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Wang Z, Zhao Z, Song Z, Xu J, Wang Y, Zhao Z, Li Y. Functional alterations of the brain default mode network and somatosensory system in trigeminal neuralgia. Sci Rep 2024; 14:10205. [PMID: 38702383 PMCID: PMC11068897 DOI: 10.1038/s41598-024-60273-6] [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: 01/02/2024] [Accepted: 04/21/2024] [Indexed: 05/06/2024] Open
Abstract
Mapping the localization of the functional brain regions in trigeminal neuralgia (TN) patients is still lacking. The study aimed to explore the functional brain alterations and influencing factors in TN patients using functional brain imaging techniques. All participants underwent functional brain imaging to collect resting-state brain activity. The significant differences in regional homogeneity (ReHo) and amplitude of low frequency (ALFF) between the TN and control groups were calculated. After familywise error (FWE) correction, the differential brain regions in ReHo values between the two groups were mainly located in bilateral middle frontal gyrus, bilateral inferior cerebellum, right superior orbital frontal gyrus, right postcentral gyrus, left inferior temporal gyrus, left middle temporal gyrus, and left gyrus rectus. The differential brain regions in ALFF values between the two groups were mainly located in the left triangular inferior frontal gyrus, left supplementary motor area, right supramarginal gyrus, and right middle frontal gyrus. With the functional impairment of the central pain area, the active areas controlling memory and emotion also change during the progression of TN. There may be different central mechanisms in TN patients of different sexes, affected sides, and degrees of nerve damage. The exact central mechanisms remain to be elucidated.
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Affiliation(s)
- Zairan Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China
| | - Zijun Zhao
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zihan Song
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiayi Xu
- Medical Records Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yizheng Wang
- Department of Pain Rehabilitation, The Forth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zongmao Zhao
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
- Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
| | - Yongning Li
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China.
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Miyata J, Sasamoto A, Ezaki T, Isobe M, Kochiyama T, Masuda N, Mori Y, Sakai Y, Sawamoto N, Tei S, Ubukata S, Aso T, Murai T, Takahashi H. Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network. Psychiatry Clin Neurosci 2024; 78:322-331. [PMID: 38414202 DOI: 10.1111/pcn.13652] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 12/15/2023] [Accepted: 01/21/2024] [Indexed: 02/29/2024]
Abstract
AIM While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear. METHODS Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter. RESULTS We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions. CONCLUSION Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.
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Grants
- Kyoto University
- JP18dm0307008 Japan Agency for Medical Research and Development
- JP21uk1024002 Japan Agency for Medical Research and Development
- JPMJMS2021 Japan Science and Technology Agency
- Novartis Pharma Research Grant
- SENSHIN Medical Research Foundation
- JP17H04248 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP18H05130 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP19H03583 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP20H05064 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP20K21567 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP21K07544 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- JP26461767 Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology KAKENHI
- Takeda Science Foundation
- Uehara Memorial Foundation
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Affiliation(s)
- Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry, Aichi Medical University, Aichi, Japan
| | - Akihiko Sasamoto
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takahiro Ezaki
- PRESTO, Japan Science and Technology Agency, Saitama, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Masanori Isobe
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York, USA
| | - Yasuo Mori
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuki Sakai
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shisei Tei
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- School of Human and Social Sciences, Tokyo International University, Tokyo, Japan
| | - Shiho Ubukata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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De la Peña-Arteaga V, Cano M, Porta-Casteràs D, Vicent-Gil M, Miquel-Giner N, Martínez-Zalacaín I, Mar-Barrutia L, López-Solà M, Andrews-Hanna JR, Soriano-Mas C, Alonso P, Serra-Blasco M, López-Solà C, Cardoner N. Mindfulness-based cognitive therapy neurobiology in treatment-resistant obsessive-compulsive disorder: A domain-related resting-state networks approach. Eur Neuropsychopharmacol 2024; 82:72-81. [PMID: 38503084 DOI: 10.1016/j.euroneuro.2024.02.011] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/15/2024] [Accepted: 02/17/2024] [Indexed: 03/21/2024]
Abstract
Mindfulness-based cognitive therapy (MBCT) stands out as a promising augmentation psychological therapy for patients with obsessive-compulsive disorder (OCD). To identify potential predictive and response biomarkers, this study examines the relationship between clinical domains and resting-state network connectivity in OCD patients undergoing a 3-month MBCT programme. Twelve OCD patients underwent two resting-state functional magnetic resonance imaging sessions at baseline and after the MBCT programme. We assessed four clinical domains: positive affect, negative affect, anxiety sensitivity, and rumination. Independent component analysis characterised resting-state networks (RSNs), and multiple regression analyses evaluated brain-clinical associations. At baseline, distinct network connectivity patterns were found for each clinical domain: parietal-subcortical, lateral prefrontal, medial prefrontal, and frontal-occipital. Predictive and response biomarkers revealed significant brain-clinical associations within two main RSNs: the ventral default mode network (vDMN) and the frontostriatal network (FSN). Key brain nodes -the precuneus and the frontopolar cortex- were identified within these networks. MBCT may modulate vDMN and FSN connectivity in OCD patients, possibly reducing symptoms across clinical domains. Each clinical domain had a unique baseline brain connectivity pattern, suggesting potential symptom-based biomarkers. Using these RSNs as predictors could enable personalised treatments and the identification of patients who would benefit most from MBCT.
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Affiliation(s)
| | - Marta Cano
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Daniel Porta-Casteràs
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Muriel Vicent-Gil
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Neus Miquel-Giner
- Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Department of Mental Health, Parc Sanitari Sant Joan de Déu, Cornellà de Llobregat, Spain
| | - Ignacio Martínez-Zalacaín
- Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Lorea Mar-Barrutia
- Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Marina López-Solà
- Department of Medicine, School of Medicine and Health Sciences, Universitat de Barcelona - UB, Barcelona, Spain
| | - Jessica R Andrews-Hanna
- Department of Psychology - Cognitive Science, University of Arizona, Tucson, United States of America
| | - Carles Soriano-Mas
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain; Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona - UB, Barcelona, Spain
| | - Pino Alonso
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, Universitat de Barcelona - UB, L'Hospitalet de Llobregat, Spain
| | - Maria Serra-Blasco
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; ICOnnecta't e-Health Program of the Institut Català d'Oncologia (ICO), L'Hospitalet de Llobregat, Spain; Psycho-oncology and Digital Health Group, Health Services Research in Cancer, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet del Llobregat, Spain.
| | - Clara López-Solà
- Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Taulí University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Sabadell, Spain; Health Clinical Psychology Section, Department of Psychiatry & Clinical Psychology, Institut Clínic de Neurociències (ICN), Hospital Clínic, Barcelona, Spain.
| | - Narcís Cardoner
- Sant Pau Mental Health Research Group, Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain; Network Centre for Biomedical Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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4
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Fernandino L, Binder JR. How does the "default mode" network contribute to semantic cognition? Brain Lang 2024; 252:105405. [PMID: 38579461 DOI: 10.1016/j.bandl.2024.105405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 02/26/2024] [Accepted: 03/23/2024] [Indexed: 04/07/2024]
Abstract
This review examines whether and how the "default mode" network (DMN) contributes to semantic processing. We review evidence implicating the DMN in the processing of individual word meanings and in sentence- and discourse-level semantics. Next, we argue that the areas comprising the DMN contribute to semantic processing by coordinating and integrating the simultaneous activity of local neuronal ensembles across multiple unimodal and multimodal cortical regions, creating a transient, global neuronal ensemble. The resulting ensemble implements an integrated simulation of phenomenological experience - that is, an embodied situation model - constructed from various modalities of experiential memory traces. These situation models, we argue, are necessary not only for semantic processing but also for aspects of cognition that are not traditionally considered semantic. Although many aspects of this proposal remain provisional, we believe it provides new insights into the relationships between semantic and non-semantic cognition and into the functions of the DMN.
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Affiliation(s)
- Leonardo Fernandino
- Department of Neurology, Medical College of Wisconsin, USA; Department of Biomedical Engineering, Medical College of Wisconsin, USA.
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, USA; Department of Biophysics, Medical College of Wisconsin, USA
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5
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Murray L, Frederick BB, Janes AC. Data-driven connectivity profiles relate to smoking cessation outcomes. Neuropsychopharmacology 2024; 49:1007-1013. [PMID: 38280945 DOI: 10.1038/s41386-024-01802-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/29/2024]
Abstract
At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.
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Affiliation(s)
- Laura Murray
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
| | - Blaise B Frederick
- McLean Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02215, USA
| | - Amy C Janes
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA
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6
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Zhang F, Li Y, Liu L, Liu Y, Wang P, Biswal BB. Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder. Psychiatry Clin Neurosci 2024; 78:291-299. [PMID: 38444215 DOI: 10.1111/pcn.13650] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/07/2024]
Abstract
AIM The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD. METHODS We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways. RESULTS Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD. CONCLUSION The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.
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Affiliation(s)
- Fanyu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yefen Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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7
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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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Kemmerer D. Transmodal neural substrates of general semantic knowledge: From single words to sentences, stories, and the default mode network. Brain Lang 2024; 252:105412. [PMID: 38574556 DOI: 10.1016/j.bandl.2024.105412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Affiliation(s)
- David Kemmerer
- Department of Speech, Language, and Hearing Sciences, Department of Psychological Sciences, Purdue University, United States.
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Long JY, Qin K, Pan N, Fan WL, Li Y. Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set. Br J Psychiatry 2024; 224:170-178. [PMID: 38602159 PMCID: PMC11039554 DOI: 10.1192/bjp.2024.41] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/20/2024] [Accepted: 02/11/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.
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Affiliation(s)
- Jing-Yi Long
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Nanfang Pan
- Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Wen-Liang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Radiology, Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yi Li
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
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De Rosa AP, d'Ambrosio A, Bisecco A, Altieri M, Cirillo M, Gallo A, Esposito F. Functional gradients reveal cortical hierarchy changes in multiple sclerosis. Hum Brain Mapp 2024; 45:e26678. [PMID: 38647001 PMCID: PMC11033924 DOI: 10.1002/hbm.26678] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alessandro d'Ambrosio
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alvino Bisecco
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Manuela Altieri
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Mario Cirillo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Antonio Gallo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabrizio Esposito
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
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11
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Bai Y, Qu J, Li D, Yin H. Neural basis underlying the relation between internet addiction tendency and sleep quality: The intrinsic default-mode network connectivity pathways. Int J Psychophysiol 2024; 195:112264. [PMID: 37977269 DOI: 10.1016/j.ijpsycho.2023.112264] [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: 08/12/2023] [Revised: 10/22/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Internet addiction (IA) tendency is considered an addictive behavior that results from excessive Internet use, and severely affecting an individual's physical health, emotion, and sleep. Although previous studies indicated that IA tendency was negatively correlated with sleep quality, the underlying neural basis of this relationship remained unclear. To address this issue, we utilized resting-state functional connectivity (RSFC) analysis to identify the neural pathways of the relationship between IA tendency and sleep quality. The behavioral results indicated a positive correlation between these two factors. And RSFC results revealed that IA tendency was positively related to the strength of functional connectivity within the default-mode network (DMN), including the right precuneus-left middle temporal gyrus (rPrcu-lMTG), the left anterior cingulate-left superior frontal gyrus (lAC-lSFG), and the left inferior parietal lobe-left medial superior frontal gyrus (lIPL-lMSFG). More importantly, mediation analysis demonstrated that IA tendency could mediate the relationship between these functional couplings and sleep quality. In conclusion, our findings suggest that intrinsic DMN connectivity may be an important neural pathways underlying the effects of IA tendency on sleep quality, and provide neural evidence for understanding the relationship between IA tendency and sleep quality.
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Affiliation(s)
- Youling Bai
- School of Education Science, Hunan Normal University, Chang Sha 410081, China; Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Chang Sha 410081, China
| | - Jianguo Qu
- School of Educational Sciences, Huaihua University, Huaihua 418000, China
| | - Dan Li
- School of Education Science, Hunan Normal University, Chang Sha 410081, China; Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Chang Sha 410081, China.
| | - Huazhan Yin
- School of Education Science, Hunan Normal University, Chang Sha 410081, China; Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Chang Sha 410081, China.
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12
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Killgore WD, Jankowski S, Henderson-Arredondo K, Lucas DA, Patel SI, Hildebrand LL, Huskey A, Dailey NS. Functional connectivity of the default mode network predicts subsequent polysomnographically measured sleep in people with symptoms of insomnia. Neuroreport 2023; 34:734-740. [PMID: 37605926 PMCID: PMC10470430 DOI: 10.1097/wnr.0000000000001949] [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: 07/29/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023]
Abstract
Insomnia is often accompanied by excessive pre-sleep rumination. Such ruminative thinking is also associated with increased connectivity of the default mode network (DMN). It is likely that DMN connectivity and associated rumination contribute to the pathogenesis of insomnia. We hypothesized that resting state functional connectivity (rsFC) between the DMN and other brain regions prior to bedtime would predict objectively measured sleep among individuals with insomnia. Twenty participants (12 female; M age = 26.9, SD = 6.6 years) with symptoms of insomnia underwent an rsFC scan in the early evening followed by a night of polysomographically (PSG) measured sleep. Connectivity of the DMN with other brain regions was regressed against several PSG sleep metrics, including time in wake, N1, N2, N3, REM, total sleep time (TST), and sleep efficiency (SE) at a cluster corrected false discovery rate (FDR) correction P < 0.05. The connectivity between DMN and cortical regions was negatively correlated with PSG indices of poorer sleep including time in wake (right angular gyrus) and N1 (precuneus) but positively correlated with time in REM (orbitofrontal cortex), TST (insula, orbitofrontal cortex, superior frontal gyrus, paracingulate gyrus), SE (orbitofrontal cortex). Connectivity between DMN and the pons was negatively correlated with SE. Among individuals with symptoms of insomnia, better sleep was predicted by rsFC between the DMN and cortical regions involved in executive functioning, consciousness, and complex cognition. Findings raise the possibility that future interventions aimed at suppressing pre-sleep DMN activation may weaken synergy between pre-sleep ruminative worry and complex cognitions, potentially ameliorating problems falling asleep.
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Affiliation(s)
- William D.S. Killgore
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Samantha Jankowski
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Kymberly Henderson-Arredondo
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Daniel A. Lucas
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Salma I. Patel
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Lindsey L. Hildebrand
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Alisa Huskey
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
| | - Natalie S. Dailey
- Social, Cognitive, and Affective Neuroscience Laboratory, Department of Psychiatry University of Arizona, Tucson, Arizona, USA
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13
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Rashidi-Ranjbar N, Rajji TK, Hawco C, Kumar S, Herrmann N, Mah L, Flint AJ, Fischer CE, Butters MA, Pollock BG, Dickie EW, Bowie CR, Soffer M, Mulsant BH, Voineskos AN. Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment. Neuropsychopharmacology 2023; 48:468-477. [PMID: 35410366 PMCID: PMC9852291 DOI: 10.1038/s41386-022-01308-2] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 02/02/2023]
Abstract
Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.
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Affiliation(s)
- Neda Rashidi-Ranjbar
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Baycrest Health Sciences, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alastair J Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Corinne E Fischer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bruce G Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Departments of Psychology and Psychiatry (CRB), Queen's University, Kingston, ON, Canada
| | - Matan Soffer
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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14
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Guardia T, Geerligs L, Tsvetanov KA, Ye R, Campbell KL. The role of the arousal system in age-related differences in cortical functional network architecture. Hum Brain Mapp 2022; 43:985-997. [PMID: 34713955 PMCID: PMC8764482 DOI: 10.1002/hbm.25701] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 05/20/2021] [Revised: 10/04/2021] [Accepted: 10/17/2021] [Indexed: 01/10/2023] Open
Abstract
A common finding in the aging literature is that of the brain's decreased within- and increased between-network functional connectivity. However, it remains unclear what is causing this shift in network organization with age. Given the essential role of the ascending arousal system (ARAS) in cortical activation and previous findings of disrupted ARAS functioning with age, it is possible that age differences in ARAS functioning contribute to disrupted cortical connectivity. We test this possibility here using resting state fMRI data from over 500 individuals across the lifespan from the Cambridge Center for Aging and Neuroscience (Cam-CAN) population-based cohort. Our results show that ARAS-cortical connectivity declines with age and, consistent with our expectations, significantly mediates some age-related differences in connectivity within and between association networks (specifically, within the default mode and between the default mode and salience networks). Additionally, connectivity between the ARAS and association networks predicted cognitive performance across several tasks over and above the effects of age and connectivity within the cortical networks themselves. These findings suggest that age differences in cortical connectivity may be driven, at least in part, by altered arousal signals from the brainstem and that ARAS-cortical connectivity relates to cognitive performance with age.
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Affiliation(s)
- Tiago Guardia
- Department of PsychologyBrock UniversitySt. CatharinesOntarioCanada
| | - Linda Geerligs
- Donders Institute for Brain, Cognition, and BehaviourRadboud UniversityNijmegenThe Netherlands
| | | | - Rong Ye
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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15
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Koelsch S, Andrews‐Hanna JR, Skouras S. Tormenting thoughts: The posterior cingulate sulcus of the default mode network regulates valence of thoughts and activity in the brain's pain network during music listening. Hum Brain Mapp 2022; 43:773-786. [PMID: 34652882 PMCID: PMC8720190 DOI: 10.1002/hbm.25686] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/06/2021] [Accepted: 10/04/2021] [Indexed: 01/08/2023] Open
Abstract
Many individuals spend a significant amount of their time "mind-wandering". Mind-wandering often includes spontaneous, nonintentional thought, and a neural correlate of this kind of thought is the default mode network (DMN). Thoughts during mind-wandering can have positive or negative valence, but only little is known about the neural correlates of positive or negative thoughts. We used resting-state functional magnetic resonance imaging (fMRI) and music to evoke mind-wandering in n = 33 participants, with positive-sounding music eliciting thoughts with more positive valence and negative-sounding music eliciting thoughts with more negative valence. Applying purely data-driven analysis methods, we show that medial orbitofrontal cortex (mOFC, part of the ventromedial prefrontal cortex) and the posterior cingulate sulcus (likely area 23c of the posterior cingulate cortex), two sub-regions of the DMN, modulate the valence of thought-contents during mind-wandering. In addition, across two independent experiments, we observed that the posterior cingulate sulcus, a region involved in pain, shows valence-specific functional connectivity with core regions of the brain's putative pain network. Our results suggest that two DMN regions (mOFC and posterior cingulate sulcus) support the formation of negative spontaneous, nonintentional thoughts, and that the interplay between these structures with regions of the putative pain network forms a neural mechanism by which thoughts can become painful.
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Affiliation(s)
- Stefan Koelsch
- Department of Biological and Medical PsychologyUniversity of BergenBergen
| | | | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergen
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16
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DiProspero ND, Keator DB, Phelan M, van Erp TGM, Doran E, Powell DK, Van Pelt KL, Schmitt FA, Head E, Lott IT, Yassa MA. Selective Impairment of Long-Range Default Mode Network Functional Connectivity as a Biomarker for Preclinical Alzheimer's Disease in People with Down Syndrome. J Alzheimers Dis 2022; 85:153-165. [PMID: 34776436 PMCID: PMC9017677 DOI: 10.3233/jad-210572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Indexed: 01/03/2023]
Abstract
BACKGROUND Down syndrome (DS) is associated with increased risk for Alzheimer's disease (AD). In neurotypical individuals, clinical AD is preceded by reduced resting state functional connectivity in the default mode network (DMN), but it is unknown whether changes in DMN connectivity predict clinical onset of AD in DS. OBJECTIVE Does lower DMN functional connectivity predict clinical onset of AD and cognitive decline in people with DS? METHODS Resting state functional MRI (rsfMRI), longitudinal neuropsychological, and clinical assessment data were collected on 15 nondemented people with DS (mean age = 51.66 years, SD = 5.34 years, range = 42-59 years) over four years, during which 4 transitioned to dementia. Amyloid-β (Aβ) PET data were acquired on 13 of the 15 participants. Resting state fMRI, neuropsychological, and clinical assessment data were also acquired on an independent, slightly younger unimpaired sample of 14 nondemented people with DS (mean age = 44.63 years, SD = 7.99 years, range = 38-61 years). RESULTS Lower functional connectivity between long-range but not short-range DMN regions predicts AD diagnosis and cognitive decline in people with DS. Aβ accumulation in the inferior parietal cortex is associated with lower regional DMN functional connectivity. CONCLUSION Reduction of long-range DMN connectivity is a potential biomarker for AD in people with DS that precedes and predicts clinical conversion.
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Affiliation(s)
- Natalie D. DiProspero
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92697
| | - David B. Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697
| | - Michael Phelan
- Institute for Memory Impairments and Neurological Disorders, UC Irvine, CA 92697
| | - Theo G. M. van Erp
- Department of Pediatrics, University of California, Irvine Medical Center, Orange, CA 92868
| | - Eric Doran
- Department of Pediatrics, University of California, Irvine Medical Center, Orange, CA 92868
| | - David K. Powell
- Department of Neuroscience, University of Kentucky Medical Center, Lexington, KY 40536
| | - Kathryn L. Van Pelt
- Sanders-Brown Center on Aging, University of Kentucky Medical Center, Lexington, KY 40536
| | - Frederick A. Schmitt
- Sanders-Brown Center on Aging, University of Kentucky Medical Center, Lexington, KY 40536
- Department of Neurology, University of Kentucky Medical Center, Lexington, KY 40536
| | - Elizabeth Head
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA 92697
| | - Ira T. Lott
- Department of Pediatrics, University of California, Irvine Medical Center, Orange, CA 92868
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92697
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697
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17
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Li J, Curley WH, Guerin B, Dougherty DD, Dalca AV, Fischl B, Horn A, Edlow BL. Mapping the subcortical connectivity of the human default mode network. Neuroimage 2021; 245:118758. [PMID: 34838949 PMCID: PMC8945548 DOI: 10.1016/j.neuroimage.2021.118758] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [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: 07/13/2021] [Revised: 10/29/2021] [Accepted: 11/23/2021] [Indexed: 01/17/2023] Open
Abstract
The default mode network (DMN) mediates self-awareness and introspection, core components of human consciousness. Therapies to restore consciousness in patients with severe brain injuries have historically targeted subcortical sites in the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia, with the goal of reactivating cortical DMN nodes. However, the subcortical connectivity of the DMN has not been fully mapped, and optimal subcortical targets for therapeutic neuromodulation of consciousness have not been identified. In this work, we created a comprehensive map of DMN subcortical connectivity by combining high-resolution functional and structural datasets with advanced signal processing methods. We analyzed 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy volunteers acquired in the Human Connectome Project. The rs-fMRI blood-oxygen-level-dependent (BOLD) data were temporally synchronized across subjects using the BrainSync algorithm. Cortical and subcortical DMN nodes were jointly analyzed and identified at the group level by applying a novel Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method to the synchronized dataset. The subcortical connectivity map was then overlaid on a 7 Tesla 100 µm ex vivo MRI dataset for neuroanatomic analysis using automated segmentation of nuclei within the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia. We further compared the NASCAR subcortical connectivity map with its counterpart generated from canonical seed-based correlation analyses. The NASCAR method revealed that BOLD signal in the central lateral nucleus of the thalamus and ventral tegmental area of the midbrain is strongly correlated with that of the DMN. In an exploratory analysis, additional subcortical sites in the median and dorsal raphe, lateral hypothalamus, and caudate nuclei were correlated with the cortical DMN. We also found that the putamen and globus pallidus are negatively correlated (i.e., anti-correlated) with the DMN, providing rs-fMRI evidence for the mesocircuit hypothesis of human consciousness, whereby a striatopallidal feedback system modulates anterior forebrain function via disinhibition of the central thalamus. Seed-based analyses yielded similar subcortical DMN connectivity, but the NASCAR result showed stronger contrast and better spatial alignment with dopamine immunostaining data. The DMN subcortical connectivity map identified here advances understanding of the subcortical regions that contribute to human consciousness and can be used to inform the selection of therapeutic targets in clinical trials for patients with disorders of consciousness.
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Affiliation(s)
- Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - William H Curley
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Bastien Guerin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Darin D Dougherty
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andreas Horn
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Movement Disorders & Neuromodulation Section, Department of Neurology, Charité - Universitätsmedizin, Berlin, Germany
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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18
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Causse M, Lepron E, Mandrick K, Peysakhovich V, Berry I, Callan D, Rémy F. Facing successfully high mental workload and stressors: An fMRI study. Hum Brain Mapp 2021; 43:1011-1031. [PMID: 34738280 PMCID: PMC8764488 DOI: 10.1002/hbm.25703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 03/30/2021] [Revised: 10/13/2021] [Accepted: 10/19/2021] [Indexed: 11/29/2022] Open
Abstract
The present fMRI study aimed at highlighting patterns of brain activations and autonomic activity when confronted with high mental workload and the threat of auditory stressors. Twenty participants performed a complex cognitive task in either safe or aversive conditions. Our results showed that increased mental workload induced recruitment of the lateral frontoparietal executive control network (ECN), along with disengagement of medial prefrontal and posterior cingulate regions of the default mode network (DMN). Mental workload also elicited an increase in heart rate and pupil diameter. Task performance did not decrease under the threat of stressors, most likely due to efficient inhibition of auditory regions, as reflected by a large decrement of activity in the superior temporal gyri. The threat of stressors was also accompanied with deactivations of limbic regions of the salience network (SN), possibly reflecting emotional regulation mechanisms through control from dorsal medial prefrontal and parietal regions, as indicated by functional connectivity analyses. Meanwhile, the threat of stressors induced enhanced ECN activity, likely for improved attentional and cognitive processes toward the task, as suggested by increased lateral prefrontal and parietal activations. These fMRI results suggest that measuring the balance between ECN, SN, and DMN recruitment could be used for objective mental state assessment. In this sense, an extra recruitment of task‐related regions and a high ratio of lateral versus medial prefrontal activity may represent a relevant marker of increased but efficient mental effort, while the opposite may indicate a disengagement from the task due to mental overload and/or stressors.
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Affiliation(s)
| | - Evelyne Lepron
- Centre de Recherche Cerveau et CognitionUniversité de Toulouse UPS and CNRSToulouseFrance
| | | | | | - Isabelle Berry
- Centre de Recherche Cerveau et CognitionUniversité de Toulouse UPS and CNRSToulouseFrance
| | - Daniel Callan
- ATR Neural Information Analysis LaboratoriesKyotoJapan
| | - Florence Rémy
- Centre de Recherche Cerveau et CognitionUniversité de Toulouse UPS and CNRSToulouseFrance
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19
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Bai L, JI G, Song Y, Sun J, Wei J, Xue F, Zhu L, Li R, Han Y, Zhang L, Yang J, Qiu B, Wu G, Zhang J, Hong J, Wang K, Zhu C. Dynamic brain connectome and high risk of mental problem in clinical nurses. Hum Brain Mapp 2021; 42:5300-5308. [PMID: 34331489 PMCID: PMC8519872 DOI: 10.1002/hbm.25617] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022] Open
Abstract
With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.
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Affiliation(s)
- Ling Bai
- School of Nursing, Anhui Medical UniversityHefeiChina
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Gong‐Jun JI
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yongxia Song
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinmei Sun
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Junjie Wei
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Fang Xue
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Lu Zhu
- Institute of Literature in Chinese Medicine, Nanjing University of Chinese MedicineNanjingChina
| | - Rui Li
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Yanfang Han
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Liu Zhang
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinying Yang
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Bensheng Qiu
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Guo‐Rong Wu
- Key Laboratory of Cognition and Personality, Southwest UniversityChongqingChina
| | - Jing Zhang
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Jingfang Hong
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
| | - Chunyan Zhu
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
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20
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Ding C, Du W, Zhang Q, Wang L, Han Y, Jiang J. Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals. Hum Brain Mapp 2021; 42:5051-5062. [PMID: 34291850 PMCID: PMC8449101 DOI: 10.1002/hbm.25599] [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: 03/10/2021] [Revised: 06/10/2021] [Accepted: 07/12/2021] [Indexed: 11/11/2022] Open
Abstract
The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography (18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area.
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Affiliation(s)
- Changchang Ding
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Wenying Du
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Qi Zhang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Luyao Wang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Biomedical Engineering InstituteHainan UniversityHaikouChina
| | - Jiehui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information EngineeringShanghai UniversityShanghaiChina
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21
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Huiskamp M, Eijlers AJC, Broeders TAA, Pasteuning J, Dekker I, Uitdehaag BMJ, Barkhof F, Wink AM, Geurts JJG, Hulst HE, Schoonheim MM. Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis. Neurology 2021; 97:e794-e802. [PMID: 34099528 PMCID: PMC8397585 DOI: 10.1212/wnl.0000000000012341] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To characterize functional network changes related to conversion to cognitive impairment in a large sample of patients with multiple sclerosis (MS) over a period of 5 years. METHODS Two hundred twenty-seven patients with MS and 59 healthy controls of the Amsterdam MS cohort underwent neuropsychological testing and resting-state fMRI at 2 time points (time interval 4.9 ± 0.9 years). At both baseline and follow-up, patients were categorized as cognitively preserved (CP; n = 123), mildly impaired (MCI; z < -1.5 on ≥2 cognitive tests, n = 32), or impaired (CI; z < -2 on ≥2 tests, n = 72), and longitudinal conversion between groups was determined. Network function was quantified with eigenvector centrality, a measure of regional network importance, which was computed for individual resting-state networks at both time points. RESULTS Over time, 18.9% of patients converted to a worse phenotype; 22 of 123 patients who were CP (17.9%) converted from CP to MCI, 10 of 123 from CP to CI (8.1%), and 12 of 32 patients with MCI converted to CI (37.5%). At baseline, default-mode network (DMN) centrality was higher in CI individuals compared to controls (p = 0.05). Longitudinally, ventral attention network (VAN) importance increased in CP, driven by stable CP and CP-to-MCI converters (p < 0.05). CONCLUSIONS Of all patients, 19% worsened in their cognitive status over 5 years. Conversion from intact cognition to impairment is related to an initial disturbed functioning of the VAN, then shifting toward DMN dysfunction in CI. Because the VAN normally relays information to the DMN, these results could indicate that in MS normal processes crucial for maintaining overall network stability are progressively disrupted as patients clinically progress.
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Affiliation(s)
- Marijn Huiskamp
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK.
| | - Anand J C Eijlers
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Tommy A A Broeders
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Jasmin Pasteuning
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Iris Dekker
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Bernard M J Uitdehaag
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Frederik Barkhof
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Alle-Meije Wink
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Jeroen J G Geurts
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Hanneke E Hulst
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Menno M Schoonheim
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
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22
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Wang Y, Metoki A, Xia Y, Zang Y, He Y, Olson IR. A large-scale structural and functional connectome of social mentalizing. Neuroimage 2021; 236:118115. [PMID: 33933599 DOI: 10.1016/j.neuroimage.2021.118115] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 12/17/2020] [Revised: 03/29/2021] [Accepted: 04/13/2021] [Indexed: 12/21/2022] Open
Abstract
Humans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the brain-wide organization and mechanisms of mentalizing processing. Key connectomic features of the mentalizing network (MTN) have been delineated in exquisite detail. We found the structural architecture of MTN is organized by two parallel subsystems and constructed redundantly by local and long-range white matter fibers. We uncovered an intrinsic functional architecture that is synchronized according to the degree of mentalizing, and its hierarchy reflects the inherent information integration order. We also examined the correspondence between the structural and functional connectivity in the network and revealed their differences in network topology, individual variance, spatial specificity, and functional specificity. Finally, we scrutinized the connectome resemblance between the default mode network and MTN and elaborated their inherent differences in dynamic patterns, laterality, and homogeneity. Overall, our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other vicinity brain networks supporting autobiographical memory, semantic memory, self-referential, moral reasoning, and mental time travel.
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Affiliation(s)
- Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Athanasia Metoki
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yinyin Zang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA, USA.
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23
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Cooper RA, Kurkela KA, Davis SW, Ritchey M. Mapping the organization and dynamics of the posterior medial network during movie watching. Neuroimage 2021; 236:118075. [PMID: 33910099 PMCID: PMC8290580 DOI: 10.1016/j.neuroimage.2021.118075] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 02/12/2021] [Accepted: 04/06/2021] [Indexed: 11/18/2022] Open
Abstract
Brain regions within a posterior medial network (PMN) are characterized by sensitivity to episodic tasks, and they also demonstrate strong functional connectivity as part of the default network. Despite its cohesive structure, delineating the intranetwork organization and functional diversity of the PMN is crucial for understanding its contributions to multidimensional event cognition. Here, we probed functional connectivity of the PMN during movie watching to identify its pattern of connections and subnetwork functions in a split-sample replication of 136 participants. Consistent with prior findings of default network fractionation, we identified distinct PMN subsystems: a Ventral PM subsystem (retrosplenial cortex, parahippocampal cortex, posterior angular gyrus) and a Dorsal PM subsystem (medial prefrontal cortex, hippocampus, precuneus, posterior cingulate cortex, anterior angular gyrus). Ventral and Dorsal PM subsystems were differentiated by functional connectivity with parahippocampal cortex and precuneus and integrated by retrosplenial cortex and posterior cingulate cortex, respectively. Finally, the distinction between PMN subsystems is functionally relevant: whereas both Dorsal and Ventral PM connectivity tracked the movie content, only Ventral PM connections increased in strength at event transitions and appeared sensitive to episodic memory. Overall, these findings reveal PMN functional pathways and the distinct functional roles of intranetwork subsystems during event cognition.
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Affiliation(s)
- Rose A Cooper
- Department of Psychology and Neuroscience, Boston College, United States.
| | - Kyle A Kurkela
- Department of Psychology and Neuroscience, Boston College, United States
| | - Simon W Davis
- Department of Neurology, Duke University School of Medicine, United States
| | - Maureen Ritchey
- Department of Psychology and Neuroscience, Boston College, United States
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24
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Mentink LJ, Guimarães JPOFT, Faber M, Sprooten E, Olde Rikkert MGM, Haak KV, Beckmann CF. Functional co-activation of the default mode network in APOE ε4-carriers: A replication study. Neuroimage 2021; 240:118304. [PMID: 34329959 DOI: 10.1016/j.neuroimage.2021.118304] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 05/12/2021] [Revised: 05/27/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
Structural and functional alterations of the brain in persons genetically at-risk for Alzheimer's disease (AD) are crucial in unravelling AD development. Filippini et al. found that the default mode network (DMN) is already affected in young APOE ε4-carriers, with increased co-activation of the DMN during rest and increased hippocampal task activation. We aimed to replicate the early findings of Filippini et al, using the APOE gene, still the principal AD risk gene, and extended this with a polygenic risk score (PRS) analysis for AD, using the Human Connectome Project dataset (HCP). We included participants from the HCP S1200 dataset (age range: 22-36 years). We studied morphometric features, functional DMN co-activation and functional task activation of recollection performance. Permutation Analysis of Linear Models (PALM) was used to test for group differences between APOE ε4-carriers and non-carriers, and to test the association with PRS. PALM controls for biases induced by the family structure of the HCP sample. Results were family-wise error rate corrected at p < 0.05. Our primary analysis did not replicate the early findings of Filippini et al. (2009). However, compared with non-carriers, APOE ε4-carriers showed increased functional activation during the encoding of subsequently recollected items in areas related to facial recognition (p<0.05, t>756.11). This increased functional activation was also positively associated with PRS (APOE variants included) (p<0.05, t>647.55). Our results are supportive for none to limited genetic effects on brain structure and function in young adults. Taking the methodological considerations of replication studies into account, the true effect of APOE ε4-carriership is likely smaller than indicated in the Filippini paper. However, it still holds that we may not yet be able to detect already present measurable effects decades before a clinical expression of AD. Since the mechanistic pathway of AD is likely to encompass many different factors, further research should be focused on the interactions of genetic risk, biomarkers, aging and lifestyle factors over the life course. Sensitive functional neuroimaging as used here may help disentangling these complex interactions.
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Affiliation(s)
- Lara J Mentink
- Department of Geriatrics, Radboudumc Alzheimer Centre, Radboud University Medical Center, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - João P O F T Guimarães
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Myrthe Faber
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Communication and Cognition, Tilburg Center for Cognition and Communication, Tilburg University, Tilburg, The Netherlands.
| | - Emma Sprooten
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Marcel G M Olde Rikkert
- Department of Geriatrics, Radboudumc Alzheimer Centre, Radboud University Medical Center, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Koen V Haak
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.
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25
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Menon DK, Stamatakis EA. Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane. Hum Brain Mapp 2021; 42:2802-2822. [PMID: 33738899 PMCID: PMC8127159 DOI: 10.1002/hbm.25405] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/10/2021] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.
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Affiliation(s)
- Andrea I. Luppi
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Andreas Ranft
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Rüdiger Ilg
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
- Department of NeurologyAsklepios ClinicBad TölzGermany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - David K. Menon
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Wolfon Brain Imaging CentreUniversity of CambridgeCambridgeUK
| | - Emmanuel A. Stamatakis
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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26
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Younes K, Rojas JC, Wolf A, Sheng‐Yang GM, Paoletti M, Toller G, Caverzasi E, Luisa Mandelli M, Illán‐Gala I, Kramer JH, Cobigo Y, Miller BL, Rosen HJ, Geschwind MD. Selective vulnerability to atrophy in sporadic Creutzfeldt-Jakob disease. Ann Clin Transl Neurol 2021; 8:1183-1199. [PMID: 33949799 PMCID: PMC8164858 DOI: 10.1002/acn3.51290] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/16/2020] [Accepted: 12/04/2020] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Identification of brain regions susceptible to quantifiable atrophy in sporadic Creutzfeldt-Jakob disease (sCJD) should allow for improved understanding of disease pathophysiology and development of structural biomarkers that might be useful in future treatment trials. Although brain atrophy is not usually present by visual assessment of MRIs in sCJD, we assessed whether using voxel-based morphometry (VBM) can detect group-wise brain atrophy in sCJD. METHODS 3T brain MRI data were analyzed with VBM in 22 sCJD participants and 26 age-matched controls. Analyses included relationships of regional brain volumes with major clinical variables and dichotomization of the cohort according to expected disease duration based on prion molecular classification (i.e., short-duration/Fast-progressors (MM1, MV1, and VV2) vs. long-duration/Slow-progressors (MV2, VV1, and MM2)). Structural equation modeling (SEM) was used to assess network-level interactions of atrophy between specific brain regions. RESULTS sCJD showed selective atrophy in cortical and subcortical regions overlapping with all but one region of the default mode network (DMN) and the insulae, thalami, and right occipital lobe. SEM showed that the effective connectivity model fit in sCJD but not controls. The presence of visual hallucinations correlated with right fusiform, bilateral thalami, and medial orbitofrontal atrophy. Interestingly, brain atrophy was present in both Fast- and Slow-progressors. Worse cognition was associated with bilateral mesial frontal, insular, temporal pole, thalamus, and cerebellum atrophy. INTERPRETATION Brain atrophy in sCJD preferentially affects specific cortical and subcortical regions, with an effective connectivity model showing strength and directionality between regions. Brain atrophy is present in Fast- and Slow-progressors, correlates with clinical findings, and is a potential biomarker in sCJD.
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Affiliation(s)
- Kyan Younes
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Julio C. Rojas
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Amy Wolf
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Goh M. Sheng‐Yang
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Matteo Paoletti
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
- Advanced Imaging and Radiomics CenterNeuroradiology DepartmentIRCCS Mondino FoundationPaviaItaly
| | - Gianina Toller
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Eduardo Caverzasi
- Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Maria Luisa Mandelli
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Ignacio Illán‐Gala
- Department of NeurologyHospital de la Santa Creu i Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Joel H. Kramer
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Yann Cobigo
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Bruce L. Miller
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Howard J. Rosen
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
| | - Michael D. Geschwind
- Department of NeurologyWeill Institute for NeurosciencesMemory and Aging CenterUniversity of California, San Francisco (UCSF)San FranciscoCalifornia
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Iordan AD, Moored KD, Katz B, Cooke KA, Buschkuehl M, Jaeggi SM, Polk TA, Peltier SJ, Jonides J, Reuter‐Lorenz PA. Age differences in functional network reconfiguration with working memory training. Hum Brain Mapp 2021; 42:1888-1909. [PMID: 33534925 PMCID: PMC7978135 DOI: 10.1002/hbm.25337] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 05/19/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 12/16/2022] Open
Abstract
Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.
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Affiliation(s)
| | - Kyle D. Moored
- Department of Mental Health, Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Benjamin Katz
- Department of Human Development and Family ScienceVirginia TechBlacksburgVirginiaUSA
| | | | | | - Susanne M. Jaeggi
- School of EducationUniversity of California‐IrvineIrvineCaliforniaUSA
| | - Thad A. Polk
- Department of PsychologyUniversity of MichiganAnn ArborMichiganUSA
| | - Scott J. Peltier
- Functional MRI LaboratoryUniversity of MichiganAnn ArborMichiganUSA
- Department of Biomedical EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - John Jonides
- Department of PsychologyUniversity of MichiganAnn ArborMichiganUSA
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DeSimone JC, Davenport EM, Urban J, Xi Y, Holcomb JM, Kelley ME, Whitlow CT, Powers AK, Stitzel JD, Maldjian JA. Mapping default mode connectivity alterations following a single season of subconcussive impact exposure in youth football. Hum Brain Mapp 2021; 42:2529-2545. [PMID: 33734521 PMCID: PMC8090779 DOI: 10.1002/hbm.25384] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 06/26/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/14/2022] Open
Abstract
Repetitive head impact (RHI) exposure in collision sports may contribute to adverse neurological outcomes in former players. In contrast to a concussion, or mild traumatic brain injury, “subconcussive” RHIs represent a more frequent and asymptomatic form of exposure. The neural network‐level signatures characterizing subconcussive RHIs in youth collision‐sport cohorts such as American Football are not known. Here, we used resting‐state functional MRI to examine default mode network (DMN) functional connectivity (FC) following a single football season in youth players (n = 50, ages 8–14) without concussion. Football players demonstrated reduced FC across widespread DMN regions compared with non‐collision sport controls at postseason but not preseason. In a subsample from the original cohort (n = 17), players revealed a negative change in FC between preseason and postseason and a positive and compensatory change in FC during the offseason across the majority of DMN regions. Lastly, significant FC changes, including between preseason and postseason and between in‐ and off‐season, were specific to players at the upper end of the head impact frequency distribution. These findings represent initial evidence of network‐level FC abnormalities following repetitive, non‐concussive RHIs in youth football. Furthermore, the number of subconcussive RHIs proved to be a key factor influencing DMN FC.
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Affiliation(s)
- Jesse C. DeSimone
- Advanced Neuroscience Imaging Research (ANSIR) LaboratoryUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Elizabeth M. Davenport
- Advanced Neuroscience Imaging Research (ANSIR) LaboratoryUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Jillian Urban
- Department of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Virginia Tech – Wake Forest School of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
| | - Yin Xi
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - James M. Holcomb
- Advanced Neuroscience Imaging Research (ANSIR) LaboratoryUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Mireille E. Kelley
- Department of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Virginia Tech – Wake Forest School of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
| | - Christopher T. Whitlow
- Virginia Tech – Wake Forest School of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Department of Radiology – NeuroradiologyWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Clinical and Translational Sciences InstituteWake Forest School of MedicineWinston SalemNorth CarolinaUSA
| | - Alexander K. Powers
- Department of NeurosurgeryWake Forest School of MedicineWinston SalemNorth CarolinaUSA
| | - Joel D. Stitzel
- Department of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Virginia Tech – Wake Forest School of Biomedical EngineeringWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Clinical and Translational Sciences InstituteWake Forest School of MedicineWinston SalemNorth CarolinaUSA
- Childress Institute for Pediatric TraumaWake Forest School of MedicineWinston SalemNorth CarolinaUSA
| | - Joseph A. Maldjian
- Advanced Neuroscience Imaging Research (ANSIR) LaboratoryUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
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de la Cruz F, Wagner G, Schumann A, Suttkus S, Güllmar D, Reichenbach JR, Bär KJ. Interrelations between dopamine and serotonin producing sites and regions of the default mode network. Hum Brain Mapp 2021; 42:811-823. [PMID: 33128416 PMCID: PMC7814772 DOI: 10.1002/hbm.25264] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 05/02/2020] [Revised: 10/05/2020] [Accepted: 10/14/2020] [Indexed: 12/13/2022] Open
Abstract
Recent functional magnetic resonance imaging (fMRI) studies showed that blood oxygenation level-dependent (BOLD) signal fluctuations in the default mode network (DMN) are functionally tightly connected to those in monoaminergic nuclei, producing dopamine (DA), and serotonin (5-HT) transmitters, in the midbrain/brainstem. We combined accelerated fMRI acquisition with spectral Granger causality and coherence analysis to investigate causal relationships between these areas. Both methods independently lead to similar results and confirm the existence of a top-down information flow in the resting-state condition, where activity in core DMN areas influences activity in the neuromodulatory centers producing DA/5-HT. We found that latencies range from milliseconds to seconds with high inter-subject variability, likely attributable to the resting condition. Our novel findings provide new insights into the functional organization of the human brain.
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Affiliation(s)
- Feliberto de la Cruz
- Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany
| | - Andy Schumann
- Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Germany
| | - Stefanie Suttkus
- Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Germany
| | - Daniel Güllmar
- Medical Physics Group, Department of Diagnostic and Interventional Radiology, Jena University Hospital, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Department of Diagnostic and Interventional Radiology, Jena University Hospital, Germany
| | - Karl-Jürgen Bär
- Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Germany
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Abstract
BACKGROUND Previous literature has extensively investigated the brain activity during response inhibition in adults with addiction. Inconsistent results including both hyper- and hypo-activities in the fronto-parietal network (FPN) and the ventral attention network (VAN) have been found in adults with addictions, compared with healthy controls (HCs). METHODS Voxel-wise meta-analyses of abnormal task-evoked regional activity were conducted for adults with substance dependence (SD) and behavioral addiction during response inhibition tasks to solve previous inconsistencies. Twenty-three functional magnetic resonance imaging studies including 479 substance users, 38 individuals with behavioral addiction and 494 HCs were identified. RESULTS Compared with HCs, all addictions showed hypo-activities in regions within FPN (inferior frontal gyrus and supramarginal gyrus) and VAN (inferior frontal gyrus, middle temporal gyrus, temporal pole and insula), and hyper-activities in the cerebellum during response inhibition. SD subgroup showed almost the same activity patterns, with an additional hypoactivation of the precentral gyrus, compared with HCs. Stronger activation of the cerebellum was associated with longer addiction duration for adults with SD. We could not conduct meta-analytic investigations into the behavioral addiction subgroup due to the small number of datasets. CONCLUSION This meta-analysis revealed altered activation of FPN, VAN and the cerebellum in adults with addiction during response inhibition tasks using non-addiction-related stimuli. Although FPN and VAN showed lower activity, the cerebellum exhibited stronger activity. These results may help to understand the neural pathology of response inhibition in addiction.
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Affiliation(s)
- Zeguo Qiu
- Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou510006, China
- School of Psychology, The University of Queensland, Brisbane4072, Australia
| | - Junjing Wang
- Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou510006, China
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31
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Moguilner S, García AM, Perl YS, Tagliazucchi E, Piguet O, Kumfor F, Reyes P, Matallana D, Sedeño L, Ibáñez A. Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study. Neuroimage 2021; 225:117522. [PMID: 33144220 PMCID: PMC7832160 DOI: 10.1016/j.neuroimage.2020.117522] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [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: 05/28/2020] [Revised: 10/14/2020] [Accepted: 10/27/2020] [Indexed: 02/07/2023] Open
Abstract
From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.
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Affiliation(s)
- Sebastian Moguilner
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; Fundación Escuela de Medicina Nuclear (FUESMEN) and Comisión Nacional de Energía Atómica (CNEA), Buenos Aires, Argentina
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Faculty of Education, National University of Cuyo (UNCuyo), Mendoza, Argentina
| | - Yonatan Sanz Perl
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Department of Physics, University of Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Department of Physics, University of Buenos Aires, Argentina
| | - Olivier Piguet
- School of Psychology and Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Fiona Kumfor
- School of Psychology and Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Pablo Reyes
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana; Mental Health Unit, Hospital Universitario Fundación Santa Fe, Bogotá, Colombia, Hospital Universitario San Ignacio. Bogotá, Colombia
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana; Mental Health Unit, Hospital Universitario Fundación Santa Fe, Bogotá, Colombia, Hospital Universitario San Ignacio. Bogotá, Colombia
| | - Lucas Sedeño
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
| | - Agustín Ibáñez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Universidad Autónoma del Caribe, Barranquilla, Colombia; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile.
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Ma J, Lin Y, Hu C, Zhang J, Yi Y, Dai Z. Integrated and segregated frequency architecture of the human brain network. Brain Struct Funct 2021; 226:335-350. [PMID: 33389041 DOI: 10.1007/s00429-020-02174-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 02/27/2020] [Accepted: 11/09/2020] [Indexed: 12/11/2022]
Abstract
The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01-0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.
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Affiliation(s)
- Junji Ma
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Ying Lin
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Chuanlin Hu
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Yangyang Yi
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China.
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Abstract
BACKGROUND Disturbances in gain and loss processing have been extensively reported in adults with addiction, a brain disorder characterized by obsession with addictive substances or behaviours. Previous studies have provided conflicting results with respect to neural abnormalities in gain processing in addiction, and few investigations into loss processing. METHODS We conducted voxel-wise meta-analyses of abnormal task-evoked regional activities in adults with substance dependence and gambling addiction during the processing of gains and losses not related to their addiction (mainly monetary). We identified 24 studies, including 465 participants with substance dependence, 81 with gambling addiction and 490 healthy controls. RESULTS Compared with healthy controls, all participants with addictions showed hypoactivations in the prefrontal cortex, striatum and insula and hyperactivations in the default mode network during gain anticipation; hyperactivations in the prefrontal cortex and both hyper- and hypoactivations in the striatum during loss anticipation; and hyperactivations in the occipital lobe during gain outcome. In the substance dependence subgroup, activity in the occipital lobe was increased during gain anticipation but decreased during loss anticipation. LIMITATIONS We were unable to conduct meta-analyses in the gambling addiction subgroup because of a limited data set. We did not investigate the effects of clinical variables because of limited information. CONCLUSION The current study identified altered brain activity associated with higher- and lower-level function during gain and loss processing for non-addiction (mainly monetary) stimuli in adults with substance dependence and gambling addiction. Adults with addiction were more sensitive to anticipatory gains than losses at higher- and lower-level brain areas. These results may help us to better understand the pathology of gain and loss processing in addiction.
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Affiliation(s)
- Zeguo Qiu
- From the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou 510006, China (Qiu, Wang); and the School of Psychology, The University of Queensland, Brisbane 4067, Australia (Qiu)
| | - Junjing Wang
- From the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou 510006, China (Qiu, Wang); and the School of Psychology, The University of Queensland, Brisbane 4067, Australia (Qiu)
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Bauer CCC, Rozenkrantz L, Caballero C, Nieto‐Castanon A, Scherer E, West MR, Mrazek M, Phillips DT, Gabrieli JDE, Whitfield‐Gabrieli S. Mindfulness training preserves sustained attention and resting state anticorrelation between default-mode network and dorsolateral prefrontal cortex: A randomized controlled trial. Hum Brain Mapp 2020; 41:5356-5369. [PMID: 32969562 PMCID: PMC7670646 DOI: 10.1002/hbm.25197] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [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: 05/11/2020] [Revised: 08/04/2020] [Accepted: 08/18/2020] [Indexed: 01/21/2023] Open
Abstract
Mindfulness training can enhance cognitive control, but the neural mechanisms underlying such enhancement in children are unknown. Here, we conducted a randomized controlled trial (RCT) with sixth graders (mean age 11.76 years) to examine the impact of 8 weeks of school-based mindfulness training, relative to coding training as an active control, on sustained attention and associated resting-state functional brain connectivity. At baseline, better performance on a sustained-attention task correlated with greater anticorrelation between the default mode network (DMN) and right dorsolateral prefrontal cortex (DLPFC), a key node of the central executive network. Following the interventions, children in the mindfulness group preserved their sustained-attention performance (i.e., fewer lapses of attention) and preserved DMN-DLPFC anticorrelation compared to children in the active control group, who exhibited declines in both sustained attention and DMN-DLPFC anticorrelation. Further, change in sustained-attention performance correlated with change in DMN-DLPFC anticorrelation only within the mindfulness group. These findings provide the first causal link between mindfulness training and both sustained attention and associated neural plasticity. Administered as a part of sixth graders' school schedule, this RCT supports the beneficial effects of school-based mindfulness training on cognitive control.
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Affiliation(s)
- Clemens C. C. Bauer
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
| | - Liron Rozenkrantz
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Camila Caballero
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Alfonso Nieto‐Castanon
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
- Department of Speech, Language and Hearing SciencesBoston UniversityBostonMassachusettsUSA
| | - Ethan Scherer
- Harvard Graduate School of EducationCambridgeMassachusettsUSA
| | - Martin R. West
- Harvard Graduate School of EducationCambridgeMassachusettsUSA
| | - Michael Mrazek
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Dawa T. Phillips
- Empowerment HoldingsSanta BarbaraCaliforniaUSA
- International Mindfulness Teachers AssociationWakefieldMassachusettsUSA
| | - John D. E. Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Harvard Graduate School of EducationCambridgeMassachusettsUSA
- MIT Integrated Learning InitiativeCambridgeMassachusettsUSA
| | - Susan Whitfield‐Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
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Salmi J, Soveri A, Salmela V, Alho K, Leppämäki S, Tani P, Koski A, Jaeggi SM, Laine M. Working memory training restores aberrant brain activity in adult attention-deficit hyperactivity disorder. Hum Brain Mapp 2020; 41:4876-4891. [PMID: 32813290 PMCID: PMC7643386 DOI: 10.1002/hbm.25164] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/25/2020] [Accepted: 07/29/2020] [Indexed: 01/01/2023] Open
Abstract
The development of treatments for attention impairments is hampered by limited knowledge about the malleability of underlying neural functions. We conducted the first randomized controlled trial to determine the modulations of brain activity associated with working memory (WM) training in adults with attention-deficit hyperactivity disorder (ADHD). At baseline, we assessed the aberrant functional brain activity in the n-back WM task by comparing 44 adults with ADHD with 18 healthy controls using fMRI. Participants with ADHD were then randomized to train on an adaptive dual n-back task or an active control task. We tested whether WM training elicits redistribution of brain activity as observed in healthy controls, and whether it might further restore aberrant activity related to ADHD. As expected, activity in areas of the default-mode (DMN), salience (SN), sensory-motor (SMN), frontoparietal (FPN), and subcortical (SCN) networks was decreased in participants with ADHD at pretest as compared with healthy controls, especially when the cognitive load was high. WM training modulated widespread FPN and SN areas, restoring some of the aberrant activity. Training effects were mainly observed as decreased brain activity during the trained task and increased activity during the untrained task, suggesting different neural mechanisms for trained and transfer tasks.
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Affiliation(s)
- Juha Salmi
- Department of Neuroscience and Biomedical EngineeringAalto UniversityEspooFinland
- Department of Psychology and Speech‐Language PathologyUniversity of TurkuTurkuFinland
- Turku Institute for Advanced StudiesUniversity of TurkuTurkuFinland
| | - Anna Soveri
- Department of Clinical MedicineUniversity of TurkuTurkuFinland
| | - Viljami Salmela
- Department of Psychology and LogopedicsUniversity of HelsinkiHelsinkiFinland
- AMI Centre, Aalto NeuroimagingAalto UniversityEspooFinland
| | - Kimmo Alho
- Department of Psychology and LogopedicsUniversity of HelsinkiHelsinkiFinland
- AMI Centre, Aalto NeuroimagingAalto UniversityEspooFinland
| | - Sami Leppämäki
- Department of PsychiatryHelsinki University HospitalHelsinkiFinland
| | - Pekka Tani
- Department of PsychiatryHelsinki University HospitalHelsinkiFinland
| | - Anniina Koski
- Department of PsychiatryHelsinki University HospitalHelsinkiFinland
| | - Susanne M. Jaeggi
- School of EducationUniversity of California IrvineIrvineCaliforniaUSA
- Department of Cognitive SciencesUniversity of California IrvineIrvineCaliforniaUSA
| | - Matti Laine
- Department of PsychologyÅbo Akademi UniversityTurkuFinland
- Brain and Mind CenterUniversity of TurkuTurkuFinland
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36
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Malagurski B, Liem F, Oschwald J, Mérillat S, Jäncke L. Longitudinal functional brain network reconfiguration in healthy aging. Hum Brain Mapp 2020; 41:4829-4845. [PMID: 32857461 PMCID: PMC7643380 DOI: 10.1002/hbm.25161] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/12/2020] [Accepted: 07/19/2020] [Indexed: 12/17/2022] Open
Abstract
Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.
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Affiliation(s)
- Brigitta Malagurski
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Jessica Oschwald
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Susan Mérillat
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Lutz Jäncke
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
- Division of Neuropsychology, Institute of PsychologyUniversity of ZurichZurichSwitzerland
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37
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Mckeown B, Strawson WH, Wang HT, Karapanagiotidis T, Vos de Wael R, Benkarim O, Turnbull A, Margulies D, Jefferies E, McCall C, Bernhardt B, Smallwood J. The relationship between individual variation in macroscale functional gradients and distinct aspects of ongoing thought. Neuroimage 2020; 220:117072. [PMID: 32585346 PMCID: PMC7573534 DOI: 10.1016/j.neuroimage.2020.117072] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/15/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Contemporary accounts of ongoing thought recognise it as a heterogeneous and multidimensional construct, varying in both form and content. An emerging body of evidence demonstrates that distinct types of experience are associated with unique neurocognitive profiles, that can be described at the whole-brain level as interactions between multiple large-scale networks. The current study sought to explore the possibility that whole-brain functional connectivity patterns at rest may be meaningfully related to patterns of ongoing thought that occurred over this period. Participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) followed by a questionnaire retrospectively assessing the content and form of their ongoing thoughts during the scan. A non-linear dimension reduction algorithm was applied to the rs-fMRI data to identify components explaining the greatest variance in whole-brain connectivity patterns. Using these data, we examined whether specific types of thought measured at the end of the scan were predictive of individual variation along the first three low-dimensional components of functional connectivity at rest. Multivariate analyses revealed that individuals for whom the connectivity of the sensorimotor system was maximally distinct from the visual system were most likely to report thoughts related to finding solutions to problems or goals and least likely to report thoughts related to the past. These results add to an emerging literature that suggests that unique patterns of experience are associated with distinct distributed neurocognitive profiles and highlight that unimodal systems may play an important role in this process.
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Affiliation(s)
- Brontë Mckeown
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom.
| | - Will H Strawson
- Neuroscience, Brighton and Sussex Medical School, University of Sussex, United Kingdom
| | - Hao-Ting Wang
- Sackler Centre for Consciousness Studies, University of Sussex, United Kingdom
| | | | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Adam Turnbull
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Daniel Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR, 7225, Paris, France
| | - Elizabeth Jefferies
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Cade McCall
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, United Kingdom
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38
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Wang X, Margulies DS, Smallwood J, Jefferies E. A gradient from long-term memory to novel cognition: Transitions through default mode and executive cortex. Neuroimage 2020; 220:117074. [PMID: 32574804 PMCID: PMC7573535 DOI: 10.1016/j.neuroimage.2020.117074] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/21/2020] [Accepted: 06/17/2020] [Indexed: 12/15/2022] Open
Abstract
Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a 'task gradient', from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain's response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations.
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Affiliation(s)
- Xiuyi Wang
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom.
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Jonathan Smallwood
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom.
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39
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Li M, Gao Y, Gao F, Anderson AW, Ding Z, Gore JC. Functional engagement of white matter in resting-state brain networks. Neuroimage 2020; 220:117096. [PMID: 32599266 PMCID: PMC7594260 DOI: 10.1016/j.neuroimage.2020.117096] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [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: 02/19/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 01/12/2023] Open
Abstract
The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - Fei Gao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA; Department of Electrical Engineering and Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
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40
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Cauda F, Nani A, Liloia D, Manuello J, Premi E, Duca S, Fox PT, Costa T. Finding specificity in structural brain alterations through Bayesian reverse inference. Hum Brain Mapp 2020; 41:4155-4172. [PMID: 32829507 PMCID: PMC7502845 DOI: 10.1002/hbm.25105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 02/20/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.
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Affiliation(s)
- Franco Cauda
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Donato Liloia
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali CiviliSpedali Civili HospitalBresciaItaly
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Sergio Duca
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging InstituteUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Tommaso Costa
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
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41
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Dong GH, Wang Z, Dong H, Wang M, Zheng Y, Ye S, Zhang J, Potenza MN. More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses. J Behav Addict 2020; 9:642-653. [PMID: 33031057 PMCID: PMC8943664 DOI: 10.1556/2006.2020.00065] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/10/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate. METHODS In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance. RESULTS The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification. DISCUSSION The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.
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Affiliation(s)
- Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Ziliang Wang
- School of Psychology, Beijing Normal University, Beijing, PR China
| | - Haohao Dong
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Yanbin Zheng
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Shuer Ye
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Jialin Zhang
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Marc N. Potenza
- Department of Psychiatry, Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
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42
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da Silva PHR, Rondinoni C, Leoni RF. Non-classical behavior of the default mode network regions during an information processing task. Brain Struct Funct 2020; 225:2553-2562. [PMID: 32939584 DOI: 10.1007/s00429-020-02143-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 04/03/2020] [Accepted: 09/08/2020] [Indexed: 01/16/2023]
Abstract
The default mode network (DMN) efficient deactivation and suppressed functional connectivity (FC) during goal-directed tasks, which require attentional resources, have been considered essential to healthy brain cognition. However, recent studies have shown that DMN regions do not always show the expected behavior. Then, we aimed to investigate the functional activation and connectivity of DMN nodes in young, healthy controls during a goal-directed task. We used an adaptation of the symbol digit modalities test (SDMT) to evaluate the information processing speed (IPS). Twenty-four subjects (10 women, age: 29 ± 7 years) underwent two functional Magnetic Resonance Imaging experiments: one during resting-state and one during a block-designed SDMT paradigm. We superimposed the templates of the DMN on the group activation map and observed the reorganization of the network. For the posterior cingulate cortex (PCC) node of the DMN, which is spatially extensive, comprising the precuneus (dorsal portion) and the posterior cingulate gyrus (PCG, ventral portion), the extent of each region was different between conditions, suggesting different functional roles for them. Therefore, for the functional connectivity (FC) analysis, we split the DMN-PCC region into two regions: left precuneus (BA 7) and PCG. The left precuneus (BA 7) was positively correlated with the left lingual gyrus (BA 17), a task-positive region, and negatively associated with the DMN nodes when comparing task performance with the resting-state condition. The other DMN regions presented the classical antagonistic role during the attentional task. In conclusion, we found that the activation and functional connectivity of the DMN is, in general, suppressed during the information processing. However, the left precuneus BA 7 presented a context-dependent modulatory behavior, working as a transient in-between hub connecting the DMN to task-positive areas. Such findings support studies that show increased activation and excitatory functional connectivity of DMN portions during goal-directed tasks. Moreover, our results may contribute to defining more precise functional correlates of IPS deficits in a wide range of clinical and neurological diseases.
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Affiliation(s)
| | - Carlo Rondinoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
| | - Renata F Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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43
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Sripada C, Angstadt M, Rutherford S, Taxali A, Shedden K. Toward a "treadmill test" for cognition: Improved prediction of general cognitive ability from the task activated brain. Hum Brain Mapp 2020; 41:3186-3197. [PMID: 32364670 PMCID: PMC7375130 DOI: 10.1002/hbm.25007] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 03/06/2020] [Accepted: 04/03/2020] [Indexed: 02/02/2023] Open
Abstract
General cognitive ability (GCA) refers to a trait-like ability that contributes to performance across diverse cognitive tasks. Identifying brain-based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole-brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2-back versus 0-back contrast achieved a 0.50 correlation with GCA scores in 10-fold cross-validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation-a brain activation pattern associated with executive processing and higher cognitive demand-are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain-based prediction of GCA.
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Affiliation(s)
- Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Mike Angstadt
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Saige Rutherford
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Aman Taxali
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Kerby Shedden
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
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44
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Jin D, Wang P, Zalesky A, Liu B, Song C, Wang D, Xu K, Yang H, Zhang Z, Yao H, Zhou B, Han T, Zuo N, Han Y, Lu J, Wang Q, Yu C, Zhang X, Zhang X, Jiang T, Zhou Y, Liu Y. Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease. Hum Brain Mapp 2020; 41:3379-3391. [PMID: 32364666 PMCID: PMC7375114 DOI: 10.1002/hbm.25023] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [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: 01/03/2020] [Revised: 03/26/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD-associated functional brain alterations using one of the world's largest resting-state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta-analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default-mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid-β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave-one-site-out cross-validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini-Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.
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Affiliation(s)
- Dan Jin
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne and Melbourne HealthMelbourneVictoriaAustralia
- Department of Biomedical EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Kaibin Xu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Nianming Zuo
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Qing Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Xinqing Zhang
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
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45
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Han S, Cui Q, Wang X, Li L, Li D, He Z, Guo X, Fan Y, Guo J, Sheng W, Lu F, Chen H. Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Hum Brain Mapp 2020; 41:3295-3304. [PMID: 32400932 PMCID: PMC7375077 DOI: 10.1002/hbm.25017] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [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: 10/17/2019] [Revised: 03/20/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022] Open
Abstract
The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.
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Affiliation(s)
- Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
- School of Public Affairs and Administration, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
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46
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Di X, Biswal BB. Intersubject consistent dynamic connectivity during natural vision revealed by functional MRI. Neuroimage 2020; 216:116698. [PMID: 32130972 PMCID: PMC10635736 DOI: 10.1016/j.neuroimage.2020.116698] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 10/10/2019] [Revised: 01/23/2020] [Accepted: 02/28/2020] [Indexed: 01/29/2023] Open
Abstract
The functional communications between brain regions are thought to be dynamic. However, it is usually difficult to elucidate whether the observed dynamic connectivity is functionally meaningful or simply due to noise during unconstrained task conditions such as resting-state. During naturalistic conditions, such as watching a movie, it has been shown that local brain activities, e.g. in the visual cortex, are consistent across subjects. Following similar logic, we propose to study intersubject correlations of the time courses of dynamic connectivity during naturalistic conditions to extract functionally meaningful dynamic connectivity patterns. We analyzed a functional MRI (fMRI) dataset when the subjects watched a short animated movie. We calculated dynamic connectivity by using sliding window technique, and quantified the intersubject correlations of the time courses of dynamic connectivity. Although the time courses of dynamic connectivity are thought to be noisier than the original signals, we found similar level of intersubject correlations of dynamic connectivity to those of regional activity. Most importantly, highly consistent dynamic connectivity could occur between regions that did not show high intersubject correlations of regional activity, and between regions with little stable functional connectivity. The analysis highlighted higher order brain regions such as the default mode network that dynamically interacted with posterior visual regions during the movie watching, which may be associated with the understanding of the movie.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07029, USA; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07029, USA; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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47
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Gesierich B, Tuladhar AM, ter Telgte A, Wiegertjes K, Konieczny MJ, Finsterwalder S, Hübner M, Pirpamer L, Koini M, Abdulkadir A, Franzmeier N, Norris DG, Marques JP, zu Eulenburg P, Ewers M, Schmidt R, de Leeuw F, Duering M. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease. Hum Brain Mapp 2020; 41:2629-2641. [PMID: 32087047 PMCID: PMC7294060 DOI: 10.1002/hbm.24967] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 10/09/2019] [Revised: 01/30/2020] [Accepted: 02/13/2020] [Indexed: 12/19/2022] Open
Abstract
While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.
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Affiliation(s)
- Benno Gesierich
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Anil Man Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Annemieke ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Sofia Finsterwalder
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Mathias Hübner
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Lukas Pirpamer
- Department of NeurologyMedical University of GrazGrazAustria
| | - Marisa Koini
- Department of NeurologyMedical University of GrazGrazAustria
| | - Ahmed Abdulkadir
- University Hospital of Old Age Psychiatry, Universitäre Psychiatrische Dienste (UPD) BernUniversity of BernBernSwitzerland
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - David G. Norris
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Peter zu Eulenburg
- German Center for Vertigo and Balance DisordersUniversity HospitalMunichGermany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | | | - Frank‐Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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48
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Luo N, Sui J, Abrol A, Lin D, Chen J, Vergara VM, Fu Z, Du Y, Damaraju E, Xu Y, Turner JA, Calhoun VD. Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum Brain Mapp 2020; 41:1725-1737. [PMID: 31876339 PMCID: PMC7267948 DOI: 10.1002/hbm.24905] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [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: 05/06/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Anees Abrol
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Dongdong Lin
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Victor M. Vergara
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Eswar Damaraju
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yong Xu
- Department of PsychiatryFirst Clinical Medical College/ First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Jessica A. Turner
- Department of PsychologyNeuroscience Institute, Georgia State UniversityAtlantaGeorgia
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- Department of PsychiatryYale University, School of MedicineNew HavenConnecticut
- Department of Psychology, Computer ScienceNeuroscience Institute, and Physics, Georgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
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49
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Blain SD, Grazioplene RG, Ma Y, DeYoung CG. Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks. Schizophr Bull 2020; 46:540-551. [PMID: 31603227 PMCID: PMC7147581 DOI: 10.1093/schbul/sbz103] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 11/14/2022]
Abstract
Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests that psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.
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Affiliation(s)
- Scott D Blain
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN
| | | | - Yizhou Ma
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN
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50
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Kashyap R, Bhattacharjee S, Yeo BTT, Chen SHA. Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality. Hum Brain Mapp 2020; 41:1261-1273. [PMID: 31773817 PMCID: PMC7267929 DOI: 10.1002/hbm.24873] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 01/08/2023] Open
Abstract
Patterns in resting-state fMRI (rs-fMRI) are widely used to characterize the trait effects of brain function. In this aspect, multiple rs-fMRI scans from single subjects can provide interesting clues about the rs-fMRI patterns, though scan-to-scan variability pose challenges. Therefore, rs-fMRI's are either concatenated or the functional connectivity is averaged. This leads to loss of information. Here, we use an alternative way to extract the rs-fMRI features that are common across all the scans by applying common-and-orthogonal-basis-extraction (COBE) technique. To address this, we employed rs-fMRI of 788 subjects from the human connectome project and estimated the common-COBE-component of each subject from the four rs-fMRI runs. Since the common-COBE-component is specific to a subject, the pattern was used to classify the subjects based on the similarity/dissimilarity of the features. The subset of subjects (n = 107) with maximal-COBE-dissimilarity (MCD) was extracted and the remaining subjects (n = 681) formed the COBE-similarity (CS) group. The distribution of weights of the common-COBE-component for the two groups across rs-fMRI networks and subcortical regions was evaluated. We found the weights in the default mode network to be lower in the MCD compared to the CS. We compared the scores of 69 behavioral measures and found six behaviors related to the use of marijuana, illicit drugs, alcohol, and tobacco; and including a measure of antisocial personality to differentiate the two groups. Gender differences were also significant. Altogether the findings suggested that subtypes exist even in healthy control population, and comparison studies (case vs. control) need to be mindful of it.
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Affiliation(s)
- Rajan Kashyap
- Centre for Research and Development in Learning (CRADLE)Nanyang Technological UniversitySingaporeSingapore
- Department of Electrical and Computer EngineeringCentre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of SingaporeSingapore
| | - Sagarika Bhattacharjee
- Psychology, School of Social Sciences (SSS)Nanyang Technological UniversitySingaporeSingapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer EngineeringCentre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of SingaporeSingapore
- Centre for Cognitive Neuroscience, Neuroscience and Behavioral Disorders ProgramDuke‐NUS Medical SchoolSingaporeSingapore
- Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMassachusetts
- NUS Graduate School for Integrative Sciences and EngineeringNational University of SingaporeSingaporeSingapore
| | - S. H. Annabel Chen
- Centre for Research and Development in Learning (CRADLE)Nanyang Technological UniversitySingaporeSingapore
- Psychology, School of Social Sciences (SSS)Nanyang Technological UniversitySingaporeSingapore
- Lee Kong Chian School of Medicine (LKC Medicine)Nanyang Technological UniversitySingaporeSingapore
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