201
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [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: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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202
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McCulloch DEW, Knudsen GM, Barrett FS, Doss MK, Carhart-Harris RL, Rosas FE, Deco G, Kringelbach ML, Preller KH, Ramaekers JG, Mason NL, Müller F, Fisher PM. Psychedelic resting-state neuroimaging: A review and perspective on balancing replication and novel analyses. Neurosci Biobehav Rev 2022; 138:104689. [PMID: 35588933 DOI: 10.1016/j.neubiorev.2022.104689] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/20/2022]
Abstract
Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.
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Affiliation(s)
| | - Gitte Moos Knudsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark; Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Frederick Streeter Barrett
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neuroscience and Department of Psychological and Brain Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Manoj K Doss
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robin Lester Carhart-Harris
- Neuroscape, Weill Institute for Neurosciences, University of California San Francisco, CA, USA; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark
| | - Katrin H Preller
- Pharmaco-Neuroimaging and Cognitive-Emotional Processing, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
| | | | - Natasha L Mason
- Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Felix Müller
- University of Basel, Department of Psychiatry (UPK), Basel, Switzerland
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203
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Charquero‐Ballester M, Kleim B, Vidaurre D, Ruff C, Stark E, Tuulari JJ, McManners H, Bar‐Haim Y, Bouquillon L, Moseley A, Williams SCR, Woolrich MW, Kringelbach ML, Ehlers A. Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks. Hum Brain Mapp 2022; 43:3207-3220. [PMID: 35393717 PMCID: PMC9188968 DOI: 10.1002/hbm.25846] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 12/03/2022] Open
Abstract
In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+ ) and healthy trauma-exposed controls (only mtDMN+ ). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function.
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Affiliation(s)
| | - Birgit Kleim
- Experimental Psychopathology and Psychotherapy, Department of PsychologyUniversity of ZurichZurichSwitzerland
- Department of Psychiatry, Psychotherapy and PsychosomaticsUniversity of ZurichZurichSwitzerland
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Christian Ruff
- Zurich Center for Neuroeconomics (ZNE), Department of EconomicsUniversity of ZurichZurichSwitzerland
| | - Eloise Stark
- Department of PsychiatryUniversity of OxfordOxfordUK
| | | | | | - Yair Bar‐Haim
- School of Psychological SciencesTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Linda Bouquillon
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Allison Moseley
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Mark W. Woolrich
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUK
- Scars of War FoundationThe Queen's CollegeOxfordUK
- Centre for Music in the BrainAarhus UniversityAarhusDenmark
| | - Anke Ehlers
- Oxford Centre for Anxiety Disorders and Trauma, Department of Experimental PsychologyUniversity of OxfordOxfordUK
- Oxford Health NHS Foundation TrustOxfordUK
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204
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Han S, Xu Y, Guo HR, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information. Hum Brain Mapp 2022; 43:4254-4265. [PMID: 35726798 PMCID: PMC9435007 DOI: 10.1002/hbm.25951] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yinhuan Xu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Hui-Rong Guo
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Junying Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
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205
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Zhu T, Becquey C, Chen Y, Lejuez CW, Li CSR, Bi J. Identifying alcohol misuse biotypes from neural connectivity markers and concurrent genetic associations. Transl Psychiatry 2022; 12:253. [PMID: 35710901 PMCID: PMC9203552 DOI: 10.1038/s41398-022-01983-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/18/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022] Open
Abstract
Alcohol use behaviors are highly heterogeneous, posing significant challenges to etiologic research of alcohol use disorder (AUD). Magnetic resonance imaging (MRI) provides intermediate endophenotypes in characterizing problem alcohol use and assessing the genetic architecture of addictive behavior. We used connectivity features derived from resting state functional MRI to subtype alcohol misuse (AM) behavior. With a machine learning pipeline of feature selection, dimension reduction, clustering, and classification we identified three AM biotypes-mild, comorbid, and moderate AM biotypes (MIA, COA, and MOA)-from a Human Connectome Project (HCP) discovery sample (194 drinkers). The three groups and controls (397 non-drinkers) demonstrated significant differences in alcohol use frequency during the heaviest 12-month drinking period (MOA > MIA; COA > non-drinkers) and were distinguished by connectivity features involving the frontal, parietal, subcortical and default mode networks. Further, COA relative to MIA, MOA and controls endorsed significantly higher scores in antisocial personality. A genetic association study identified that an alcohol use and antisocial behavior related variant rs16930842 from LINC01414 was significantly associated with COA. Using a replication HCP sample (28 drinkers and 46 non-drinkers), we found that subtyping helped in classifying AM from controls (area under the curve or AUC = 0.70, P < 0.005) in comparison to classifiers without subtyping (AUC = 0.60, not significant) and successfully reproduced the genetic association. Together, the results suggest functional connectivities as important features in classifying AM subgroups and the utility of reducing the heterogeneity in connectivity features among AM subgroups in advancing the research of etiological neural markers of AUD.
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Affiliation(s)
- Tan Zhu
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Chloe Becquey
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Yu Chen
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Carl W Lejuez
- Department of Psychological Sciences, College of Liberal Arts and Sciences, University of Connecticut, Storrs, CT, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
- Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jinbo Bi
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA.
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206
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Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Sheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nat Hum Behav 2022; 6:782-795. [PMID: 35241793 PMCID: PMC9232838 DOI: 10.1038/s41562-022-01301-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/14/2022] [Indexed: 11/15/2022]
Abstract
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Qi Lin
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Sheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA. .,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
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207
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He T, An L, Chen P, Chen J, Feng J, Bzdok D, Holmes AJ, Eickhoff SB, Yeo BTT. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nat Neurosci 2022; 25:795-804. [PMID: 35578132 PMCID: PMC9202200 DOI: 10.1038/s41593-022-01059-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/23/2022] [Indexed: 12/12/2022]
Abstract
We propose a simple framework-meta-matching-to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = -0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.
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Affiliation(s)
- Tong He
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Lijun An
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Pansheng Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal QC, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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208
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Ly MT, Scarneo-Miller SE, Lepley AS, Coleman K, Hirschhorn R, Yeargin S, Casa DJ, Chen CM. Combining MRI and cognitive evaluation to classify concussion in university athletes. Brain Imaging Behav 2022; 16:2175-2187. [PMID: 35639240 DOI: 10.1007/s11682-022-00687-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 11/26/2022]
Abstract
Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R2 = 0.32, p < .001), and performed worse on tests of concentration (∆R2 = 0.07, p < .05) and delayed memory (∆R2 = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.
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Affiliation(s)
- Monica T Ly
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA.
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA.
- Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA.
| | - Samantha E Scarneo-Miller
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- Division of Athletic Training, School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Adam S Lepley
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- School of Kinesiology, Exercise and Sport Science Initiative, University of Michigan, Ann Arbor, MI, USA
| | - Kelly Coleman
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- Department of Health & Movement Sciences, Southern Connecticut State University, New Haven, CT, USA
| | - Rebecca Hirschhorn
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA
| | - Susan Yeargin
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Douglas J Casa
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
| | - Chi-Ming Chen
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
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209
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Cohesive parcellation of the human brain using resting-state fMRI. J Neurosci Methods 2022; 377:109629. [PMID: 35618164 DOI: 10.1016/j.jneumeth.2022.109629] [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: 08/17/2021] [Revised: 04/14/2022] [Accepted: 05/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
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Tinaz S, Kamel S, Aravala SS, Elfil M, Bayoumi A, Patel A, Scheinost D, Sinha R, Hampson M. Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial. Neuroimage Clin 2022; 34:102980. [PMID: 35247729 PMCID: PMC8897714 DOI: 10.1016/j.nicl.2022.102980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/23/2022] [Accepted: 02/28/2022] [Indexed: 11/06/2022]
Abstract
Parkinson’s disease causes difficulty with sustained motor performance. Insula and dorsomedial frontal cortex (dmFC) are implicated in motivating movement. Regulation of insula-dmFC functional connectivity with neurofeedback (NF) failed. Motor imagery practice regardless of NF improved motor function and body awareness. Visual imagery practice without NF also improved motor function.
Background Parkinson’s disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD. Methods We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10–12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively. Results The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome). Conclusions The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.
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Affiliation(s)
- Sule Tinaz
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA.
| | - Serageldin Kamel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Sai S Aravala
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Mohamed Elfil
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Ahmed Bayoumi
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Amar Patel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA
| | - Rajita Sinha
- Yale School of Medicine, Yale Stress Center, 2 Church St South, Suite 209, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA; Yale School of Medicine, Department of Neuroscience, 333 Cedar St, SHM-L-200, New Haven, CT 06510, USA
| | - Michelle Hampson
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA
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Tejavibulya L, Peterson H, Greene A, Gao S, Rolison M, Noble S, Scheinost D. Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. Neuroimage 2022; 252:119040. [PMID: 35272202 PMCID: PMC9013515 DOI: 10.1016/j.neuroimage.2022.119040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 11/15/2022] Open
Abstract
Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole-brain functional connectivity (i.e., functional connectomes) due to handedness have been relatively understudied beyond pre-specified networks of interest. Here, we compared functional connectomes of left- and right-handed individuals at the whole brain level. We explored differences in functional connectivity of previously established regions of interest, and showed differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional connectivity of left- and right-handed individuals are not specific to networks of interest, but extend across every region of the brain. In particular, we found that connections between and within the cerebellum show distinct patterns of connectivity. To put these effects into context, we show that the effect sizes associated with handedness differences account for a similar amount of individual differences in the connectome as sex differences. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals and underscore that handedness effects are neurobiologically meaningful in addition to being statistically significant.
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Affiliation(s)
- Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Abigail Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD PhD Program, Yale School of Medicine, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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212
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The effect of perceived stress on cognition is mediated by personality and the underlying neural mechanism. Transl Psychiatry 2022; 12:199. [PMID: 35550503 PMCID: PMC9098451 DOI: 10.1038/s41398-022-01929-7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 11/30/2022] Open
Abstract
Perceived stress impairs cognitive function across the adult lifespan, but the extent to which cognition decline is variable across individuals. Individual differences in the stress response are described as personality traits. Substantial individual differences in the magnitude of cognitive impairment that is induced by short-term perceived stress are poorly understood. The present study tested the hypothesis that the relationship between short-term perceived stress and different aspects of cognition is mediated by personality traits. The study included 1066 participants with behavior and neuroimaging data from the Human Connectome Project after excluding individuals with missing variables. In the result, the parallel multiple mediation model demonstrated that the influence of perceived stress on the total and crystalized cognition is mainly mediated by neuroticism (indirect effect = -0.04, p < 0.05) and conscientiousness (indirect effect = 0.05, p < 0.05) in adults. Cortical thickness value (n = 1066) of the right superior frontal gyrus (SFG) showed not only positive correlations with short-term perceived stress and neuroticism, but negative associations with cognition. The chain mediation model found that the right SFG and neuroticism play a small but significant chain mediating effect between stress and total cognition. The strength of the resting-state functional connectivity (n = 968) between the left orbitofrontal cortex versus the left superior medial frontal cortex was positively correlated with crystallized cognition and negatively associated with conscientiousness. These results extend previous findings by the impacts of short-term perceived stress on cognitive function is mediated by neuroticism and the right SFG was the underlying neural mechanism.
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213
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Dan T, Huang Z, Cai H, Lyday RG, Laurienti PJ, Wu G. Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold. Hum Brain Mapp 2022; 43:3970-3986. [PMID: 35538672 PMCID: PMC9374896 DOI: 10.1002/hbm.25897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022] Open
Abstract
Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold‐based geometric neural network for functional brain networks (called “Geo‐Net4Net” for short) to learn the intrinsic low‐dimensional feature representations of resting‐state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low‐dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive‐definite (SPD) form of the correlation matrices. Due to the lack of well‐defined ground truth in the resting state, existing learning‐based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self‐supervise the feature representation learning of resting‐state functional networks by leveraging the task‐based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo‐Net4Net allows us to establish a more reasonable understanding of resting‐state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task‐based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo‐Net4Net not only achieves more accurate change detection results than other state‐of‐the‐art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function.
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Affiliation(s)
- Tingting Dan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zhuobin Huang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Robert G Lyday
- Department of Radiology, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Carolina Institute for Developmental Disabilities (CIDD), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,UNC NeuroScience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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214
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Sun J, Zhao R, He Z, Chang M, Wang F, Wei W, Zhang X, Zhu Y, Xi Y, Yang X, Qin W. Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective. Hum Brain Mapp 2022; 43:3824-3839. [PMID: 35524680 PMCID: PMC9294309 DOI: 10.1002/hbm.25886] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 12/25/2022] Open
Abstract
Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.
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Affiliation(s)
- Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Zhaoyang He
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Mengying Chang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Fumin Wang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Wei Wei
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Xiaodan Zhang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
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215
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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216
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Yeung AWK, More S, Wu J, Eickhoff SB. Reporting details of neuroimaging studies on individual traits prediction: A literature survey. Neuroimage 2022; 256:119275. [PMID: 35513295 DOI: 10.1016/j.neuroimage.2022.119275] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] Open
Abstract
Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.
| | - Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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217
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Brain functional connectivities that mediate the association between childhood traumatic events, and adult mental health and cognition. EBioMedicine 2022; 79:104002. [PMID: 35472671 PMCID: PMC9058958 DOI: 10.1016/j.ebiom.2022.104002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/16/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Childhood traumatic events are risk factors for psychopathology, but large-scale studies of how childhood traumatic events relate to mental health and cognition in adulthood, and how the brain is involved, are needed. METHODS The associations between childhood traumatic events (such as abuse and neglect, and defined by the 'Childhood Trauma' questions in the UK Biobank database) and brain functional connectivity, mental health problems, and cognitive performance were investigated by a univariate correlation analysis with 19,535 participants aged 45-79 from the UK Biobank dataset. The results were replicated with 17,747 independent participants in the second release from the same dataset. FINDINGS Childhood traumatic events were significantly associated with mental health problems in adulthood including anxiety (r=0.19, p<1.0 × 10-323), depression (r=0.21, p<1.0 × 10-323), and self-harm (r=0.24, p<1.0 × 10-323), and with adult cognitive performance including fluid intelligence (r=-0.05, p=2.8 × 10-10) and prospective memory (r=-0.04, p=6.8 × 10-8). Functional connectivities of the medial and lateral temporal cortex, the precuneus, the medial orbitofrontal cortex; and the superior, middle and inferior prefrontal cortex extending back to precentral regions were negatively correlated with the childhood traumatic events (FDR corrected, p<0.01). These lower functional connectivities significantly mediated the associations between childhood traumatic events and addiction, anxiety, depression and well-being (all p<1.0 × 10-3), and cognitive performance. The association between childhood traumatic events and behavioural measures and functional connectivity were confirmed in a replication with different participants in the second release of the UK Biobank dataset. INTERPRETATION Childhood traumatic events are strongly associated with adult mental health problems mediated by brain functional connectivities in brain areas involved in executive function, emotion, face processing, and memory. This understanding may help with prevention and treatment. FUNDING Funding was provided by the National Key R&D Program of China (No. 2018YFC1312900 and No. 2019YFA0709502).
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218
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Wang X, Yoo K, Chen H, Zou T, Wang H, Gao Q, Meng L, Hu X, Li R. Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling. NPJ Parkinsons Dis 2022; 8:49. [PMID: 35459232 PMCID: PMC9033778 DOI: 10.1038/s41531-022-00315-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
Motor impairment is a core clinical feature of Parkinson’s disease (PD). Although the decoupled brain connectivity has been widely reported in previous neuroimaging studies, how the functional connectome is involved in motor dysfunction has not been well elucidated in PD patients. Here we developed a distributed brain signature by predicting clinical motor scores of PD patients across multicenter datasets (total n = 236). We decomposed the Pearson’s correlation into accordance and discordance via a temporal discrete procedure, which can capture coupling and anti-coupling respectively. Using different profiles of functional connectivity, we trained candidate predictive models and tested them on independent and heterogeneous PD samples. We showed that the antagonistic model measured by discordance had the best sensitivity and generalizability in all validations and it was dubbed as Parkinson’s antagonistic motor signature (PAMS). The PAMS was dominated by the subcortical, somatomotor, visual, cerebellum, default-mode, and frontoparietal networks, and the motor-visual stream accounted for the most part of predictive weights among network pairs. Additional stage-specific analysis showed that the predicted scores generated from the antagonistic model tended to be higher than the observed scores in the early course of PD, indicating that the functional signature may vary more sensitively with the neurodegenerative process than clinical behaviors. Together, these findings suggest that motor dysfunction of PD is represented as antagonistic interactions within multi-level brain systems. The signature shows great potential in the early motor evaluation and developing new therapeutic approaches for PD in the clinical realm.
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Affiliation(s)
- Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 610054, People's Republic of China.
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
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219
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Zhi D, King M, Hernandez-Castillo CR, Diedrichsen J. Evaluating brain parcellations using the distance-controlled boundary coefficient. Hum Brain Mapp 2022; 43:3706-3720. [PMID: 35451538 PMCID: PMC9294308 DOI: 10.1002/hbm.25878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/13/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.
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Affiliation(s)
- Da Zhi
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada
| | - Maedbh King
- Department of Psychology, University of California, Berkeley, California, USA
| | | | - Jörn Diedrichsen
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada.,Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
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220
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Gbadeyan O, Teng J, Prakash RS. Predicting response time variability from task and resting-state functional connectivity in the aging brain. Neuroimage 2022; 250:118890. [PMID: 35007719 PMCID: PMC9063711 DOI: 10.1016/j.neuroimage.2022.118890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/22/2022] Open
Abstract
Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.
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Affiliation(s)
- Oyetunde Gbadeyan
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - James Teng
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA.
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221
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Bahrami M, Simpson SL, Burdette JH, Lyday RG, Quandt SA, Chen H, Arcury TA, Laurienti PJ. Altered Default Mode Network Associated with Pesticide Exposure in Latinx Children from Rural Farmworker Families. Neuroimage 2022; 256:119179. [PMID: 35429626 PMCID: PMC9251855 DOI: 10.1016/j.neuroimage.2022.119179] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/03/2022] [Accepted: 04/03/2022] [Indexed: 01/21/2023] Open
Abstract
Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jonathan H Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert G Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sara A Quandt
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Haiying Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Thomas A Arcury
- Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
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222
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Han S, Xu Y, Guo H, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis. Hum Brain Mapp 2022; 43:3037-3046. [PMID: 35384125 PMCID: PMC9188970 DOI: 10.1002/hbm.25833] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 01/31/2023] Open
Abstract
Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.
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223
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Burdette JH, Bahrami M, Laurienti PJ, Simpson S, Nicklas BJ, Fanning J, Rejeski WJ. Longitudinal relationship of baseline functional brain networks with intentional weight loss in older adults. Obesity (Silver Spring) 2022; 30:902-910. [PMID: 35333443 PMCID: PMC8969753 DOI: 10.1002/oby.23396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The goal of this study was to determine whether the degree of weight loss after 6 months of a behavior-based intervention is related to baseline connectivity within two functional networks (FNs) of interest, FN1 and FN2, in a group of older adults with obesity. METHODS Baseline functional magnetic resonance imaging data were collected following an overnight fast in 71 older adults with obesity involved in a weight-loss intervention. Functional brain networks in a resting state and during a food-cue task were analyzed using a mixed-regression framework to examine the relationships between baseline networks and 6-month change in weight. RESULTS During the resting condition, the relationship of baseline brain functional connectivity and network clustering in FN1, which includes the visual cortex and sensorimotor areas, was significantly associated with 6-month weight loss. During the food-cue condition, 6-month weight loss was significantly associated with the relationship between baseline brain connectivity and network global efficiency in FN2, which includes executive control, attention, and limbic regions. CONCLUSION These findings provide further insight into complex functional circuits in the brain related to successful weight loss and may ultimately aid in developing tailored behavior-based treatment regimens that target specific brain circuitry.
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Affiliation(s)
- Jonathan H. Burdette
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Mohsen Bahrami
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Biomedical EngineeringVirginia Tech‐Wake Forest School of Biomedical Engineering and SciencesWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Paul J. Laurienti
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Sean L. Simpson
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Biostatistics and Data ScienceWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Barbara J. Nicklas
- Section on Geriatric MedicineDepartment of Internal MedicineWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Jason Fanning
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - W. Jack Rejeski
- Section on Geriatric MedicineDepartment of Internal MedicineWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
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224
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Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome. Commun Biol 2022; 5:261. [PMID: 35332230 PMCID: PMC8948277 DOI: 10.1038/s42003-022-03185-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 02/08/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints. Mantwill et al. analysed the relationship between functional connectivity markers that are specific to individuals (i.e. brain fingerprints) and those predictive of behaviour. They reveal that these markers are unrelated and suggest an alternative perspective on the basis of individual signatures.
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225
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Zhang D, Zhou L, Yang A, Li S, Chang C, Liu J, Zhou K. A connectome-based neuromarker of nonverbal number acuity and arithmetic skills. Cereb Cortex 2022; 33:881-894. [PMID: 35254408 PMCID: PMC9890459 DOI: 10.1093/cercor/bhac108] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 02/04/2023] Open
Abstract
The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual's ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.
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Affiliation(s)
- Dai Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Anmin Yang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Shanshan Li
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Chunqi Chang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, No. 30, Shuangqing Street, Haidian District, Beijing 100084, China
| | - Ke Zhou
- Corresponding author: Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China.
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226
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Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [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: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
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Affiliation(s)
- Sage Hahn
- Corresponding author: Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, 100 South Prospect Street Burlington, Vermont 05401, United States.
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
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227
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Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
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228
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Han S, Zheng R, Li S, Zhou B, Jiang Y, Wang C, Wei Y, Pang J, Li H, Zhang Y, Chen Y, Cheng J. Integrative Functional, Molecular, and Transcriptomic Analyses of Altered Intrinsic Timescale Gradient in Depression. Front Neurosci 2022; 16:826609. [PMID: 35250462 PMCID: PMC8891525 DOI: 10.3389/fnins.2022.826609] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
The pathophysiology and pharmacology of depression are hypothesized to be related to the imbalance of excitation–inhibition that gives rise to hierarchical dynamics (or intrinsic timescale gradient), further supporting a hierarchy of cortical functions. On this assumption, intrinsic timescale gradient is theoretically altered in depression. However, it remains unknown. We investigated altered intrinsic timescale gradient recently developed to measure hierarchical brain dynamics gradient and its underlying molecular architecture and brain-wide gene expression in depression. We first presented replicable intrinsic timescale gradient in two independent Chinese Han datasets and then investigated altered intrinsic timescale gradient and its possible underlying molecular and transcriptional bases in patients with depression. As a result, patients with depression showed stage-specifically shorter timescales compared with healthy controls according to illness duration. The shorter timescales were spatially correlated with monoamine receptor/transporter densities, suggesting the underlying molecular basis of timescale aberrance and providing clues to treatment. In addition, we identified that timescale aberrance-related genes ontologically enriched for synapse-related and neurotransmitter (receptor) terms, elaborating the underlying transcriptional basis of timescale aberrance. These findings revealed atypical timescale gradient in depression and built a link between neuroimaging, transcriptome, and neurotransmitter information, facilitating an integrative understanding of depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- *Correspondence: Shaoqiang Han,
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jianyue Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Yuan Chen,
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Jingliang Cheng,
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Bahrami M, Laurienti PJ, Shappell HM, Dagenbach D, Simpson SL. A mixed-modeling framework for whole-brain dynamic network
analysis. Netw Neurosci 2022; 6:591-613. [PMID: 35733427 PMCID: PMC9208000 DOI: 10.1162/netn_a_00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/09/2022] [Indexed: 11/15/2022] Open
Abstract
The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather M. Shappell
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
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230
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Functional brain connectomes reflect acute and chronic cannabis use. Sci Rep 2022; 12:2449. [PMID: 35165360 PMCID: PMC8844352 DOI: 10.1038/s41598-022-06509-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
AbstractResting state fMRI has been employed to identify alterations in functional connectivity within or between brain regions following acute and chronic exposure to Δ9-tetrahydrocannabinol (THC), the psychoactive component in cannabis. Most studies focused a priori on a limited number of local brain areas or circuits, without considering the impact of cannabis on whole-brain network organization. The present study attempted to identify changes in the whole-brain human functional connectome as assessed with ultra-high field (7T) resting state scans of cannabis users (N = 26) during placebo and following vaporization of cannabis. Two distinct data-driven methodologies, i.e. network-based statistics (NBS) and connICA, were used to identify changes in functional connectomes associated with acute cannabis intoxication and history of cannabis use. Both methodologies revealed a broad state of hyperconnectivity within the entire range of major brain networks in chronic cannabis users compared to occasional cannabis users, which might be reflective of an adaptive network reorganization following prolonged cannabis exposure. The connICA methodology also extracted a distinct spatial connectivity pattern of hypoconnectivity involving the dorsal attention, limbic, subcortical and cerebellum networks and of hyperconnectivity between the default mode and ventral attention network, that was associated with the feeling of subjective high during THC intoxication. Whole-brain network approaches identified spatial patterns in functional brain connectomes that distinguished acute from chronic cannabis use, and offer an important utility for probing the interplay between short and long-term alterations in functional brain dynamics when progressing from occasional to chronic use of cannabis.
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231
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Fallahi A, Pooyan M, Habibabadi JM, Hashemi-Fesharaki SS, Tabatabaei NH, Ay M, Nazem-Zadeh MR. A novel approach for extracting functional brain networks involved in mesial temporal lobe epilepsy based on self organizing maps. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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232
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Tomlinson CE, Laurienti PJ, Lyday RG, Simpson SL. A regression framework for brain network distance metrics. Netw Neurosci 2022; 6:49-68. [PMID: 35350586 PMCID: PMC8942614 DOI: 10.1162/netn_a_00214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.
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Affiliation(s)
- Chal E. Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
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233
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Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 2022; 246:118774. [PMID: 34861391 PMCID: PMC10569447 DOI: 10.1016/j.neuroimage.2021.118774] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/26/2021] [Accepted: 11/29/2021] [Indexed: 12/23/2022] Open
Abstract
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Boris Duka
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
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234
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Miranda M, Campo CG, Birba A, Neely A, Hernandez FDT, Faure E, Costa GR, Ibáñez A, García A. An action-concept processing advantage in a patient with a double motor cortex. Brain Cogn 2022; 156:105831. [PMID: 34922210 PMCID: PMC9944406 DOI: 10.1016/j.bandc.2021.105831] [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: 10/04/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 02/08/2023]
Abstract
Patients with atrophy in motor brain regions exhibit selective deficits in processing action-related meanings, suggesting a link between movement conceptualization and the amount of regional tissue. Here we examine such a relation in a unique opposite model: a rare patient with a double cortex (due to subcortical band heterotopia) in primary/supplementary motor regions, and no double cortex in multimodal semantic regions. We measured behavioral performance in action- and object-concept processing as well and resting-state functional connectivity. Both dimensions involved comparisons with healthy controls. Results revealed preserved accuracy in action and object categories for the patient. However, unlike controls, the patient exhibited faster performance for action than object concepts, a difference that was uninfluenced by general cognitive abilities. Moreover, this pattern was accompanied by heightened functional connectivity between the bilateral primary motor cortices. This suggests that a functionally active double motor cortex may entail action-processing advantages. Our findings offer new constraints for models of action semantics and motor-region function at large.
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Affiliation(s)
- Magdalena Miranda
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Instituto de Neurociencia Cognitiva y Traslacional (INCyT), Buenos Aires, Argentina
| | - Cecilia Gonzalez Campo
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
| | - Agustina Birba
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina,Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Alejandra Neely
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | | | - Evelyng Faure
- Department of Radiology, Clínica las Condes, Santiago, Chile,Advanced Epilepsy Center, Clínica las Condes, Santiago, Chile
| | - Gonzalo Rojas Costa
- Department of Radiology, Clínica las Condes, Santiago, Chile,Advanced Epilepsy Center, Clínica las Condes, Santiago, Chile,Health Innovation Center, Clínica las Condes, Santiago, Chile
| | - Agustín Ibáñez
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina,Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile,Global Brain Health Institute, University of California-San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland
| | - Adolfo García
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute, University of California-San Francisco, San Francisco, CA, United States; and Trinity College Dublin, Dublin, Ireland; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
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235
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Cai B, Zhou Z, Zhang A, Zhang G, Xiao L, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences. J Neurosci Methods 2022; 372:109539. [PMID: 35219769 DOI: 10.1016/j.jneumeth.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
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236
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Ip KI, Sisk LM, Horien C, Conley MI, Rapuano KM, Rosenberg MD, Greene AS, Scheinost D, Constable RT, Casey BJ, Baskin-Sommers A, Gee DG. Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth. J Cogn Neurosci 2022; 34:1810-1841. [PMID: 35104356 DOI: 10.1162/jocn_a_01826] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Exposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED are a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 years at baseline, n = 4038, 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline and indirectly predicted symptoms 1 year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial OFC (mOFC) regions within the frontoparietal network. Although more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multilevel socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions.
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Affiliation(s)
- Ka I Ip
- Yale University, New Haven, CT
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237
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Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study. Front Neurosci 2022; 15:785595. [PMID: 35087373 PMCID: PMC8787107 DOI: 10.3389/fnins.2021.785595] [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: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.
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Affiliation(s)
- Jing Wang
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Pengfei Ke
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Jinyu Zang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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238
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McCulloch DEW, Madsen MK, Stenbæk DS, Kristiansen S, Ozenne B, Jensen PS, Knudsen GM, Fisher PM. Lasting effects of a single psilocybin dose on resting-state functional connectivity in healthy individuals. J Psychopharmacol 2022; 36:74-84. [PMID: 34189985 PMCID: PMC8801642 DOI: 10.1177/02698811211026454] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
BACKGROUND Psilocybin is a psychedelic drug that has shown lasting positive effects on clinical symptoms and self-reported well-being following a single dose. There has been little research into the long-term effects of psilocybin on brain connectivity in humans. AIM Evaluate changes in resting-state functional connectivity (RSFC) at 1 week and 3 months after one psilocybin dose in 10 healthy psychedelic-naïve volunteers and explore associations between change in RSFC and related measures. METHODS Participants received 0.2-0.3 mg/kg psilocybin in a controlled setting. Participants completed resting-state functional magnetic resonance imaging (fMRI) scans at baseline, 1-week and 3-month post-administration and [11C]Cimbi-36 PET scans at baseline and 1 week. We examined changes in within-network, between-network and region-to-region RSFC. We explored associations between changes in RSFC and psilocybin-induced phenomenology as well as changes in psychological measures and neocortex serotonin 2A receptor binding. RESULTS Psilocybin was well tolerated and produced positive changes in well-being. At 1 week only, executive control network (ECN) RSFC was significantly decreased (Cohen's d = -1.73, pFWE = 0.010). We observed no other significant changes in RSFC at 1 week or 3 months, nor changes in region-to-region RSFC. Exploratory analyses indicated that decreased ECN RSFC at 1 week predicted increased mindfulness at 3 months (r = -0.65). CONCLUSIONS These findings in a small cohort indicate that psilocybin affects ECN function within the psychedelic 'afterglow' period. Our findings implicate ECN modulation as mediating psilocybin-induced, long-lasting increases in mindfulness. Although our findings implicate a neural pathway mediating lasting psilocybin effects, it is notable that changes in neuroimaging measures at 3 months, when personality changes are observed, remain to be identified.
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Affiliation(s)
| | - Martin Korsbak Madsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Dea Siggaard Stenbæk
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Sara Kristiansen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Peter Steen Jensen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Patrick MacDonald Fisher, Neurobiology Research Unit, Rigshospitalet Building 8057, 8 Inge Lehmanns Vej, Copenhagen 2100, Denmark.
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239
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Kashyap P, Shenk TE, Svaldi DO, Lycke RJ, Lee TA, Tamer GG, Nauman EA, Talavage TM. Normalized Brain Tissue–Level Evaluation of Volumetric Changes of Youth Athletes Participating in Collision Sports. Neurotrauma Rep 2022; 3:57-69. [PMID: 35112108 PMCID: PMC8804236 DOI: 10.1089/neur.2021.0060] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Observations of short-term changes in the neural health of youth athletes participating in collision sports (e.g., football and soccer) have highlighted a need to explore potential structural alterations in brain tissue volumes for these persons. Studies have shown biochemical, vascular, functional connectivity, and white matter diffusivity changes in the brain physiology of these athletes that are strongly correlated with repetitive head acceleration exposure. Here, research is presented that highlights regional anatomical volumetric measures that change longitudinally with accrued subconcussive trauma. A novel pipeline is introduced that provides simplified data analysis on standard-space template to quantify group-level longitudinal volumetric changes within these populations. For both sports, results highlight incremental relative regional volumetric changes in the subcortical cerebrospinal fluid that are strongly correlated with head exposure events greater than a 50-G threshold at the short-term post-season assessment. Moreover, longitudinal regional gray matter volumes are observed to decrease with time, only returning to baseline/pre-participation levels after sufficient (5–6 months) rest from collision-based exposure. These temporal structural volumetric alterations are significantly different from normal aging observed in sex- and age-matched controls participating in non-collision sports. Future work involves modeling repetitive head exposure thresholds with multi-modal image analysis and understanding the underlying physiological reason. A possible pathophysiological pathway is presented, highlighting the probable metabolic regulatory mechanisms. Continual participation in collision-based activities may represent a risk wherein recovery cannot occur. Even when present, the degree of the eventual recovery remains to be explored, but has strong implications for the well-being of collision-sport participants.
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Affiliation(s)
- Pratik Kashyap
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Trey E. Shenk
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Diana O. Svaldi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Roy J. Lycke
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Taylor A. Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Gregory G. Tamer
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Eric A. Nauman
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Thomas M. Talavage
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio, USA
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240
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Niu H, Li W, Wang G, Hu Q, Hao R, Li T, Zhang F, Cheng T. Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder. Front Psychiatry 2022; 13:973921. [PMID: 35958666 PMCID: PMC9360427 DOI: 10.3389/fpsyt.2022.973921] [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: 06/20/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification. METHODS Seventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance. RESULTS Experimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification. CONCLUSION These findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.
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Affiliation(s)
- Heng Niu
- Department of MRI, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Weirong Li
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Guiquan Wang
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Qiong Hu
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Rui Hao
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Tianliang Li
- Department of Ultrasound, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Fan Zhang
- Department of Medical Imaging, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Tao Cheng
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
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241
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Kremneva E, Sinitsyn D, Dobrynina L, Suslina A, Krotenkova M. Resting state functional MRI in neurology and psychiatry. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:5-14. [DOI: 10.17116/jnevro20221220215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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242
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Yuan Z, Lin X, Li P, Gao YJ, Yuan K, Yan W, Zhang YX, Liu L, Zhu XM, Zhang YJ, Bao YP, Chang SH, Lu L, Shi L. The neural correlation of emotion recognition ability and depressive symptoms-evidence from the HCP database. Front Psychiatry 2022; 13:1090369. [PMID: 36762291 PMCID: PMC9905428 DOI: 10.3389/fpsyt.2022.1090369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 12/14/2022] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION Negative bias of emotional face is the core feature of depression, but its underlying neurobiological mechanism is still unclear. The neuroimaging findings of negative emotional recognition and depressive symptoms are inconsistent. METHODS The neural association between depressive symptoms and negative emotional bias were analyzed by measuring the associations between resting state functional connectivity (FC), brain structures, negative emotional bias, and depressive problems. Then, we performed a mediation analysis to assess the potential overlapping neuroimaging mechanisms. RESULTS We found a negative correlation between depressive symptoms and emotional recognition. Secondly, the structure and function of the inferior and lateral orbitofrontal gyrus are related to depressive symptoms and emotional recognition. Thirdly, the thickness of the inferior orbitofrontal cortex and the FC between the inferior orbitofrontal gyrus and fusiform gyrus, precuneate and cingulate gyrus mediated and even predicted the interaction between emotion recognition and depressive symptoms. Finally, in response to a negative stimulus, the activation of the frontal pole and precuneus lobe associated with the inferior orbitofrontal gyrus was higher in participants with depressive symptoms. CONCLUSION The core brain regions centered on the inferior orbitofrontal cortex such as middle temporal gyrus, precuneus lobe, frontal pole, insula and cingulate gyrus are the potential neuroimaging basis for the interaction between depressive symptoms and emotional recognition.
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Affiliation(s)
- Ze Yuan
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China.,Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Xiao Lin
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Peng Li
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yu-Jun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kai Yuan
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Wei Yan
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yu-Xin Zhang
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China.,Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lin Liu
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China.,Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Xi-Mei Zhu
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yi-Jing Zhang
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yan-Ping Bao
- Beijing Key Laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,School of Public Health, Peking University, Beijing, China
| | - Su-Hua Chang
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Lin Lu
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China.,Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key Laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China
| | - Le Shi
- Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
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243
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Langenecker SA, Westlund Schreiner M, Thomas LR, Bessette KL, DelDonno SR, Jenkins LM, Easter RE, Stange JP, Pocius SL, Dillahunt A, Love TM, Phan KL, Koppelmans V, Paulus M, Lindquist MA, Caffo B, Mickey BJ, Welsh RC. Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:76-84. [PMID: 34271215 PMCID: PMC8748287 DOI: 10.1016/j.bpsc.2021.06.014] [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: 01/07/2021] [Revised: 05/14/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease. METHODS We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions. RESULTS There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network). CONCLUSIONS In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.
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Affiliation(s)
| | | | - Leah R Thomas
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychology, University of Utah, Salt Lake City, Utah
| | - Katie L Bessette
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Sophia R DelDonno
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Lisanne M Jenkins
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, Illinois
| | - Rebecca E Easter
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Jonathan P Stange
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois; Department of Psychology, University of Southern California, Los Angeles, California
| | | | - Alina Dillahunt
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Tiffany M Love
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | | | - Brian Caffo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Brian J Mickey
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Robert C Welsh
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
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244
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Zhou W, Wang Y, Wang M, Wang Z, Zheng H, Wang M, Potenza MN, Dong G. Connectome-based prediction of craving for gaming in internet gaming disorder. Addict Biol 2022; 27:e13076. [PMID: 34176197 DOI: 10.1111/adb.13076] [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] [Received: 02/09/2021] [Revised: 06/15/2021] [Accepted: 06/18/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Craving-related brain responses have been associated with the emergence and maintenance of addictions. However, little is known about brain network organizations underlying cravings in internet gaming disorder (IGD). METHODS Sixty-six IGD subjects and 61 matched individuals with recreational game use (RGU) were scanned while performing a cue-craving task. A recently developed whole-brain analysis approach, connectome-based predictive modelling (CPM) with leave-one-out cross-validation was conducted to identify networks that predicted craving responses in IGD. Then, the craving network was tested in different brain states (cue-craving under deprivation) to investigate replicability. RESULTS CPM identified an IGD craving network, as indicated by a significant correspondence between predicted and actual craving values (r = 0.49, p < 0.001), characterized by within-network default mode (DMN) connectivity and connectivity between canonical networks implicated in executive/cognitive control (frontoparietal, medial frontal, DMN) and reward responsiveness (subcortical, motor/sensory). Network strength in the cue-craving task during gaming deprivation also predicted IGD craving scores (r = 0.43, p = 0.017), indicating network replication across brain states. CONCLUSIONS The CPM results demonstrate that individual differences in cognitive, attention, and control network function can predict craving intensities in IGD subjects. These networks may be targets for potential interventions using brain modulation.
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Affiliation(s)
- Wei‐ran Zhou
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou Zhejiang Province China
- Institutes of Psychological Sciences Hangzhou Normal University Hangzhou Zhejiang Province China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou Zhejiang Province China
- College of Education Hangzhou Normal University Hangzhou Zhejiang Province China
| | - Yi‐ming Wang
- College of Education Hangzhou Normal University Hangzhou Zhejiang Province China
| | - Min Wang
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou Zhejiang Province China
- Institutes of Psychological Sciences Hangzhou Normal University Hangzhou Zhejiang Province China
| | - Zi‐liang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai China
| | - Meng‐jing Wang
- Southeast University ‐ Monash University Joint Graduate School Southeast University Nanjing China
| | - Marc N. Potenza
- Department of Psychiatry and the Child Study Center Yale University School of Medicine New Haven Connecticut USA
- Department of Neuroscience Yale University New Haven Connecticut USA
- Connecticut Council on Problem Gambling Wethersfield Connecticut USA
- Connecticut Mental Health Center New Haven Connecticut USA
| | - Guang‐Heng Dong
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou Zhejiang Province China
- Institutes of Psychological Sciences Hangzhou Normal University Hangzhou Zhejiang Province China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou Zhejiang Province China
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245
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Wu H, Zhou C, Bai X, Liu X, Chen J, Wen J, Guo T, Wu J, Guan X, Gao T, Gu L, Huang P, Xu X, Zhang B, Zhang M. Identifying a whole-brain connectome-based model in drug-naïve Parkinson's disease for predicting motor impairment. Hum Brain Mapp 2021; 43:1984-1996. [PMID: 34970835 PMCID: PMC8933250 DOI: 10.1002/hbm.25768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/12/2021] [Accepted: 12/20/2021] [Indexed: 12/17/2022] Open
Abstract
Identifying a whole‐brain connectome‐based predictive model in drug‐naïve patients with Parkinson's disease and verifying its predictions on drug‐managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain–behavior associations. In this study, we constructed a predictive model from the resting‐state functional data of 47 drug‐naïve patients by using a connectome‐based approach. This model was subsequently validated in 115 drug‐managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson's Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (rtrue) between predicted and observed scores. As a result, a connectome‐based model for predicting individual motor impairment in drug‐naïve patients was identified with significant performance (rtrue = .845, p < .001, ppermu = .002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor‐impairment‐related network contained more within‐network connections in the motor, visual‐related, and default mode networks, whereas the positive motor‐impairment‐related network was constructed mostly with between‐network connections coupling the motor‐visual, motor‐limbic, and motor‐basal ganglia networks. Finally, this predictive model constructed around drug‐naïve patients was confirmed with significant predictive efficacy on drug‐managed patients (r = .209, p = .025), suggesting a generalizability in Parkinson's disease patients under long‐term drug influence. In conclusion, this study identified a whole‐brain connectome‐based model that could predict the severity of motor impairment in Parkinson's patients and furthers our understanding of the functional underpinnings of the disease.
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Affiliation(s)
- Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Casanova R, Lyday RG, Bahrami M, Burdette JH, Simpson SL, Laurienti PJ. Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques. Front Neuroinform 2021; 15:740143. [PMID: 35002665 PMCID: PMC8739961 DOI: 10.3389/fninf.2021.740143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning. Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics. Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly. Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Jonathan H. Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Sean L. Simpson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
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Lee LH, Chen CH, Chang WC, Lee PL, Shyu KK, Chen MH, Hsu JW, Bai YM, Su TP, Tu PC. Evaluating the Performance of Machine Learning Models for Automatic Diagnosis of Patients with Schizophrenia Based on a Single Site Dataset of 440 Participants. Eur Psychiatry 2021; 65:e1. [PMID: 34937587 PMCID: PMC8792868 DOI: 10.1192/j.eurpsy.2021.2248] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Methods Results Conclusions
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Affiliation(s)
- Lung-Hao Lee
- Department of Electrical Engineering, National Central University, Taiwan.,Department of Medical Humanities and Education, College of Medicine, Kaohsiung Medical University, Taiwan.,Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan
| | - Chang-Hao Chen
- Department of Electrical Engineering, National Central University, Taiwan.,Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan
| | - Wan-Chen Chang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Po-Lei Lee
- Department of Electrical Engineering, National Central University, Taiwan.,Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan
| | - Kuo-Kai Shyu
- Department of Electrical Engineering, National Central University, Taiwan.,Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Ju-Wei Hsu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming Chiao Tung University, Taipei, Taiwan.,Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Pei-Chi Tu
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.,Institute of Philosophy of Mind and Cognition, National Yang-Ming Chiao Tung University, Taipei, Taiwan
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Yun JY, Kim YK. Graph theory approach for the structural-functional brain connectome of depression. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110401. [PMID: 34265367 DOI: 10.1016/j.pnpbp.2021.110401] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 01/22/2023]
Abstract
To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea; Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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249
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Domingos C, Picó-Pérez M, Magalhães R, Moreira M, Sousa N, Pêgo JM, Santos NC. Free-Living Physical Activity Measured With a Wearable Device Is Associated With Larger Hippocampus Volume and Greater Functional Connectivity in Healthy Older Adults: An Observational, Cross-Sectional Study in Northern Portugal. Front Aging Neurosci 2021; 13:729060. [PMID: 34916921 PMCID: PMC8670087 DOI: 10.3389/fnagi.2021.729060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/26/2021] [Indexed: 01/10/2023] Open
Abstract
Several studies using neuroimaging techniques have established a positive relationship between physical activity (PA) and brain structure and function in older populations. However, the use of subjective measures of PA and the lack of multimodal neuroimaging approaches have limited the understanding of this association. This study aims to explore the associations between PA and brain structure and function by objectively evaluating PA. Community-dwelling cognitively healthy older adults (without diagnosed cognitive, neurological or degenerative disease) were recruited from local health centers and local gyms. In a cross-sectional design, participants were evaluated regarding cognitive, clinical, anthropometric, physical performance, and lifestyle characteristics. A 3 T magnetic resonance imaging (MRI) was performed for structural and functional brain measures. PA time and level was assessed via a Xiaomi Mi Band 2® worn for 15 consecutive days. Participants (n = 110, after inclusion/exclusion criteria and completion of all evaluations) were 58 females (56%), with an average age of 68.42 years old (SD = 3.12), most were active. Multiple regression analysis revealed that higher time spent in vigorous PA associated with larger left parahippocampal gyrus and right hippocampus volumes. Furthermore, the analysis of the functional connectome indicated a greater functional connectivity (FC) between the frontal gyrus, cingulate gyrus, occipital inferior lobe for light, moderate, and total PA time, and sedentary time associated with lower FC in the same networks. Overall, the structural and functional findings may provide evidence on the relevant association between PA and brain health in aging.
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Affiliation(s)
- Célia Domingos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.,iCognitus4ALL - IT Solutions, Braga, Portugal.,Clinical Academic Center - Braga (2CA-B), Braga, Portugal
| | - Maria Picó-Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.,Clinical Academic Center - Braga (2CA-B), Braga, Portugal
| | - Ricardo Magalhães
- NeuroSpin, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Mariana Moreira
- ENCONTRAR+SE-Association for the Promotion of Mental Health, Porto, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.,Associação Centro de Medicina P5 (P5), School of Medicine, University of Minho, Braga, Portugal
| | - José Miguel Pêgo
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.,iCognitus4ALL - IT Solutions, Braga, Portugal.,Clinical Academic Center - Braga (2CA-B), Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal.,Clinical Academic Center - Braga (2CA-B), Braga, Portugal.,Associação Centro de Medicina P5 (P5), School of Medicine, University of Minho, Braga, Portugal
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250
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Wong TY, Zhang H, White T, Xu L, Qiu A. Common functional brain networks between attention deficit and disruptive behaviors in youth. Neuroimage 2021; 245:118732. [PMID: 34813970 DOI: 10.1016/j.neuroimage.2021.118732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/06/2021] [Accepted: 11/13/2021] [Indexed: 11/18/2022] Open
Abstract
Attention deficits (AD) and disruptive behavior (DB) are highly comorbid youth externalizing behaviors. This study aimed to study reliable functional brain networks shared by AD and DB in youth aged from 8 to 21 years from the Philadelphia Neurodevelopmental Cohort (PNC). The PNC study assessed AD and DB behaviors via Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS). This study employed sparse canonical correlation analysis (SCCA) to examine the correlation of AD and DB behaviors with resting-state functional connectivity maps of the brain regions identified via activation likelihood estimation (ALE) meta-analyses on attention deficit/hyperactivity disorder (ADHD) and DB disorder (DBD). Our meta-analyses identified that the middle cingulate cortex, pre-supplementary motor area (pre-SMA), and striatum had a great consensus in existing ADHD studies and the amygdala and inferior parietal lobule were consistently found in existing DBD studies. Our SCCA analysis revealed that the AD and DB behavioral items relevant to inattention and delinquency were correlated with the functional connectivity of the pre-SMA with the ventral attentional and frontoparietal networks (FPN), and the striatum with the default mode (DMN) and dorsal attentional networks. The AD and DB behavioral items relevant to inattention and irritability were associated with the functional connectivity between the amygdala and the DMN and FPN. Our findings suggest that the functional organization of the ADHD- and DBD-related brain regions provides insights on the shared neural basis in AD and DB.
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Affiliation(s)
- Ting Yat Wong
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, Singapore 117583, Singapore
| | - Han Zhang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, Singapore 117583, Singapore
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Liyuan Xu
- School of Computer Engineering and Science, Shanghai University, China
| | - Anqi Qiu
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, Singapore 117583, Singapore; NUS (Suzhou) Research Institute, National University of Singapore, China; School of Computer Engineering and Science, Shanghai University, China; The N.1 Institute for Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore; Department of Biomedical Engineering, the Johns Hopkins University, United States.
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