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Fazio G, Olivo D, Wolf ND, Hirjak D, Schmitgen MM, Werler F, Witteman M, Kubera KM, Calhoun VD, Reith W, Wolf RC, Sambataro F. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study. Addict Biol 2024; 29:e13395. [PMID: 38709211 PMCID: PMC11072977 DOI: 10.1111/adb.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 02/05/2024] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.
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Affiliation(s)
- Giovanni Fazio
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Daniele Olivo
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Nadine D. Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Mike M. Schmitgen
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Florian Werler
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Miriam Witteman
- Department of Psychiatry and PsychotherapySaarland UniversitySaarbrückenGermany
| | - Katharina M. Kubera
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Wolfgang Reith
- Department of NeuroradiologySaarland UniversitySaarbrückenGermany
| | - Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Fabio Sambataro
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
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Lindquist MA. Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data. bioRxiv 2024:2023.10.06.561221. [PMID: 37873165 PMCID: PMC10592634 DOI: 10.1101/2023.10.06.561221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Resting-state fMRI (rs-fMRI) data is used to study the intrinsic functional connectivity (FC) in the human brain. In the past decade, interest has focused on studying the temporal dynamics of FC on short timescales, ranging from seconds to minutes. These studies of time-varying FC (TVFC) have enabled the classification of whole-brain dynamic FC profiles into distinct "brain states", defined as recurring whole-brain connectivity profiles reliably observed across subjects and sessions. The analysis of rs-fMRI data is complicated by the fact that the measured BOLD signal consists of changes induced by neuronal activation, as well as non-neuronal nuisance fluctuations that should be removed prior to further analysis. Thus, the data undergoes significant preprocesing prior to analysis. In previous work [24], we illustrated the potential pitfalls involved with using modular preprocessing pipelines, showing how later preprocessing steps can reintroduce correlation with signal previously removed from the data. Here we show that the problem runs deeper, and that certain statistical analysis techniques can potentially interact with preprocessing and reintroduce correlations with previously removed signal. One such technique is the popular sliding window analysis, used to compute TVFC. In this paper, we discuss the problem both theoretically and empirically in application to test-retest rs-fMRI data. Importantly, we show that we are able to obtain essentially the same brain states and state transitions when analyzing motion induced signal as we do when analyzing the preprocessed but windowed data. Our results cast doubt on whether the estimated brain states obtained using sliding window analysis are neuronal in nature, or simply reflect non-neuronal nuisance signal variation (e.g., motion).
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3
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Zou H. iHBPs-VWDC: variable-length window-based dynamic connectivity approach for identifying hormone-binding proteins. J Biomol Struct Dyn 2023:1-10. [PMID: 37978902 DOI: 10.1080/07391102.2023.2283150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023]
Abstract
Hormone-binding proteins (HBPs) are soluble carrier proteins that play a vital role in the growth and development of living organisms. Identifying HBPs accurately is crucial for understanding their functions. However, traditional wet lab experimental methods are labor intensive and cost ineffective. Therefore, there is a need for computational methods to efficiently identify HBPs. In this study, a machine learning method based on support vector machine (SVM) was proposed for the accurate and efficient identification of HBPs. The encoding of protein sequences involved using fifty different physicochemical (PC) properties. A variable-length window-based dynamic connectivity method was applied to capture the connection information between two different PC properties through two distinct strategies. The canonical correlation analysis algorithm was then used to fuse features obtained from these approaches. Feature selection was performed using the F-score approach to choose the most discriminative features. Finally, these selected features were fed into the SVM to discriminate between HBPs and non-HBPs. The proposed method achieved high classification accuracies of 99.19%, 96.77%, and 94.57% on the main dataset and two independent datasets, respectively, as demonstrated in the jackknife test. Comparative results showed that our proposed method outperforms existing approaches on the same datasets, indicating its potential as a useful tool for identifying HBPs. The Matlab codes and datasets used in the current study are freely available at https://figshare.com/articles/online_resource/iHBPs-VWDC/23559834.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China
- Jiangxi Engineering Research Center of Unattended Perception System and Artificial Intelligence Technology, Jiangxi Science and Technology Normal University, Nanchang, China
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4
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Levakov G, Sporns O, Avidan G. Fine-scale dynamics of functional connectivity in the face-processing network during movie watching. Cell Rep 2023; 42:112585. [PMID: 37285265 DOI: 10.1016/j.celrep.2023.112585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/02/2023] [Accepted: 05/16/2023] [Indexed: 06/09/2023] Open
Abstract
Mapping the human face-processing network is typically done during rest or using isolated, static face images, overlooking widespread cortical interactions obtained in response to naturalistic face dynamics and context. To determine how inter-subject functional correlation (ISFC) relates to face recognition scores, we measure cortical connectivity patterns in response to a dynamic movie in typical adults (N = 517). We find a positive correlation with recognition scores in edges connecting the occipital visual and anterior temporal regions and a negative correlation in edges connecting the attentional dorsal, frontal default, and occipital visual regions. We measure the inter-subject stimulus-evoked response at a single TR resolution and demonstrate that co-fluctuations in face-selective edges are related to activity in core face-selective regions and that the ISFC patterns peak during boundaries between movie segments rather than during the presence of faces. Our approach demonstrates how face processing is linked to fine-scale dynamics in attentional, memory, and perceptual neural circuitry.
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Affiliation(s)
- Gidon Levakov
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Galia Avidan
- Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Ramirez-Mahaluf JP, Tepper Á, Alliende LM, Mena C, Castañeda CP, Iruretagoyena B, Nachar R, Reyes-Madrigal F, León-Ortiz P, Mora-Durán R, Ossandon T, Gonzalez-Valderrama A, Undurraga J, de la Fuente-Sandoval C, Crossley NA. Dysconnectivity in Schizophrenia Revisited: Abnormal Temporal Organization of Dynamic Functional Connectivity in Patients With a First Episode of Psychosis. Schizophr Bull 2023; 49:706-716. [PMID: 36472382 PMCID: PMC10154721 DOI: 10.1093/schbul/sbac187] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND HYPOTHESIS Abnormal functional connectivity between brain regions is a consistent finding in schizophrenia, including functional magnetic resonance imaging (fMRI) studies. Recent studies have highlighted that connectivity changes in time in healthy subjects. We here examined the temporal changes in functional connectivity in patients with a first episode of psychosis (FEP). Specifically, we analyzed the temporal order in which whole-brain organization states were visited. STUDY DESIGN Two case-control studies, including in each sample a subgroup scanned a second time after treatment. Chilean sample included 79 patients with a FEP and 83 healthy controls. Mexican sample included 21 antipsychotic-naïve FEP patients and 15 healthy controls. Characteristics of the temporal trajectories between whole-brain functional connectivity meta-states were examined via resting-state functional MRI using elements of network science. We compared the cohorts of cases and controls and explored their differences as well as potential associations with symptoms, cognition, and antipsychotic medication doses. STUDY RESULTS We found that the temporal sequence in which patients' brain dynamics visited the different states was more redundant and segregated. Patients were less flexible than controls in changing their network in time from different configurations, and explored the whole landscape of possible states in a less efficient way. These changes were related to the dose of antipsychotics the patients were receiving. We replicated the relationship with antipsychotic medication in the antipsychotic-naïve FEP sample scanned before and after treatment. CONCLUSIONS We conclude that psychosis is related to a temporal disorganization of the brain's dynamic functional connectivity, and this is associated with antipsychotic medication use.
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Affiliation(s)
- Juan P Ramirez-Mahaluf
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ángeles Tepper
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Luz Maria Alliende
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carlos Mena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Carmen Paz Castañeda
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Barbara Iruretagoyena
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Ruben Nachar
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Pablo León-Ortiz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Ricardo Mora-Durán
- Emergency Department, Hospital Fray Bernardino Álvarez, Mexico City, Mexico
| | - Tomas Ossandon
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Center for Integrative Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alfonso Gonzalez-Valderrama
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
- Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
- Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Nicolas A Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Católica de, Santiago, Chile
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 DOI: 10.1002/hbm.26291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Juanning Si
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Beijing Institute of Geriatrics, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
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Panico F, Schintu S, Trojano L. Editorial: Uncovering the neural correlates of prism adaptation: Evidence from the brain network approach. Front Psychol 2022; 13:1076307. [PMID: 36457912 PMCID: PMC9706180 DOI: 10.3389/fpsyg.2022.1076307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 08/01/2023] Open
Affiliation(s)
- Francesco Panico
- Department of Psychology, University of Campania ‘Luigi Vanvitelli', Caserta, Italy
| | - Selene Schintu
- CIMeC—Center for Mind/Brain Sciences, University of Trento, Trento, Italy
- Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
- Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, United States
| | - Luigi Trojano
- Department of Psychology, University of Campania ‘Luigi Vanvitelli', Caserta, Italy
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8
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Mozelewski TG, Robbins ZJ, Scheller RM. Forecasting the influence of conservation strategies on landscape connectivity. Conserv Biol 2022; 36:e13904. [PMID: 35212035 DOI: 10.1111/cobi.13904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 06/14/2023]
Abstract
Maintaining and enhancing landscape connectivity reduces biodiversity declines due to habitat fragmentation. Uncertainty remains, however, about the effectiveness of conservation for enhancing connectivity for multiple species on dynamic landscapes, especially over long time horizons. We forecasted landscape connectivity from 2020 to 2100 under four common conservation land-acquisition strategies: acquiring the lowest cost land, acquiring land clustered around already established conservation areas, acquiring land with high geodiversity characteristics, and acquiring land opportunistically. We used graph theoretic metrics to quantify landscape connectivity across these four strategies, evaluating connectivity for four ecologically relevant species guilds that represent endpoints along a spectrum of vagility and habitat specificity: long- versus short-distance dispersal ability and habitat specialists versus generalists. We applied our method to central North Carolina and incorporated landscape dynamics, including forest growth, succession, disturbance, and management. Landscape connectivity improved for specialist species under all conservation strategies employed, although increases were highly variable across strategies. For generalist species, connectivity improvements were negligible. Overall, clustering the development of new protected areas around land already designated for conservation yielded the largest improvements in connectivity; increases were several orders of magnitude beyond current landscape connectivity for long- and short-distance dispersing specialist species. Conserving the lowest cost land contributed the least to connectivity. Our approach provides insight into the connectivity contributions of a suite of conservation alternatives prior to on-the-ground implementation and, therefore, can inform connectivity planning to maximize conservation benefit.
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Affiliation(s)
- Tina G Mozelewski
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
| | - Zachary J Robbins
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
| | - Robert M Scheller
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
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Zhang G, Li N, Liu H, Zheng H, Zheng W. Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake. Front Neurosci 2022; 16:974778. [PMID: 36203810 PMCID: PMC9531019 DOI: 10.3389/fnins.2022.974778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/02/2022] [Indexed: 02/04/2023] Open
Abstract
AIMS Currently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity. MATERIALS AND METHODS Twenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions. RESULTS Three optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain's main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants' subjective feelings after drinking. CONCLUSION The current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.
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Affiliation(s)
- Gengbiao Zhang
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Ni Li
- The Family Medicine Branch, Department of Radiology, The First Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongkun Liu
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongyi Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Wenbin Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
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10
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Yang L, Liu Q, Zhou Y, Wang X, Wu T, Chen Z. No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals. Front Public Health 2022; 9:734370. [PMID: 35096727 PMCID: PMC8793026 DOI: 10.3389/fpubh.2021.734370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022] Open
Abstract
Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.
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Affiliation(s)
- Lei Yang
- China Academy of Information and Communications Technology, Beijing, China
| | - Qingmeng Liu
- China Academy of Information and Communications Technology, Beijing, China
| | - Yu Zhou
- China Academy of Information and Communications Technology, Beijing, China
| | - Xing Wang
- China Academy of Information and Communications Technology, Beijing, China
| | - Tongning Wu
- China Academy of Information and Communications Technology, Beijing, China
| | - Zhiye Chen
- Hainan Hospital of Chinese People's Liberation Army General Hospital, Hainan, China
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11
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Endemann C, Krause BM, Nourski KV, Banks MI, Veen BV. Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data. Neuroimage 2022; 254:119057. [PMID: 35354095 PMCID: PMC9360562 DOI: 10.1016/j.neuroimage.2022.119057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/04/2022] [Accepted: 03/03/2022] [Indexed: 12/01/2022] Open
Abstract
Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 - 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.
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Affiliation(s)
| | - Bryan M Krause
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA
| | - Matthew I Banks
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA; Department of Neuroscience, University of Wisconsin, Madison, WI, USA.
| | - Barry Van Veen
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA
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12
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Warthen KG, Welsh RC, Sanford B, Koppelmans V, Burmeister M, Mickey BJ. Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network. Front Syst Neurosci 2021; 15:629488. [PMID: 34867217 PMCID: PMC8636673 DOI: 10.3389/fnsys.2021.629488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 10/25/2021] [Indexed: 11/24/2022] Open
Abstract
Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.
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Affiliation(s)
- Katherine G. Warthen
- Department of Biomedical Engineering, The University of Utah, Salt Lake City, UT, United States
| | - Robert C. Welsh
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
| | - Benjamin Sanford
- Department of Psychiatry, University of Michigan, Michigan, MI, United States
| | - Vincent Koppelmans
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
| | - Margit Burmeister
- Michigan Neuroscience Institute and Departments of Computational Medicine & Bioinformatics, Human Genetics and Psychiatry, The University of Michigan, Michigan, MI, United States
| | - Brian J. Mickey
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
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13
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Park HJ, Eo J, Pae C, Son J, Park SM, Kang J. State-Dependent Effective Connectivity in Resting-State fMRI. Front Neural Circuits 2021; 15:719364. [PMID: 34776875 PMCID: PMC8579116 DOI: 10.3389/fncir.2021.719364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023] Open
Abstract
The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.
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Affiliation(s)
- Hae-Jeong Park
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jinseok Eo
- Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Chongwon Pae
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Junho Son
- Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Sung Min Park
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea
| | - Jiyoung Kang
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
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14
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Gao S, Mishne G, Scheinost D. Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Hum Brain Mapp 2021; 42:4510-4524. [PMID: 34184812 PMCID: PMC8410525 DOI: 10.1002/hbm.25561] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/26/2021] [Accepted: 05/30/2021] [Indexed: 02/02/2023] Open
Abstract
Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable.
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Affiliation(s)
- Siyuan Gao
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | - Gal Mishne
- Halıcıoğlu Data Science Institute, University of California San DiegoLa JollaCaliforniaUSA
- Neurosciences Graduate Program, University of California San DiegoLa JollaCaliforniaUSA
| | - Dustin Scheinost
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
- Department of Statistics and Data ScienceYale UniversityNew HavenConnecticutUSA
- Child Study Center, Yale School of MedicineNew HavenConnecticutUSA
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15
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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16
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Barber AD, Hegarty CE, Lindquist M, Karlsgodt KH. Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks. Cereb Cortex 2021; 31:2834-2844. [PMID: 33429433 DOI: 10.1093/cercor/bhaa391] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 01/26/2023] Open
Abstract
Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.
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Affiliation(s)
- Anita D Barber
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York, 11004, USA.,Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, New York, 11030, USA.,Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | | | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, 21205, USA
| | - Katherine H Karlsgodt
- Department of Psychology, University of California, Los Angeles, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 90095, USA
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17
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Reichert BE, Fletcher RJ, Kitchens WM. The demographic contributions of connectivity versus local dynamics to population growth of an endangered bird. J Anim Ecol 2020; 90:574-584. [PMID: 33179773 DOI: 10.1111/1365-2656.13387] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 10/07/2020] [Indexed: 11/30/2022]
Abstract
Conservation and management increasingly focus on connectivity, because connectivity driven by variation in immigration rates across landscapes is thought to be crucial for maintaining local population and metapopulation persistence. Yet, efforts to quantify the relative role of immigration on population growth across the entire range of species and over time have been lacking. We assessed whether immigration limited local and range-wide population growth of the endangered snail kite Rostrhamus sociabilis in Florida, USA, over 18 years using multi-state, reverse-time modelling that accounts for imperfect detection of individuals and unobservable states. Demographic contributions of immigration varied depending on the dynamics and geographic position of the local populations, were scale-dependent and changed over time. By comparing the relative contributions of immigration versus local demography for periods of significant change in local abundance, we found empirical evidence for a disproportionately large role of immigration in facilitating population growth of a centrally located population-a connectivity 'hub'. The importance of connectivity changed depending of the spatial scale considered, such that immigration was a more important driver of population growth at small versus large spatial scales. Furthermore, the contribution of immigration was much greater during time periods when local population size was small, emphasizing abundance-dependent rescue effects. Our findings suggest that efforts aimed at improving local breeding habitat will likely be most effective at increasing snail kite population growth. More broadly, our results provide much needed information on the role of connectivity for population growth, suggesting that connectivity conservation may have the greatest benefits when efforts focus on centrally located habitat patches and small populations. Furthermore, our results highlight that connectivity is highly dynamic over time and that interpreting the effects of connectivity at local scales may not transfer to region-wide dynamics.
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Affiliation(s)
- Brian E Reichert
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | - Robert J Fletcher
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | - Wiley M Kitchens
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
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18
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Mills EP, Alshelh Z, Kosanovic D, Di Pietro F, Vickers ER, Macey PM, Henderson LA. Altered Brainstem Pain-Modulation Circuitry Connectivity During Spontaneous Pain Intensity Fluctuations. J Pain Res 2020; 13:2223-2235. [PMID: 32943915 PMCID: PMC7481287 DOI: 10.2147/jpr.s252594] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 07/07/2020] [Indexed: 11/23/2022] Open
Abstract
Background Chronic pain, particularly that following nerve injury, can occur in the absence of external stimuli. Although the ongoing pain is sometimes continuous, in many individuals the intensity of their pain fluctuates. Experimental animal studies have shown that the brainstem contains circuits that modulate nociceptive information at the primary afferent synapse and these circuits are involved in maintaining ongoing continuous neuropathic pain. However, it remains unknown if these circuits are involved in regulating fluctuations of ongoing neuropathic pain in humans. Methods We used functional magnetic resonance imaging to determine whether in 19 subjects with painful trigeminal neuropathy, brainstem pain-modulation circuitry function changes according to moment-to-moment fluctuations in spontaneous pain intensity as rated online over a 12-minute period. Results We found that when pain intensity was spontaneously high, connectivity strengths between regions of the brainstem endogenous pain-modulating circuitry-the midbrain periaqueductal gray, rostral ventromedial medulla (RVM), and the spinal trigeminal nucleus (SpV)-were high, and vice-versa (when pain was low, connectivity was low). Additionally, sliding-window connectivity analysis using 50-second windows revealed a significant positive relationship between ongoing pain intensity and RVM-SpV connectivity over the duration of the 12-minute scan. Conclusion These data reveal that moment-to-moment changes in brainstem pain-modulation circuitry functioning likely contribute to fluctuations in spontaneous pain intensity in individuals with chronic neuropathic pain.
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Affiliation(s)
- Emily P Mills
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
| | - Zeynab Alshelh
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
| | - Danny Kosanovic
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
| | - Flavia Di Pietro
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
| | - E Russell Vickers
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
| | - Paul M Macey
- School of Nursing and Brain Research Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Luke A Henderson
- Department of Anatomy and Histology, University of Sydney, Sydney, NSW 2006, Australia
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19
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Hahn A, Strandberg TO, Stomrud E, Nilsson M, van Westen D, Palmqvist S, Ossenkoppele R, Hansson O. Association Between Earliest Amyloid Uptake and Functional Connectivity in Cognitively Unimpaired Elderly. Cereb Cortex 2020; 29:2173-2182. [PMID: 30877785 PMCID: PMC6458901 DOI: 10.1093/cercor/bhz020] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 01/25/2019] [Indexed: 12/19/2022] Open
Abstract
Alterations in cognitive performance have been noted in nondemented subjects with elevated accumulation of amyloid-β (Aβ) fibrils. However, it is not yet understood whether brain function is already influenced by Aβ deposition during the very earliest stages of the disease. We therefore investigated associations between [18F]Flutemetamol PET, resting-state functional connectivity, gray and white matter structure and cognitive performance in 133 cognitively normal elderly that exhibited normal global Aβ PET levels. [18F]Flutemetamol uptake in regions known to accumulate Aβ fibrils early in preclinical AD (i.e., mainly certain parts of the default-mode network) was positively associated with dynamic but not static functional connectivity (r = 0.77). Dynamic functional connectivity was further related to better cognitive performance (r = 0.21–0.72). No significant associations were found for Aβ uptake with gray matter volume or white matter diffusivity. The findings demonstrate that the earliest accumulation of Aβ fibrils is associated with increased functional connectivity, which occurs before any structural alterations. The enhanced functional connectivity may reflect a compensatory mechanism to maintain high cognitive performance in the presence of increasing amyloid accumulation during the earliest phases of AD.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Tor O Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Diagnostic Radiology, Lund University, Sweden.,Imaging and Function, Skåne University Health Care, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden.,Department of Neurology, Skåne University Hospital, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden.,Department of Neurology and Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, HV, The Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
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20
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Yoo HB, Moya BE, Filbey FM. Dynamic functional connectivity between nucleus accumbens and the central executive network relates to chronic cannabis use. Hum Brain Mapp 2020; 41:3637-3654. [PMID: 32432821 PMCID: PMC7416060 DOI: 10.1002/hbm.25036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 04/13/2020] [Accepted: 05/05/2020] [Indexed: 01/05/2023] Open
Abstract
The neural mechanisms of drug cue‐reactivity regarding the temporal fluctuations of functional connectivity, namely the dynamic connectivity, are sparsely studied. Quantifying the task‐modulated variability in dynamic functional connectivity at cue exposure can aid the understanding. We analyzed changes in dynamic connectivity in 54 adult cannabis users and 90 controls during a cannabis cue exposure task. The variability was measured as standard deviation in the (a) connectivity weights of the default mode, the central executive, and the salience networks and two reward loci (amygdalae and nuclei accumbens); and (b) topological indexes of the whole brain (global efficiency, modularity and network resilience). These were compared for the main effects of task conditions and the group (users vs. controls), and correlated with pre‐ and during‐scan subjective craving. The variability of connectivity weights between the central executive network and nuclei accumbens was increased in users throughout the cue exposure task, and, was positively correlated with during‐scan craving for cannabis. The variability of modularity was not different by groups, but positively correlated with prescan craving. The variability of dynamic connectivity during cannabis cue exposure task between the central executive network and the nuclei accumbens, and, the level of modularity, seem to relate to the neural underpinning of cannabis use and the subjective craving.
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Affiliation(s)
- Hye Bin Yoo
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, TX, USA.,Department of Neurological Surgery, University of Texas Southwestern, Dallas, TX, USA
| | - Blake Edward Moya
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, TX, USA
| | - Francesca M Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, TX, USA
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21
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Petrican R, Palombo DJ, Sheldon S, Levine B. The Neural Dynamics of Individual Differences in Episodic Autobiographical Memory. eNeuro 2020; 7:ENEURO.0531-19.2020. [PMID: 32060035 PMCID: PMC7171291 DOI: 10.1523/eneuro.0531-19.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/30/2020] [Accepted: 01/31/2020] [Indexed: 11/24/2022] Open
Abstract
The ability to mentally travel to specific events from one's past, dubbed episodic autobiographical memory (E-AM), contributes to adaptive functioning. Nonetheless, the mechanisms underlying its typical interindividual variation remain poorly understood. To address this issue, we capitalize on existing evidence that successful performance on E-AM tasks draws on the ability to visualize past episodes and reinstate their unique spatiotemporal context. Hence, here, we test whether features of the brain's functional architecture relevant to perceptual versus conceptual processes shape individual differences in both self-rated E-AM and laboratory-based episodic memory (EM) for random visual scene sequences (visual EM). We propose that superior subjective E-AM and visual EM are associated with greater similarity in static neural organization patterns, potentially indicating greater efficiency in switching, between rest and mental states relevant to encoding perceptual information. Complementarily, we postulate that impoverished subjective E-AM and visual EM are linked to dynamic brain organization patterns implying a predisposition towards semanticizing novel perceptual information. Analyses were conducted on resting state and task-based fMRI data from 329 participants (160 women) in the Human Connectome Project (HCP) who completed visual and verbal EM assessments, and an independent gender diverse sample (N = 59) who self-rated their E-AM. Interindividual differences in subjective E-AM were linked to the same neural mechanisms underlying visual, but not verbal, EM, in general agreement with the hypothesized static and dynamic brain organization patterns. Our results suggest that higher E-AM entails more efficient processing of temporally extended information sequences, whereas lower E-AM entails more efficient semantic or gist-based processing.
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Affiliation(s)
- Raluca Petrican
- School of Psychology, Cardiff University, Cardiff, Wales CF10 3AT, UK
| | - Daniela J Palombo
- Department of Psychology, University of British Columbia, Vancouver, British Columbia V6T 1Z4 Canada
| | - Signy Sheldon
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Brian Levine
- Rotman Research Institute and Departments of Psychology and Neurology, University of Toronto, Toronto, Ontario M6A 2E1, Canada
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22
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Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Front Neurosci 2019; 13:803. [PMID: 31507353 PMCID: PMC6716456 DOI: 10.3389/fnins.2019.00803] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/17/2019] [Indexed: 01/08/2023] Open
Abstract
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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Affiliation(s)
- D Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Thomas S Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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23
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Abstract
Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.
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Affiliation(s)
- Derek M Smith
- 1 School of Psychology, Georgia Institute of Technology , Atlanta, Georgia
| | - Yrian Zhao
- 2 Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Shella D Keilholz
- 2 Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Eric H Schumacher
- 1 School of Psychology, Georgia Institute of Technology , Atlanta, Georgia
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24
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Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O’Brien JT, Blamire AM, Taylor JP. Dysfunctional brain dynamics and their origin in Lewy body dementia. Brain 2019; 142:1767-1782. [PMID: 30938426 PMCID: PMC6536851 DOI: 10.1093/brain/awz069] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 01/06/2019] [Accepted: 01/27/2019] [Indexed: 01/08/2023] Open
Abstract
Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer's disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain's dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer's disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer's disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer's disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = - 0.53, P = 0.003) and thalamic networks (r = - 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer's disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics.
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Affiliation(s)
- Julia Schumacher
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Luis R Peraza
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Alan J Thomas
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
- Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne, UK
| | - Peter Gallagher
- Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne, UK
| | - John T O’Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, UK
| | - Andrew M Blamire
- Institute of Cellular Medicine and Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
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25
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Harlalka V, Bapi RS, Vinod PK, Roy D. Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder. Front Hum Neurosci 2019; 13:6. [PMID: 30774589 PMCID: PMC6367662 DOI: 10.3389/fnhum.2019.00006] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/08/2019] [Indexed: 01/10/2023] Open
Abstract
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.
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Affiliation(s)
- Vatika Harlalka
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Raju S. Bapi
- Cognitive Science Lab, IIIT Hyderabad, Hyderabad, India
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
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26
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Miller RL, Abrol A, Adali T, Levin-Schwarz Y, Calhoun VD. Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations. Front Neurosci 2018; 12:551. [PMID: 30237758 PMCID: PMC6135983 DOI: 10.3389/fnins.2018.00551] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 07/20/2018] [Indexed: 12/16/2022] Open
Abstract
Studies of resting state functional MRI (rs-fRMI) are increasingly focused on “dynamics”, or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.
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Affiliation(s)
- Robyn L Miller
- The Mind Research Network, Albuquerque, NM, United States
| | - Anees Abrol
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Yuri Levin-Schwarz
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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27
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Rosenthal G, Sporns O, Avidan G. Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network. Cereb Cortex 2018; 27:4823-4834. [PMID: 27620978 DOI: 10.1093/cercor/bhw279] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Accepted: 08/16/2016] [Indexed: 11/12/2022] Open
Abstract
Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.
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Affiliation(s)
- Gideon Rosenthal
- Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, PO Box 653, Beer-Sheva 8410501, Israel.,The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Galia Avidan
- Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, PO Box 653, Beer-Sheva 8410501, Israel.,The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,Department of Psychology, Ben-Gurion University of the Negev, PO Box 653, Beer-Sheva 8410501, Israel
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28
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Keilholz S, Caballero-Gaudes C, Bandettini P, Deco G, Calhoun V. Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions. Brain Connect 2018; 7:465-481. [PMID: 28874061 DOI: 10.1089/brain.2017.0543] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.
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Affiliation(s)
- Shella Keilholz
- 1 Department of Biomedical Engineering, Emory University/Georgia Institute of Technology , Atlanta, Georgia
| | | | - Peter Bandettini
- 3 Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland.,4 Functional MRI Core Facility, NIMH, NIH, Bethesda, Maryland
| | - Gustavo Deco
- 5 Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain .,6 Institució Catalana de la Recerca i Estudis Avançats (ICREA) , Barcelona, Spain.,7 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, Germany .,8 School of Psychological Sciences, Monash University , Melbourne, Australia
| | - Vince Calhoun
- 9 The Mind Research Network, Albuquerque, New Mexico.,10 Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico
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29
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Williams N, Henson RN. Recent advances in functional neuroimaging analysis for cognitive neuroscience. Brain Neurosci Adv 2018; 2:2398212817752727. [PMID: 32285010 PMCID: PMC7119434 DOI: 10.1177/2398212817752727] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 12/10/2017] [Indexed: 11/17/2022] Open
Abstract
Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.
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Affiliation(s)
- Nitin Williams
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Richard N. Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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30
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Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
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31
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Zhang H, Chen X, Zhang Y, Shen D. Test-Retest Reliability of "High-Order" Functional Connectivity in Young Healthy Adults. Front Neurosci 2017; 11:439. [PMID: 28824362 PMCID: PMC5539178 DOI: 10.3389/fnins.2017.00439] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/18/2017] [Indexed: 12/16/2022] Open
Abstract
Functional connectivity (FC) has become a leading method for resting-state functional magnetic resonance imaging (rs-fMRI) analysis. However, the majority of the previous studies utilized pairwise, temporal synchronization-based FC. Recently, high-order FC (HOFC) methods were proposed with the idea of computing "correlation of correlations" to capture high-level, more complex associations among the brain regions. There are two types of HOFC. The first type is topographical profile similarity-based HOFC (tHOFC) and its variant, associated HOFC (aHOFC), for capturing different levels of HOFC. Instead of measuring the similarity of the original rs-fMRI signals with the traditional FC (low-order FC, or LOFC), tHOFC measures the similarity of LOFC profiles (i.e., a set of LOFC values between a region and all other regions) between each pair of brain regions. The second type is dynamics-based HOFC (dHOFC) which defines the quadruple relationship among every four brain regions by first calculating two pairwise dynamic LOFC "time series" and then measuring their temporal synchronization (i.e., temporal correlation of the LOFC fluctuations, not the BOLD fluctuations). Applications have shown the superiority of HOFC in both disease biomarker detection and individualized diagnosis than LOFC. However, no study has been carried out for the assessment of test-retest reliability of different HOFC metrics. In this paper, we systematically evaluate the reliability of the two types of HOFC methods using test-retest rs-fMRI data from 25 (12 females, age 24.48 ± 2.55 years) young healthy adults with seven repeated scans (with interval = 3-8 days). We found that all HOFC metrics have satisfactory reliability, specifically (1) fair-to-good for tHOFC and aHOFC, and (2) fair-to-moderate for dHOFC with relatively strong connectivity strength. We further give an in-depth analysis of the biological meanings of each HOFC metric and highlight their differences compared to the LOFC from the aspects of cross-level information exchanges, within-/between-network connectivity, and modulatory connectivity. In addition, how the dynamic analysis parameter (i.e., sliding window length) affects dHOFC reliability is also investigated. Our study reveals unique functional associations characterized by the HOFC metrics. Guidance and recommendations for future applications and clinical research using HOFC are provided. This study has made a further step toward unveiling more complex human brain connectome.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Xiaobo Chen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Yu Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea UniversitySeoul, South Korea
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32
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Reynolds CA, Menke H, Andrew M, Blunt MJ, Krevor S. Dynamic fluid connectivity during steady-state multiphase flow in a sandstone. Proc Natl Acad Sci U S A 2017; 114:8187-92. [PMID: 28716946 DOI: 10.1073/pnas.1702834114] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The current conceptual picture of steady-state multiphase Darcy flow in porous media is that the fluid phases organize into separate flow pathways with stable interfaces. Here we demonstrate a previously unobserved type of steady-state flow behavior, which we term "dynamic connectivity," using fast pore-scale X-ray imaging. We image the flow of N2 and brine through a permeable sandstone at subsurface reservoir conditions, and low capillary numbers, and at constant fluid saturation. At any instant, the network of pores filled with the nonwetting phase is not necessarily connected. Flow occurs along pathways that periodically reconnect, like cars controlled by traffic lights. This behavior is consistent with an energy balance, where some of the energy of the injected fluids is sporadically converted to create new interfaces.
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33
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Cocchi L, Yang Z, Zalesky A, Stelzer J, Hearne LJ, Gollo LL, Mattingley JB. Neural decoding of visual stimuli varies with fluctuations in global network efficiency. Hum Brain Mapp 2017; 38:3069-3080. [PMID: 28342260 DOI: 10.1002/hbm.23574] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 02/25/2017] [Accepted: 03/07/2017] [Indexed: 12/14/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and nonvisual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialized, stimulus driven neural processes. Hum Brain Mapp 38:3069-3080, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Luca Cocchi
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Zhengyi Yang
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Johannes Stelzer
- Department of Biomedical Magnetic Resonance Imaging, University Hospital Tuebingen, Germany.,Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
| | - Luke J Hearne
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | | | - Jason B Mattingley
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,School of Psychology, The University of Queensland, Brisbane, Australia
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Rangaprakash D, Deshpande G, Daniel TA, Goodman AM, Robinson JL, Salibi N, Katz JS, Denney TS, Dretsch MN. Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild-traumatic brain injury and posttraumatic stress disorder. Hum Brain Mapp 2017; 38:2843-2864. [PMID: 28295837 DOI: 10.1002/hbm.23551] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Revised: 12/24/2016] [Accepted: 02/16/2017] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVES Military service members risk acquiring posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI), with high comorbidity. Owing to overlapping symptomatology in chronic mTBI or postconcussion syndrome (PCS) and PTSD, it is difficult to assess the etiology of a patient's condition without objective measures. Using resting-state functional MRI in a novel framework, we tested the hypothesis that their neural signatures are characterized by functionally hyperconnected brain regions which are less variable over time. Additionally, we predicted that such connectivities possessed the highest ability in predicting the diagnostic membership of a novel subject (top-predictors) in addition to being statistically significant. METHODS U.S. Army Soldiers (N = 87) with PTSD and comorbid PCS + PTSD were recruited along with combat controls. Static and dynamic functional connectivities were evaluated. Group differences were obtained in accordance with our hypothesis. Machine learning classification (MLC) was employed to determine top predictors. RESULTS From whole-brain connectivity, we identified the hippocampus-striatum connectivity to be significantly altered in accordance with our hypothesis. Diffusion tractography revealed compromised white-matter integrity between aforementioned regions only in the PCS + PTSD group, suggesting a structural etiology for the PCS + PTSD group rather than being an extreme subset of PTSD. Employing MLC, connectivities provided worst-case accuracy of 84% (9% more than psychological measures). Additionally, the hippocampus-striatum connectivities were found to be top predictors and thus a potential biomarker of PTSD/mTBI. CONCLUSIONS PTSD/mTBI are associated with hippocampal-striatal hyperconnectivity from which it is difficult to disengage, leading to a habit-like response toward episodic traumatic memories, which fits well with behavioral manifestations of combat-related PTSD/mTBI. Hum Brain Mapp 38:2843-2864, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, Alabama
| | - Thomas A Daniel
- Department of Psychology, Auburn University, Auburn, Alabama.,Department of Psychology, Westfield State University, Westfield, Massachusetts
| | - Adam M Goodman
- Department of Psychology, Auburn University, Auburn, Alabama.,Department of Psychology, University of Alabama Birmingham, Birmingham, Alabama
| | - Jennifer L Robinson
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, Alabama
| | - Nouha Salibi
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,MR R&D, Siemens Healthcare, Malvern, Pennsylvania
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, Alabama
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, Alabama
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virginia
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Prčkovska V, Huijbers W, Schultz A, Ortiz-Teran L, Peña-Gomez C, Villoslada P, Johnson K, Sperling R, Sepulcre J. Epicenters of dynamic connectivity in the adaptation of the ventral visual system. Hum Brain Mapp 2016; 38:1965-1976. [PMID: 28029725 DOI: 10.1002/hbm.23497] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 11/29/2016] [Accepted: 12/02/2016] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVES AND DESIGN Neuronal responses adapt to familiar and repeated sensory stimuli. Enhanced synchrony across wide brain systems has been postulated as a potential mechanism for this adaptation phenomenon. Here, we used recently developed graph theory methods to investigate hidden connectivity features of dynamic synchrony changes during a visual repetition paradigm. Particularly, we focused on strength connectivity changes occurring at local and distant brain neighborhoods. PRINCIPAL OBSERVATIONS We found that connectivity reorganization in visual modal cortex-such as local suppressed connectivity in primary visual areas and distant suppressed connectivity in fusiform areas-is accompanied by enhanced local and distant connectivity in higher cognitive processing areas in multimodal and association cortex. Moreover, we found a shift of the dynamic functional connections from primary-visual-fusiform to primary-multimodal/association cortex. CONCLUSIONS These findings suggest that repetition-suppression is made possible by reorganization of functional connectivity that enables communication between low- and high-order areas. Hum Brain Mapp 38:1965-1976, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Vesna Prčkovska
- Center of Neuroimmunology, Institut d'Investigacions Biomedica August Pi Sunyer, Barcelona, Spain.,Gordon Center For Medical Imaging, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, Massachusetts
| | - Willem Huijbers
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Boston, Massachusetts.,Department of Population Health Sciences, German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Aaron Schultz
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Boston, Massachusetts
| | - Laura Ortiz-Teran
- Gordon Center For Medical Imaging, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, Massachusetts
| | - Cleofe Peña-Gomez
- Gordon Center For Medical Imaging, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, Massachusetts
| | - Pablo Villoslada
- Center of Neuroimmunology, Institut d'Investigacions Biomedica August Pi Sunyer, Barcelona, Spain.,Department of Neurology, University of California, San Francisco, California
| | - Keith Johnson
- Gordon Center For Medical Imaging, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Centre for Alzheimer Research and Treatment, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Reisa Sperling
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Boston, Massachusetts.,Department of Neurology, Centre for Alzheimer Research and Treatment, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jorge Sepulcre
- Gordon Center For Medical Imaging, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Boston, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Boston, Massachusetts
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Miller RL, Vergara VM, Keator DB, Calhoun VD. A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow. IEEE Trans Biomed Eng 2016; 63:2525-2539. [PMID: 27541329 PMCID: PMC5737021 DOI: 10.1109/tbme.2016.2600637] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We introduce a method for analyzing dynamically changing functional magnetic resonance imaging (fMRI) network connectivity estimates as they vary within and between broad functional domains. The method captures evidence of intertemporal directionality in cross joint functional-domain influence and extends standard whole-brain dynamic network connectivity approaches into additional functionally meaningful dimensions by evaluating transition probabilities between clustered intradomain and interdomain connectivity patterns. Results: In applying this method to a large (N = 314) multisite resting-state fMRI dataset balanced between schizophrenia patients and healthy controls, we find evidence of joint functional domains that are global catalyzers, broadly shaping downstream functional relationships throughout the brain. Multiple interesting differences between patients and controls in both time-varying joint functional-domain connectivity patterns and in cross joint functional-domain intertemporal information flow were identified. Conclusion and Significance: Our proposed approach, thus, unifies the concepts of brain connectivity and interdomain connectivity and provides a powerful new way to evaluate functional connectivity data in the context of both the healthy and diseased brain.
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Yin D, Liu W, Zeljic K, Wang Z, Lv Q, Fan M, Cheng W, Wang Z. Dissociable Changes of Frontal and Parietal Cortices in Inherent Functional Flexibility across the Human Life Span. J Neurosci 2016; 36:10060-74. [PMID: 27683903 DOI: 10.1523/JNEUROSCI.1476-16.2016] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 08/11/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED Extensive evidence suggests that frontoparietal regions can dynamically update their pattern of functional connectivity, supporting cognitive control and adaptive implementation of task demands. However, it is largely unknown whether this flexibly functional reconfiguration is intrinsic and occurs even in the absence of overt tasks. Based on recent advances in dynamics of resting-state functional resonance imaging (fMRI), we propose a probabilistic framework in which dynamic reconfiguration of intrinsic functional connectivity between each brain region and others can be represented as a probability distribution. A complexity measurement (i.e., entropy) was used to quantify functional flexibility, which characterizes heterogeneous connectivity between a particular region and others over time. Following this framework, we identified both functionally flexible and specialized regions over the human life span (112 healthy subjects from 13 to 76 years old). Across brainwide regions, we found regions showing high flexibility mainly in the higher-order association cortex, such as the lateral prefrontal cortex (LPFC), lateral parietal cortex, and lateral temporal lobules. In contrast, visual, auditory, and sensory areas exhibited low flexibility. Furthermore, we observed that flexibility of the right LPFC improved during maturation and reduced due to normal aging, with the opposite occurring for the left lateral parietal cortex. Our findings reveal dissociable changes of frontal and parietal cortices over the life span in terms of inherent functional flexibility. This study not only provides a new framework to quantify the spatiotemporal behavior of spontaneous brain activity, but also sheds light on the organizational principle behind changes in brain function across the human life span. SIGNIFICANCE STATEMENT Recent neuroscientific research has demonstrated that the human capability of adaptive task control is primarily the result of the flexible operation of frontal brain networks. However, it remains unclear whether this flexibly functional reconfiguration is intrinsic and occurs in the absence of an overt task. In this study, we propose a probabilistic framework to quantify the functional flexibility of each brain region using resting-state fMRI. We identify regions showing high flexibility mainly in the higher-order association cortex. In contrast, primary and unimodal visual and sensory areas show low flexibility. On the other hand, our findings reveal dissociable changes of frontal and parietal cortices in terms of inherent functional flexibility over the life span.
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38
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Shine JM, Koyejo O, Poldrack RA. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A 2016; 113:9888-91. [PMID: 27528672 DOI: 10.1073/pnas.1604898113] [Citation(s) in RCA: 155] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.
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Di X, Fu Z, Chan SC, Hung YS, Biswal BB, Zhang Z. Task-related functional connectivity dynamics in a block-designed visual experiment. Front Hum Neurosci 2015; 9:543. [PMID: 26483660 PMCID: PMC4588125 DOI: 10.3389/fnhum.2015.00543] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 09/16/2015] [Indexed: 01/04/2023] Open
Abstract
Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward) among several visual regions of the bilateral middle occipital gyrus (MOG) and the bilateral fusiform gyrus (FuG). Importantly, only the FCs between higher visual regions (MOG) and lower visual regions (FuG) exhibited such dynamic patterns. The results suggested that simply assuming a sustained FC during a task block may be insufficient to capture distinct task-related FC changes. The investigation of FC dynamics in tasks could improve our understanding of condition shifts and the coordination between different activated brain regions.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark, NJ, USA
| | - Zening Fu
- Department of Electrical and Electronic Engineering, The University of Hong Kong Hong Kong, Hong Kong
| | - Shing Chow Chan
- Department of Electrical and Electronic Engineering, The University of Hong Kong Hong Kong, Hong Kong
| | - Yeung Sam Hung
- Department of Electrical and Electronic Engineering, The University of Hong Kong Hong Kong, Hong Kong
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark, NJ, USA
| | - Zhiguo Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong Hong Kong, Hong Kong ; School of Chemical and Biomedical Engineering and School of Electrical and Electronic Engineering, Nanyang Technological University Singapore, Singapore
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Abstract
The study of large-scale functional interactions in the human brain with functional magnetic resonance imaging (fMRI) extends almost to the first applications of this technology. Due to historical reasons and preconceptions about the limitations of this brain imaging method, most studies have focused on assessing connectivity over extended periods of time. It is now clear that fMRI can resolve the temporal dynamics of functional connectivity, like other faster imaging techniques such as electroencephalography and magnetoencephalography (albeit on a different temporal scale). However, the indirect nature of fMRI measurements can hinder the interpretability of the results. After briefly summarizing recent advances in the field, we discuss how the simultaneous combination of fMRI with electrophysiological activity measurements can contribute to a better understanding of dynamic functional connectivity in humans both during rest and task, wakefulness, and other brain states.
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Affiliation(s)
- Enzo Tagliazucchi
- Institute for Medical Psychology, Christian Albrechts University , Kiel , Germany ; Department of Neurology and Brain Imaging Center, Goethe University Frankfurt , Frankfurt , Germany
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt , Frankfurt , Germany ; Department of Neurology, University Hospital Schleswig Holstein , Kiel , Germany
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Abstract
Studies of large-scale brain functional connectivity using the resting-state functional magnetic resonance imaging have advanced our understanding of human brain functions. Although the evidence of dynamic functional connectivity is accumulating, the variations of functional connectivity over time have not been well characterized. In the present study, we aimed to associate the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting-state scan by comparing functional connectivity differences between when a network had higher and lower intrinsic activities. The activities of the salience network, default mode network (DMN), and motor network were associated with changes of resting-state functional connectivity. Higher activity of the salience network was accompanied by greater functional connectivity between the fronto-parietal regions and the DMN regions, and between the regions within the DMN. Higher DMN activity was associated with less connectivity between the regions within the DMN, and greater connectivity between the regions within the fronto-parietal network. Higher motor network activity was correlated with greater connectivity between the regions within the motor network, and smaller connectivity between the DMN regions and fronto-parietal regions, and between the DMN regions and the motor regions. In addition, the whole brain network modularity was positively correlated with the motor network activity, suggesting that the brain is more segregated as sub-systems when the motor network is intrinsically activated. Together, these results demonstrate the association between the resting-state connectivity variations and the intrinsic activities of specific networks, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA
| | - Bharat B. Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA
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42
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Abstract
Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
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Abstract
Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.
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Affiliation(s)
- Tara M Madhyastha
- 1 Department of Radiology, University of Washington , Seattle, Washington
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Liu X, Chang C, Duyn JH. Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Front Syst Neurosci 2013; 7:101. [PMID: 24550788 PMCID: PMC3913885 DOI: 10.3389/fnsys.2013.00101] [Citation(s) in RCA: 122] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 11/15/2013] [Indexed: 01/23/2023] Open
Abstract
Recent fMRI studies have shown that analysis of the human brain's spontaneous activity may provide a powerful approach to reveal its functional organization. Dedicated methods have been proposed to investigate co-variation of signals from different brain regions, with the goal of revealing neuronal networks (NNs) that may serve specialized functions. However, these analysis methods generally do not take into account a potential non-stationary (variable) interaction between brain regions, and as a result have limited effectiveness. To address this, we propose a novel analysis method that uses clustering analysis to sort and selectively average fMRI activity time frames to produce a set of co-activation patterns. Compared to the established networks extracted with conventional analysis methods, these co-activation patterns demonstrate novel network features with apparent relevance to the brain's functional organization.
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Affiliation(s)
- Xiao Liu
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA
| | - Catie Chang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD, USA
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Cribben I, Wager TD, Lindquist MA. Detecting functional connectivity change points for single-subject fMRI data. Front Comput Neurosci 2013; 7:143. [PMID: 24198781 PMCID: PMC3812660 DOI: 10.3389/fncom.2013.00143] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 09/30/2013] [Indexed: 11/13/2022] Open
Abstract
Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.
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Affiliation(s)
- Ivor Cribben
- Department of Finance and Statistical Analysis, Alberta School of Business, University of Alberta Edmonton, AB, Canada
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Tagliazucchi E, von Wegner F, Morzelewski A, Brodbeck V, Laufs H. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci 2012; 6:339. [PMID: 23293596 PMCID: PMC3531919 DOI: 10.3389/fnhum.2012.00339] [Citation(s) in RCA: 209] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2012] [Accepted: 12/09/2012] [Indexed: 12/11/2022] Open
Abstract
Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations. This dynamic functional connectivity has recently been demonstrated in spontaneous fluctuations of the Blood Oxygen Level-Dependent (BOLD) signal, measured with functional Magnetic Resonance Imaging (fMRI). We analyzed temporal fluctuations in BOLD connectivity and their electrophysiological correlates, by means of long (≈50 min) joint electroencephalographic (EEG) and fMRI recordings obtained from two populations: 15 awake subjects and 13 subjects undergoing vigilance transitions. We identified positive and negative correlations between EEG spectral power (extracted from electrodes covering different scalp regions) and fMRI BOLD connectivity in a network of 90 cortical and subcortical regions (with millimeter spatial resolution). In particular, increased alpha (8-12 Hz) and beta (15-30 Hz) power were related to decreased functional connectivity, whereas gamma (30-60 Hz) power correlated positively with BOLD connectivity between specific brain regions. These patterns were altered for subjects undergoing vigilance changes, with slower oscillations being correlated with functional connectivity increases. Dynamic BOLD functional connectivity was reflected in the fluctuations of graph theoretical indices of network structure, with changes in frontal and central alpha power correlating with average path length. Our results strongly suggest that fluctuations of BOLD functional connectivity have a neurophysiological origin. Positive correlations with gamma can be interpreted as facilitating increased BOLD connectivity needed to integrate brain regions for cognitive performance. Negative correlations with alpha suggest a temporary functional weakening of local and long-range connectivity, associated with an idling state.
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Affiliation(s)
- Enzo Tagliazucchi
- Neurology Department and Brain Imaging Center, Goethe University Frankfurt am Main, Germany
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