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Liddell BJ, Das P, Malhi GS, Jobson L, Lau W, Felmingham KL, Nickerson A, Askovic M, Aroche J, Coello M, Bryant RA. Self-construal modulates default mode network connectivity in refugees with PTSD. J Affect Disord 2024; 361:268-276. [PMID: 38866252 DOI: 10.1016/j.jad.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND While self-construal and posttraumatic stress disorder (PTSD) are independently associated with altered self-referential processes and underlying default mode network (DMN) functioning, no study has examined how self-construal affects DMN connectivity in PTSD. METHODS A final sample of 93 refugee participants (48 with DSM-5 PTSD or sub-syndromal PTSD and 45 matched trauma-exposed controls) completed a 5-minute resting state fMRI scan to enable the observation of connectivity in the DMN and other core networks. A self-construal index was calculated by substracting scores on the collectivistic and individualistic sub-scales of the Self Construal Scale. RESULTS Independent components analysis identified 9 active networks-of-interest, and functional network connectivity was determined. A significant interaction effect between PTSD and self-construal index was observed in the anterior ventromedial DMN, with spatial maps localizing this to the left ventromedial prefrontal cortex (vmPFC), extending to the ventral anterior cingulate cortex. This effect revealed that connectivity in the vMPFC showed greater reductions in those with PTSD with higher levels of collectivistic self-construal. LIMITATIONS This is an observational study and causality cannot be assumed. The specialized sample of refugees means that the findings may not generalize to other trauma-exposed populations. CONCLUSIONS Such a finding indicates that self-construal may shape the core neural architecture of PTSD, given that functional disruptions to the vmPFC underpin the core mechanisms of extinction learning, emotion dysregulation and self-referential processing in PTSD. Results have important implications for understanding the universality of neural disturbances in PTSD, and suggest that self-construal could be an important consideration in the assessment and treatment of post-traumatic stress reactions.
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Affiliation(s)
- Belinda J Liddell
- School of Psychological Sciences, University of Newcastle, Australia; School of Psychology, UNSW Sydney, Australia.
| | - Pritha Das
- School of Psychological Sciences, University of Newcastle, Australia; Academic Department of Psychiatry, Northern Sydney Local Health District, CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia
| | - Gin S Malhi
- Academic Department of Psychiatry, Northern Sydney Local Health District, CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia; University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, New South Wales, Australia.; Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Winnie Lau
- Phoenix Australia, University of Melbourne, Australia
| | - Kim L Felmingham
- School of Psychological Sciences, University of Melbourne, Australia
| | | | - Mirjana Askovic
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Jorge Aroche
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Mariano Coello
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024; 47:608-621. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
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3
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Krukow P, Rodríguez-González V, Kopiś-Posiej N, Gómez C, Poza J. Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states. Sci Rep 2024; 14:17442. [PMID: 39075178 PMCID: PMC11286934 DOI: 10.1038/s41598-024-68532-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.
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Affiliation(s)
- Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland.
| | - Victor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Natalia Kopiś-Posiej
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Valladolid, Spain
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4
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Asendorf AL, Theis H, Tittgemeyer M, Timmermann L, Fink GR, Drzezga A, Eggers C, Ruppert‐Junck MC, Pedrosa DJ, Hoenig MC, van Eimeren T. Dynamic properties in functional connectivity changes and striatal dopamine deficiency in Parkinson's disease. Hum Brain Mapp 2024; 45:e26776. [PMID: 38958131 PMCID: PMC11220510 DOI: 10.1002/hbm.26776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine-related changes in large-scale brain network dynamics and its implications in clinical features. We pooled data from two disease-control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting-state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single-photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age- and sex-matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age- and sex-matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F-DOPA-positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F-DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre-processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k-means clustering were conducted separately for each cohort to derive dFC states (reemerging intra- and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L-dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large-scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. PRACTITIONER POINTS: Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD-specific changes in dynamic functional connectivity. Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state. Results only in the PPMI cohort suggest striatal dopamine availability influences large-scale network dynamics that are relevant in motor control.
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Affiliation(s)
- Adrian L. Asendorf
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Hendrik Theis
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Translational Neurocircuitry GroupCologneGermany
- University of Cologne, Cologne Excellence Cluster on Cellular Stress Responses in Aging‐Associated Diseases (CECAD)CologneGermany
| | | | - Gereon R. Fink
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Research Centre Juelich, Institute of Neuroscience and Medicine III, Cognitive NeuroscienceJuelichGermany
| | - Alexander Drzezga
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Carsten Eggers
- Department of NeurologyMarburgGermany
- Department of NeurologyUniversity of Duisburg‐Essen, Knappschaftskrankenhaus BottropBottropGermany
| | | | - David J. Pedrosa
- Universities of Marburg and Gießen, Center for Mind, Brain, and Behavior‐CMBBMarburgGermany
| | - Merle C. Hoenig
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Research Center Juelich, Institute of Neuroscience and Medicine II, Molecular Organization of the BrainJuelichGermany
| | - Thilo van Eimeren
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
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5
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Sun F, Liu Z, Yang J, Fan Z, Wang F, Yang J. Aberrant brain dynamics in major depressive disorder during working memory task. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01854-4. [PMID: 38976050 DOI: 10.1007/s00406-024-01854-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 06/17/2024] [Indexed: 07/09/2024]
Abstract
Working memory (WM) is a distributed and dynamic process, and WM deficits are recognized as one of the top-ranked endophenotype candidates for major depressive disorders (MDD). However, there is a lack of knowledge of brain temporal-spatial profile of WM deficits in MDD. We used the dynamical degree centrality (dDC) to investigate the whole-brain temporal-spatial profile in 40 MDD and 40 controls during an n-back task with 2 conditions (i.e., '0back' and '2back'). We explored the dDC temporal variability and clustered meta-stable states in 2 groups during different WM conditions. Pearson's correlation analysis was used to evaluate the relationship between the altered dynamics with clinical symptoms and WM performance. Compared with controls, under '2back vs. 0back' contrast, patients showed an elevated dDC variability in wide range of brain regions, including the middle frontal gyrus, orbital part of inferior frontal gyrus (IFGorb), hippocampus, and middle temporal gyrus. Furthermore, the increased dDC variability in the hippocampus and IFGorb correlated with worse WM performance. However, there were no significant group-related differences in the meta-stable states were observed. This study demonstrated the increased WM-related instability (i.e., the elevated dDC variability) was represented in MDD, and enhancing stability may help patients achieve better WM performance.
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Affiliation(s)
- Fuping Sun
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jun Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Zebin Fan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Feiwen Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jie Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
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6
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Wang C, He J, Feng X, Qi X, Hong Z, Dun W, Zhang M, Liu J. Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study. Brain Imaging Behav 2024:10.1007/s11682-024-00901-x. [PMID: 38954259 DOI: 10.1007/s11682-024-00901-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 07/04/2024]
Abstract
Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.
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Affiliation(s)
- Chenxi Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR China
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an, 710126, PR China
| | - Juan He
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No. 277, West Yanta Road, Xi'an, Shaanxi, 710061, PR China
| | - Xinyue Feng
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR China
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an, 710126, PR China
| | - Xingang Qi
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR China
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an, 710126, PR China
| | - Zilong Hong
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR China
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an, 710126, PR China
| | - Wanghuan Dun
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No. 277, West Yanta Road, Xi'an, Shaanxi, 710061, PR China.
| | - Ming Zhang
- Department of Rehabilitation Medicine, First Affiliated Hospital of Xi'an Jiaotong University, No. 277, West Yanta Road, Xi'an, Shaanxi, 710061, PR China.
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR China.
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an, 710126, PR China.
- Department of Rehabilitation Medicine, First Affiliated Hospital of Xi'an Jiaotong University, No. 277, West Yanta Road, Xi'an, Shaanxi, 710061, PR China.
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Blair DS, Miller RL, Calhoun VD. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. ENTROPY (BASEL, SWITZERLAND) 2024; 26:545. [PMID: 39056908 PMCID: PMC11275472 DOI: 10.3390/e26070545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.
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Affiliation(s)
- David Sutherland Blair
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30303, USA (V.D.C.)
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Mithani K, Richards OL, Ebden M, Malik N, Greuter L, Suresh H, Niazi F, Gouveia FV, Widjaja E, Weiss S, Donner E, Otsubo H, Ochi A, Jain P, Yau I, Kerr EN, Rutka JT, Drake JM, Weil AG, Ibrahim GM. Intraoperative changes in large-scale thalamic circuitry following laser ablation of hypothalamic hamartomas. Neuroimage Clin 2024; 42:103613. [PMID: 38714093 PMCID: PMC11098953 DOI: 10.1016/j.nicl.2024.103613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/08/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
BACKGROUND AND OBJECTIVES Gelastic seizures due to hypothalamic hamartomas (HH) are challenging to treat, in part due to an incomplete understanding of seizure propagation pathways. Although magnetic resonance imaging-guided laser interstitial thermal therapy (MRgLITT) is a promising intervention to disconnect HH from ictal propagation networks, the optimal site of ablation to achieve seizure freedom is not known. In this study, we investigated intraoperative post-ablation changes in resting-state functional connectivity to identify large-scale networks associated with successful disconnection of HH. METHODS Children who underwent MRgLITT for HH at two institutions were consecutively recruited and followed for a minimum of one year. Seizure freedom was defined as Engel score of 1A at the last available follow-up. Immediate pre- and post- ablation resting-state functional MRI scans were acquired while maintaining a constant depth of general anesthetic. Multivariable generalized linear models were used to identify intraoperative changes in large-scale connectivity associated with seizure outcomes. RESULTS Twelve patients underwent MRgLITT for HH, five of whom were seizure-free at their last follow-up. Intraprocedural changes in thalamocortical circuitry involving the anterior cingulate cortex were associated with seizure-freedom. Children who were seizure-free demonstrated an increase and decrease in connectivity to the pregenual and dorsal anterior cingulate cortices, respectively. In addition, children who became seizure-free demonstrated increased thalamic connectivity to the periaqueductal gray immediately following MRgLITT. DISCUSSION Successful disconnection of HH is associated with intraoperative, large-scale changes in thalamocortical connectivity. These changes provide novel insights into the large-scale basis of gelastic seizures and may represent intraoperative biomarkers of treatment success.
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Affiliation(s)
- Karim Mithani
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Oliver L Richards
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Mark Ebden
- Neurosciences & Mental Health, SickKids Research Institute, Toronto, Ontario, Canada
| | - Noor Malik
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ladina Greuter
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hrishikesh Suresh
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Farbod Niazi
- Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | | | - Elysa Widjaja
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Shelly Weiss
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Donner
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Otsubo
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ayako Ochi
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Puneet Jain
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ivanna Yau
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth N Kerr
- Department of Psychology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - James T Rutka
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - James M Drake
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alexander G Weil
- Division of Neurosurgery, Centre Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada
| | - George M Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health, SickKids Research Institute, Toronto, Ontario, Canada
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9
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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10
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Xu J, Chen H, Hu Z, Ke Z, Qin R, Chen Y, Xu Y. Characteristic patterns of functional connectivity-mediated cerebral small vessel disease-related cognitive impairment and depression. Cereb Cortex 2024; 34:bhad468. [PMID: 38061698 DOI: 10.1093/cercor/bhad468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 01/19/2024] Open
Abstract
Cerebral small vessel disease is common in most individuals aged 60 years or older, and it is associated with cognitive dysfunction, depression, anxiety disorder, and mobility problems. Currently, many cerebral small vessel disease patients have both cognitive impairment and depressive symptoms, but the relationship between the 2 is unclear. The present research combined static and dynamic functional network connectivity methods to explore the patterns of functional networks in cerebral small vessel disease individuals with cognitive impairment and depression (cerebral small vessel disease-mild cognitive impairment with depression) and their relationship. We found specific functional network patterns in the cerebral small vessel disease-mild cognitive impairment with depression individuals (P < 0.05). The cerebral small vessel disease individuals with depression exhibited unstable dynamic functional network connectivity states (transitions likelihood: P = 0.040). In addition, we found that the connections within the lateral visual network between the sensorimotor network and ventral attention network could mediate white matter hyperintensity-related cognitive impairment (indirect effect: 0.064; 95% CI: 0.003, 0.170) and depression (indirect effect: -0.415; 95% CI: -1.080, -0.011). Cognitive function can negatively regulate white matter hyperintensity-related depression. These findings elucidate the association between cognitive impairment and depression and provide new insights into the underlying mechanism of cerebral small vessel disease-related cognitive dysfunction and depression.
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Affiliation(s)
- Jingxian Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Haifeng Chen
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu 210023, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Zhihong Ke
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Ying Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu 210023, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, 321 Zhongshan Road, Nanjing, Jiangsu 210008, China
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11
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Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population. RESEARCH SQUARE 2024:rs.3.rs-3586731. [PMID: 38260417 PMCID: PMC10802706 DOI: 10.21203/rs.3.rs-3586731/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Georgia Institute of Technology, Emory University and Georgia State University
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12
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Chen J, Iraji A, Fu Z, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD. Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301013. [PMID: 38260328 PMCID: PMC10802663 DOI: 10.1101/2024.01.09.24301013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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13
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Torabi M, Mitsis GD, Poline JB. On the variability of dynamic functional connectivity assessment methods. Gigascience 2024; 13:giae009. [PMID: 38587470 PMCID: PMC11000510 DOI: 10.1093/gigascience/giae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/05/2023] [Accepted: 02/15/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods. METHODS We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity. RESULTS Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages. CONCLUSIONS Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.
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Affiliation(s)
- Mohammad Torabi
- Graduate Program in Biological and Biomedical Engineering, McGill University, Duff Medical Building, 3775 rue University, Montreal H3A 2B4, Canada
- Department of Bioengineering, McGill University, 3480 University Street, Montreal H3A 0E9, Canada
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal H3A 2B4, Canada
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, 3480 University Street, Montreal H3A 0E9, Canada
| | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal H3A 2B4, Canada
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14
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Hu L, Katz ES, Stamoulis C. Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference. Neuroimage 2023; 284:120459. [PMID: 37977408 DOI: 10.1016/j.neuroimage.2023.120459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.
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Affiliation(s)
- Linfeng Hu
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard School of Public Health, Department of Biostatistics, Boston, MA 02115, USA
| | - Eliot S Katz
- Johns Hopkins All Children's Hospital, St. Petersburg, FL 33701, USA
| | - Catherine Stamoulis
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA.
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15
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Dimitriadis SI, Routley B, Linden DEJ, Singh KD. Multiplexity of human brain oscillations as a personal brain signature. Hum Brain Mapp 2023; 44:5624-5640. [PMID: 37668332 PMCID: PMC10619372 DOI: 10.1002/hbm.26466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/06/2023] Open
Abstract
Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.
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Affiliation(s)
- Stavros I. Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- Department of Clinical Psychology and PsychobiologyUniversity of BarcelonaBarcelonaSpain
| | - B. Routley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
| | - David E. J. Linden
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- School for Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Krish D. Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
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16
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Qiao C, Gao B, Liu Y, Hu X, Hu W, Calhoun VD, Wang YP. Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity. Med Image Anal 2023; 90:102941. [PMID: 37683445 DOI: 10.1016/j.media.2023.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical. This is especially the case with neuroimaging, where we are often interested in identifying biomarkers underlying brain development or disorders. Herein we propose an explainable deep learning approach by elucidating the information transmission mechanism between two layers of a deep network with a joint feature selection strategy that considers several shallow-layer explainable machine learning models and sparse learning of the deep network. At the end, we apply and validate the proposed approach to the analysis of dynamic brain functional connectivity (FC) from fMRI in a brain development study. Our approach can identify the differences within and between functional brain networks over age during development. The results indicate that the brain network transits from undifferentiated structures to more specialized and organized ones, and the information processing ability becomes more efficient as age increases. In addition, we detect two developmental patterns in the brain network: the FCs in regions related to visual and sound processing and mental regulation become weakened, while those between regions corresponding to emotional processing and cognitive activities are enhanced.
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Affiliation(s)
- Chen Qiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Bin Gao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Yuechen Liu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Xinyu Hu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Wenxing Hu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, USA; Emory University, Atlanta, GA 30303, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
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17
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Di X, Xu T, Uddin LQ, Biswal BB. Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations. Dev Cogn Neurosci 2023; 63:101280. [PMID: 37480715 PMCID: PMC10393546 DOI: 10.1016/j.dcn.2023.101280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/24/2023] Open
Abstract
Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5-22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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18
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Miller RL, Vergara VM, Calhoun VD. Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083298 DOI: 10.1109/embc40787.2023.10341101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
While analysis of temporal signal fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role of spatially localized directional diffusion in both signal propagation and emergent large-scale functional integration remains almost entirely neglected. We are proposing an extensible framework to capture and analyze spatially localized fMRI directional signal flow dynamics. The approach is validated in a large resting-state fMRI schizophrenia study where it uncovers significant and novel relationships between hyperlocal spatial dynamics and subject diagnostic status.
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19
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Fallahi A, Hashemi-Fesharaki SS, Hoseini-Tabatabaei N, Pooyan M, Nazem-Zadeh MR. Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082832 DOI: 10.1109/embc40787.2023.10339957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.
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20
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Di X, Xu T, Uddin LQ, Biswal BB. Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526311. [PMID: 36778481 PMCID: PMC9915503 DOI: 10.1101/2023.01.30.526311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5 to 22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Bharat B. Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
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21
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Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
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Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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22
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Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:687-703. [PMID: 36880528 PMCID: PMC10281251 DOI: 10.1111/acer.15046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-state static functional connectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. METHODS Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. RESULTS During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. CONCLUSIONS There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.
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Affiliation(s)
| | - Megan E. Schendel
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Lucinda Flynn
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Cassandra Cerros
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Dina E. Hill
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
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23
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Altered static and dynamic functional network connectivity in post-stroke cognitive impairment. Neurosci Lett 2023; 799:137097. [PMID: 36716911 DOI: 10.1016/j.neulet.2023.137097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.
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24
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Noro Y, Li R, Matsui T, Jimura K. A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations. Front Neuroinform 2023; 16:960607. [PMID: 36713290 PMCID: PMC9878402 DOI: 10.3389/fninf.2022.960607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.
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Affiliation(s)
- Yusuke Noro
- Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Ruixiang Li
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan,PRESTO, Japan Science and Technology Agency, Tokyo, Japan,Teppei Matsui ✉
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi, Japan,*Correspondence: Koji Jimura ✉
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25
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Novi SL, Carvalho AC, Forti RM, Cendes F, Yasuda CL, Mesquita RC. Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study. NEUROPHOTONICS 2023; 10:013510. [PMID: 36756003 PMCID: PMC9896013 DOI: 10.1117/1.nph.10.1.013510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/10/2023] [Indexed: 06/18/2023]
Abstract
SIGNIFICANCE Brain fingerprinting refers to identifying participants based on their functional patterns. Despite its success with functional magnetic resonance imaging (fMRI), brain fingerprinting with functional near-infrared spectroscopy (fNIRS) still lacks adequate validation. AIM We investigated how fNIRS-specific acquisition features (limited spatial information and nonneural contributions) influence resting-state functional connectivity (rsFC) patterns at the intra-subject level and, therefore, brain fingerprinting. APPROACH We performed multiple simultaneous fNIRS and fMRI measurements in 29 healthy participants at rest. Data were preprocessed following the best practices, including the removal of motion artifacts and global physiology. The rsFC maps were extracted with the Pearson correlation coefficient. Brain fingerprinting was tested with pairwise metrics and a simple linear classifier. RESULTS Our results show that average classification accuracy with fNIRS ranges from 75% to 98%, depending on the number of runs and brain regions used for classification. Under the right conditions, brain fingerprinting with fNIRS is close to the 99.9% accuracy found with fMRI. Overall, the classification accuracy is more impacted by the number of runs and the spatial coverage than the choice of the classification algorithm. CONCLUSIONS This work provides evidence that brain fingerprinting with fNIRS is robust and reliable for extracting unique individual features at the intra-subject level once relevant spatiotemporal constraints are correctly employed.
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Affiliation(s)
- Sergio L. Novi
- University of Campinas, “Gleb Wataghin” Institute of Physics, Campinas, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | - Alex C. Carvalho
- University of Campinas, “Gleb Wataghin” Institute of Physics, Campinas, Brazil
- University of Campinas, Laboratory of Neuroimaging, Campinas, Brazil
| | - R. M. Forti
- University of Campinas, “Gleb Wataghin” Institute of Physics, Campinas, Brazil
- The Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Fernado Cendes
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
- University of Campinas, School of Medical Sciences, Department of Neurology, Campinas, Brazil
| | - Clarissa L. Yasuda
- University of Campinas, Laboratory of Neuroimaging, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
- University of Campinas, School of Medical Sciences, Department of Neurology, Campinas, Brazil
| | - Rickson C. Mesquita
- University of Campinas, “Gleb Wataghin” Institute of Physics, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
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26
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Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder. Clin Neuroradiol 2022; 32:1087-1096. [PMID: 35543744 DOI: 10.1007/s00062-022-01173-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78 adult ASD patients from autism brain imaging data exchange datasets, and 65 age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13 independent components (ICs) within 11 resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores. RESULTS Compared with controls, ASD patients had higher fraction time and mean dwell time in state 2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state 2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS. CONCLUSION The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.
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27
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Rahaman MA, Chen J, Fu Z, Lewis N, Iraji A, van Erp TGM, Calhoun VD. Deep multimodal predictome for studying mental disorders. Hum Brain Mapp 2022; 44:509-522. [PMID: 36574598 PMCID: PMC9842924 DOI: 10.1002/hbm.26077] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/29/2022] [Accepted: 08/16/2022] [Indexed: 01/25/2023] Open
Abstract
Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.
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Affiliation(s)
- Md Abdur Rahaman
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Noah Lewis
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Armin Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA,Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Vince D. Calhoun
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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28
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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29
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Peng L, Luo Z, Zeng LL, Hou C, Shen H, Zhou Z, Hu D. Parcellating the human brain using resting-state dynamic functional connectivity. Cereb Cortex 2022; 33:3575-3590. [PMID: 35965076 DOI: 10.1093/cercor/bhac293] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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30
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Park J, Barahona‐Torres N, Jang S, Mok KY, Kim HJ, Han S, Cho K, Zhou X, Fu AKY, Ip NY, Seo J, Choi M, Jeong H, Hwang D, Lee DY, Byun MS, Yi D, Han JW, Mook‐Jung I, Hardy J. Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201212. [PMID: 35694866 PMCID: PMC9376815 DOI: 10.1002/advs.202201212] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/26/2022] [Indexed: 05/05/2023]
Abstract
Recent multi-omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer's disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood-based biomarkers) using Multi-Omics Factor Analysis+ (MOFA+), along with systems-biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aβ+) are revealed and the features of them are identified based on the top-rated targets constructing multi-omics factors of both whole (M-TPAD) and immune-focused models (M-IPAD). The authors explored the characteristics of subtypes and possible key-drivers for AD pathogenesis. Further in-depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aβ- and 90 Aβ+), induced pluripotent stem cell-derived human brain organoids/microglia (n = 12 including 5 Aβ-, 5 Aβ+, and CRISPR-Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi-omics analysis and network modelling.
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Affiliation(s)
- Jong‐Chan Park
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Natalia Barahona‐Torres
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
| | - So‐Yeong Jang
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Kin Y. Mok
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
| | - Haeng Jun Kim
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Sun‐Ho Han
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Kwang‐Hyun Cho
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Xiaopu Zhou
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Amy K. Y. Fu
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Nancy Y. Ip
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Jieun Seo
- Department of Laboratory MedicineSeverance HospitalYonsei University College of MedicineSeoul03722Republic of Korea
| | - Murim Choi
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Hyobin Jeong
- European Molecular Biology LaboratoryGenome Biology UnitHeidelberg69117Germany
| | - Daehee Hwang
- Department of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Dong Young Lee
- Institute of Human Behavioral MedicineMedical Research CenterSeoul National UniversitySeoul03080Republic of Korea
- Department of PsychiatryCollege of medicineSeoul National UniversitySeoul03080Republic of Korea
- Department of NeuropsychiatrySeoul National University HospitalSeoul03080Republic of Korea
| | - Min Soo Byun
- Department of PsychiatryPusan National University Yangsan HospitalYangsan50612Republic of Korea
| | - Dahyun Yi
- Biomedical Research InstituteSeoul National University HospitalSeoul03082Republic of Korea
| | - Jong Won Han
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Inhee Mook‐Jung
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - John Hardy
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
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31
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Abrol A, Calhoun V. Discovery and Replication of Time-Resolved Functional Network Connectivity Differences in Adolescence and Adulthood in over 50K fMRI Datasets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1855-1858. [PMID: 36085722 DOI: 10.1109/embc48229.2022.9870916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
There remains an open question about whether and in what context brain function varies in adolescence and adulthood. In this work, we systematically study the functional brain networks of adolescents and adults, outlining the significant differences in the developing brain detected via time-resolved functional network connectivity (trFNC) derived from a fully automated independent component analysis pipeline applied to resting-state fMRI data in over 50K individuals. We then statistically analyze the transient, recurrent, and robust brain state profiles in both groups. We confirmed the results in independent replication datasets for both groups. Our findings indicate a strengthening of a state reflecting functional coupling within the visual, motor, and auditory domains and anticorrelation with all other domains in a unique adult state profile, a pattern consistently less modular in adolescents. This new insight into possible integration, strengthening, and modularization of resting-state brain connections beyond childhood convergently indicates that the highlighted temporal dynamics likely reflect robust differences in brain function in adolescents versus adults.
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Hancock F, Cabral J, Luppi AI, Rosas FE, Mediano PAM, Dipasquale O, Turkheimer FE. Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity. Neuroimage 2022; 259:119433. [PMID: 35781077 PMCID: PMC9339663 DOI: 10.1016/j.neuroimage.2022.119433] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/25/2022] [Accepted: 06/29/2022] [Indexed: 12/21/2022] Open
Abstract
Spatiotemporal patterns of phase-locking tend to be time-invariant. Global metastability is representative and stable in a cohort of heathy young adults. dFC characteristics are in general unique to any fMRI acquisition. Dynamical and informational complexity are interrelated. Complexity science contributes to a coherent description of brain dynamics.
Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Portugal
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge; Department of Clinical Neurosciences, University of Cambridge; Leverhulme Centre for the Future of Intelligence, University of Cambridge; Alan Turing Institute, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD; Data Science Institute, Imperial College London, London SW7 2AZ; Centre for Complexity Science, Imperial College London, London SW7 2AZ
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB; Department of Psychology, Queen Mary University of London, London E1 4NS
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Koubiyr I, Broeders TA, Deloire M, Brochet B, Tourdias T, Geurts JJ, Schoonheim MM, Ruet A. Altered functional brain states predict cognitive decline 5 years after a clinically isolated syndrome. Mult Scler 2022; 28:1973-1982. [PMID: 35735004 DOI: 10.1177/13524585221101470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cognitive impairment occurs in the earliest stages of multiple sclerosis (MS) together with altered functional connectivity (FC). OBJECTIVE The aim of this study was to investigate the evolution of dynamic FC states in early MS and their role in shaping cognitive decline. METHODS Overall, 32 patients were enrolled after their first neurological episode suggestive of MS and underwent cognitive evaluation and resting-state functional MRI (fMRI) over 5 years. In addition, 28 healthy controls were included at baseline. RESULTS Cognitive performance was stable during the first year and declined after 5 years.At baseline, the number of transitions between states was lower in MS compared to controls (p = 0.01). Over time, frequency of high FC states decreased in patients (p = 0.047) and increased in state with low FC (p = 0.035). Cognitive performance at Year 5 was best predicted by the mean connectivity of high FC state at Year 1. CONCLUSION Patients with early MS showed reduced functional network dynamics at baseline. Longitudinal changes showed longer time spent in a state of low FC but less time spent and more connectivity disturbance in more integrative states with high within- and between-network FC. Disturbed FC within this more integrative state was predictive of future cognitive decline.
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Affiliation(s)
- Ismail Koubiyr
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
| | - Tommy Aa Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Bruno Brochet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
| | - Thomas Tourdias
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France; CHU de Bordeaux, Bordeaux, France
| | - Jeroen Jg Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno Michiel Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aurélie Ruet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France; CHU de Bordeaux, Bordeaux, France
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Kang DW, Wang SM, Um YH, Kim NY, Lee CU, Lim HK. Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study. Front Aging Neurosci 2022; 14:871323. [PMID: 35677201 PMCID: PMC9168227 DOI: 10.3389/fnagi.2022.871323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 12/19/2022] Open
Abstract
BackgroundA growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.ObjectiveThis study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.MethodsOne hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.ResultsThe present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.ConclusionThis study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Nak-Young Kim
- Department of Psychiatry, Keyo Hospital, Uiwang, South Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- *Correspondence: Hyun Kook Lim
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Jiang P, Sun J, Zhou X, Lu L, Li L, Xu J, Huang X, Li J, Gong Q. Dynamics of intrinsic whole-brain functional connectivity in abstinent males with methamphetamine use disorder. DRUG AND ALCOHOL DEPENDENCE REPORTS 2022; 3:100065. [PMID: 36845989 PMCID: PMC9949309 DOI: 10.1016/j.dadr.2022.100065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 04/08/2022] [Accepted: 05/11/2022] [Indexed: 11/30/2022]
Abstract
Background The global prevalence of methamphetamine use disorder (MUD) and the associated economic burden are increasing, but effective pharmacological treatment is lacking. Therefore, understanding the neurological mechanisms underlying MUD is essential to develop clinical strategies and improve patient care. Individuals with MUD can show static brain network abnormalities during the resting state, but their alterations in dynamic functional network connectivity (dFNC) are unclear. Methods In this study, we obtained resting-state functional magnetic resonance imaging from 42 males with MUD and 41 healthy controls. Sliding-window and spatial independent component analyses with a k-means clustering algorithm were used to assess the recurring functional connectivity states. The temporal properties of the dFNC, including fraction and dwelling time of each state and the number of transitions between different states, were compared between the two groups. In addition, the relationships between the temporal properties of the dFNC and clinical characteristics of the MUDs, including their anxiety and depressive symptoms, were further explored. Results While the two groups shared many similarities in their dFNC, the occurrence of a highly integrated functional network state and a state featuring balanced integration and segregation in the MUDs significantly correlated with the total drug usage (Spearman's rho = 0.47, P = 0.002) and duration of abstinence (Spearman's rho = 0.38, P = 0.013), respectively. Conclusions The observed results in our study demonstrate that methamphetamines can affect dFNC, which may reflect the drug's influence on cognitive abilities. Our study justifies further studies into the effects of MUD on dynamic neural mechanisms.
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Affiliation(s)
- Ping Jiang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China,Corresponding authors at: Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China.
| | - Jiayu Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaobo Zhou
- Department of Psychosomatics, Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Lei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Jiajun Xu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Jing Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China,Corresponding authors at: Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China,Corresponding authors at: Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China.
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Iraji A, Faghiri A, Fu Z, Kochunov P, Adhikari BM, Belger A, Ford JM, McEwen S, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, Van Erp TGM, Chang C, Calhoun VD. Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping. Neuroimage 2022; 251:119013. [PMID: 35189361 PMCID: PMC9107614 DOI: 10.1016/j.neuroimage.2022.119013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/05/2022] Open
Abstract
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
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Affiliation(s)
- A Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America.
| | - A Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America
| | - Z Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - B M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - A Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - J M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - G D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - J A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States of America
| | - T G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - C Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - V D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America.
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Miller RL, Vergara VM, Pearlson GD, Calhoun VD. Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs. Front Neurosci 2022; 16:770468. [PMID: 35516809 PMCID: PMC9063321 DOI: 10.3389/fnins.2022.770468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/24/2022] [Indexed: 11/28/2022] Open
Abstract
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.
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Affiliation(s)
- Robyn L. Miller
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- *Correspondence: Robyn L. Miller,
| | - Victor M. Vergara
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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Fu Z, Sui J, Espinoza R, Narr K, Qi S, Sendi MSE, Abbot CC, Calhoun VD. Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:312-322. [PMID: 34303848 PMCID: PMC8783932 DOI: 10.1016/j.bpsc.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. METHODS In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. RESULTS Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. CONCLUSIONS These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Katherine Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Christopher C. Abbot
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States,Corresponding author: Dr. Christopher C. Abbott, Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States, , Phone: 505-272-0406
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States
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Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor. Sci Rep 2022; 12:2396. [PMID: 35165343 PMCID: PMC8844001 DOI: 10.1038/s41598-022-06342-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 01/11/2022] [Indexed: 11/17/2022] Open
Abstract
We conducted a study to understand how dynamic functional brain connectivity contributes to the moderating effect of trait mindfulness on the stress response. 40 male participants provided subjective reports of stress, cortisol assays, and functional MRI before and after undergoing a social stressor. Self-reported trait mindfulness was also collected. Experiencing stress led to significant decreases in the prevalence of a connectivity state previously associated with mindfulness, but no changes in two connectivity states with prior links to arousal. Connectivity did not return to baseline 30 min after stress. Higher trait mindfulness was associated with attenuated affective and neuroendocrine stress response, and smaller decreases in the mindfulness-related connectivity state. In contrast, we found no association between affective response and functional connectivity. Taken together, these data allow us to construct a preliminary brain-behaviour model of how mindfulness dampens stress reactivity and demonstrate the utility of time-varying functional connectivity in understanding psychological state changes.
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Kraft D, Fiebach CJ. Probing the association between resting-state brain network dynamics and psychological resilience. Netw Neurosci 2022; 6:175-195. [PMID: 36605891 PMCID: PMC9810279 DOI: 10.1162/netn_a_00216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/08/2021] [Indexed: 01/07/2023] Open
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
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Affiliation(s)
- Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany,* Corresponding Author:
| | - Christian J. Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany,Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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41
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Rahaman MA, Damaraju E, Turner JA, van Erp TG, Mathalon D, Vaidya J, Muller B, Pearlson G, Calhoun VD. Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals. Brain Connect 2022; 12:61-73. [PMID: 34049447 PMCID: PMC8867091 DOI: 10.1089/brain.2020.0896] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.
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Affiliation(s)
- Md Abdur Rahaman
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Address correspondence to: Md Abdur Rahaman, Department of Computational Science and Engineering, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jessica A. Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Theo G.M. van Erp
- Center for the Neurobiology of Learning and Memory, Department of Psychiatry and Human Behavior, University of California Irvine, California, USA.,Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, California, USA
| | - Daniel Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, California, USA
| | - Jatin Vaidya
- Department of Psychiatry, Cognitive Brain Development Laboratory, University of Iowa Health Care, Iowa, USA
| | - Bryon Muller
- Department of Psychiatry, University of Minnesota, Minnesota, USA
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, Connecticut, USA
| | - Vince D. Calhoun
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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Torture exposure and the functional brain: investigating disruptions to intrinsic network connectivity using resting state fMRI. Transl Psychiatry 2022; 12:37. [PMID: 35082270 PMCID: PMC8791936 DOI: 10.1038/s41398-022-01795-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/13/2021] [Accepted: 01/11/2022] [Indexed: 11/24/2022] Open
Abstract
Torture has profound psychological and physiological consequences for survivors. While some brain structures and functions appear altered in torture survivors, it is unclear how torture exposure influences functional connectivity within and between core intrinsic brain networks. In this study, 37 torture survivors (TS) and 62 non-torture survivors (NTS) participated in a resting-state fMRI scan. Data-driven independent components analysis identified active intrinsic networks. Group differences in functional connectivity in the default mode network (DMN), salience network (SN) and central executive network (CEN) of the triple network model, as well any prefrontal network, were examined while controlling for PTSD symptoms and exposure to other potentially traumatic events. The analysis identified 25 networks; eight comprised our networks of interest. Within-network group differences were observed in the left CEN (lCEN), where the TS group showed less spectral power in the low-frequency band. Differential internetwork dynamic connectivity patterns were observed, where the TS group showed stronger positive coupling between the lCEN and anterior dorsomedial and ventromedial DMN, and stronger negative coupling between a lateral frontal network and the lCEN and anterior dorsomedial DMN (when contrasted with the NTS group). Group differences were not attributed to torture severity or dissociative symptoms. Torture survivors showed disrupted dynamic functional connectivity between a laterally-aligned lCEN that serves top-down control functions over external processes and the midline DMN that underpins internal self-referential processes, which may be an adaptive response to mitigate the worst effects of the torture experience. This study provides a critical step in mapping the neural signature of torture exposure to guide treatment development and selection.
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43
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Yang W, Xu X, Wang C, Cheng Y, Li Y, Xu S, Li J. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia. Brain Imaging Behav 2022; 16:1294-1302. [PMID: 34997915 DOI: 10.1007/s11682-021-00592-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/20/2021] [Indexed: 01/28/2023]
Abstract
Schizophrenia is a chronic mental disorder characterized by continuous or relapsing episodes of psychosis. While previous studies have detected functional network connectivity alterations in patients with schizophrenia, and most have focused on static functional connectivity. However, brain activity is believed to change dynamically over time. Therefore, we computed dynamic functional network connectivity using the sliding window method in 38 patients with schizophrenia and 31 healthy controls. We found that patients with schizophrenia exhibited higher occurrences in the weakly and sparsely connected state (state 3) than healthy controls, positively correlated with negative symptoms. In addition, patients exhibited fewer occurrences in a strongly connected state (state 4) than healthy controls. Lastly, the dynamic functional network connectivity between the right executive-control network and the medial visual network was decreased in schizophrenia patients compared to healthy controls. Our results further prove that brain activity is dynamic, and that alterations of dynamic functional network connectivity features might be a fundamental neural mechanism in schizophrenia.
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Affiliation(s)
- Weiliang Yang
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Xuexin Xu
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Chunxiang Wang
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Yongying Cheng
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Yan Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Shuli Xu
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
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Rahaman MA, Chen J, Fu Z, Lewis N, Iraji A, Calhoun VD. Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3267-3272. [PMID: 34891938 DOI: 10.1109/embc46164.2021.9630693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlying neural and biological mechanisms of these disorders. Combining neuroimaging and genomic data with a multi-modal 'predictome' paves the way for biologically informed markers and may improve prediction reliability. With that, we develop a multi-modal deep learning framework by fusing data from different modalities to capture the interaction between the latent features and evaluate their complementary information in characterizing schizophrenia. Our deep model uses structural MRI, functional MRI, and genome-wide polymorphism data to perform the classification task. It includes a multi-layer feed-forward network, an encoder, and a long short-term memory (LSTM) unit with attention to learn the latent features and adopt a joint training scheme capturing synergies between the modalities. The hybrid network also uses different regularizers for addressing the inherent overfitting and modality-specific bias in the multi-modal setup. Next, we run the network through a saliency model to analyze the learned features. Integrating modalities enhances the performance of the classifier, and our framework acquired 88% (P < 0.0001) accuracy on a dataset of 437 subjects. The trimodal accuracy is comparable to the state-of-the-art performance on a data collection of this size and outperforms the unimodal and bimodal baselines we compared. Model introspection was used to expose the salient neural features and genes/biological pathways associated with schizophrenia. To our best knowledge, this is the first approach that fuses genomic information with structural and functional MRI biomarkers for predicting schizophrenia. We believe this type of modality blending can better explain the disorder's dynamics by adding cross-modal prospects.Clinical Relevance- This study combinedly learns imaging and genomic features for the classification of schizophrenia. The data fusion scheme extracts modality interactions, and the saliency experiments report multiple functional and structural networks closely connected to the disorder.
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Dissemination in time and space in presymptomatic granulin mutation carriers: a GENFI spatial chronnectome study. Neurobiol Aging 2021; 108:155-167. [PMID: 34607248 DOI: 10.1016/j.neurobiolaging.2021.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/28/2021] [Accepted: 09/01/2021] [Indexed: 11/24/2022]
Abstract
The presymptomatic brain changes of granulin (GRN) disease, preceding by years frontotemporal dementia, has not been fully characterized. New approaches focus on the spatial chronnectome can capture both spatial network configurations and their dynamic changes over time. To investigate the spatial dynamics in 141 presymptomatic GRN mutation carriers and 282 noncarriers from the Genetic Frontotemporal dementia research Initiative cohort. We considered time-varying patterns of the default mode network, the language network, and the salience network, each summarized into 4 distinct recurring spatial configurations. Dwell time (DT) (the time each individual spends in each spatial state of each network), fractional occupacy (FO) (the total percentage of time spent by each individual in a state of a specific network) and total transition number (the total number of transitions performed by each individual in a specifict state) were considered. Correlations between DT, FO, and transition number and estimated years from expected symptom onset (EYO) and clinical performances were assessed. Presymptomatic GRN mutation carriers spent significantly more time in those spatial states characterised by greater activation of the insula and the parietal cortices, as compared to noncarriers (p < 0.05, FDR-corrected). A significant correlation between DT and FO of these spatial states and EYO was found, the longer the time spent in the spatial states, the closer the EYO. DT and FO significantly correlated with performances at tests tapping processing speed, with worse scores associated with increased spatial states' DT. Our results demonstrated that presymptomatic GRN disease presents a complex dynamic reorganization of brain connectivity. Change in both the spatial and temporal aspects of brain network connectivity could provide a unique glimpse into brain function and potentially allowing a more sophisticated evaluation of the earliest disease changes and the understanding of possible mechanisms in GRN disease.
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46
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Gu XQ, Liu Y, Gu JB, Li LF, Fu LL, Han XM. Correlations between hippocampal functional connectivity, structural changes, and clinical data in patients with relapsing-remitting multiple sclerosis: a case-control study using multimodal magnetic resonance imaging. Neural Regen Res 2021; 17:1115-1124. [PMID: 34558540 PMCID: PMC8552851 DOI: 10.4103/1673-5374.324855] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Multiple sclerosis is associated with structural and functional brain alterations leading to cognitive impairments across multiple domains including attention, memory, and the speed of information processing. The hippocampus, which is a brain important structure involved in memory, undergoes microstructural changes in the early stage of multiple sclerosis. In this study, we analyzed hippocampal function and structure in patients with relapsing-remitting multiple sclerosis and explored correlations between the functional connectivity of the hippocampus to the whole brain, changes in local brain function and microstructure, and cognitive function at rest. We retrospectively analyzed data from 20 relapsing-remitting multiple sclerosis patients admitted to the Department of Neurology at the China-Japan Union Hospital of Jilin University, China, from April 2015 to November 2019. Sixteen healthy volunteers were recruited as the healthy control group. All participants were evaluated using a scale of extended disability status and the Montreal cognitive assessment within 1 week before and after head diffusion tensor imaging and functional magnetic resonance imaging. Compared with the healthy control group, the patients with relapsing-remitting multiple sclerosis had lower Montreal cognitive assessment scores and regions of simultaneously enhanced and attenuated whole-brain functional connectivity and local functional connectivity in the bilateral hippocampus. Hippocampal diffusion tensor imaging data showed that, compared with the healthy control group, patients with relapsing-remitting multiple sclerosis had lower hippocampal fractional anisotropy values and higher mean diffusivity values, suggesting abnormal hippocampal structure. The left hippocampus whole-brain functional connectivity was negatively correlated with the Montreal cognitive assessment score (r = −0.698, P = 0.025), and whole-brain functional connectivity of the right hippocampus was negatively correlated with extended disability status scale score (r = −0.649, P = 0.042). The mean diffusivity value of the left hippocampus was negatively correlated with the Montreal cognitive assessment score (r = −0.729, P = 0.017) and positively correlated with the extended disability status scale score (r = 0.653, P = 0.041). The right hippocampal mean diffusivity value was positively correlated with the extended disability status scale score (r = 0.684, P = 0.029). These data suggest that the functional connectivity and presence of structural abnormalities in the hippocampus in patients with relapse-remission multiple sclerosis are correlated with the degree of cognitive function and extent of disability. This study was approved by the Ethics Committee of China-Japan Union Hospital of Jilin University, China (approval No. 201702202) on February 22, 2017.
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Affiliation(s)
- Xin-Quan Gu
- China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Ying Liu
- Cardre's Ward, Changchun Central hospital, Changchun, Jilin Province, China
| | - Jie-Bing Gu
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Lin-Fang Li
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Ling-Ling Fu
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Xue-Mei Han
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
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47
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Liégeois R, Yeo BTT, Van De Ville D. Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis. Neuroimage 2021; 243:118518. [PMID: 34469853 DOI: 10.1016/j.neuroimage.2021.118518] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/19/2021] [Accepted: 08/25/2021] [Indexed: 11/27/2022] Open
Abstract
Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be 'trivial', i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in representing brain functional dynamics.
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Affiliation(s)
- Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
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48
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Dong T, Huang Q, Huang S, Xin J, Jia Q, Gao Y, Shen H, Tang Y, Zhang H. Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging. Front Psychol 2021; 12:717519. [PMID: 34526937 PMCID: PMC8435858 DOI: 10.3389/fpsyg.2021.717519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022] Open
Abstract
Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.
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Affiliation(s)
- Tingting Dong
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiuping Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Shucai Huang
- The Fourth People’s Hospital of Wuhu, Wuhu, China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiaolan Jia
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hongxian Shen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
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Calhoun VD, Pearlson GD, Sui J. Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples. Curr Opin Neurol 2021; 34:469-479. [PMID: 34054110 PMCID: PMC8263510 DOI: 10.1097/wco.0000000000000967] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW The 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments. RECENT FINDINGS We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy. SUMMARY The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.
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Affiliation(s)
- Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
| | - Godfrey D Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
- Institute of Automation, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, Beijing, China
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50
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Liang X, Pang X, Zhao J, Yu L, Wu P, Li X, Wei W, Zheng J. Altered static and dynamic functional network connectivity in temporal lobe epilepsy with different disease duration and their relationships with attention. J Neurosci Res 2021; 99:2688-2705. [PMID: 34269468 DOI: 10.1002/jnr.24915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/13/2021] [Accepted: 06/14/2021] [Indexed: 11/09/2022]
Abstract
The brain network alterations associated with temporal lobe epilepsy (TLE) progression are still unclear. The purpose of this study was to investigate altered patterns of static and dynamic functional network connectivity (sFNC and dFNC) in TLE with different durations of disease. In this study, 19 TLE patients with a disease duration of ≤5 years (TLE-SD), 24 TLE patients with a disease duration of >5 years (TLE-LD), and 21 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and attention network test. We used group independent component analysis to determine the target resting-state networks. Sliding window correlation and k-means clustering analysis methods were used to obtain different dFNC states, temporal properties, and temporal variability. We then compared sFNC and dFNC between groups and found that compared with HCs, TLE-SD patients had increased sFNC between the dorsal attention network and sensorimotor network/visual network (VN), but decreased sFNC between the inferior-posterior default mode network and VN. In the strongly connected dFNC state, TLE-SD patients spent more time, had greater mean dwell time, and showed greater inconsistent abnormal network connectivity. There was a significant negative correlation between the temporal variability of auditory network- left fronto-parietal network connectivity and orienting effect. No significant differences in sFNC and dFNC were detected between TLE-LD and HC groups. These findings suggest that the damage and functional brain network abnormalities gradually occur in TLE patients after the onset of epilepsy, which might lead to functional network reorganization and compensatory remodeling as the disease progresses.
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Affiliation(s)
- Xiulin Liang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaomin Pang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jingyuan Zhao
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Lu Yu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peirong Wu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xinrong Li
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wutong Wei
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
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