1
|
Park K, Chang I, Kim S. Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network. Neuroimage 2024; 301:120884. [PMID: 39378912 DOI: 10.1016/j.neuroimage.2024.120884] [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: 11/30/2023] [Revised: 10/04/2024] [Accepted: 10/05/2024] [Indexed: 10/10/2024] Open
Abstract
Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.
Collapse
Affiliation(s)
- Kyeongwon Park
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Iksoo Chang
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea; Supercomputing Bigdata Center, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Sangyeol Kim
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea.
| |
Collapse
|
2
|
Mirzaeian S, Faghiri A, Calhoun VD, Iraji A. A Telescopic Independent Component Analysis on Functional Magnetic Resonance Imaging Data Set. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.19.581086. [PMID: 39386484 PMCID: PMC11463639 DOI: 10.1101/2024.02.19.581086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of DMN, VS, and RFPN. In addition, TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources. Author summary Our study introduces "telescopic independent component analysis (TICA)", a novel approach using a recursive ICA strategy for exploring brain function across multiple spatial scales using fMRI data. TICA constructs spatial hierarchies and identifies functional sources such as default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). By applying TICA, we uncovered hierarchical structures within these networks and revealed differences in the DMN between healthy individuals and those with schizophrenia. This approach offers a promising new tool for studying function dynamics comprehensively.
Collapse
|
3
|
Thompson KI, Schneider CJ, Lopez-Roque JA, Wakschlag LS, Karim HT, Perlman SB. A network approach to the investigation of childhood irritability: probing frustration using social stimuli. J Child Psychol Psychiatry 2024; 65:959-972. [PMID: 38124618 PMCID: PMC11161318 DOI: 10.1111/jcpp.13937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/31/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli. METHODS Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives. RESULTS ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p < .05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p < .05, FDR-corrected). CONCLUSIONS Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability.
Collapse
Affiliation(s)
- Khalil I Thompson
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Clayton J Schneider
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Justin A Lopez-Roque
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Lauren S Wakschlag
- Department of Medical Social Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburg School of Medicine, Pittsburgh, PA, USA
| | - Susan B Perlman
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| |
Collapse
|
4
|
Blair DS, Miller RL, Calhoun VD. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. ENTROPY (BASEL, SWITZERLAND) 2024; 26:545. [PMID: 39056908 PMCID: PMC11275472 DOI: 10.3390/e26070545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.
Collapse
Affiliation(s)
- David Sutherland Blair
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30303, USA (V.D.C.)
| | | | | |
Collapse
|
5
|
Farahibozorg SR, Harrison SJ, Bijsterbosch JD, Woolrich MW, Smith SM. Multiscale Modes of Functional Brain Connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596120. [PMID: 38854078 PMCID: PMC11160636 DOI: 10.1101/2024.05.28.596120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a paradigm shift in functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.
Collapse
Affiliation(s)
- S Rezvan Farahibozorg
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Dept. of Clinical Neuroscience, Oxford University, Oxford, UK
| | - Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Dept. of Clinical Neuroscience, Oxford University, Oxford, UK
| | | | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Dept. of Clinical Neuroscience, Oxford University, Oxford, UK
- OHBA, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, UK
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Dept. of Clinical Neuroscience, Oxford University, Oxford, UK
| |
Collapse
|
6
|
Ganesan S, Misaki M, Zalesky A, Tsuchiyagaito A. Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.05.24306889. [PMID: 38766116 PMCID: PMC11100839 DOI: 10.1101/2024.05.05.24306889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD. Methods We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B). Results Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037). Limitations The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback. Conclusion We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.
Collapse
Affiliation(s)
- Saampras Ganesan
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
- Contemplative Studies Centre, Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| |
Collapse
|
7
|
Wei W, Zhang K, Chang J, Zhang S, Ma L, Wang H, Zhang M, Zu Z, Yang L, Chen F, Fan C, Li X. Analyzing 20 years of Resting-State fMRI Research: Trends and collaborative networks revealed. Brain Res 2024; 1822:148634. [PMID: 37848120 DOI: 10.1016/j.brainres.2023.148634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/19/2023] [Accepted: 10/14/2023] [Indexed: 10/19/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.
Collapse
Affiliation(s)
- Wenzhuo Wei
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Kaiyuan Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jin Chang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Shuyu Zhang
- School of Psychology, the Australian National University, Australian
| | - Lijun Ma
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Huixue Wang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Mi Zhang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Zhenyue Zu
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Linxi Yang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Fenglan Chen
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Chuan Fan
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
| | - Xiaoming Li
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China.
| |
Collapse
|
8
|
Stoyanov D, Paunova R, Dichev J, Kandilarova S, Khorev V, Kurkin S. Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale. World J Clin Cases 2023; 11:8458-8474. [PMID: 38188204 PMCID: PMC10768520 DOI: 10.12998/wjcc.v11.i36.8458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/10/2023] [Accepted: 12/05/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders. AIM To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD). METHODS 60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN. RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task. CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.
Collapse
Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Rositsa Paunova
- Research Institute, Medical University, Plovdiv 4002, Bulgaria
| | - Julian Dichev
- Faculty of Medicine, Medical University, Plovdiv 4002, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University, Plovdiv 4002, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
| |
Collapse
|
9
|
Iraji A, Fu Z, Faghiri A, Duda M, Chen J, Rachakonda S, DeRamus T, Kochunov P, Adhikari BM, Belger A, Ford JM, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Bustillo JR, Yang K, Ishizuka K, Faria A, Sawa A, Hutchison K, Osuch EA, Theberge J, Abbott C, Mueller BA, Zhi D, Zhuo C, Liu S, Xu Y, Salman M, Liu J, Du Y, Sui J, Adali T, Calhoun VD. Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets. Hum Brain Mapp 2023; 44:5729-5748. [PMID: 37787573 PMCID: PMC10619392 DOI: 10.1002/hbm.26472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/30/2023] [Accepted: 06/19/2023] [Indexed: 10/04/2023] Open
Abstract
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
Collapse
Affiliation(s)
- A. Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Z. Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - A. Faghiri
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - M. Duda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - J. Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - S. Rachakonda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - T. DeRamus
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - A. Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - J. M. Ford
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - D. H. Mathalon
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - G. D. Pearlson
- Departments of Psychiatry and Neuroscience, School of MedicineYale UniversityNew HavenConnecticutUSA
| | - S. G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - A. Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. A. Turner
- Department of Psychiatry and Behavioral HealthOhio State University Medical Center in ColumbusColumbusOhioUSA
| | - T. G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - K. Yang
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - K. Ishizuka
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Faria
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Pharmacology, and Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Mental HealthJohns Hopkins University Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - K. Hutchison
- Department of PsychologyUniversity of ColoradoBoulderColoradoUSA
| | - E. A. Osuch
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - J. Theberge
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - C. Abbott
- Department of Psychiatry (CCA)University of New MexicoAlbuquerqueNew MexicoUSA
| | - B. A. Mueller
- Department of PsychiatryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - D. Zhi
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - C. Zhuo
- Tianjin Mental Health CenterNankai University Affiliated Anding HospitalTianjinChina
| | - S. Liu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Y. Xu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - M. Salman
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - J. Liu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Y. Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - J. Sui
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - T. Adali
- Department of CSEEUniversity of Maryland Baltimore CountyBaltimoreMarylandUSA
| | - V. D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| |
Collapse
|
10
|
Papadaki E, Koustakas T, Werner A, Lindenberger U, Kühn S, Wenger E. Resting-state functional connectivity in an auditory network differs between aspiring professional and amateur musicians and correlates with performance. Brain Struct Funct 2023; 228:2147-2163. [PMID: 37792073 PMCID: PMC10587189 DOI: 10.1007/s00429-023-02711-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/10/2023] [Indexed: 10/05/2023]
Abstract
Auditory experience-dependent plasticity is often studied in the domain of musical expertise. Available evidence suggests that years of musical practice are associated with structural and functional changes in auditory cortex and related brain regions. Resting-state functional magnetic resonance imaging (MRI) can be used to investigate neural correlates of musical training and expertise beyond specific task influences. Here, we compared two groups of musicians with varying expertise: 24 aspiring professional musicians preparing for their entrance exam at Universities of Arts versus 17 amateur musicians without any such aspirations but who also performed music on a regular basis. We used an interval recognition task to define task-relevant brain regions and computed functional connectivity and graph-theoretical measures in this network on separately acquired resting-state data. Aspiring professionals performed significantly better on all behavioral indicators including interval recognition and also showed significantly greater network strength and global efficiency than amateur musicians. Critically, both average network strength and global efficiency were correlated with interval recognition task performance assessed in the scanner, and with an additional measure of interval identification ability. These findings demonstrate that task-informed resting-state fMRI can capture connectivity differences that correspond to expertise-related differences in behavior.
Collapse
Affiliation(s)
- Eleftheria Papadaki
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
- International Max Planck Research School on the Life Course (LIFE), Berlin, Germany.
| | - Theodoros Koustakas
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - André Werner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, London, UK
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
- Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Elisabeth Wenger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| |
Collapse
|
11
|
Kinsey S, Kazimierczak K, Camazón PA, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp TGM, Dhamala M, Calhoun VD, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.566292. [PMID: 38014169 PMCID: PMC10680735 DOI: 10.1101/2023.11.16.566292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approach's ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.
Collapse
Affiliation(s)
- Spencer Kinsey
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | - Pablo Andrés Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Tülay Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, TX
| | - Bhim Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Judith Ford
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Mukesh Dhamala
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
12
|
Jiang K, Xu Q. Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate. Front Res Metr Anal 2023; 8:1239726. [PMID: 37920784 PMCID: PMC10618672 DOI: 10.3389/frma.2023.1239726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/25/2023] [Indexed: 11/04/2023] Open
Abstract
Introduction This study examines the associations between time series, termed "coherency," using spectral analysis. Coherence squared, analogous to the squared correlation coefficient, serves as a metric to quantify the degree of interdependence and co-evolution of individual nodes. Methods We utilized spectral analysis to compute coherence squared, unveiling relationships and co-evolution patterns among individual nodes. The resultant matrix of these relationships was subjected to network analysis. Results By conducting a case study analyzing tweets associated with the co-hashtags #StopAsianHate and #BlackLivesMatter, we present a novel approach utilizing coherency network analysis to investigate the dynamics of social media text. Frequency domain analysis aided in calculating coherence squared, effectively illustrating the relationships and co-evolution of individual nodes. Furthermore, an analysis of the phase spectrum's slope facilitated the determination of time lag and potential causality direction between highly co-evolved node pairs. Discussion Our findings underline the potential of coherency network analysis in comprehending the intricate dynamics of social media text. This approach offers valuable insights into how topics, sentiments, or movements manifest and evolve within the digital realm. Future research should explore diverse datasets and domains to broaden our understanding of this novel analytical technique.
Collapse
Affiliation(s)
- Ke Jiang
- School of Communications, Elon University, Elon, NC, United States
| | | |
Collapse
|
13
|
Jackson RL, Humphreys GF, Rice GE, Binney RJ, Lambon Ralph MA. A network-level test of the role of the co-activated default mode network in episodic recall and social cognition. Cortex 2023; 165:141-159. [PMID: 37285763 PMCID: PMC10284259 DOI: 10.1016/j.cortex.2022.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/10/2022] [Accepted: 12/19/2022] [Indexed: 06/09/2023]
Abstract
Resting-state network research is extremely influential, yet the functions of many networks remain unknown. In part, this is due to typical (e.g., univariate) analyses independently testing the function of individual regions and not examining the full set of regions that form a network whilst co-activated. Connectivity is dynamic and the function of a region may change based on its current connections. Therefore, determining the function of a network requires assessment at this network-level. Yet popular theories implicating the default mode network (DMN) in episodic memory and social cognition, rest principally upon analyses performed at the level of individual brain regions. Here we use independent component analysis to formally test the role of the DMN in episodic and social processing at the network level. As well as an episodic retrieval task, two independent datasets were employed to assess DMN function across the breadth of social cognition; a person knowledge judgement and a theory of mind task. Each task dataset was separated into networks of co-activated regions. In each, the co-activated DMN, was identified through comparison to an a priori template and its relation to the task model assessed. This co-activated DMN did not show greater activity in episodic or social tasks than high-level baseline conditions. Thus, no evidence was found to support hypotheses that the co-activated DMN is involved in explicit episodic or social tasks at a network-level. The networks associated with these processes are described. Implications for prior univariate findings and the functional significance of the co-activated DMN are considered.
Collapse
Affiliation(s)
- Rebecca L Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, York, UK; MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Gina F Humphreys
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Grace E Rice
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | | |
Collapse
|
14
|
Nozais V, Forkel SJ, Petit L, Talozzi L, Corbetta M, Thiebaut de Schotten M, Joliot M. Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain. Commun Biol 2023; 6:726. [PMID: 37452124 PMCID: PMC10349117 DOI: 10.1038/s42003-023-05107-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.
Collapse
Affiliation(s)
- Victor Nozais
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, the Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Laurent Petit
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
| | - Lia Talozzi
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine and Padova Neuroscience Center, University of Padua, Padova, PD, 32122, Italy
| | - Michel Thiebaut de Schotten
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Marc Joliot
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
| |
Collapse
|
15
|
Duda M, Faghiri A, Belger A, Bustillo JR, Ford JM, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Sui J, Van Erp TGM, Calhoun VD. Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547840. [PMID: 37461731 PMCID: PMC10350020 DOI: 10.1101/2023.07.05.547840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
Collapse
Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| |
Collapse
|
16
|
Yan X, Kong R, Xue A, Yang Q, Orban C, An L, Holmes AJ, Qian X, Chen J, Zuo XN, Zhou JH, Fortier MV, Tan AP, Gluckman P, Chong YS, Meaney MJ, Bzdok D, Eickhoff SB, Yeo BTT. Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. Neuroimage 2023; 273:120010. [PMID: 36918136 PMCID: PMC10212507 DOI: 10.1016/j.neuroimage.2023.120010] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/25/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023] Open
Abstract
Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).
Collapse
Affiliation(s)
- Xiaoxuan Yan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Aihuiping Xue
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Qing Yang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, Unites States of America
| | - Xing Qian
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; National Basic Public Science Data Center, China
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore; Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Peter Gluckman
- UK Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, Unites States of America.
| |
Collapse
|
17
|
Jia Y, Jariwala N, Hinkley LBN, Nagarajan S, Subramaniam K. Abnormal resting-state functional connectivity underlies cognitive and clinical symptoms in patients with schizophrenia. Front Hum Neurosci 2023; 17:1077923. [PMID: 36875232 PMCID: PMC9976937 DOI: 10.3389/fnhum.2023.1077923] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/31/2023] [Indexed: 02/17/2023] Open
Abstract
Introduction The cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity. Methods We capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC). Results We found that SZ showed significant global disrupted functional connectivity in delta-theta (2-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition. Discussion The multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.
Collapse
Affiliation(s)
- Yingxin Jia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Namasvi Jariwala
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Leighton B. N. Hinkley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Karuna Subramaniam
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
18
|
Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
Collapse
Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| |
Collapse
|
19
|
Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
Collapse
Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| |
Collapse
|
20
|
Riedel P, Lee J, Watson CG, Jimenez AM, Reavis EA, Green MF. Reorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder. Psychiatry Res Neuroimaging 2022; 327:111556. [PMID: 36327867 PMCID: PMC10611423 DOI: 10.1016/j.pscychresns.2022.111556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/13/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Functional connectome organization is altered in schizophrenia (SZ) and bipolar disorder (BD). However, it remains unclear whether network reorganization during a task relative to rest is also altered in these disorders. This study examined connectome organization in patients with SZ (N = 43) and BD (N = 42) versus healthy controls (HC; N = 39) using fMRI data during a visual object-perception task and at rest. Graph analyses were conducted for the whole-brain network using indices selected a priori: three reflecting network segregation (clustering coefficient, local efficiency, modularity), two reflecting integration (characteristic path length, global efficiency). Group differences were limited to network segregation and were more evident in SZ (clustering coefficient, modularity) than in BD (clustering coefficient) compared to HC. State differences were found across groups for segregation (local efficiency) and integration (characteristic path length). There was no group-by-state interaction for any graph index. In summary, aberrant network organization compared to HC was confirmed, and was more evident in SZ than in BD. Yet, reorganization was largely intact in both disorders. These findings help to constrain models of dysconnection in SZ and BD, suggesting that the extent of functional dysconnectivity in these disorders tends to persist across changes in mental state.
Collapse
Affiliation(s)
- Philipp Riedel
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Würzburger Straße 35, Dresden 01187, Germany.
| | - Junghee Lee
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Behavioral Neurobiology, School of Medicine, The University of Alabama at Birmingham, SC 560, 1720 2nd Ave S, Birmingham, AL 35294-0017, USA
| | - Christopher G Watson
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Amy M Jimenez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Eric A Reavis
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Michael F Green
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| |
Collapse
|
21
|
Ye Y, Wang C, Lan X, Li W, Fu L, Zhang F, Liu H, Wu K, Zhou Y, Ning Y. Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder. Neuroimage Clin 2022; 36:103230. [PMID: 36274375 PMCID: PMC9668631 DOI: 10.1016/j.nicl.2022.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy. METHODS In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups. RESULTS Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission. CONCLUSION These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.
Collapse
Affiliation(s)
- Yanxiang Ye
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Chengyu Wang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Weicheng Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Ling Fu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Fan Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Haiyan Liu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
| |
Collapse
|
22
|
Zheng YQ, Farahibozorg SR, Gong W, Rafipoor H, Jbabdi S, Smith S. Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner. Neuroimage 2022; 259:119418. [PMID: 35777635 PMCID: PMC10933828 DOI: 10.1016/j.neuroimage.2022.119418] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/06/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023] Open
Abstract
Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.
Collapse
Affiliation(s)
- Ying-Qiu Zheng
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK.
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK
| | - Weikang Gong
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK
| | - Hossein Rafipoor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK
| |
Collapse
|
23
|
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon DH, Mueller BA, Pearlson G, Potkin SG, Preda A, Turner J, van Erp T, Sui J, Calhoun VD. Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales. Brain Connect 2022; 12:617-628. [PMID: 34541879 PMCID: PMC9529308 DOI: 10.1089/brain.2021.0079] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.
Collapse
Affiliation(s)
- Xing Meng
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Judith Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
- San Francisco VA Medical Center, San Francisco, California, USA
| | - Sara McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
- San Francisco VA Medical Center, San Francisco, California, USA
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, Connecticut, USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jessica Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
| | - Theo van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
| |
Collapse
|
24
|
Lee SW, Kim E, Jang TY, Choi H, Kim S, Song H, Hwang MJ, Chang Y, Lee SJ. Alterations of Power Spectral Density in Salience Network during Thought-action Fusion Induction Paradigm in Obsessive-compulsive Disorder. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2022; 20:415-426. [PMID: 35879026 PMCID: PMC9329118 DOI: 10.9758/cpn.2022.20.3.415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/30/2021] [Accepted: 04/29/2021] [Indexed: 11/18/2022]
Abstract
Objective Methods Results Conclusion
Collapse
Affiliation(s)
- Sang Won Lee
- Department of Psychiatry, Kyungpook National University Chilgok Hospital, Daegu, Korea
- Department of Psychiatry, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Eunji Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Tae Yang Jang
- Department of Psychiatry, School of Medicine, Kyungpook National University, Daegu, Korea
- Department of Psychiatry, Kyungpook National University Hospital, Daegu, Korea
| | - Heajung Choi
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Seungho Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Huijin Song
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | | | - Yongmin Chang
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea
| | - Seung Jae Lee
- Department of Psychiatry, School of Medicine, Kyungpook National University, Daegu, Korea
- Department of Psychiatry, Kyungpook National University Hospital, Daegu, Korea
| |
Collapse
|
25
|
Palmucci M, Tagliazucchi E. Divergences Between Resting State Networks and Meta-Analytic Maps Of Task-Evoked Brain Activity. Open Neuroimag J 2022. [DOI: 10.2174/18744400-v15-e2206270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Spontaneous human neural activity is organized into resting state networks, complex patterns of synchronized activity that account for the major part of brain metabolism. The correspondence between these patterns and those elicited by the performance of cognitive tasks would suggest that spontaneous brain activity originates from the stream of ongoing cognitive processing.
Objective:
To investigate a large number of meta-analytic activation maps obtained from Neurosynth (www.neurosynth.org), establishing the extent of task-rest similarity in large-scale human brain activity.
Methods:
We applied a hierarchical module detection algorithm to the Neurosynth activation map similarity network, and then compared the average activation maps for each module with a set of resting state networks by means of spatial correlations.
Results:
We found that the correspondence between resting state networks and task-evoked activity tended to hold only for the largest spatial scales. We also established that this correspondence could be biased by the inclusion of maps related to neuroanatomical terms in the database (e.g. “parietal”, “occipital”, “cingulate”, etc.).
Conclusion:
Our results establish divergences between brain activity patterns related to spontaneous cognition and the spatial configuration of RSN, suggesting that anatomically-constrained homeostatic processes could play an important role in the inception and shaping of human resting state activity fluctuations.
Collapse
|
26
|
Wu Q, Huang Q, Liu C, Wu H. Oxytocin modulates social brain network correlations in resting and task state. Cereb Cortex 2022; 33:3607-3620. [PMID: 36005833 DOI: 10.1093/cercor/bhac295] [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: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/13/2022] Open
Abstract
The effects of oxytocin (OT) on the social brain can be tracked upon assessing the neural activity in resting and task states, and developing a system-level framework for characterizing the state-based functional relationships of its distinct effect. Here, we contribute to this framework by examining how OT modulates social brain network correlations during resting and task states, using fMRI. First, we investigated network activation, followed by an analysis of the relationships between networks and individual differences. Subsequently, we evaluated the functional connectivity in both states. Finally, the relationship between networks across states was represented by the predictive power of networks in the resting state for task-evoked activities. The differences in the predicted accuracy between the subjects displayed individual variations in this relationship. Our results showed that the activity of the dorsal default mode network in the resting state had the largest predictive power for task-evoked activation of the precuneus network (PN) only in the OT group. The results also demonstrated that OT reduced the individual variation in PN in the prediction process. These findings suggest a distributed but modulatory effect of OT on the association between resting and task-dependent brain networks.
Collapse
Affiliation(s)
- Qingyuan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China
| |
Collapse
|
27
|
Guo JR, Shi JY, Dong QY, Cao YB, Li D, Chen HJ. Altered dynamic spontaneous neural activity in minimal hepatic encephalopathy. Front Neurol 2022; 13:963551. [PMID: 36061995 PMCID: PMC9439282 DOI: 10.3389/fneur.2022.963551] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
Background and aims: Abnormal regional neural activity has been identified by the analysis of the static amplitude of low-frequency fluctuation (ALFF) in the setting of minimal hepatic encephalopathy (MHE). Brain activity is highly dynamic. This work sought to evaluate the temporal variability of ALFF to reveal MHE-related alterations in the dynamics of spontaneous neural activity. Methods A total of 29 healthy controls and 49 patients with cirrhosis [including 20 patients with MHE and 29 patients without MHE (NHE)] who underwent resting-state functional magnetic resonance imaging and Psychometric Hepatic Encephalopathy Score (PHES) examination were enrolled in this investigation. Utilizing a sliding-window approach, we calculated the dynamic ALFF (dALFF) variability to reflect the temporal dynamics of regional neural activity. An analysis of the correlation between dALFF variability and PHES was performed, and receiver operating characteristic (ROC) curve analysis to determine the potential of the dALFF variability index in identifying MHE was completed. Results The dALFF variability in the bilateral precuneus/posterior cingulate gyrus and left middle frontal gyrus progressively decreased from NHE to MHE group. In cirrhotic patients, the value of dALFF variability in the bilateral precuneus/posterior cingulate gyrus was positively correlated with their neurocognitive performance (r = 0.383 and P = 0.007). The index of dALFF variability in the bilateral precuneus/posterior cingulate gyrus could be used to distinguish NHE and MHE patients, with moderate power (area under the ROC curve = 0.712 and P = 0.012). Conclusion Our findings highlight the existence of aberrant dynamic brain function in MHE, which could underlie the neural basis of cognitive impairments and could be associated with the development of the disease. Analyzing dALFF could facilitate new biomarker identification for MHE.
Collapse
Affiliation(s)
- Jie-Ru Guo
- Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Yan Shi
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Qiu-Yi Dong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yun-Bin Cao
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Dan Li
- Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou, China
- Dan Li
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- *Correspondence: Hua-Jun Chen
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
Li X, Yan R, Yue Z, Zhang M, Ren J, Wu B. Abnormal Dynamic Functional Connectivity in Patients With End-Stage Renal Disease. Front Neurosci 2022; 16:852822. [PMID: 35669490 PMCID: PMC9163405 DOI: 10.3389/fnins.2022.852822] [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: 01/11/2022] [Accepted: 04/06/2022] [Indexed: 11/23/2022] Open
Abstract
Dynamic functional connectivity (FC) analysis can capture time-varying properties of connectivity; however, studies focusing on dynamic FC in patients with end-stage renal disease (ESRD) are very limited. This is the first study to explore the dynamic aspects of whole-brain FC and topological properties in ESRD patients. Resting-state functional magnetic resonance imaging data were acquired from 100 ESRD patients [50 hemodialysis (HD) patients and 50 non-dialysis patients] and 64 healthy controls (HCs). Independent component analysis, a sliding-window approach and graph-theory methods were used to study the dynamic FC properties. The intrinsic brain FC were clustered into four configuration states. Compared with HCs, both patient groups spent longer time in State 3, in which decreased FC between subnetworks of the default mode network (DMN) and between the dorsal DMN and language network was observed in these patients, and a further reduction in FC between the DMN subnetworks was found in HD patients compared to non-dialysis patients. The number of transitions and the variability of global and local efficiency progressively decreased from that in HCs to that of non-dialysis patients to that of HD patients. The completion time of Trail Making Test A and Trail Making Test B positively correlated with the mean dwell time of State 3 and negatively correlated with the number of transitions in ESRD patients. Our findings suggest impaired functional flexibility of network connections and state-specific FC disruptions in patients with ESRD, which may underlie their cognitive deficits. HD may have an adverse effect on time-varying FC.
Collapse
|
30
|
Zhang L, Zhang R, Han S, Womer FY, Wei Y, Duan J, Chang M, Li C, Feng R, Liu J, Zhao P, Jiang X, Wei S, Yin Z, Zhang Y, Zhang Y, Zhang X, Tang Y, Wang F. Three major psychiatric disorders share specific dynamic alterations of intrinsic brain activity. Schizophr Res 2022; 243:322-329. [PMID: 34244046 DOI: 10.1016/j.schres.2021.06.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 05/21/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Increasing evidence suggests that major psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ) share biological, neuropsychological and clinical features, despite the criteria for their respective diagnoses being different. Neuroimaging studies have shown disrupted 'static' neural connectivity in these disorders. However, the changes in brain dynamics across the three psychiatric disorders remain unknown. METHODS We aim to examine the connections and divergencies of the dynamic amplitude of low-frequency fluctuation (dALFF) in MDD, BD and SZ. In total, 901 participants [MDD, 229; BD, 146; SZ, 142; and healthy controls (HCs), 384] received resting-state functional magnetic resonance imaging. The dALFF was calculated using sliding-window analysis and compared across three psychiatric disorders. RESULTS We found significant increases of dALFF in the right fusiform, right hippocampus, right parahippocampal in participants with MDD, BD and SZ compared to HC. We also found specific increased dALFF changes in caudate and left thalamus for SZ and BD and decreased dALFF changes in calcarine and lingual for SZ and MDD. CONCLUSION Our study found significant intrinsic brain activity changes in the limbic system and primary visual area in MDD, BD, and SZ, which indicates these areas disruptions are core neurobiological features shared among three psychiatric disorders. Meanwhile, our findings also indicate that specific alterations in MDD, BD, and SZ provide neuroimaging evidence for the differential diagnosis of the three mental disorders.
Collapse
Affiliation(s)
- Luheng Zhang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ran Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Shaoqiang Han
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, PR China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Yange Wei
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Jia Duan
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Miao Chang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Chao Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ruiqi Feng
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Juan Liu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Pengfei Zhao
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Shengnan Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Zhiyang Yin
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yifan Zhang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, PR China.
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China; Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.
| |
Collapse
|
31
|
Li Y, Zhou Z, Li Q, Li T, Julian IN, Guo H, Chen J. Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network. Front Neurosci 2022; 16:889105. [PMID: 35578623 PMCID: PMC9106560 DOI: 10.3389/fnins.2022.889105] [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: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
Collapse
Affiliation(s)
- Yao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zihao Zhou
- College of Mathematics, Taiyuan University of Technology, Taiyuan, China
| | - Qifan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ibegbu Nnamdi Julian
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| |
Collapse
|
32
|
Li Y, Li Q, Li T, Zhou Z, Xu Y, Yang Y, Chen J, Guo H. Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data. Front Neurosci 2022; 16:848363. [PMID: 35495049 PMCID: PMC9043754 DOI: 10.3389/fnins.2022.848363] [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: 01/04/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.
Collapse
Affiliation(s)
- Yao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Qifan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zijing Zhou
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| |
Collapse
|
33
|
The overlapping modular organization of human brain functional networks across the adult lifespan. Neuroimage 2022; 253:119125. [PMID: 35331872 DOI: 10.1016/j.neuroimage.2022.119125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 03/02/2022] [Accepted: 03/19/2022] [Indexed: 01/06/2023] Open
Abstract
Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.
Collapse
|
34
|
Hollunder B, Rajamani N, Siddiqi SH, Finke C, Kühn AA, Mayberg HS, Fox MD, Neudorfer C, Horn A. Toward personalized medicine in connectomic deep brain stimulation. Prog Neurobiol 2022; 210:102211. [PMID: 34958874 DOI: 10.1016/j.pneurobio.2021.102211] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/15/2021] [Accepted: 12/22/2021] [Indexed: 02/08/2023]
Abstract
At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.
Collapse
Affiliation(s)
- Barbara Hollunder
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Nanditha Rajamani
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA
| | - Clemens Neudorfer
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andreas Horn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
35
|
Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
Collapse
Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | |
Collapse
|
36
|
Rahaman MA, Damaraju E, Turner JA, van Erp TG, Mathalon D, Vaidya J, Muller B, Pearlson G, Calhoun VD. Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals. Brain Connect 2022; 12:61-73. [PMID: 34049447 PMCID: PMC8867091 DOI: 10.1089/brain.2020.0896] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.
Collapse
Affiliation(s)
- Md Abdur Rahaman
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Address correspondence to: Md Abdur Rahaman, Department of Computational Science and Engineering, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jessica A. Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Theo G.M. van Erp
- Center for the Neurobiology of Learning and Memory, Department of Psychiatry and Human Behavior, University of California Irvine, California, USA.,Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, California, USA
| | - Daniel Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, California, USA
| | - Jatin Vaidya
- Department of Psychiatry, Cognitive Brain Development Laboratory, University of Iowa Health Care, Iowa, USA
| | - Bryon Muller
- Department of Psychiatry, University of Minnesota, Minnesota, USA
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, Connecticut, USA
| | - Vince D. Calhoun
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| |
Collapse
|
37
|
OUP accepted manuscript. Cereb Cortex 2022; 32:4869-4884. [DOI: 10.1093/cercor/bhab521] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/02/2021] [Accepted: 12/17/2021] [Indexed: 11/14/2022] Open
|
38
|
Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
Collapse
|
39
|
Luo W, Constable RT. Inside information: Systematic within-node functional connectivity changes observed across tasks or groups. Neuroimage 2021; 247:118792. [PMID: 34896289 PMCID: PMC8840325 DOI: 10.1016/j.neuroimage.2021.118792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/16/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
Collapse
Affiliation(s)
- Wenjing Luo
- Department of Biomedical Engineering, Yale University School of Medicine USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
| |
Collapse
|
40
|
Ganesan S, Lv J, Zalesky A. Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics. Hum Brain Mapp 2021; 43:1403-1418. [PMID: 34859934 PMCID: PMC8837593 DOI: 10.1002/hbm.25732] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 10/28/2021] [Accepted: 11/14/2021] [Indexed: 01/02/2023] Open
Abstract
Behavioral traits are rarely considered in task‐evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an individual engages with the task, and thus lead to heterogeneity in task‐evoked brain responses. We aimed to investigate whether interindividual variation in behavior associates with the accuracy of predicting task‐evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi‐timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large‐scale networks enabled individual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interindividual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for individuals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interindividual variation in behavior when investigating context‐dependent dynamic functional connectivity.
Collapse
Affiliation(s)
- Saampras Ganesan
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of MelbourneMelbourneVictoriaAustralia
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVictoriaAustralia
| | - Jinglei Lv
- School of Biomedical EngineeringUniversity of SydneySydneyNew South WalesAustralia
- Brain and Mind CentreUniversity of SydneySydneyNew South WalesAustralia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of MelbourneMelbourneVictoriaAustralia
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVictoriaAustralia
| |
Collapse
|
41
|
Popiel NJM, Metrow C, Laforge G, Owen AM, Stojanoski B, Soddu A. Exploring electroencephalography with a model inspired by quantum mechanics. Sci Rep 2021; 11:19771. [PMID: 34611185 PMCID: PMC8492705 DOI: 10.1038/s41598-021-97960-7] [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: 04/28/2021] [Accepted: 08/30/2021] [Indexed: 12/05/2022] Open
Abstract
An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.
Collapse
Affiliation(s)
- Nicholas J M Popiel
- The Department of Physics and Astronomy, The University of Western Ontario, London, ON, N6A 5B7, Canada.,Cavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, UK
| | - Colin Metrow
- The Department of Physics and Astronomy, The University of Western Ontario, London, ON, N6A 5B7, Canada
| | - Geoffrey Laforge
- The Brain and Mind Institute, The University of Western Ontario, London, ON, N6A 5B7, Canada
| | - Adrian M Owen
- The Brain and Mind Institute, The University of Western Ontario, London, ON, N6A 5B7, Canada.,The Department of Psychology, The University of Western Ontario, London, ON, N6A 5B7, Canada.,The Department of Physiology and Pharmacology, The University of Western Ontario, London, ON, N6A 5B7, Canada
| | - Bobby Stojanoski
- The Department of Psychology, The University of Western Ontario, London, ON, N6A 5B7, Canada.,Faculty of Social Science and Humanities, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada
| | - Andrea Soddu
- The Department of Physics and Astronomy, The University of Western Ontario, London, ON, N6A 5B7, Canada. .,The Brain and Mind Institute, The University of Western Ontario, London, ON, N6A 5B7, Canada.
| |
Collapse
|
42
|
Increased decision latency in alcohol use disorder reflects altered resting-state synchrony in the anterior salience network. Sci Rep 2021; 11:19581. [PMID: 34599268 PMCID: PMC8486863 DOI: 10.1038/s41598-021-99211-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 09/22/2021] [Indexed: 12/12/2022] Open
Abstract
Increased decision latency in alcohol use disorder (AUD) has been generally explained in terms of psychomotor slowing. Recent results suggest that AUD patients' slowed decision-making might rather reflect alterations in the neural circuitry underlying the engagement of controlled processing by salient stimuli. We addressed this hypothesis by testing a relationship between decision latency at the Cambridge Gambling Task (CGT) and intrinsic brain activity in 22 individuals with AUD and 19 matched controls. CGT deliberation time was related to two complementary facets of resting-state fMRI activity, i.e. coherence and intensity, representing early biomarkers of functional changes in the intrinsic brain architecture. For both metrics, we assessed a multiple regression (to test a relationship with deliberation time in the whole sample), and an interaction analysis (to test a significantly different relationship with decision latency across groups). AUD patients' slowed deliberation time (p < 0.025) reflected distinct facets of altered intrinsic activity in the cingulate node of the anterior salience network previously associated with the "output" motor stage of response selection. Its heightened activity in AUD patients compared with controls, tracking choice latency (p < 0.025 corrected), might represent a compensation mechanism counterbalancing the concurrent decrease of its internal coherent activity (p < 0.025 corrected). These findings provide novel insights into the intrinsic neural mechanisms underlying increased decision latency in AUD, involving decreased temporal synchronicity in networks promoting executive control by behaviourally relevant stimuli. These results pave the way to further studies assessing more subtle facets of decision-making in AUD, and their possible changes with rehabilitative treatment.
Collapse
|
43
|
Farahibozorg SR, Bijsterbosch JD, Gong W, Jbabdi S, Smith SM, Harrison SJ, Woolrich MW. Hierarchical modelling of functional brain networks in population and individuals from big fMRI data. Neuroimage 2021; 243:118513. [PMID: 34450262 PMCID: PMC8526871 DOI: 10.1016/j.neuroimage.2021.118513] [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: 01/30/2021] [Revised: 06/30/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data. sPROFUMO hierarchically estimates fMRI networks for the population and every individual. We characterised high dimensional resting state fMRI networks from UK Biobank. Model outperforms ICA and dual regression for estimation of individual-specific network topography. We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.
Collapse
Affiliation(s)
- Seyedeh-Rezvan Farahibozorg
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom.
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, United States
| | - Weikang Gong
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; New Zealand Brain Research Institute, University of Otago, Christchurch, New Zealand
| | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; OHBA, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United Kingdom
| |
Collapse
|
44
|
Ahmadi M, Kazemi K, Kuc K, Cybulska-Klosowicz A, Helfroush MS, Aarabi A. Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder. J Neural Eng 2021; 18. [PMID: 34289458 DOI: 10.1088/1741-2552/ac16b3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity (dFC) between 113 children with inattentive (46 ADHDI) and combined (67 ADHDC) ADHD and 76 typically developing (TD) children using resting-state functional MRI data. For dynamic connectivity analysis, the data were first decomposed into 100 independent components, among which 88 were classified into eight well-known resting-state networks (RSNs). Three discrete FC states were then identified using k-means clustering and used to estimate transition probabilities between states in both patient and control groups using a hidden Markov model. Our results showed state-dependent alterations in intra and inter-network connectivity in both ADHD subtypes in comparison with TD. Spending less time than healthy controls in state 1, both ADHDIand ADHDCwere characterized with weaker intra-hemispheric connectivity with functional asymmetries. In this state, ADHDIfurther showed weaker inter-hemispheric connectivity. The patients spent more time in state 2, exhibiting characteristic abnormalities in corticosubcortical and corticocerebellar connectivity. In state 3, a less frequently state observed across the ADHD and TD children, ADHDCwas differentiated from ADHDIby significant alterations in FC between bilateral temporal regions and other brain areas in comparison with TD. Across all three states, several strategic brain regions, mostly bilateral, exhibited significant alterations in both static functional connectivity (sFC) and dFC in the ADHD groups compared to TD, including inferior, middle and superior temporal gyri, middle frontal gyri, insula, anterior cingulum cortex, precuneus, calcarine, fusiform, superior motor area, and cerebellum. Our results show distributed abnormalities in sFC and dFC between different large-scale RSNs including cortical and subcortical regions in both ADHD subtypes compared to TD. Our findings show that the dynamic changes in brain FC can better explain the underlying pathophysiology of ADHD such as deficits in visual cognition, attention, memory and emotion processing, and cognitive and motor control.
Collapse
Affiliation(s)
- Maliheh Ahmadi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Katarzyna Kuc
- SWPS University of Social Sciences and Humanities, Warsaw, Poland
| | - Anita Cybulska-Klosowicz
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (LNFP EA4559), University Research Center (CURS), University Hospital, Amiens, France.,Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
| |
Collapse
|
45
|
Orkan Olcay B, Özgören M, Karaçalı B. On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels. Neural Netw 2021; 143:452-474. [PMID: 34273721 DOI: 10.1016/j.neunet.2021.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/04/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.
Collapse
Affiliation(s)
- B Orkan Olcay
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, Near East University, 99138, Nicosia, Cyprus.
| | - Bilge Karaçalı
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| |
Collapse
|
46
|
Xu L, Bolt T, Nomi JS, Li J, Zheng X, Fu M, Kendrick KM, Becker B, Uddin LQ. Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy. Soc Cogn Affect Neurosci 2021; 15:225-233. [PMID: 32128580 PMCID: PMC7304508 DOI: 10.1093/scan/nsaa025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/20/2020] [Accepted: 02/29/2020] [Indexed: 12/20/2022] Open
Abstract
Recent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS) is an approach for exploratory analysis of task-fMRI data that reveals brain networks dynamically synchronized to task-features across participants. We applied ISPS to task-fMRI data assessing vicarious pain empathy in healthy participants (n = 238). The task employed physical (limb) and affective (face) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may modulate and support the physical pain empathic response. No robust network synchronization was observed for affective pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experiences. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain, but not affective pain, across individuals.
Collapse
Affiliation(s)
- Lei Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Taylor Bolt
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Jialin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Xiaoxiao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Meina Fu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33124, USA
| |
Collapse
|
47
|
Kubera KM, Wolf ND, Rashidi M, Hirjak D, Northoff G, Schmitgen MM, Romanov DV, Sambataro F, Frasch K, Wolf RC. Functional Decoupling of Language and Self-Reference Networks in Patients with Persistent Auditory Verbal Hallucinations. Neuropsychobiology 2021; 79:345-351. [PMID: 32485705 DOI: 10.1159/000507630] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/29/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND Accumulating neuroimaging evidence suggests that abnormal intrinsic neural activity could underlie auditory verbal hallucinations (AVH) in patients with schizophrenia. However, little is known about the functional interplay between distinct intrinsic neural networks and their association with AVH. METHODS We investigated functional network connectivity (FNC) of distinct resting-state networks as well as the relationship between FNC strength and AVH symptom severity. Resting-state functional MRI data at 3 T were obtained for 14 healthy controls and 10 patients with schizophrenia presenting with persistent AVH. The data were analyzed using a spatial group independent component analysis, followed by constrained maximal lag correlations to determine FNC within and between groups. RESULTS Four components of interest, comprising language, attention, executive control networks, as well as the default-mode network (DMN), were selected for subsequent FNC analyses. Patients with persistent AVH showed lower FNC between the language network and the DMN (p < 0.05, corrected for false discovery rate). FNC strength, however, was not significantly related to symptom severity, as measured by the Psychotic Symptom Rating Scale. CONCLUSION These findings suggest that disrupted FNC between a speech-related system and a network subserving self-referential processing is associated with AVH. The data are consistent with a model of disrupted self-attribution of speech generation and perception.
Collapse
Affiliation(s)
- Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Nadine D Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Mahmoud Rashidi
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
| | - Mike M Schmitgen
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Dmitry V Romanov
- Department of Psychiatry and Psychosomatics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Mental Health Research Center, Moscow, Russian Federation
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padua, Padua, Italy.,Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Karel Frasch
- Department of Psychiatry and Psychotherapy, District Hospital Donauwörth, Donauwörth, Germany.,Department of Psychiatry and Psychotherapy II, District Hospital Günzburg, University of Ulm, Günzburg, Germany
| | - Robert Christian Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany,
| |
Collapse
|
48
|
Yang L, Yan Y, Li Y, Hu X, Lu J, Chan P, Yan T, Han Y. Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer's disease: a resting-state fMRI study. Brain Imaging Behav 2021; 14:2187-2201. [PMID: 31478145 DOI: 10.1007/s11682-019-00169-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly individuals. We conducted this study to examine whether alterations in the fractional amplitudes of low-frequency fluctuations (fALFF) in the AD spectrum were frequency-dependent and symptom-relevant. A total of 43 patients with subjective cognitive decline (SCD), 52 with amnestic mild cognitive impairment (aMCI), 44 with Alzheimer's dementia (d-AD) and 55 well-matched controls participated in resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitudes were measured using fALFF within the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands. Repeated-measures analysis of variance was performed on fALFF within two bands and correlated with neuropsychological test scores. The significant main effects of frequency and group on fALFF differed widely across brain regions. There were more varied areas in the slow-5 band than the slow-4 band. The fALFF associated with primary disease effects was mainly distributed in the parietal lobe. Obvious frequency band and group interaction effects were observed in the left angular gyrus, left calcarine fissure and surrounding cortex, left superior cerebellum, left cuneus and right lingual gyrus. Neuropsychological tests scores were significantly correlated with the fALFF magnitude of the left cuneus and right lingual in the slow-5 band. Our results suggested that the AD continuum had abnormal amplitudes in intrinsic brain activity, and these abnormalities were frequency-dependent and mainly associated with the slow-5 band rather than the slow-4 band. This may guide the frequency choice of future rs-fMRI studies and provide new insights into the neuropathophysiology of AD.
Collapse
Affiliation(s)
- Liu Yang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China
| | - Yan Yan
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China
| | - Yuxia Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China
| | - Xiaochen Hu
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Piu Chan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China.,Beijing Institute of Geriatrics, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China. .,Beijing Institute of Geriatrics, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
| |
Collapse
|
49
|
Yang W, Pilozzi A, Huang X. An Overview of ICA/BSS-Based Application to Alzheimer's Brain Signal Processing. Biomedicines 2021; 9:386. [PMID: 33917280 PMCID: PMC8067382 DOI: 10.3390/biomedicines9040386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.
Collapse
Affiliation(s)
- Wenlu Yang
- Department of Electrical Engineering, Information Engineering College, Shanghai Maritime University, Shanghai 200135, China;
| | - Alexander Pilozzi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
| | - Xudong Huang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
| |
Collapse
|
50
|
Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis. Neuroinformatics 2021; 19:619-637. [PMID: 33543442 DOI: 10.1007/s12021-021-09514-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2021] [Indexed: 12/12/2022]
Abstract
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.
Collapse
|