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Zhang X, Xu R, Ma H, Qian Y, Zhu J. Brain Structural and Functional Damage Network Localization of Suicide. Biol Psychiatry 2024; 95:1091-1099. [PMID: 38215816 DOI: 10.1016/j.biopsych.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Extensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide. METHODS To clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities. RESULTS The suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex. CONCLUSIONS Our findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Ruoxuan Xu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Haining Ma
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
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2
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Moretto M, Luciani BF, Zigiotto L, Saviola F, Tambalo S, Cabalo DG, Annicchiarico L, Venturini M, Jovicich J, Sarubbo S. Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation of a New Tool for Planning Brain Resections. Neurosurgery 2024:00006123-990000000-01188. [PMID: 38836617 DOI: 10.1227/neu.0000000000003012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/29/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Precise mapping of functional networks in patients with brain tumor is essential for tailoring personalized treatment strategies. Resting-state functional MRI (rs-fMRI) offers an alternative to task-based fMRI, capable of capturing multiple networks within a single acquisition, without necessitating task engagement. This study demonstrates a strong concordance between preoperative rs-fMRI maps and the gold standard intraoperative direct electric stimulation (DES) mapping during awake surgery. METHODS We conducted an analysis involving 28 patients with glioma who underwent awake surgery with DES mapping. A total of 100 DES recordings were collected to map sensorimotor (SMN), language (LANG), visual (VIS), and speech articulation cognitive domains. Preoperative rs-fMRI maps were generated using an updated version of the ReStNeuMap software, specifically designed for rs-fMRI data preprocessing and automatic detection of 7 resting-state networks (SMN, LANG, VIS, speech articulation, default mode, frontoparietal, and visuospatial). To evaluate the agreement between these networks and those mapped with invasive cortical mapping, we computed patient-specific distances between them and intraoperative DES recordings. RESULTS Automatically detected preoperative functional networks exhibited excellent agreement with intraoperative DES recordings. When we spatially compared DES points with their corresponding networks, we found that SMN, VIS, and speech articulatory DES points fell within the corresponding network (median distance = 0 mm), whereas for LANG a median distance of 1.6 mm was reported. CONCLUSION Our findings show the remarkable consistency between key functional networks mapped noninvasively using presurgical rs-fMRI and invasive cortical mapping. This evidence highlights the utility of rs-fMRI for personalized presurgical planning, particularly in scenarios where awake surgery with DES is not feasible to protect eloquent areas during tumor resection. We have made the updated tool for automated functional network estimation publicly available, facilitating broader utilization of rs-fMRI mapping in various clinical contexts, including presurgical planning, functional reorganization over follow-up periods, and informing future treatments such as radiotherapy.
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Affiliation(s)
- Manuela Moretto
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Luca Zigiotto
- Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy
- Department of Psychology, University of Trento, Trento, Italy
| | - Francesca Saviola
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Stefano Tambalo
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | - Donna Gift Cabalo
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Luciano Annicchiarico
- Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy
| | - Martina Venturini
- Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
- Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy
- Department of Cellular, Computation and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Centre for Medical Sciences (CISMED), University of Trento, Trento, Italy
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3
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Chen F, Chen Q, Zhu Y, Long C, Lu J, Jiang Y, Zhang X, Zhang B. Alterations in Dynamic Functional Connectivity in Patients with Cerebral Small Vessel Disease. Transl Stroke Res 2024; 15:580-590. [PMID: 36967436 PMCID: PMC11106163 DOI: 10.1007/s12975-023-01148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/03/2023] [Accepted: 03/14/2023] [Indexed: 03/28/2023]
Abstract
Cerebral small vessel disease (CSVD) is a common disease that seriously endangers people's health, and is easily overlooked by both patients and clinicians due to its near-silent onset. Dynamic functional connectivity (DFC) is a new concept focusing on the dynamic features and patterns of brain networks that represents a powerful tool for gaining novel insight into neurological diseases. To assess alterations in DFC in CSVD patients, and the correlation of DFC with cognitive function. We enrolled 35 CSVD patients and 31 normal control subjects (NC). Resting-state functional MRI (rs-fMRI) with a sliding-window approach and k-means clustering based on independent component analysis (ICA) was used to evaluate DFC. The temporal properties of fractional windows and the mean dwell time in each state, as well as the number of transitions between each pair of DFC states, were calculated. Additionally, we assessed the functional connectivity (FC) strength of the dynamic states and the associations of altered neuroimaging measures with cognitive performance. A dynamic analysis of all included subjects suggested four distinct functional connectivity states. Compared with the NC group, the CSVD group had more fractional windows and longer mean dwell times in state 4 characterized by sparse FC both inter-network and intra-networks. Additionally, the CSVD group had a reduced number of windows and shorter mean dwell times compared to the NC group in state 3 characterized by highly positive FC between the somatomotor and visual networks, and negative FC in the basal ganglia and somatomotor and visual networks. The number of transitions between state 2 and state 3 and between state 3 and state 4 was significantly reduced in the CSVD group compared to the NC group. Moreover, there was a significant difference in the FC strength between the two groups, and the altered temporal properties of DFC were significantly related to cognitive performance. Our study indicated that CSVD is characterized by altered temporal properties in DFC that may be sensitive neuroimaging biomarkers for early disease identification. Further study of DFC alterations could help us to better understand the progressive dysfunction of networks in CSVD patients.
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Affiliation(s)
- Futao Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Qian Chen
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Yajing Zhu
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Cong Long
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Yaoxian Jiang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, China.
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China.
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China.
- Institute of Brain Science, Nanjing University, Nanjing, China.
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4
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Zhang Q, Zhang W, Zhang P, Zhao Z, Yang L, Zheng F, Zhang L, Huang G, Zhang J, Zheng W, Ma R, Yao Z, Hu B. Altered dynamic functional connectivity in rectal cancer patients with and without chemotherapy: a resting-state fMRI study. Int J Neurosci 2024; 134:584-594. [PMID: 36178032 DOI: 10.1080/00207454.2022.2130295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/11/2022] [Accepted: 09/01/2022] [Indexed: 10/17/2022]
Abstract
Purpose: Understanding the mechanism of brain functional alterations in rectal cancer (RC) patients is of great significance to improve the prognosis and quality of life of patients. Additionally, the influence of chemotherapy on brain function in RC patients is still unclear. In this study, we aimed to investigate the alterations of brain functional network dynamics in RC patients and explore the effects of chemotherapy on temporal dynamics of dynamic functional connectivity (DFC). Methods: The group independent component analysis (GICA) and sliding window method were applied to investigate abnormalities of DFC based on resting-state functional magnetic resonance imaging (rs-fMRI) of 18 RC patients without chemotherapy (RC_NC), 21 RC patients with chemotherapy (RC_C) and 33 healthy controls (HC). Then, the Spearman correlation between aberrant properties and clinical measures was calculated. Results: Two discrete states were identified. Compared to HC, RC_NC exhibited increased mean dwell time (MDT) and fractional windows (FW) in state 2 and decreased transition numbers between the two states. Notably, three temporal properties in RC_C showed an intermediate trend in comparison with RC_NC and HC. Furthermore, RC_C also demonstrated abnormal intra- and inter-network connections, involving the visual (VIS), default mode (DM), and cognitive control (CC) networks, and most connections related to VIS were correlated with the severity of anxiety and depression. Conclusions: Our study suggested that abnormal DFC patterns could be manifested in RC patients and chemotherapy would further correct abnormalities of network dynamics, which may provide new insights into the brain functional alterations in patients with RC from the time-varying connectivity perspective.
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Affiliation(s)
- Qin Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, PRChina
| | - Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou, PRChina
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, PRChina
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, PRChina
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Fang Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Lingyu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, PRChina
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou, PRChina
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, PRChina
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, PRChina
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Rong Ma
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, PR China
- Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, PR China
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5
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Wan X, Tang Y, Wu Y, Xu Z, Chen W, Chen F, Luo C, Wang F. Abnormal functional connectivity of white-matter networks and gray-white matter functional networks in patients with NMOSD. Brain Res Bull 2024; 211:110949. [PMID: 38615889 DOI: 10.1016/j.brainresbull.2024.110949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/10/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Cognitive impairment (CI) has been reported in 29-70% of patients with neuromyelitis optica spectrum disorder (NMOSD). Abnormal white matter (WM) functional networks that correlate with cognitive functions have not been studied well in patients with NMOSD. The aim of the current study was to investigate functional connectivity (FC), spontaneous activity, and functional covariance connectivity (FCC) abnormalities of WM functional networks in patients with NMOSD and their correlation with cognitive performance. Twenty-four patients with NMOSD and 24 healthy controls (HCs) were included in the study. Participants underwent brain resting-state functional magnetic resonance imaging (fMRI) and the Montreal Cognitive Assessment (MoCA). Eight WM networks and nine gray matter (GM) networks were created. In patients, WM networks, including WM1-4, WM1-8, WM2-6, WM2-7, WM2-8, WM4-8, WM5-8 showed reduced FC (P < 0.05). All WM networks except WM1 showed decreased spontaneous activity (P < 0.05). The major GM networks demonstrated increased/decreased FC (P < 0.05), whereas GM7-WM7, GM8-WM4, GM8-WM6 and GM8-WM8 displayed decreased FC (P < 0.05). The MoCA results showed that two-thirds (16/24) of the patients had CI. FC and FCC in WM networks were correlated negatively with the MoCA scores (P < 0.05). WM functional networks are multi-layered. Abnormal FC of WM functional networks and GM functional networks may be responsible for CI.
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Affiliation(s)
- Xincui Wan
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Yingjie Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yu Wu
- Department of Neurology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Zhenming Xu
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Wangsheng Chen
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Fei Wang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
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Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 DOI: 10.1038/s41593-024-01618-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
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Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
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Vassiliadis P, Beanato E, Popa T, Windel F, Morishita T, Neufeld E, Duque J, Derosiere G, Wessel MJ, Hummel FC. Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills. Nat Hum Behav 2024:10.1038/s41562-024-01901-z. [PMID: 38811696 DOI: 10.1038/s41562-024-01901-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, we exploited a recently developed non-invasive deep brain stimulation technique called transcranial temporal interference stimulation (tTIS) during reinforcement motor learning with concurrent neuroimaging, in a randomized, sham-controlled, double-blind study. Striatal tTIS applied at 80 Hz, but not at 20 Hz, abolished the benefits of reinforcement on motor learning. This effect was related to a selective modulation of neural activity within the striatum. Moreover, 80 Hz, but not 20 Hz, tTIS increased the neuromodulatory influence of the striatum on frontal areas involved in reinforcement motor learning. These results show that tTIS can non-invasively and selectively modulate a striatal mechanism involved in reinforcement learning, expanding our tools for the study of causal relationships between deep brain structures and human behaviour.
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Affiliation(s)
- Pierre Vassiliadis
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Traian Popa
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Fabienne Windel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Julie Duque
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Gerard Derosiere
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
- Lyon Neuroscience Research Center, Impact Team, Inserm U1028, CNRS UMR5292, Lyon 1 University, Bron, France
| | - Maximilian J Wessel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland.
- Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland.
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Cai W, Young CB, Yuan R, Lee B, Ryman S, Kim J, Yang L, Levine TF, Henderson VW, Poston KL, Menon V. Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease. Proc Natl Acad Sci U S A 2024; 121:e2316149121. [PMID: 38768342 PMCID: PMC11145286 DOI: 10.1073/pnas.2316149121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.
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Affiliation(s)
- Weidong Cai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
| | - Christina B. Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Rui Yuan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Byeongwook Lee
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Sephira Ryman
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Jeehyun Kim
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Laurice Yang
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Taylor F. Levine
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Victor W. Henderson
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA94305
| | - Kathleen L. Poston
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA94305
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
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9
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Xing L, Guo Z, Long Z. Energy landscape analysis of brain network dynamics in Alzheimer's disease. Front Aging Neurosci 2024; 16:1375091. [PMID: 38813531 PMCID: PMC11133694 DOI: 10.3389/fnagi.2024.1375091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Background Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study. Methods This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients. Results The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance. Conclusion The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states.
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Affiliation(s)
- Le Xing
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhitao Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
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10
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Huang D, Wang Y, Fan L, Yu Y, Zhao Z, Zeng P, Wang K, Li N, Shen H. Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface. Brain Sci 2024; 14:498. [PMID: 38790476 PMCID: PMC11120245 DOI: 10.3390/brainsci14050498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.
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Affiliation(s)
- Dingyong Huang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yingjie Wang
- College of Physical Education and Health, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China;
| | - Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yang Yu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Ziyu Zhao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Pu Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Kunqing Wang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Na Li
- Radiology Department, Xiangya 3rd Hospital, Central South University, Changsha 410013, China;
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
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11
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Chung MK, Che JB, Nair VA, Ramos CG, Mathis JR, Prabhakaran V, Meyerand E, Hermann BP, Binder JR, Struck AF. Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy. ARXIV 2024:arXiv:2405.07835v1. [PMID: 38800648 PMCID: PMC11118617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.
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Affiliation(s)
- Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | | | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, USA
| | | | | | | | - Elizabeth Meyerand
- Departments of Medical Physics & Biomedical Engineering, University of Wisconsin-Madison, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin-Madison, USA
| | | | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, USA
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12
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Howard KA, Ahmad SS, Chavez JV, Hoogerwoerd H, McIntosh RC. The central executive network moderates the relationship between posttraumatic stress symptom severity and gastrointestinal related issues. Sci Rep 2024; 14:10695. [PMID: 38724613 PMCID: PMC11082173 DOI: 10.1038/s41598-024-61418-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
Although most adults experience at least one traumatic event in their lifetime, a smaller proportion will go on to be clinically diagnosed with post-traumatic stress disorder (PTSD). Persons diagnosed with PTSD have a greater likelihood of developing gastrointestinal (GI) disorders. However, the extent to which subclinical levels of post-traumatic stress (PTS) correspond with the incidence of GI issues in a normative sample is unclear. Resting state fMRI, medical history, psychological survey, and anthropometric data were acquired from the Enhanced Nathan Kline Institute-Rockland Sample (n = 378; age range 18-85.6 years). The primary aim of this study was to test the main effect of subclinical PTS symptom severity on the number of endorsed GI issues. The secondary aim was to test the moderating effect of high versus low resting state functional connectivity (rsFC) of the central executive network (CEN) on the relationship between PTS symptom severity and GI issues. Trauma Symptom Checklist-40 (TSC-40) scores were positively associated with the number of endorsed GI issues (b = -0.038, SE = .009, p < .001). The interaction between TSC-40 scores and rsFC within the CEN was significant on GI issues after controlling for sociodemographic and cardiometabolic variables (b = -0.031, SE = .016, p < .05), such that above average rsFC within the CEN buffered the effect of TSC-40 scores on GI issues. Our findings of higher rsFC within the CEN moderating the magnitude of coincidence in PTS and GI symptom severity may reflect the mitigating role of executive control processes in the putative stress signaling mechanisms that contribute to gut dysbiosis.
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Affiliation(s)
- Kia A Howard
- Department of Psychology, University of Miami, Coral Gables, FL, 33146, USA
| | - Salman S Ahmad
- Department of Psychology, University of Miami, Coral Gables, FL, 33146, USA
| | - Jennifer V Chavez
- Department of Environmental Health Sciences, Robert Stempel College of Public Health & Social Work, Florida International University, Miami, FL, 33199, USA
| | - Hannah Hoogerwoerd
- Department of Psychology, University of Miami, Coral Gables, FL, 33146, USA
| | - Roger C McIntosh
- Department of Psychology, University of Miami, Coral Gables, FL, 33146, USA.
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13
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Ke M, Luo X, Guo Y, Zhang J, Ren X, Liu G. Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy. Neurol Sci 2024:10.1007/s10072-024-07506-8. [PMID: 38704479 DOI: 10.1007/s10072-024-07506-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/27/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. RESULTS Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. CONCLUSIONS Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
| | - Xiaofei Luo
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Yi Guo
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Juli Zhang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Xupeng Ren
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Guangyao Liu
- Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030, China.
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14
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Guo B, Mao T, Tao R, Fu S, Deng Y, Liu Z, Wang M, Wang R, Zhao W, Chai Y, Jiang C, Rao H. Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task. Cereb Cortex 2024; 34:bhae212. [PMID: 38771245 DOI: 10.1093/cercor/bhae212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/19/2024] [Accepted: 05/09/2024] [Indexed: 05/22/2024] Open
Abstract
Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.
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Affiliation(s)
- Bowen Guo
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Tianxin Mao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Ruiwen Tao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Shanna Fu
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Yao Deng
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Zhihui Liu
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Mengmeng Wang
- Business School, NingboTech University, Ningbo 315199, China
| | - Ruosi Wang
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Weiwei Zhao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Ya Chai
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Caihong Jiang
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States
- Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States
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15
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Koslov SR, Kable JW, Foster BL. Dissociable Contributions of the Medial Parietal Cortex to Recognition Memory. J Neurosci 2024; 44:e2220232024. [PMID: 38527809 PMCID: PMC11063824 DOI: 10.1523/jneurosci.2220-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/15/2024] [Indexed: 03/27/2024] Open
Abstract
Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its distinct functional characteristics during different retrieval tasks. Specifically, while recognition and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct spatial patterns of response within MPC. However, other studies have emphasized alternate MPC functional dissociations in terms of brain network connectivity profiles or stimulus category selectivity. As the unique contributions of MPC to episodic memory remain unclear, adjudicating between these different accounts can provide better consensus regarding MPC function. Therefore, we used a precision-neuroimaging dataset (7T functional magnetic resonance imaging) to examine how MPC regions are differentially engaged during recognition memory and how these task-related dissociations may also reflect distinct connectivity and stimulus category functional profiles. We observed interleaved, though spatially distinct, subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli. In addition, this dissociation was further accentuated by functional subregions displaying distinct profiles of connectivity with the hippocampus during task and rest. Finally, we show that recent observations of dissociable person and place selectivity within the MPC reflect category-specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, by examining precision functional mapping within individuals, these data suggest that previously distinct observations of functional dissociation within MPC conform to a common principle of organization throughout hippocampal-neocortical memory systems.
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Affiliation(s)
- Seth R Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Brett L Foster
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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16
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Zhao S, Fang L, Yang Y, Tang G, Luo G, Han J, Liu T, Hu X. Task sub-type states decoding via group deep bidirectional recurrent neural network. Med Image Anal 2024; 94:103136. [PMID: 38489895 DOI: 10.1016/j.media.2024.103136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 01/31/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Decoding brain states under different cognitive tasks from functional magnetic resonance imaging (fMRI) data has attracted great attention in the neuroimaging filed. However, the well-known temporal dependency in fMRI sequences has not been fully exploited in existing studies, due to the limited temporal-modeling capacity of the backbone machine learning algorithms and rigid training sample organization strategies upon which the brain decoding methods are built. To address these limitations, we propose a novel method for fine-grain brain state decoding, namely, group deep bidirectional recurrent neural network (Group-DBRNN) model. We first propose a training sample organization strategy that consists of a group-task sample generation module and a multiple-scale random fragment strategy (MRFS) module to collect training samples that contain rich task-relevant brain activity contrast (i.e., the comparison of neural activity patterns between different tasks) and maintain the temporal dependency. We then develop a novel decoding model by replacing the unidirectional RNNs that are widely used in existing brain state decoding studies with bidirectional stacked RNNs to better capture the temporal dependency, and by introducing a multi-task interaction layer (MTIL) module to effectively model the task-relevant brain activity contrast. Our experimental results on the Human Connectome Project task fMRI dataset (7 tasks consisting of 23 task sub-type states) show that the proposed model achieves an average decoding accuracy of 94.7% over the 23 fine-grain sub-type states. Meanwhile, our extensive interpretations of the intermediate features learned in the proposed model via visualizations and quantitative assessments of their discriminability and inter-subject alignment evidence that the proposed model can effectively capture the temporal dependency and task-relevant contrast.
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Affiliation(s)
- Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Research & Development Institute of Northwestern Polytechnical University in Shenzhen, China
| | - Long Fang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yang Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Guochang Tang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Guoxin Luo
- Department of Ophthalmology, Nanyang First People's Hospital Affiliated to Henan University, Nanyang 473000, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tianming Liu
- School of Computing, The University of Georgia, GA, USA
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
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17
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Lopez S, Hampel H, Chiesa PA, Del Percio C, Noce G, Lizio R, Teipel SJ, Dyrba M, González-Escamilla G, Bakardjian H, Cavedo E, Lista S, Vergallo A, Lemercier P, Spinelli G, Grothe MJ, Potier MC, Stocchi F, Ferri R, Habert MO, Dubois B, Babiloni C. The association between posterior resting-state EEG alpha rhythms and functional MRI connectivity in older adults with subjective memory complaint. Neurobiol Aging 2024; 137:62-77. [PMID: 38431999 DOI: 10.1016/j.neurobiolaging.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 02/09/2024] [Accepted: 02/19/2024] [Indexed: 03/05/2024]
Abstract
Resting-state eyes-closed electroencephalographic (rsEEG) alpha rhythms are dominant in posterior cortical areas in healthy adults and are abnormal in subjective memory complaint (SMC) persons with Alzheimer's disease amyloidosis. This exploratory study in 161 SMC participants tested the relationships between those rhythms and seed-based resting-state functional magnetic resonance imaging (rs-fMRI) connectivity between thalamus and visual cortical networks as a function of brain amyloid burden, revealed by positron emission tomography and cognitive reserve, measured by educational attainment. The SMC participants were divided into 4 groups according to 2 factors: Education (Edu+ and Edu-) and Amyloid burden (Amy+ and Amy-). There was a statistical interaction (p < 0.05) between the two factors, and the subgroup analysis using estimated marginal means showed a positive association between the mentioned rs-fMRI connectivity and the posterior rsEEG alpha rhythms in the SMC participants with low brain amyloidosis and high CR (Amy-/Edu+). These results suggest that in SMC persons, early Alzheimer's disease amyloidosis may contrast the beneficial effects of cognitive reserve on neurophysiological oscillatory mechanisms at alpha frequencies and connectivity between the thalamus and visual cortical networks.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy
| | - Harald Hampel
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France
| | - Patrizia Andrea Chiesa
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris F-75013, France; Institut du Cerveau et de la Moelle épinière, ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Paris F- 75013, France
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy
| | - Stefan J Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany; German Center for Neurodegenerative Diseases (DZNE), Greifswald, Rostock, Germany
| | - Martin Dyrba
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Gabriel González-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Hovagim Bakardjian
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris F-75013, France; Centre pour l'Acquisition et le Traitement des Images, (CATI platform), France; Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, Sorbonne University, LIB, Paris F-75006, France
| | - Enrica Cavedo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France
| | - Simone Lista
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France
| | - Andrea Vergallo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France
| | - Pablo Lemercier
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris F-75013, France; Centre pour l'Acquisition et le Traitement des Images, (CATI platform), France; Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, Sorbonne University, LIB, Paris F-75006, France
| | - Giuseppe Spinelli
- Centre pour l'Acquisition et le Traitement des Images, (CATI platform), France; Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, Sorbonne University, LIB, Paris F-75006, France
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Greifswald, Rostock, Germany
| | - Marie-Claude Potier
- Institut du Cerveau et de la Moelle épinière, ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Paris F- 75013, France
| | - Fabrizio Stocchi
- IRCCS San Raffaele, Rome, Italy; Telematic University, San Raffaele, Rome, Italy
| | | | - Marie-Odile Habert
- Centre pour l'Acquisition et le Traitement des Images, (CATI platform), France; Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, Sorbonne University, LIB, Paris F-75006, France; AP-HP, Pitié-Salpêtrière Hospital, Department of Nuclear Medicine, Paris F-75013, France
| | - Bruno Dubois
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris F-75013, France; Institut du Cerveau et de la Moelle épinière, ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Paris F- 75013, France
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy; San Raffaele Cassino, Cassino, FR, Italy.
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Mekbib DB, Cai M, Wu D, Dai W, Liu X, Zhao L. Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring. J Magn Reson Imaging 2024; 59:1630-1642. [PMID: 37584329 DOI: 10.1002/jmri.28958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded. PURPOSE To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD. STUDY TYPE Prospective. SUBJECTS Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female). FIELD STRENGTH/SEQUENCE 3-T, T1-weighted gradient echo and EPI sequences. ASSESSMENT Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings. STATISTICAL TESTS Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05. RESULTS With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline. DATA CONCLUSION The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Destaw Bayabil Mekbib
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Department of Physics and Statistics, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
| | - Miao Cai
- Department of Neurology, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA
| | - Xiaoli Liu
- Department of Neurology, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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Caminiti SP, Galli A, Jonghi-Lavarini L, Boccalini C, Nicastro N, Chiti A, Garibotto V, Perani D. Mapping brain metabolism, connectivity and neurotransmitters topography in early and late onset dementia with lewy bodies. Parkinsonism Relat Disord 2024; 122:106061. [PMID: 38430691 DOI: 10.1016/j.parkreldis.2024.106061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/17/2024] [Accepted: 02/21/2024] [Indexed: 03/05/2024]
Abstract
INTRODUCTION Early-onset dementia with Lewy bodies (EO-DLB) is associated with rapid cognitive decline and severe neuropsychiatric symptoms at onset. METHODS Using FDG-PET imaging for 62 patients (21 EO-DLB, 41 LO (late-onset)-DLB), we explored brain hypometabolism, and metabolic connectivity in the whole-brain network and resting-state networks (RSNs). We also evaluated the spatial association between brain hypometabolism and neurotransmitter pathways topography. RESULTS Direct comparisons between the two clinical subgroups showed that EO-DLB was characterized by a lower metabolism in posterior cingulate/precuneus and occipital cortex. Metabolic connectivity analysis revealed significant alterations in posterior regions in both EO-DLB and LO-DLB. The EO-DLB, however, showed more severe loss of connectivity between occipital and parietal nodes and hyperconnectivity between frontal and cerebellar nodes. Spatial topography association analysis indicated significant correlations between neurotransmitter maps (i.e. acetylcholine, GABA, serotonin, dopamine) and brain hypometabolism in both EO and LO-DLB, with significantly higher metabolic correlation in the presynaptic serotonergic system for EO-DLB, supporting its major dysfunction. CONCLUSIONS Our study revealed greater brain hypometabolism and loss of connectivity in posterior brain region in EO- than LO-DLB. Serotonergic mapping emerges as a relevant factor for further investigation addressing clinical differences between DLB subtypes.
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Affiliation(s)
- Silvia Paola Caminiti
- Vita-Salute San Raffaele University, Milan, Italy; IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alice Galli
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Cecilia Boccalini
- Vita-Salute San Raffaele University, Milan, Italy; IRCCS San Raffaele Scientific Institute, Milan, Italy; Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Nicastro
- Division of Neurorehabilitation, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Switzerland
| | - Arturo Chiti
- Vita-Salute San Raffaele University, Milan, Italy; IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy; IRCCS San Raffaele Scientific Institute, Milan, Italy.
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20
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Ironside M, Duda JM, Moser AD, Holsen LM, Zuo CS, Du F, Perlo S, Richards CE, Chen X, Nickerson LD, Null KE, Esfand SM, Alexander MM, Crowley DJ, Lauze M, Misra M, Goldstein JM, Pizzagalli DA. Association of Lower Rostral Anterior Cingulate GABA+ and Dysregulated Cortisol Stress Response With Altered Functional Connectivity in Young Adults With Lifetime Depression: A Multimodal Imaging Investigation of Trait and State Effects. Am J Psychiatry 2024:appiajp20230382. [PMID: 38685857 DOI: 10.1176/appi.ajp.20230382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Preclinical work suggests that excess glucocorticoids and reduced cortical γ-aminobutyric acid (GABA) may affect sex-dependent differences in brain regions implicated in stress regulation and depressive phenotypes. The authors sought to address a critical gap in knowledge, namely, how stress circuitry is functionally affected by glucocorticoids and GABA in current or remitted major depressive disorder (MDD). METHODS Multimodal imaging data were collected from 130 young adults (ages 18-25), of whom 44 had current MDD, 42 had remitted MDD, and 44 were healthy comparison subjects. GABA+ (γ-aminobutyric acid and macromolecules) was assessed using magnetic resonance spectroscopy, and task-related functional MRI data were collected under acute stress and analyzed using data-driven network modeling. RESULTS Across modalities, trait-related abnormalities emerged. Relative to healthy comparison subjects, both clinical groups were characterized by lower rostral anterior cingulate cortex (rACC) GABA+ and frontoparietal network amplitude but higher amplitude in salience and stress-related networks. For the remitted MDD group, differences from the healthy comparison group emerged in the context of elevated cortisol levels, whereas the MDD group had lower cortisol levels than the healthy comparison group. In the comparison group, frontoparietal and stress-related network connectivity was positively associated with cortisol level (highlighting putative top-down regulation of stress), but the opposite relationship emerged in the MDD and remitted MDD groups. Finally, rACC GABA+ was associated with stress-induced changes in connectivity between overlapping default mode and salience networks. CONCLUSIONS Lifetime MDD was characterized by reduced rACC GABA+ as well as dysregulated cortisol-related interactions between top-down control (frontoparietal) and threat (task-related) networks. These findings warrant further investigation of the role of GABA in the vulnerability to and treatment of MDD.
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Affiliation(s)
- Maria Ironside
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Jessica M Duda
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Amelia D Moser
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Laura M Holsen
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Chun S Zuo
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Fei Du
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Sarah Perlo
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Christine E Richards
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Xi Chen
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Lisa D Nickerson
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Kaylee E Null
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Shiba M Esfand
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Madeline M Alexander
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - David J Crowley
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Meghan Lauze
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Madhusmita Misra
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Jill M Goldstein
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
| | - Diego A Pizzagalli
- Center for Depression, Anxiety, and Stress Research (Ironside, Duda, Moser, Perlo, Richards, Null, Esfand, Alexander, Crowley, Pizzagalli) and McLean Imaging Center (Zuo, Du, Chen, Nickerson, Pizzagalli), McLean Hospital, Belmont, Mass.; Laureate Institute for Brain Research, Tulsa, Okla. (Ironside); Harvard Medical School, Boston (Holsen, Zuo, Du, Chen, Nickerson, Misra, Goldstein, Pizzagalli); Division of Women's Health, Department of Medicine (Holsen), and Department of Psychiatry, Brigham and Women's Hospital, Boston (Holsen); Division of Pediatric Endocrinology (Lauze, Misra), Department of Psychiatry (Goldstein), and Innovation Center on Sex Differences in Medicine (Holsen, Misra, Goldstein), Massachusetts General Hospital, Boston
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Yassin W, de Moura FB, Withey SL, Cao L, Kangas BD, Bergman J, Kohut SJ. Resting state networks of awake adolescent and adult squirrel monkeys using ultra-high field (9.4T) functional magnetic resonance imaging. eNeuro 2024; 11:ENEURO.0173-23.2024. [PMID: 38627065 DOI: 10.1523/eneuro.0173-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 04/30/2024] Open
Abstract
Resting state networks (RSNs) are increasingly forwarded as candidate biomarkers for neuropsychiatric disorders. Such biomarkers may provide objective measures for evaluating novel therapeutic interventions in nonhuman primates often used in translational neuroimaging research. This study aimed to characterize the RSNs of awake squirrel monkeys and compare the characteristics of those networks in adolescent and adult subjects. Twenty-seven squirrel monkeys (n=12 adolescents [6 male/6 female] ∼2.5 years and n=15 adults [7 male/8 female] ∼9.5 years) were gradually acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Group level independent component (ICA) analysis (30 ICs) with dual regression was used to detect and compare RSNs. Twenty ICs corresponding to physiologically meaningful networks representing a range of neural functions, including motor, sensory, reward, and cognitive processes were identified in both adolescent and adult monkeys. The reproducibility of these RSNs was evaluated across several ICA model orders. Adults showed a trend for greater connectivity compared to adolescent subjects in two of the networks of interest: (1) in the right occipital region with the OFC network and (2) in the left temporal cortex, bilateral occipital cortex, and cerebellum with the posterior cingulate network. However, when age was entered into the above model, this trend for significance was lost. These results demonstrate that squirrel monkey RSNs are stable and consistent with RSNs previously identified in humans, rodents, and other nonhuman primate species. These data also identify several networks in adolescence that are conserved and others that may change into adulthood.Significance Statement Functional magnetic resonance imaging procedures have revealed important information about how the brain is modified by experimental manipulations, disease states, and aging throughout the lifespan. Preclinical neuroimaging, especially in nonhuman primates, has become a frequently used means to answer targeted questions related to brain resting-state functional connectivity. The present study characterized resting state networks (RSNs) in adult and adolescent squirrel monkeys; twenty RSNs corresponding to networks representing a range of neural functions were identified. The RSNs identified here can be utilized in future studies examining the effects of experimental manipulations on brain connectivity in squirrel monkeys. These data also may be useful for comparative analysis with other primate species to provide an evolutionary perspective for understanding brain function and organization.
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Affiliation(s)
- Walin Yassin
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Fernando B de Moura
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Sarah L Withey
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Lei Cao
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
| | - Brian D Kangas
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Jack Bergman
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Stephen J Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
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22
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Spencer APC, Goodfellow M, Chakkarapani E, Brooks JCW. Resting-state functional connectivity in children cooled for neonatal encephalopathy. Brain Commun 2024; 6:fcae154. [PMID: 38741661 PMCID: PMC11089421 DOI: 10.1093/braincomms/fcae154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/21/2024] [Accepted: 04/28/2024] [Indexed: 05/16/2024] Open
Abstract
Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol BS2 8DX, UK
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- Department of Radiology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK
- Department of Mathematics and Statistics, University of Exeter, Exeter EX4 4QF, UK
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- Neonatal Intensive Care Unit, St Michaels Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol BS2 8EG, UK
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol BS2 8DX, UK
- University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), University of East Anglia, Norwich NR4 7TJ, UK
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23
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Ye B, Wu Y, Cao M, Xu C, Zhou C, Zhang X. Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01803-1. [PMID: 38662092 DOI: 10.1007/s00406-024-01803-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/19/2024] [Indexed: 04/26/2024]
Abstract
This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.
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Affiliation(s)
- Biying Ye
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Yiqiao Wu
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Mingjun Cao
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Chanhuan Xu
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No.264 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No.264 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
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24
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Albrecht F, Johansson H, Ekman U, Poulakis K, Bezuidenhout L, Pereira JB, Franzén E. Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease. Sci Rep 2024; 14:9513. [PMID: 38664471 PMCID: PMC11045833 DOI: 10.1038/s41598-024-60050-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.
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Affiliation(s)
- Franziska Albrecht
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden.
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden.
| | - Hanna Johansson
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
- Stockholm Sjukhem Foundation, Stockholm, Sweden
| | - Urban Ekman
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Medical Psychology, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Poulakis
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lucian Bezuidenhout
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
| | - Joana B Pereira
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Erika Franzén
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
- Stockholm Sjukhem Foundation, Stockholm, Sweden
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25
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Tilton-Bolowsky V, Stockbridge MD, Hillis AE. Remapping and Reconnecting the Language Network after Stroke. Brain Sci 2024; 14:419. [PMID: 38790398 PMCID: PMC11117613 DOI: 10.3390/brainsci14050419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified.
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Affiliation(s)
| | | | - Argye E. Hillis
- Departments of Neurology, Physical Medicine & Rehabilitation, and Cognitive Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (V.T.-B.); (M.D.S.)
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26
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Zhu K, Chang J, Zhang S, Li Y, Zuo J, Ni H, Xie B, Yao J, Xu Z, Bian S, Yan T, Wu X, Chen S, Jin W, Wang Y, Xu P, Song P, Wu Y, Shen C, Zhu J, Yu Y, Dong F. The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain. Neuroimage 2024; 290:120558. [PMID: 38437909 DOI: 10.1016/j.neuroimage.2024.120558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/06/2024] Open
Abstract
The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.
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Affiliation(s)
- Kun Zhu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jianchao Chang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Siya Zhang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China
| | - Yan Li
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Junxun Zuo
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Haoyu Ni
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Bingyong Xie
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiyuan Yao
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Zhibin Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Sicheng Bian
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Tingfei Yan
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Xianyong Wu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Orthopedics, Anqing First People's Hospital of Anhui Medical University, Anqing, PR China
| | - Senlin Chen
- Department of Orthopedics, Dongcheng branch of The First Affiliated Hospital of Anhui Medical University (Feidong People's Hospital), Hefei, PR China
| | - Weiming Jin
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Ying Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peng Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peiwen Song
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yuanyuan Wu
- Department of Medical Imaging, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Cailiang Shen
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Fulong Dong
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China.
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27
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Moguilner S, Herzog R, Perl YS, Medel V, Cruzat J, Coronel C, Kringelbach M, Deco G, Ibáñez A, Tagliazucchi E. Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition. Alzheimers Res Ther 2024; 16:79. [PMID: 38605416 PMCID: PMC11008050 DOI: 10.1186/s13195-024-01449-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed. METHODS We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition. RESULTS Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity. CONCLUSIONS Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.
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Affiliation(s)
- Sebastian Moguilner
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), 1207 1651 4th St, 3rd Floor, San Francisco, CA, 94143, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
- Trinity College Dublin, Lloyd Building Trinity College Dublin, Dublin, D02 PN40, Ireland
| | - Rubén Herzog
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Yonatan Sanz Perl
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, 1425, Argentina
- Institute of Applied and Interdisciplinary Physics and Department of Physics, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA, 1428, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Plaça de La Mercè, 10-12, Barcelona, 08002, Spain
| | - Vicente Medel
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Valparaíso, 2381850, Chile
| | - Josefina Cruzat
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Carlos Coronel
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Morten Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, St.Cross Rd, Oxford, OX1 3JA, UK
- Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Blvd. 82, Aarhus, 8200, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Plaça de La Mercè, 10-12, Barcelona, 08002, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, Leipzig, 04103, Germany
- Institució Catalana de Recerca I Estudis Avancats (ICREA), Passeig de Lluís Companys, 23, Barcelona, 08010, Spain
- Turner Institute for Brain and Mental Health, Monash University, 770 Blackburn Rd,, Clayton, VIC, 3168, Australia
| | - Agustín Ibáñez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile.
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), 1207 1651 4th St, 3rd Floor, San Francisco, CA, 94143, USA.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina.
- Trinity College Institute of Neuroscience, Trinity College Dublin, 152 - 160 Pearse St, Dublin, D02 R590, Ireland.
- Trinity College Dublin, Lloyd Building Trinity College Dublin, Dublin, D02 PN40, Ireland.
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina.
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, 1425, Argentina.
- Institute of Applied and Interdisciplinary Physics and Department of Physics, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA, 1428, Argentina.
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28
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Pierpaoli C, Nayak A, Hafiz R, Irfanoglu MO, Chen G, Taylor P, Hallett M, Hoa M, Pham D, Chou YY, Moses AD, van der Merwe AJ, Lippa SM, Brewer CC, Zalewski CK, Zampieri C, Turtzo LC, Shahim P, Chan L. Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents. JAMA 2024; 331:1122-1134. [PMID: 38497822 PMCID: PMC10949155 DOI: 10.1001/jama.2024.2424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/13/2024] [Indexed: 03/19/2024]
Abstract
Importance US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. Objective To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. Design, Setting, and Participants This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. Exposure AHIs. Main Outcomes and Measures Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Results Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. Conclusions and Relevance In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.
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Affiliation(s)
- Carlo Pierpaoli
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - Amritha Nayak
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Rakibul Hafiz
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - M. Okan Irfanoglu
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - Gang Chen
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland
| | - Paul Taylor
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland
| | - Mark Hallett
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, Maryland
| | - Michael Hoa
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Dzung Pham
- The Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland
| | - Yi-Yu Chou
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Anita D. Moses
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - André J. van der Merwe
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Sara M. Lippa
- National Intrepid Center of Excellence Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Carmen C. Brewer
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Chris K. Zalewski
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Cris Zampieri
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - L. Christine Turtzo
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, Maryland
| | - Pashtun Shahim
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Leighton Chan
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
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Leocadi M, Canu E, Sarasso E, Gardoni A, Basaia S, Calderaro D, Castelnovo V, Volontè MA, Filippi M, Agosta F. Dual-task gait training improves cognition and resting-state functional connectivity in Parkinson's disease with postural instability and gait disorders. J Neurol 2024; 271:2031-2041. [PMID: 38189921 DOI: 10.1007/s00415-023-12151-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVES To assess whether dual-task gait/balance training with action observation training (AOT) and motor imagery (MI) ameliorates cognitive performance and resting-state (RS) brain functional connectivity (FC) in Parkinson's disease (PD) patients with postural instability and gait disorders (PIGD). METHODS 21 PD-PIGD patients were randomized into 2 groups: (1) DUAL-TASK + AOT-MI group performed a 6-week training consisting of AOT-MI combined with practicing observed-imagined gait and balance exercises; and (2) DUAL-TASK group performed the same exercises combined with landscape-videos observation. At baseline and after training, all patients underwent a computerized cognitive assessment, while 17 patients had also RS-fMRI scans. Cognitive and RS-FC changes (and their relationships) over time within and between groups were assessed. RESULTS After training, all PD-PIGD patients improved accuracy in a test assessing executive-attentive (mainly dual-task) skills. DUAL-TASK + AOT-MI patients showed increased RS-FC within the anterior salience network (aSAL), and reduced RS-FC within the anterior default mode network (aDMN), right executive control network and precuneus network. DUAL-TASK patients showed increased RS-FC within the visuospatial network, only. Group × Time interaction showed that, compared to DUAL-TASK group, DUAL-TASK + AOT-MI cases had reduced RS-FC within the aDMN, which correlated with higher accuracy in a dual-task executive-attentive test. CONCLUSIONS In PD-PIGD patients, both trainings promote cognitive improvement and brain functional reorganization. DUAL-TASK + AOT-MI training induced specific functional reorganization changes of extra-motor brain networks, which were related with improvement in dual-task performance.
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Affiliation(s)
- Michela Leocadi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Elisabetta Sarasso
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genoa, Genoa, Italy
| | - Andrea Gardoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Davide Calderaro
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Veronica Castelnovo
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | | | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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30
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Áfra E, Janszky J, Perlaki G, Orsi G, Nagy SA, Arató Á, Szente A, Alhour HAM, Kis-Jakab G, Darnai G. Altered functional brain networks in problematic smartphone and social media use: resting-state fMRI study. Brain Imaging Behav 2024; 18:292-301. [PMID: 38049599 DOI: 10.1007/s11682-023-00825-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2023] [Indexed: 12/06/2023]
Abstract
Nowadays, the limitless availability to the World Wide Web can lead to general Internet misuse and dependence. Currently, smartphone and social media use belong to the most prevalent Internet-related behavioral addiction forms. However, the neurobiological background of these Internet-related behavioral addictions is not sufficiently explored. In this study, these addiction forms were assessed with self-reported questionnaires. Resting-state functional magnetic resonance imaging was acquired for all participants (n = 59, 29 males) to examine functional brain networks. The resting-state networks that were discovered using independent component analysis were analyzed to estimate within network differences. Significant negative associations with social media addiction and smartphone addiction were found in the language network, the lateral visual networks, the auditory network, the sensorimotor network, the executive network and the frontoparietal network. These results suggest that problematic smartphone and social media use are associated with sensory processing and higher cognitive functioning.
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Affiliation(s)
- Eszter Áfra
- Department of Behavioral Sciences, Medical School, University of Pécs, Pécs, Hungary
| | - József Janszky
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary.
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary.
- Department of Neurology, University of Pécs, Pécs, Hungary.
| | - Gábor Perlaki
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
- Department of Neurosurgery, Medical School, University of Pécs, Pécs, Hungary
| | - Gergely Orsi
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
- Department of Neurosurgery, Medical School, University of Pécs, Pécs, Hungary
| | - Szilvia Anett Nagy
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
- Department of Neurosurgery, Medical School, University of Pécs, Pécs, Hungary
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Ákos Arató
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
| | - Anna Szente
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
| | | | - Gréta Kis-Jakab
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
| | - Gergely Darnai
- Department of Behavioral Sciences, Medical School, University of Pécs, Pécs, Hungary
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
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Bagarinao E, Maesawa S, Kato S, Mutoh M, Ito Y, Ishizaki T, Tanei T, Tsuboi T, Suzuki M, Watanabe H, Hoshiyama M, Isoda H, Katsuno M, Sobue G, Saito R. Cerebellar and thalamic connector hubs facilitate the involvement of visual and cognitive networks in essential tremor. Parkinsonism Relat Disord 2024; 121:106034. [PMID: 38382401 DOI: 10.1016/j.parkreldis.2024.106034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
INTRODUCTION Connector hubs are specialized brain regions that connect multiple brain networks and therefore have the potential to affect the functions of multiple systems. This study aims to examine the involvement of connector hub regions in essential tremor. METHODS We examined whole-brain functional connectivity alterations across multiple brain networks in 27 patients with essential tremor and 27 age- and sex-matched healthy controls to identify affected hub regions using a network metric called functional connectivity overlap ratio estimated from resting-state functional MRI. We also evaluated the relationships of affected hubs with cognitive and tremor scores in all patients and with motor function improvement scores in 15 patients who underwent postoperative follow-up evaluations after focused ultrasound thalamotomy. RESULTS We have identified affected connector hubs in the cerebellum and thalamus. Specifically, the dentate nucleus in the cerebellum and the dorsomedial thalamus exhibited more extensive connections with the sensorimotor network in patients. Moreover, the connections of the thalamic pulvinar with the visual network were also significantly widespread in the patient group. The connections of these connector hub regions with cognitive networks were negatively associated (FDR q < 0.05) with cognitive, tremor, and motor function improvement scores. CONCLUSION In patients with essential tremor, connector hub regions within the cerebellum and thalamus exhibited widespread functional connections with sensorimotor and visual networks, leading to alternative pathways outside the classical tremor axis. Their connections with cognitive networks also affect patients' cognitive function.
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Affiliation(s)
- Epifanio Bagarinao
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan; Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan; Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Sachiko Kato
- Focused Ultrasound Therapy Center, Nagoya Kyoritsu Hospital, Nagoya, Aichi, Japan
| | - Manabu Mutoh
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yoshiki Ito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomotaka Ishizaki
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takafumi Tanei
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takashi Tsuboi
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masashi Suzuki
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan; Department of Neurology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Minoru Hoshiyama
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan; Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Haruo Isoda
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan; Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Masahisa Katsuno
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan; Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan; Department of Clinical Research Education, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan; Aichi Medical University, Nagakute, Aichi, Japan
| | - Ryuta Saito
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan; Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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32
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Liu H, Zhang G, Zheng H, Tan H, Zhuang J, Li W, Wu B, Zheng W. Dynamic Dysregulation of the Triple Network of the Brain in Mild Traumatic Brain Injury and Its Relationship With Cognitive Performance. J Neurotrauma 2024; 41:879-886. [PMID: 37128187 DOI: 10.1089/neu.2022.0257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
A triple network model consisting of a default network, a salience network, and a central executive network has recently been used to understand connectivity patterns in cognitively normal versus dysfunctional brains. This study aimed to explore changes in the dynamic connectivity of triplet network in mild traumatic brain injury (mTBI) and its relationship to cognitive performance. In this work, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 30 mTBI patients and 30 healthy controls (HCs). Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Further, we analyzed the relationship between changes in dynamic functional connectivity (FC) parameters and clinical variables in mTBI patients. The results showed that the dynamic functional connectivity of the brain triple network was clustered into five states. Compared with HC, mTBI patients spent longer in state 1, which is characterized by weakened dorsal default mode network (DMN) and anterior salience network (SN) connectivity, and state 3, which is characterized by a positive correlation between DMN and SN internal connectivity. Mild TBI patients had fewer metastases in different states than HC patients. In addition, the mean residence time in state 1 correlated with Montreal Cognitive Assessment scores in mTBI patients; the number of transitions between states correlated with Glasgow Coma Score in mTBI patients. Taken together, our findings suggest that the dynamic properties of FC in the triple network of mTBI patients are abnormal, and provide a new perspective on the pathophysiological mechanism of cognitive impairment from the perspective of dynamic FC.
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Affiliation(s)
- Hongkun Liu
- Department of Radiology, Huizhou Central People's Hospital, Huizhou, China
| | - Gengbiao Zhang
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Hongyi Zheng
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Hui Tan
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Jiayan Zhuang
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Weijia Li
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Bixia Wu
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Wenbin Zheng
- Department of Radiology, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
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Wang Y, Zhou J, Chen X, Liu R, Zhang Z, Feng L, Feng Y, Wang G, Zhou Y. Effects of escitalopram therapy on effective connectivity among core brain networks in major depressive disorder. J Affect Disord 2024; 350:39-48. [PMID: 38220106 DOI: 10.1016/j.jad.2024.01.115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND Patients with major depressive disorder (MDD) have abnormal functional interaction among large-scale brain networks, indicated by aberrant effective connectivity of the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). However, it remains unclear whether antidepressants can normalize the altered effective connectivity in MDD. METHODS In this study, we collected resting-state functional magnetic resonance imaging data from 46 unmedicated patients with MDD at baseline and after 12 weeks of escitalopram treatment. We also collected data from 58 healthy controls (HCs) at the same time point with the same interval. Using spectral dynamic causal modeling and parametric empirical Bayes, we examined group differences, time effect and their interaction on the casual interactions among the regions of interest in the three networks. RESULTS Compared with HCs, patients with MDD showed increased positive (excitatory) connections within the DMN, decreased positive connections within the SN and DAN, decreased absolute value of negative (inhibitory) connectivity from the SN and DAN to the DMN, and decreased positive connections between the DAN and the SN. Furthermore, we found that six connections related to the DAN showed decreased group differences in effective connectivity between MDD and HCs during follow-up compared to the baseline. CONCLUSIONS Our findings suggest that escitalopram therapy can partly improve the disrupted effective connectivity among high-order brain functional networks in MDD. These findings deepened our understanding of the neural basis of antidepressants' effect on brain function in patients with MDD.
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Affiliation(s)
- Yun Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiongying Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Rui Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zhifang Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Lei Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yuan Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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34
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Gu J, Deng K, Luo X, Ma W, Tang X. Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery. Cereb Cortex 2024; 34:bhae139. [PMID: 38629796 DOI: 10.1093/cercor/bhae139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/19/2024] Open
Abstract
Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.
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Affiliation(s)
- Jin Gu
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, China
- Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, No. 999, Xi'an Road, Pidu District, Chengdu, China
| | - Kexin Deng
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, China
| | - Xiaoqi Luo
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, China
| | - Wanli Ma
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, China
| | - Xuegang Tang
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, China
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35
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Vaccaro AG, Wu H, Iyer R, Shakthivel S, Christie NC, Damasio A, Kaplan J. Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain. Cereb Cortex 2024; 34:bhae122. [PMID: 38566509 DOI: 10.1093/cercor/bhae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.
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Affiliation(s)
- Anthony G Vaccaro
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Helen Wu
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Rishab Iyer
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Shruti Shakthivel
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Nina C Christie
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Antonio Damasio
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Jonas Kaplan
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
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36
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Olgiati E, Violante IR, Xu S, Sinclair TG, Li LM, Crow JN, Kapsetaki ME, Calvo R, Li K, Nayar M, Grossman N, Patel MC, Wise RJS, Malhotra PA. Targeted non-invasive brain stimulation boosts attention and modulates contralesional brain networks following right hemisphere stroke. Neuroimage Clin 2024; 42:103599. [PMID: 38608376 PMCID: PMC11019269 DOI: 10.1016/j.nicl.2024.103599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Right hemisphere stroke patients frequently present with a combination of lateralised and non-lateralised attentional deficits characteristic of the neglect syndrome. Attentional deficits are associated with poor functional outcome and are challenging to treat, with non-lateralised deficits often persisting into the chronic stage and representing a common complaint among patients and families. In this study, we investigated the effects of non-invasive brain stimulation on non-lateralised attentional deficits in right-hemispheric stroke. In a randomised double-blind sham-controlled crossover study, twenty-two patients received real and sham transcranial Direct Current Stimulation (tDCS) whilst performing a non-lateralised attentional task. A high definition tDCS montage guided by stimulation modelling was employed to maximise current delivery over the right dorsolateral prefrontal cortex, a key node in the vigilance network. In a parallel study, we examined brain network response to this tDCS montage by carrying out concurrent fMRI during stimulation in healthy participants and patients. At the group level, stimulation improved target detection in patients, reducing overall error rate when compared with sham stimulation. TDCS boosted performance throughout the duration of the task, with its effects briefly outlasting stimulation cessation. Exploratory lesion analysis indicated that response to stimulation was related to lesion location rather than volume. In particular, reduced stimulation response was associated with damage to the thalamus and postcentral gyrus. Concurrent stimulation-fMRI revealed that tDCS did not affect local connectivity but influenced functional connectivity within large-scale networks in the contralesional hemisphere. This combined behavioural and functional imaging approach shows that brain stimulation targeted to surviving tissue in the ipsilesional hemisphere improves non-lateralised attentional deficits following stroke. This effect may be exerted via contralesional network effects.
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Affiliation(s)
- Elena Olgiati
- Imperial College London, Department of Brain Sciences, UK; Imperial College Healthcare NHS Trust, UK.
| | - Ines R Violante
- Imperial College London, Department of Brain Sciences, UK; University of Surrey, Department of Psychology, UK
| | - Shuler Xu
- Imperial College London, Department of Brain Sciences, UK; University College London, UK
| | | | - Lucia M Li
- Imperial College London, Department of Brain Sciences, UK; UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK
| | - Jennifer N Crow
- Imperial College London, Department of Brain Sciences, UK; Imperial College Healthcare NHS Trust, UK
| | | | - Roberta Calvo
- UTHealth, Department of Neurobiology and Anatomy, McGovern Medical School, Houston, US
| | - Korina Li
- Imperial College London, Department of Brain Sciences, UK; University College London, UK
| | | | - Nir Grossman
- Imperial College London, Department of Brain Sciences, UK; UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK
| | - Maneesh C Patel
- Imperial College London, Department of Brain Sciences, UK; Imperial College Healthcare NHS Trust, UK
| | - Richard J S Wise
- Imperial College London, Department of Brain Sciences, UK; Imperial College Healthcare NHS Trust, UK
| | - Paresh A Malhotra
- Imperial College London, Department of Brain Sciences, UK; Imperial College Healthcare NHS Trust, UK; UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK
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Kumar U, Dhanik K, Mishra M, Pandey HR, Keshri A. Mapping the unique neural engagement in deaf individuals during picture, word, and sign language processing: fMRI study. Brain Imaging Behav 2024:10.1007/s11682-024-00878-7. [PMID: 38523177 DOI: 10.1007/s11682-024-00878-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 03/26/2024]
Abstract
Employing functional magnetic resonance imaging (fMRI) techniques, we conducted a comprehensive analysis of neural responses during sign language, picture, and word processing tasks in a cohort of 35 deaf participants and contrasted these responses with those of 35 hearing counterparts. Our voxel-based analysis unveiled distinct patterns of brain activation during language processing tasks. Deaf individuals exhibited robust bilateral activation in the superior temporal regions during sign language processing, signifying the profound neural adaptations associated with sign comprehension. Similarly, during picture processing, the deaf cohort displayed activation in the right angular, right calcarine, right middle temporal, and left angular gyrus regions, elucidating the neural dynamics engaged in visual processing tasks. Intriguingly, during word processing, the deaf group engaged the right insula and right fusiform gyrus, suggesting compensatory mechanisms at play during linguistic tasks. Notably, the control group failed to manifest additional or distinctive regions in any of the tasks when compared to the deaf cohort, underscoring the unique neural signatures within the deaf population. Multivariate Pattern Analysis (MVPA) of functional connectivity provided a more nuanced perspective on connectivity patterns across tasks. Deaf participants exhibited significant activation in a myriad of brain regions, including bilateral planum temporale (PT), postcentral gyrus, insula, and inferior frontal regions, among others. These findings underscore the intricate neural adaptations in response to auditory deprivation. Seed-based connectivity analysis, utilizing the PT as a seed region, revealed unique connectivity pattern across tasks. These connectivity dynamics provide valuable insights into the neural interplay associated with cross-modal plasticity.
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Affiliation(s)
- Uttam Kumar
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, Uttar Pradesh, 226014, India.
| | - Kalpana Dhanik
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, Uttar Pradesh, 226014, India
| | - Mrutyunjaya Mishra
- Department of Special Education (Hearing Impairments), Dr. Shakuntala Misra National Rehabilitation University, Lucknow, India
| | - Himanshu R Pandey
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, Uttar Pradesh, 226014, India
| | - Amit Keshri
- Department of Neuro-Otology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India
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Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
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Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
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Ju U, Wallraven C. Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole-brain analysis. Hum Brain Mapp 2024; 45:e26652. [PMID: 38488473 PMCID: PMC10941534 DOI: 10.1002/hbm.26652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 03/18/2024] Open
Abstract
Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating-based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time-resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.
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Affiliation(s)
- Uijong Ju
- Department of Information DisplayKyung Hee UniversitySeoulSouth Korea
| | - Christian Wallraven
- Department of Brain and Cognitive EngineeringKorea UniversitySouth Korea
- Department of Artificial IntelligenceKorea UniversitySouth Korea
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Huang W, Fang X, Li S, Mao R, Ye C, Liu W, Deng Y, Lin G. Abnormal characteristic static and dynamic functional network connectivity in idiopathic normal pressure hydrocephalus. CNS Neurosci Ther 2024; 30:e14178. [PMID: 36949617 PMCID: PMC10915979 DOI: 10.1111/cns.14178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
AIMS Idiopathic Normal pressure hydrocephalus (iNPH) is a neurodegenerative disease characterized by gait disturbance, dementia, and urinary dysfunction. The neural network mechanisms underlying this phenomenon is currently unknown. METHODS To investigate the resting-state functional connectivity (rs-FC) abnormalities of iNPH-related brain connectivity from static and dynamic perspectives and the correlation of these abnormalities with clinical symptoms before and 3-month after shunt. We investigated both static and dynamic functional network connectivity (sFNC and dFNC, respectively) in 33 iNPH patients and 23 healthy controls (HCs). RESULTS The sFNC and dFNC of networks were generally decreased in iNPH patients. The reduction in sFNC within the default mode network (DMN) and between the somatomotor network (SMN) and visual network (VN) were related to symptoms. The temporal properties of dFNC and its temporal variability in state-4 were sensitive to the identification of iNPH and were correlated with symptoms. The temporal variability in the dorsal attention network (DAN) increased, and the average instantaneous FC was altered among networks in iNPH. These features were partially associated with clinical symptoms. CONCLUSION The dFNC may be a more sensitive biomarker for altered network function in iNPH, providing us with extra information on the mechanisms of iNPH.
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Affiliation(s)
- Wenjun Huang
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Xuhao Fang
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Shihong Li
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Renling Mao
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Chuntao Ye
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Wei Liu
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Yao Deng
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Guangwu Lin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
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Ding JR, Feng C, Zhang H, Li Y, Tang Z, Chen Q, Ding X, Wang M, Ding Z. Changes in Resting-State Networks in Children with Growth Hormone Deficiency. Brain Connect 2024; 14:84-91. [PMID: 38264988 DOI: 10.1089/brain.2023.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024] Open
Abstract
Purpose: Growth hormone deficiency (GHD) refers to the partial or complete lack of growth hormone. Short stature and slow growth are characteristic of patients with GHD. Previous neuroimaging studies have suggested that GHD may cause cognitive and behavioral impairments in patients. Resting-state networks (RSNs) are regions of the brain that exhibit synchronous activity and are closely related to our cognition and behavior. Therefore, the purpose of the current study was to explore cognitive and behavioral abnormalities in children with GHD by investigating changes in RSNs. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 26 children with GHD and 15 healthy controls (HCs) were obtained. Independent component analysis was used to identify seven RSNs from rs-fMRI data. Group differences in RSNs were estimated using two-sample t-tests. Correlation analysis was employed to investigate the associations among the areas of difference and clinical measures. Results: Compared with HCs, children with GHD had significant differences in the salience network (SN), default mode network (DMN), language network (LN), and sensorimotor network (SMN). Moreover, within the SN, the functional connectivity (FC) value of the right posterior supramarginal gyrus was negatively correlated with the adrenocorticotropic hormone and the FC value of the left anterior inferior parietal gyrus was positively correlated with insulin-like growth factor 1. Conclusions: These results suggest that alterations in RSNs may account for abnormal cognition and behavior in children with GHD, such as decreased motor function, language withdrawal, anxiety, and social anxiety. These findings provide neuroimaging support for uncovering the pathophysiological mechanisms of GHD in children. Impact statement Children with growth hormone deficiency (GHD) generally experience cognitive and behavioral abnormalities. However, there are few neuroimaging studies on children with GHD. Moreover, prior research has not investigated the aberrant brain function in patients with GHD from the perspective of brain functional networks. Therefore, this study employed the independent component analysis method to investigate alterations within seven commonly observed resting-state networks due to GHD. The results showed that children with GHD had significant differences in the salience network, default mode network, language network, and sensorimotor network. This provides neuroimaging support for revealing the pathophysiological mechanisms of GHD in children.
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Affiliation(s)
- Ju-Rong Ding
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Chenyu Feng
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Hui Zhang
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Yuan Li
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Zhiling Tang
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Qiang Chen
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P.R. China
- Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, P.R. China
| | - Xin Ding
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, P.R. China
| | - Mei Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China
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Kroon E, Toenders YJ, Kuhns LN, Cousijn J, Filbey F. Resting state functional connectivity in dependent cannabis users: The moderating role of cannabis attitudes. Drug Alcohol Depend 2024; 256:111090. [PMID: 38301388 DOI: 10.1016/j.drugalcdep.2024.111090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/04/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND The global increase in lenient cannabis policy has been paralleled by reduced harm perception, which has been associated with cannabis use initiation and persistent use. However, it is unclear how cannabis attitudes might affect the brain processes underlying cannabis use. METHODS Resting state functional connectivity (RSFC) within and between the executive control network (ECN), salience network (SN), and default mode network (DMN) was assessed in 110 near-daily cannabis users with cannabis use disorder (CUD) and 79 controls from The Netherlands and Texas, USA. Participants completed a questionnaire assessing the perceived benefits and harms of cannabis use from their personal, friends-family's, and country-state's perspectives and reported on their cannabis use (gram/week), CUD severity, and cannabis-related problems. RESULTS RSFC within the dorsal SN was lower in cannabis users than controls, while no group differences in between-network RSFC were observed. Furthermore, heavier cannabis use was associated with lower dorsal SN RSFC in the cannabis group. Perceived benefits and harms of cannabis - from personal, friends-family's, and country-state's perspectives - moderated associations of cannabis use, CUD severity, and cannabis use-related problems with within-network RSFC of the SN, ECN, and DMN. Personal perceived benefits and country-state perceived harms moderated the association between CUD severity and RSFC between the ventral and dorsal DMN. CONCLUSIONS This study highlights the importance of considering individual differences in the perceived harms and benefits of cannabis use as a factor in the associations between brain functioning and cannabis use, CUD severity, and cannabis use-related problems.
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Affiliation(s)
- E Kroon
- Neuroscience of Addiction Lab, Department of Psychology, Education & Child Studies, Erasmus University Rotterdam, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Y J Toenders
- Developmental and Educational Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands; Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands
| | - L N Kuhns
- Neuroscience of Addiction Lab, Department of Psychology, Education & Child Studies, Erasmus University Rotterdam, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - J Cousijn
- Neuroscience of Addiction Lab, Department of Psychology, Education & Child Studies, Erasmus University Rotterdam, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - F Filbey
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
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43
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Li Y, Ran Y, Yao M, Chen Q. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study. Neurobiol Dis 2024; 192:106425. [PMID: 38296113 DOI: 10.1016/j.nbd.2024.106425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Yun Ran
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Maohua Yao
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
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Zhi S, Zhao W, Huang Y, Li Y, Wang X, Li J, Liu S, Xu Y. Neuroticism and openness exhibit an anti-correlation pattern to dissociable default mode network: using resting connectivity and structural equation modeling analysis. Brain Imaging Behav 2024:10.1007/s11682-024-00869-8. [PMID: 38409462 DOI: 10.1007/s11682-024-00869-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2024] [Indexed: 02/28/2024]
Abstract
The default mode network (DMN) can be subdivided into ventral and dorsal subsystems, which serve affective cognition and mental sense construction, respectively. An internally dissociated pattern of anti-correlations was observed between these two subsystems. Although numerous studies on neuroticism and openness have demonstrated the neurological functions of the DMN, little is known about whether different subsystems and hubs regions within the network are engaged in different functions in response to the two traits. We recruited 223 healthy volunteers in this study and collected their resting-state functional magnetic resonance imaging (fMRI) and NEO Five-Factor Inventory scores. We used independent component analysis (ICA) to obtain the DMN, before further decomposing it into the ventral and dorsal subsystems. Then, the network coherence of hubs regions within subsystems was extracted to construct two structural equation models (SEM) to explore the relationship between neuroticism and openness traits and DMN. We observed that the ventral DMN could significantly predict positive openness and negative neuroticism. The dorsal DMN was diametrically opposed. Additionally, the medial prefrontal cortex (mPFC) and middle temporal gyrus (MTG), both of which are core hubs of the subnetworks within the DMN, are significantly positively correlated with neuroticism and openness. These findings may point to a biological basis that neuroticism and openness are engaged in opposite mechanisms and support the hypothesis about the functional dissociation of the DMN.
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Affiliation(s)
- Shengwen Zhi
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Wentao Zhao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yifei Huang
- School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, China
| | - Yue Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xiao Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, 030001, Taiyuan, P.R. China.
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Yong Xu
- Department of Psychiatry, Taiyuan Central Hospital of Shanxi Medical University, Taiyuan, 030032, China.
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Kuang LD, Li HQ, Zhang J, Gui Y, Zhang J. Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework. J Neural Eng 2024; 21:016032. [PMID: 38335544 DOI: 10.1088/1741-2552/ad27ee] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/09/2024] [Indexed: 02/12/2024]
Abstract
Objective.Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.Approach.The proposed SLRCPD approach imposes two constraints. First, the L1regularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.Main results.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.Significance.This study significantly excavates spatio-temporal patterns for schizophrenia disease.
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Affiliation(s)
- Li-Dan Kuang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - He-Qiang Li
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jianming Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Yan Gui
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jin Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
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Mo F, Zhao H, Li Y, Cai H, Song Y, Wang R, Yu Y, Zhu J. Network Localization of State and Trait of Auditory Verbal Hallucinations in Schizophrenia. Schizophr Bull 2024:sbae020. [PMID: 38401526 DOI: 10.1093/schbul/sbae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/26/2024]
Abstract
BACKGROUND AND HYPOTHESIS Neuroimaging studies investigating the neural substrates of auditory verbal hallucinations (AVH) in schizophrenia have yielded mixed results, which may be reconciled by network localization. We sought to examine whether AVH-state and AVH-trait brain alterations in schizophrenia localize to common or distinct networks. STUDY DESIGN We initially identified AVH-state and AVH-trait brain alterations in schizophrenia reported in 48 previous studies. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we then leveraged novel functional connectivity network mapping to construct AVH-state and AVH-trait dysfunctional networks. STUDY RESULTS The neuroanatomically heterogeneous AVH-state and AVH-trait brain alterations in schizophrenia localized to distinct and specific networks. The AVH-state dysfunctional network comprised a broadly distributed set of brain regions mainly involving the auditory, salience, basal ganglia, language, and sensorimotor networks. Contrastingly, the AVH-trait dysfunctional network manifested as a pattern of circumscribed brain regions principally implicating the caudate and inferior frontal gyrus. Additionally, the AVH-state dysfunctional network aligned with the neuromodulation targets for effective treatment of AVH, indicating possible clinical relevance. CONCLUSIONS Apart from unifying the seemingly irreproducible neuroimaging results across prior AVH studies, our findings suggest different neural mechanisms underlying AVH state and trait in schizophrenia from a network perspective and more broadly may inform future neuromodulation treatment for AVH.
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Affiliation(s)
- Fan Mo
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Han Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yifan Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yang Song
- Department of Pain, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
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47
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Liu X, Jiao G, Zhou F, Kendrick KM, Yao D, Gong Q, Xiang S, Jia T, Zhang XY, Zhang J, Feng J, Becker B. A neural signature for the subjective experience of threat anticipation under uncertainty. Nat Commun 2024; 15:1544. [PMID: 38378947 PMCID: PMC10879105 DOI: 10.1038/s41467-024-45433-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.
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Affiliation(s)
- Xiqin Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Guojuan Jiao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
- MOE Key Laboratory of Cognition and Personality, Chongqing, China
| | - Keith M Kendrick
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Dezhong Yao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Benjamin Becker
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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48
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Zeng X, Han X, Zheng D, Jiang P, Yuan Z. Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis. Psychol Med 2024; 54:473-487. [PMID: 38047402 DOI: 10.1017/s0033291723003434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.
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Affiliation(s)
- Xinglin Zeng
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, 999078, China
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Dong Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ping Jiang
- West China Medical Publishers, West China Hospital of Sichuan University, Chengdu, 610041, People's Republic of China
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, 999078, China
- Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
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49
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Li X, Zhang W, Bi Y, Chen J, Fu L, Zhang Z, Chen Q, Zhang X, Zhu Z, Zhang B. Non-alcoholic fatty liver disease is associated with brain function disruption in type 2 diabetes patients without cognitive impairment. Diabetes Obes Metab 2024; 26:650-662. [PMID: 37961040 DOI: 10.1111/dom.15354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023]
Abstract
AIMS To investigate the neural static and dynamic intrinsic activity of intra-/inter-network topology among patients with type 2 diabetes (T2D) with non-alcoholic fatty liver disease (NAFLD) and those without NAFLD (T2NAFLD group and T2noNAFLD group, respectively) and to assess the relationship with metabolism. METHODS Fifty-six patients with T2NAFLD, 78 with T2noNAFLD, and 55 healthy controls (HCs) were recruited to the study. Participants had normal cognition and underwent functional magnetic resonance imaging scans, clinical measurements, and global cognition evaluation. Independent component analysis was used to identify frequency spectrum parameters, static functional network connectivity, and temporal properties of dynamic functional network connectivity (P < 0.05, false discovery rate-corrected). Statistical analysis involved one-way analysis of covariance with post hoc, partial correlation and canonical correlation analyses. RESULTS Our findings showed that: (i) T2NAFLD patients had more disordered glucose and lipid metabolism, had more severe insulin resistance, and were more obese than T2noNAFLD patients; (ii) T2D patients exhibited disrupted brain function, as evidenced by alterations in intra-/inter-network topology, even without clinically measurable cognitive impairment; (iii) T2NAFLD patients had more significant reductions in the frequency spectrum parameters of cognitive executive and visual networks than those with T2noNAFLD; and (iv) altered brain function in T2D patients was correlated with postprandial glucose, high-density lipoprotein cholesterol, and waist-hip ratio. CONCLUSION This study may provide novel insights into neuroimaging correlates for underlying pathophysiological processes inducing brain damage in T2NAFLD. Thus, controlling blood glucose levels, lipid levels and abdominal obesity may reduce brain damage risk in such patients.
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Affiliation(s)
- Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Wen Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Yan Bi
- Department of Endocrinology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Linqing Fu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhou Zhang
- Department of Endocrinology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qian Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Institute of Brain Science, Nanjing University, Nanjing, China
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50
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Thakuri DS, Bhattarai P, Wong DF, Chand GB. Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling. Brain Connect 2024; 14:70-79. [PMID: 38164105 PMCID: PMC10890948 DOI: 10.1089/brain.2023.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.
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Affiliation(s)
- Deepa S. Thakuri
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Departments of Medicine and Radiology, University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Puskar Bhattarai
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Dean F. Wong
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Departments of Neuroscience, Psychiatry, and Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ganesh B. Chand
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA
- Institute of Clinical and Translational Sciences, Washington University School of Medicine, St. Louis, Missouri, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, USA
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