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Wang L, Li T, Gu R, Feng C. Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression. Neuroimage 2024; 299:120824. [PMID: 39214437 DOI: 10.1016/j.neuroimage.2024.120824] [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: 05/28/2024] [Revised: 08/01/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.
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Affiliation(s)
- Li Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education; School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Normal College, Hubei Center for Brain and Mental Health Research, Jingchu University of Technology, Jingmen, China
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Ruolei Gu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education; School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
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2
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Li Q, Lai X, Li T, Madsen KH, Xiao J, Hu K, Feng C, Fu D, Liu X. Brain responses to self- and other- unfairness under resource distribution context: Meta-analysis of fMRI studies. Neuroimage 2024; 297:120707. [PMID: 38942102 DOI: 10.1016/j.neuroimage.2024.120707] [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: 01/15/2024] [Revised: 06/06/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024] Open
Abstract
Under resource distribution context, individuals have a strong aversion to unfair treatment not only toward themselves but also toward others. However, there is no clear consensus regarding the commonality and distinction between these two types of unfairness. Moreover, many neuroimaging studies have investigated how people evaluate and respond to unfairness in the abovementioned two contexts, but the consistency of the results remains to be investigated. To resolve these two issues, we sought to summarize existing findings regarding unfairness to self and others and to further elucidate the neural underpinnings related to distinguishing evaluation and response processes through meta-analyses of previous neuroimaging studies. Our results indicated that both types of unfairness consistently activate the affective and conflict-related anterior insula (AI) and dorsal anterior cingulate cortex/supplementary motor area (dACC/SMA), but the activations related to unfairness to self appeared stronger than those related to others, suggesting that individuals had negative reactions to both unfairness and a greater aversive response toward unfairness to self. During the evaluation process, unfairness to self activated the bilateral AI, dACC, and right dorsolateral prefrontal cortex (DLPFC), regions associated with unfairness aversion, conflict, and cognitive control, indicating reactive, emotional and automatic responses. In contrast, unfairness to others activated areas associated with theory of mind, the inferior parietal lobule and temporoparietal junction (IPL-TPJ), suggesting that making rational judgments from the perspective of others was needed. During the response, unfairness to self activated the affective-related left AI and striatum, whereas unfairness to others activated cognitive control areas, the left DLPFC and the thalamus. This indicated that the former maintained the traits of automaticity and emotionality, whereas the latter necessitated cognitive control. These findings provide a fine-grained description of the common and distinct neurocognitive mechanisms underlying unfairness to self and unfairness to others. Overall, this study not only validates the inequity aversion model but also provides direct evidence of neural mechanisms for neurobiological models of fairness.
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Affiliation(s)
- Qi Li
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, PR China
| | - Xinyu Lai
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, PR China; Sino-Danish Center for Education and Research, Beijing, PR China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, PR China; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, PR China
| | - Kristoffer Hougaard Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Jing Xiao
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, PR China
| | - Kesong Hu
- Department of Psychology, University of Arkansas, Little Rock, AR, USA
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
| | - Di Fu
- School of Psychology, University of Surrey, Surrey, England.
| | - Xun Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Sun S, Yan C, Qu S, Luo G, Liu X, Tian F, Dong Q, Li X, Hu B. Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111076. [PMID: 38972502 DOI: 10.1016/j.pnpbp.2024.111076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/02/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
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Affiliation(s)
- Shuting Sun
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Fuze Tian
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
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Zhu Q, Li S, Meng X, Xu Q, Zhang Z, Shao W, Zhang D. Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2381-2394. [PMID: 38319754 DOI: 10.1109/tmi.2024.3363014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.
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Fazio G, Olivo D, Wolf ND, Hirjak D, Schmitgen MM, Werler F, Witteman M, Kubera KM, Calhoun VD, Reith W, Wolf RC, Sambataro F. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study. Addict Biol 2024; 29:e13395. [PMID: 38709211 PMCID: PMC11072977 DOI: 10.1111/adb.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 02/05/2024] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.
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Affiliation(s)
- Giovanni Fazio
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Daniele Olivo
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Nadine D. Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Mike M. Schmitgen
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Florian Werler
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Miriam Witteman
- Department of Psychiatry and PsychotherapySaarland UniversitySaarbrückenGermany
| | - Katharina M. Kubera
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Wolfgang Reith
- Department of NeuroradiologySaarland UniversitySaarbrückenGermany
| | - Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Fabio Sambataro
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane JM, Malhotra AK. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol Psychiatry 2024; 29:929-938. [PMID: 38177349 PMCID: PMC11176002 DOI: 10.1038/s41380-023-02381-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA.
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John M Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anil K Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
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Bailey NW, Fulcher BD, Caldwell B, Hill AT, Fitzgibbon B, van Dijk H, Fitzgerald PB. Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction. Neural Netw 2024; 171:171-185. [PMID: 38091761 DOI: 10.1016/j.neunet.2023.12.007] [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: 06/24/2023] [Revised: 11/02/2023] [Accepted: 12/04/2023] [Indexed: 01/29/2024]
Abstract
Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.
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Affiliation(s)
- Neil W Bailey
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia.
| | - Ben D Fulcher
- School of Physics, University of Sydney, Camperdown, NSW, Australia
| | - Bridget Caldwell
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Victoria, Australia
| | - Bernadette Fitzgibbon
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia
| | - Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Kingdom of the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, University Maastricht, Maastricht, the Kingdom of the Netherlands
| | - Paul B Fitzgerald
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia
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8
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Li Y, Ran Y, Yao M, Chen Q. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study. Neurobiol Dis 2024; 192:106425. [PMID: 38296113 DOI: 10.1016/j.nbd.2024.106425] [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: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Yun Ran
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Maohua Yao
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
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9
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Huang J. The Commonality and Individuality of Human Brains When Performing Tasks. Brain Sci 2024; 14:125. [PMID: 38391700 PMCID: PMC10887153 DOI: 10.3390/brainsci14020125] [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: 12/12/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
It is imperative to study individual brain functioning toward understanding the neural bases responsible for individual behavioral and clinical traits. The complex and dynamic brain activity varies from area to area and from time to time across the entire brain, and BOLD-fMRI measures this spatiotemporal activity at large-scale systems level. We present a novel method to investigate task-evoked whole brain activity that varies not only from person to person but also from task trial to trial within each task type, offering a means of characterizing the individuality of human brains when performing tasks. For each task trial, the temporal correlation of task-evoked ideal time signal with the time signal of every point in the brain yields a full spatial map that characterizes the whole brain's functional co-activity (FC) relative to the task-evoked ideal response. For any two task trials, regardless of whether they are the same task or not, the spatial correlation of their corresponding two FC maps over the entire brain quantifies the similarity between these two maps, offering a means of investigating the variation in the whole brain activity trial to trial. The results demonstrated a substantially varied whole brain activity from trial to trial for each task category. The degree of this variation was task type-dependent and varied from subject to subject, showing a remarkable individuality of human brains when performing tasks. It demonstrates the potential of using the presented method to investigate the relationship of the whole brain activity with individual behavioral and clinical traits.
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Affiliation(s)
- Jie Huang
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
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10
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Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population. RESEARCH SQUARE 2024:rs.3.rs-3586731. [PMID: 38260417 PMCID: PMC10802706 DOI: 10.21203/rs.3.rs-3586731/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Georgia Institute of Technology, Emory University and Georgia State University
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11
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Zhou DD, Li HZ, Wang W, Kuang L. Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis. Sci Data 2024; 11:38. [PMID: 38182586 PMCID: PMC10770397 DOI: 10.1038/s41597-023-02892-8] [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: 07/29/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024] Open
Abstract
We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.
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Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Hong-Zhi Li
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Li Kuang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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12
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Lee J, Lee J. Discovering individual fingerprints in resting-state functional connectivity using deep neural networks. Hum Brain Mapp 2024; 45:e26561. [PMID: 38096866 PMCID: PMC10789221 DOI: 10.1002/hbm.26561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/13/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024] Open
Abstract
Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.
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Affiliation(s)
- Juhyeon Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
- Interdisciplinary Program in Precision Public HealthKorea UniversitySeoulSouth Korea
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyBostonMassachusettsUSA
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13
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Dimitriadis SI, Routley B, Linden DEJ, Singh KD. Multiplexity of human brain oscillations as a personal brain signature. Hum Brain Mapp 2023; 44:5624-5640. [PMID: 37668332 PMCID: PMC10619372 DOI: 10.1002/hbm.26466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/06/2023] Open
Abstract
Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.
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Affiliation(s)
- Stavros I. Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- Department of Clinical Psychology and PsychobiologyUniversity of BarcelonaBarcelonaSpain
| | - B. Routley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
| | - David E. J. Linden
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- School for Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Krish D. Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
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14
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Guan Y, Ma H, Liu J, Xu L, Zhang Y, Tian L. The abilities of movie-watching functional connectivity in individual identifications and individualized predictions. Brain Imaging Behav 2023; 17:628-638. [PMID: 37553449 DOI: 10.1007/s11682-023-00785-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2023] [Indexed: 08/10/2023]
Abstract
Quite a few studies have been performed based on movie-watching functional connectivity (FC). As compared to its resting-state counterpart, however, there is still much to know about its abilities in individual identifications and individualized predictions. To pave the way for appropriate usage of movie-watching FC, we systemically evaluated the minimum number of time points, as well as the exact functional networks, supporting individual identifications and individualized predictions of apparent traits based on it. We performed the study based on the 7T movie-watching fMRI data included in the HCP S1200 Release, and took IQ as the test case for the prediction analyses. The results indicate that movie-watching FC based on only 15 time points can support successful individual identifications (99.47%), and the connectivity contributed more to identifications were much associated with higher-order cognitive processes (the secondary visual network, the frontoparietal network and the posterior multimodal network). For individualized predictions of IQ, it was found that successful predictions necessitated 60 time points (predicted vs. actual IQ correlation significant at P < 0.05, based on 5,000 permutations), and the prediction accuracy increased logarithmically with the number of time points used for connectivity calculation. Furthermore, the connectivity that contributed more to individual identifications exhibited the strongest prediction ability. Collectively, our findings demonstrate that movie-watching FC can capture rich information about human brain function, and its ability in individualized predictions depends heavily on the length of fMRI scans.
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Affiliation(s)
- Yun Guan
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
- Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China
| | - Hao Ma
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Jiangcong Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Le Xu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Yang Zhang
- Department of Orthopedics, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, 100700, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
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15
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Liu J, Chen L, Chang H, Rudoler J, Al-Zughoul AB, Kang JB, Abrams DA, Menon V. Replicable Patterns of Memory Impairments in Children With Autism and Their Links to Hyperconnected Brain Circuits. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1113-1123. [PMID: 37196984 PMCID: PMC10646152 DOI: 10.1016/j.bpsc.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/07/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Memory impairments have profound implications for social communication and educational outcomes in children with autism spectrum disorder (ASD). However, the precise nature of memory dysfunction in children with ASD and the underlying neural circuit mechanisms remain poorly understood. The default mode network (DMN) is a brain network that is associated with memory and cognitive function, and DMN dysfunction is among the most replicable and robust brain signatures of ASD. METHODS We used a comprehensive battery of standardized episodic memory assessments and functional circuit analyses in 25 8- to 12-year-old children with ASD and 29 matched typically developing control children. RESULTS Memory performance was reduced in children with ASD compared with control children. General and face memory emerged as distinct dimensions of memory difficulties in ASD. Importantly, findings of diminished episodic memory in children with ASD were replicated in 2 independent data sets. Analysis of intrinsic functional circuits associated with the DMN revealed that general and face memory deficits were associated with distinct, hyperconnected circuits: Aberrant hippocampal connectivity predicted diminished general memory while aberrant posterior cingulate cortex connectivity predicted diminished face memory. Notably, aberrant hippocampal-posterior cingulate cortex circuitry was a common feature of diminished general and face memory in ASD. CONCLUSIONS Our results represent a comprehensive appraisal of episodic memory function in children with ASD and identify extensive and replicable patterns of memory reductions in children with ASD that are linked to dysfunction of distinct DMN-related circuits. These findings highlight a role for DMN dysfunction in ASD that extends beyond face memory to general memory function.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
| | - Lang Chen
- Department of Psychology, Santa Clara University, Santa Clara, California
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Jeremy Rudoler
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Ahmad Belal Al-Zughoul
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Julia Boram Kang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Daniel A Abrams
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, California
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, California.
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16
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Chen H, Li M, Qin Z, Yang Z, Lv T, Yao W, Hu Z, Qin R, Zhao H, Bai F. Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease. Eur Radiol Exp 2023; 7:63. [PMID: 37872457 PMCID: PMC10593644 DOI: 10.1186/s41747-023-00376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/17/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer's disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response. METHODS Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline. RESULTS We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54). CONCLUSION Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response. RELEVANCE STATEMENT Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease. KEY POINTS • rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Mengyun Li
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhiming Qin
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Tingyu Lv
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weina Yao
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
- Geriatric Medicine Center, Affiliated Hospital of Medical School, Taikang Xianlin Drum Tower Hospital, Nanjing University, Nanjing, China.
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17
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Zhou DD, Peng XY, Zhao L, Ma LL, Hu JH, Jiang ZH, He XQ, Wang W, Chen R, Kuang L. Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model. J Psychiatr Res 2023; 165:197-204. [PMID: 37517240 DOI: 10.1016/j.jpsychires.2023.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/30/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.
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Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-Yu Peng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Zhao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling-Li Ma
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin-Hui Hu
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Hao Jiang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Qing He
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Ran Chen
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Li Kuang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China; Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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18
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Li Y, Ma X, Sunderraman R, Ji S, Kundu S. Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence. Hum Brain Mapp 2023; 44:4772-4791. [PMID: 37466292 PMCID: PMC10400788 DOI: 10.1002/hbm.26415] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/08/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.
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Affiliation(s)
- Yang Li
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Xin Ma
- Department of BiostatisticsColumbia UniversityNew YorkNew YorkUSA
| | - Raj Sunderraman
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Shihao Ji
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Suprateek Kundu
- Department of BiostatisticsThe University of Texas at MD Anderson Cancer CenterHoustonTexasUSA
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19
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Rubio J, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal D, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Maholtra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. RESEARCH SQUARE 2023:rs.3.rs-3185688. [PMID: 37609149 PMCID: PMC10441472 DOI: 10.21203/rs.3.rs-3185688/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Northwell Health
| | - Todd Lencz
- Zucker School of Medicine at Hofstra/Northwell
| | - Hengyi Cao
- The Feinstein Institute for Medical Research
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20
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Malhotra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.17.23292779. [PMID: 37503088 PMCID: PMC10371185 DOI: 10.1101/2023.07.17.23292779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anil Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
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21
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Yuan Y, Duan Y, Li W, Ren J, Li Z, Yang C. Differences in the Default Mode Network of Temporal Lobe Epilepsy Patients Detected by Hilbert-Huang Transform Based Dynamic Functional Connectivity. Brain Topogr 2023:10.1007/s10548-023-00966-9. [PMID: 37115390 DOI: 10.1007/s10548-023-00966-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/15/2023] [Indexed: 04/29/2023]
Abstract
Resting-state functional connectivity, constructed via functional magnetic resonance imaging, has become an essential tool for exploring brain functions. Aside from the methods focusing on the static state, investigating dynamic functional connectivity can better uncover the fundamental properties of brain networks. Hilbert-Huang transform (HHT) is a novel time-frequency technique that can adapt to both non-linear and non-stationary signals, which may be an effective tool for investigating dynamic functional connectivity. To perform the present study, we investigated time-frequency dynamic functional connectivity among 11 brain regions of the default mode network by first projecting the coherence into the time and frequency domains, and subsequently by identifying clusters in the time-frequency domain using k-means clustering. Experiments on 14 temporal lobe epilepsy (TLE) patients and 21 age and sex-matched healthy controls were performed. The results show that functional connections in the brain regions of the hippocampal formation, parahippocampal gyrus, and retrosplenial cortex (Rsp) were reduced in the TLE group. However, the connections in the brain regions of the posterior inferior parietal lobule, ventral medial prefrontal cortex, and the core subsystem could hardly be detected in TLE patients. The findings not only demonstrate the feasibility of utilizing HHT in dynamic functional connectivity for epilepsy research, but also indicate that TLE may cause damage to memory functions, disorders of processing self-related tasks, and impairment of constructing a mental scene.
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Affiliation(s)
- Ye Yuan
- Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, China
- Department of Bioengineering, Imperial College London, London, UK
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
| | - Wan Li
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Jiechuan Ren
- Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, China.
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22
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Yang D, Li J, Ke Z, Qin R, Mao C, Huang L, Mo Y, Hu Z, Lv W, Huang Y, Zhang B, Xu Y. Subsystem mechanisms of default mode network underlying white matter hyperintensity-related cognitive impairment. Hum Brain Mapp 2023; 44:2365-2379. [PMID: 36722495 PMCID: PMC10028636 DOI: 10.1002/hbm.26215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
Abstract
Functional changes of default mode network (DMN) have been proven to be closely associated with white matter hyperintensity (WMH) related cognitive impairment (CI). However, subsystem mechanisms of DMN underlying WMH-related CI remain unclear. The present study recruited WMH patients (n = 206) with mild CI and normal cognition, as well as healthy controls (HC, n = 102). Static/dynamic functional connectivity (FC) of the DMN's three subsystems were calculated using resting-state functional MRI. K-means clustering analyses were performed to extract distinct dynamic connectivity states. Compared with the WMH-NC group, the WMH-MCI group displayed lower static FC within medial temporal lobe (MTL) and core subsystem, between core-MTL subsystem, as well as between core and dorsal medial prefrontal cortex subsystem. All these static alterations were positively associated with information processing speed (IPS). Regarding dynamic FC, the WMH-MCI group exhibited higher dynamic FC within MTL subsystem than the HC and WMH-NC groups. Altered dynamic FC within MTL subsystem mediated the relationship between WMH and memory span (indirect effect: -0.2251, 95% confidence interval [-0.6295, -0.0267]). Additionally, dynamic FCs of DMN subsystems could be clustered into two recurring states. For dynamic FCs within MTL subsystem, WMH-MCI subjects exhibited longer mean dwell time (MDT) and higher reoccurrence fraction (RF) in a sparsely connected state (State 2). Altered MDT and RF in State 2 were negatively associated with IPS. Taken together, these findings indicated static/dynamic FC of DMN subsystems can provide relevant information on cognitive decline from different aspects, which provides a comprehensive view of subsystem mechanisms of DMN underlying WMH-related CI.
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Affiliation(s)
- Dan Yang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiangnan Li
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Zhihong Ke
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - ChengLu Mao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Lili Huang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Yuting Mo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Weiping Lv
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Yanan Huang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
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23
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Tepper Á, Vásquez Núñez J, Ramirez-Mahaluf JP, Aguirre JM, Barbagelata D, Maldonado E, Díaz Dellarossa C, Nachar R, González-Valderrama A, Undurraga J, Goñi J, Crossley N. Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis. Neuroimage Clin 2023; 38:103391. [PMID: 37003128 PMCID: PMC10102560 DOI: 10.1016/j.nicl.2023.103391] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.
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Affiliation(s)
- Ángeles Tepper
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile
| | | | | | - Juan Manuel Aguirre
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Daniella Barbagelata
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Elisa Maldonado
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Ruben Nachar
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile
| | - Alfonso González-Valderrama
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Joaquín Goñi
- CONNplexity Lab. Purdue University. West Lafayette, Indiana USA
| | - Nicolás Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
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24
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Kurtin DL, Scott G, Hebron H, Skeldon AC, Violante IR. Task-based differences in brain state dynamics and their relation to cognitive ability. Neuroimage 2023; 271:119945. [PMID: 36870433 DOI: 10.1016/j.neuroimage.2023.119945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Transient patterns of interregional connectivity form and dissipate in response to varying cognitive demands. Yet, it is not clear how different cognitive demands influence brain state dynamics, and whether these dynamics relate to general cognitive ability. Here, using functional magnetic resonance imaging (fMRI) data, we characterised shared, recurrent, global brain states in 187 participants across the working memory, emotion, language, and relation tasks from the Human Connectome Project. Brain states were determined using Leading Eigenvector Dynamics Analysis (LEiDA). In addition to the LEiDA-based metrics of brain state lifetimes and probabilities, we also computed information-theoretic measures of Block Decomposition Method of complexity, Lempel-Ziv complexity and transition entropy. Information theoretic metrics are notable in their ability to compute relationships amongst sequences of states over time, compared to lifetime and probability, which capture the behaviour of each state in isolation. We then related task-based brain state metrics to fluid intelligence. We observed that brain states exhibited stable topology across a range of numbers of clusters (K = 2:15). Most metrics of brain state dynamics, including state lifetime, probability, and all information theoretic metrics, reliably differed between tasks. However, relationships between state dynamic metrics and cognitive abilities varied according to the task, the metric, and the value of K, indicating that there are contextual relationships between task-dependant state dynamics and trait cognitive ability. This study provides evidence that the brain reconfigures across time in response to cognitive demands, and that there are contextual, rather than generalisable, relationships amongst task, state dynamics, and cognitive ability.
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Affiliation(s)
- Danielle L Kurtin
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
| | - Gregory Scott
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Henry Hebron
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK; UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK
| | - Anne C Skeldon
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK; Department of Mathematics, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
| | - Ines R Violante
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
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25
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Long Y, Liu X, Liu Z. Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives. Brain Sci 2023; 13:brainsci13030429. [PMID: 36979239 PMCID: PMC10046056 DOI: 10.3390/brainsci13030429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.
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26
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Cutts SA, Faskowitz J, Betzel RF, Sporns O. Uncovering individual differences in fine-scale dynamics of functional connectivity. Cereb Cortex 2023; 33:2375-2394. [PMID: 35690591 DOI: 10.1093/cercor/bhac214] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
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Affiliation(s)
- Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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27
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Li YL, Zheng MX, Hua XY, Gao X, Wu JJ, Shan CL, Zhang JP, Wei D, Xu JG. Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study. Brain Struct Funct 2023; 228:761-773. [PMID: 36749387 DOI: 10.1007/s00429-023-02616-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/25/2023] [Indexed: 02/08/2023]
Abstract
The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The 18F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China.,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Jun-Peng Zhang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China. .,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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28
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Naik A, Kenyon R, Taheri A, BergerWolf T, Ibrahim B, Shinagawa Y, Llano D. V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data. J Neurosci Res 2023; 101:217-231. [PMID: 36309817 PMCID: PMC9742979 DOI: 10.1002/jnr.25139] [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: 04/07/2022] [Revised: 09/07/2022] [Accepted: 10/12/2022] [Indexed: 12/14/2022]
Abstract
Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.
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Affiliation(s)
- A.G. Naik
- Department of Computer Science, University of Illinois at Chicago, USA
| | - R.V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, USA
| | - A. Taheri
- Department of Computer Science, University of Illinois at Chicago, USA
| | - T. BergerWolf
- Department of Computer Science Engineering, Ohio State University, USA
| | - B. Ibrahim
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - Y. Shinagawa
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - D.A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
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Li X, Jia X, Liu Y, Bai G, Pan Y, Ji Q, Mo Z, Zhao W, Wei Y, Wang S, Yin B, Zhang J, Bai L. Brain dynamics in triple-network interactions and its relation to multiple cognitive impairments in mild traumatic brain injury. Cereb Cortex 2023:6969137. [PMID: 36610729 DOI: 10.1093/cercor/bhac529] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 01/09/2023] Open
Abstract
Traumatic brain injury (TBI) disrupt the coordinated activity of triple-network and produce impairments across several cognitive domains. The triple-network model posits a key role of the salience network (SN) that regulates interactions with the central executive network (CEN) and default mode network (DMN). However, the aberrant dynamic interactions among triple-network and associations with neurobehavioral symptoms in mild TBI was still unclear. In present study, we used brain network interaction index (NII) and dynamic functional connectivity to examine the time-varying cross-network interactions among the triple-network in 109 acute patients, 41 chronic patients, and 65 healthy controls. Dynamic cross-network interactions were significantly increased and more variable in mild TBI compared to controls. Crucially, mild TBI exhibited an increased NII as enhanced integrations between the SN and CEN while reduced coupling of the SN with DMN. The increased NII also implied much severer and multiple domains of cognitive impairments at both acute and chronic mild TBI. Abnormities in time-varying engagement of triple-network is a clinically relevant neurobiological signature of psychopathology in mild TBI. The findings provided align with and advance an emerging perspective on the importance of aberrant brain dynamics associated with highly disparate cognitive and behavioral outcomes in trauma.
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Affiliation(s)
- Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaoyan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yuling Liu
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Guanghui Bai
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Yizhen Pan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Qiuyu Ji
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhaoyi Mo
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Wenpu Zhao
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yixin Wei
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Bo Yin
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Jie Zhang
- Department of Radiation Medicine, School of Preventive Medicine, Air Force Medical University, Xi'an 710032, China
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
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A Holistic Analysis of Individual Brain Activity Revealed the Relationship of Brain Areal Activity with the Entire Brain's Activity. Brain Sci 2022; 13:brainsci13010006. [PMID: 36671988 PMCID: PMC9855953 DOI: 10.3390/brainsci13010006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The relationship between brain areal activity and the entire brain's activity is unknown, and understanding this relationship is imperative for understanding the neural mechanisms of human brain function at systems level. The complex activity of human brains varies from area to area and from time to time across the whole brain. BOLD-fMRI measures this spatiotemporal activity at a large-scale systems level. The BOLD time signal of an area reflects a collective neuronal activity of over one million neurons under that area, and the temporal correlation of this time signal with that of every point in the brain yields a full spatial map that characterizes the entire brain's functional co-activity (FC) relative to that area's activity. Here we show a quantitative relationship between brain areal activity and the activity of the entire brain. The temporal correlation coefficient r of the signal time courses of two areas quantifies the degree of co-activity between the two areas, and the spatial correlation coefficient R of their corresponding two FC maps quantifies the co-activity between these two maps. We found that a modified sigmoid function quantified this R with r, i.e., Rr=1+ra-1-ra1+ra+1-ra, revealing a relationship between the activity of brain areas and that of the entire brain. The parameter a in this equation was found to be associated with the mean degree of the temporal co-activity among all brain areas, and its value was brain functional state dependent too. Our study demonstrated a novel approach for analyzing fMRI data to holistically characterize the entire brain's activity quantitatively for any brain functional state in individual humans.
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31
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Zhang J, Yin M, Shu D, Liu D. The establishment of the general microexpression recognition ability and its relevant brain activity. Front Hum Neurosci 2022; 16:894702. [PMID: 36569473 PMCID: PMC9774033 DOI: 10.3389/fnhum.2022.894702] [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: 03/12/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people's true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual's microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual's microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the "higher-order" areas to be more concise and critical than those of different types of microexpression recognition.
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Affiliation(s)
- Jianxin Zhang
- Jiangsu Province Engineering Research Center of Microexpression Intelligent Sensing and Security Prevention and Control, Nanjing, China,School of Education, Jiangnan University, Wuxi, China
| | - Ming Yin
- Jiangsu Province Engineering Research Center of Microexpression Intelligent Sensing and Security Prevention and Control, Nanjing, China,Jiangsu Police Institute, Nanjing, China
| | - Deming Shu
- School of Education, Soochow University, Soochow, China,*Correspondence: Deming Shu,
| | - Dianzhi Liu
- School of Education, Soochow University, Soochow, China,*Correspondence: Deming Shu,
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32
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Liu N, Liu Q, Yang Z, Xu J, Fu G, Zhou Y, Li H, Wang Y, Liu L, Qian Q. Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies. CNS Neurosci Ther 2022; 29:60-69. [PMID: 36468409 PMCID: PMC9804052 DOI: 10.1111/cns.14032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/21/2022] [Accepted: 10/09/2022] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered. METHODS In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed-based whole-brain resting-state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta-analysis was performed to investigate the abnormal connectivity in different age groups (all-age group, younger: <12 years, older: ≥12 years). Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset. RESULTS As with previous studies, scarce results were found in the all-age group. However, we found that the younger group consistently exhibited hyper-connectivity between different parts of the cortex and left middle frontal gyrus, and hypo-connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper-connectivity between the cortex and right precuneus/sub-gyral/cingulate gyrus. Besides, the abnormal cortico-cortical and cortico-subcortical functional connectivity in children, and the abnormal cortico-cortical functional connectivity in adults were verified in our independent dataset. CONCLUSION Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico-cortical and cortico-subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico-cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD.
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Affiliation(s)
- Ningning Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qianrong Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Ziqi Yang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Jie Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen UniversityShenzhenChina,Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Guanghui Fu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yi Zhou
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yufeng Wang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
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33
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Li Y, Liu A, Fu X, Mckeown MJ, Wang ZJ, Chen X. Atlas-guided parcellation: Individualized functionally-homogenous parcellation in cerebral cortex. Comput Biol Med 2022; 150:106078. [PMID: 36155266 DOI: 10.1016/j.compbiomed.2022.106078] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/23/2022] [Accepted: 09/03/2022] [Indexed: 11/03/2022]
Abstract
Resting-state Magnetic resonance imaging-based parcellation aims to group the voxels/vertices non-invasively based on their connectivity profiles, which has achieved great success in understanding the fundamental organizational principles of the human brain. Given the substantial inter-individual variability, the increasing number of studies focus on individual parcellation. However, current methods perform individual parcellations independently or are based on the group prior, requiring expensive computational costs, precise parcel alignment, and extra group information. In this work, an efficient and flexible parcellation framework of individual cerebral cortex was proposed based on a region growing algorithm by merging the unassigned and neighbor vertex with the highest-correlated parcel iteratively. It considered both consistency with prior atlases and individualized functional homogeneity of parcels, which can be applied to a single individual without parcel alignment and group information. The proposed framework was leveraged to 100 unrelated subjects for functional homogeneity comparison and individual identification, and 186 patients with Parkison's disease for symptom prediction. Results demonstrated our framework outperformed other methods in functional homogeneity, and the generated parcellations provided 100% individual identification accuracy. Moreover, the default mode network (DMN) exhibited higher functional homogeneity, intra-subject parcel reproducibility and fingerprinting accuracy, while the sensorimotor network did the opposite, reflecting that the DMN is the most representative, stable, and individual-identifiable network in the resting state. The correlation analysis showed that the severity of the disease symptoms was related negatively to the similarity of individual parcellation and the atlases of healthy populations. The disease severity can be correctly predicted using machine learning models based on individual topographic features such as parcel similarity and parcel size. In summary, the proposed framework not only significantly improves the functional homogeneity but also captures individualized and disease-related brain topography, serving as a potential tool to explore brain function and disease in the future.
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Affiliation(s)
- Yu Li
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China
| | - Aiping Liu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.
| | - Xueyang Fu
- School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China
| | - Martin J Mckeown
- Pacific Parkinson's Research Centre, Vancouver, British Columbia, V6E 2M6, Canada; Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, V6T 2B5, Canada
| | - Z Jane Wang
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
| | - Xun Chen
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China
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34
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Shu Y, Chen L, Li K, Li H, Kong L, Liu X, Li P, Xie W, Zeng Y, Peng D. Abnormal cerebellar-prefrontal cortical pathways in obstructive sleep apnea with/without mild cognitive impairment. Front Neurosci 2022; 16:1002184. [PMID: 36340771 PMCID: PMC9630600 DOI: 10.3389/fnins.2022.1002184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/26/2022] [Indexed: 12/04/2022] Open
Abstract
Obstructive sleep apnea (OSA), a common respiratory sleep disorder, is often associated with mild cognitive impairment (MCI), which is a precursor stage to Alzheimer’s disease (AD). However, the neuroimaging changes in patients with OSA with/without MCI are still under discussion. This study aimed to investigate the temporal variability of spontaneous brain activity in OSA. Fifty-two OSA patients (26 with OSA with MCI (OSA-MCI), 26 OSA without MCI (OSA-nMCI), and 26 healthy controls (HCs) underwent MRI scans and scale questionnaires. A dynamic amplitude of low-frequency fluctuation (dALFF) evaluation was performed to examine the time-varying nature of OSA-MCI and OSA-nMCI. Compared with OSA-MCI, OSA-nMCI had increased dALFF in the posterior cerebellar and right superior frontal gyrus; compared with HCs, OSA-nMCI patients showed increased dALFF in the right posterior cerebellum. A positive correlation between the bilateral posterior cerebellar lobes and right superior frontal gyrus was observed in OSA-MCI patients; however, in OSA-nMCI patients, a positive correlation was observed only between the bilateral posterior cerebellar lobes. The dALFF value of the left posterior cerebellar lobe was positively correlated with the apnea-hypopnea index (AHI), epworth sleepiness scale (ESS) score, and arousal index in OSA-nMCIs, while the dALFF value of the right posterior cerebellum was positively correlated with the AHI and negatively correlated with the lowest oxygen saturation (SaO2). This study argues that OSA-nMCIs and OSA-MCIs exhibit different temporal variabilities in dynamic brain functions, OSA-nMCIs may have variable intermediate states. We concluded that the functional abnormalities of the cerebellar-prefrontal cortex pathway in OSA-MCIs may cause cognitive impairment with OSA.
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Affiliation(s)
- Yongqiang Shu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Liting Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Kunyao Li
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Haijun Li
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Linghong Kong
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiang Liu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Panmei Li
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Wei Xie
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yaping Zeng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Dechang Peng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- *Correspondence: Dechang Peng,
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35
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Zhang J, Zhuang L, Jiang J, Yang M, Li S, Tang X, Ma Y, Liu L, Ding G. Brain fingerprints along the language hierarchy. Front Hum Neurosci 2022; 16:982905. [PMID: 36188171 PMCID: PMC9521489 DOI: 10.3389/fnhum.2022.982905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Recent studies have shown that the brain functional connectome constitutes a unique fingerprint that allows the identification of individuals from a group. However, what information encoded in the brain that makes us unique remains elusive. Here, we addressed this issue by examining how individual identifiability changed along the language hierarchy. Subjects underwent fMRI scanning during rest and when listening to short stories played backward, scrambled at the sentence level, and played forward. Identification for individuals was performed between two scan sessions for each task as well as between the rest and task sessions. We found that individual identifiability tends to increase along the language hierarchy: the more complex the task is, the better subjects can be distinguished from each other based on their whole-brain functional connectivity profiles. A similar principle is found at the functional network level: compared to the low-order network (the auditory network), the high-order network is more individualized (the frontoparietal network). Moreover, in both cases, the increase in individual identifiability is accompanied by the increase in inter-subject variability of functional connectivities. These findings advance the understanding of the source of brain individualization and have potential implications for developing robust connectivity-based biomarkers.
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Affiliation(s)
- Juan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liping Zhuang
- Department of Psychology, School of Arts and Sciences, Beijing Normal University, Zhuhai, China
| | - Jiahao Jiang
- School of Psychology, Beijing Normal University, Beijing, China
| | - Menghan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Shijie Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiangrong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yingbo Ma
- School of Psychology, Beijing Normal University, Beijing, China
| | - Lanfang Liu
- Department of Psychology, School of Arts and Sciences, Beijing Normal University, Zhuhai, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Center for Cognition and Neuroergonomics, Beijing Normal University, Zhuhai, China
- Lanfang Liu,
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- *Correspondence: Guosheng Ding,
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36
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Gao M, Lam CLM, Lui WM, Lau KK, Lee TMC. Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease. Brain Commun 2022; 4:fcac213. [PMID: 36072648 PMCID: PMC9438963 DOI: 10.1093/braincomms/fcac213] [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: 03/18/2022] [Revised: 06/09/2022] [Accepted: 08/19/2022] [Indexed: 11/26/2022] Open
Abstract
Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23–49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24–54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: ρ = 0.63, Pcorr = 0.017) and 6 months after surgery (positive network: ρ = 0.62, Pcorr = 0.010; negative network: ρ = 0.55, Pcorr = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study’s findings offer important insight into the clinical management of patients with moyamoya disease.
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Affiliation(s)
- Mengxia Gao
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong , Hong Kong 999077 , China
- Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong , Hong Kong 999077 , China
| | - Charlene L M Lam
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong , Hong Kong 999077 , China
- Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong , Hong Kong 999077 , China
| | - Wai M Lui
- Division of Neurosurgery, Queen Mary Hospital , Hong Kong 999077 , China
| | - Kui Kai Lau
- Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong , Hong Kong 999077 , China
- Division of Neurology, Department of Medicine, The University of Hong Kong , Hong Kong 999077 , China
| | - Tatia M C Lee
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong , Hong Kong 999077 , China
- Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong , Hong Kong 999077 , China
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37
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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Litwińczuk MC, Trujillo-Barreto N, Muhlert N, Cloutman L, Woollams A. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. Neuroimage 2022; 262:119531. [PMID: 35931312 DOI: 10.1016/j.neuroimage.2022.119531] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022] Open
Abstract
The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).
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Affiliation(s)
| | | | - Nils Muhlert
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Lauren Cloutman
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Anna Woollams
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
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Jiang R, Woo CW, Qi S, Wu J, Sui J. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE SIGNAL PROCESSING MAGAZINE 2022; 39:107-118. [PMID: 36712588 PMCID: PMC9880880 DOI: 10.1109/msp.2022.3155951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA, 06520
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 211106
| | - Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, China, 100069
| | - Jing Sui
- State Key Laboratory of Brain Cognition and Learning, Beijing Normal University, Beijing, China, 100875
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40
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Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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41
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White matter substrates of functional connectivity dynamics in the human brain. Neuroimage 2022; 258:119391. [PMID: 35716842 DOI: 10.1016/j.neuroimage.2022.119391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.
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42
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Sun J, Zhao R, He Z, Chang M, Wang F, Wei W, Zhang X, Zhu Y, Xi Y, Yang X, Qin W. Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective. Hum Brain Mapp 2022; 43:3824-3839. [PMID: 35524680 PMCID: PMC9294309 DOI: 10.1002/hbm.25886] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 12/25/2022] Open
Abstract
Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.
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Affiliation(s)
- Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Zhaoyang He
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Mengying Chang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Fumin Wang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Wei Wei
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Xiaodan Zhang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
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43
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Wang YM, Cai XL, Zhang RT, Zhang YJ, Zhou HY, Wang Y, Wang Y, Huang J, Wang YY, Cheung EFC, Chan RCK. Altered brain structural and functional connectivity in schizotypy. Psychol Med 2022; 52:834-843. [PMID: 32677599 DOI: 10.1017/s0033291720002445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of 'brain disconnection' and 'brain connectivity compensation' to 'brain connectivity decompensation'. METHODS In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated. RESULTS We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network. CONCLUSIONS These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.
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Affiliation(s)
- Yong-Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Xin-Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Rui-Ting Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi-Jing Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Han-Yu Zhou
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Ya Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Jia Huang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yan-Yu Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, Weifang Medical University, Shandong Province, PR China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region, PR China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
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44
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Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome. Commun Biol 2022; 5:261. [PMID: 35332230 PMCID: PMC8948277 DOI: 10.1038/s42003-022-03185-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 02/08/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints. Mantwill et al. analysed the relationship between functional connectivity markers that are specific to individuals (i.e. brain fingerprints) and those predictive of behaviour. They reveal that these markers are unrelated and suggest an alternative perspective on the basis of individual signatures.
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45
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Zhang R, Tam SKTS, Wong NML, Wu J, Tao J, Chen L, Lin K, Lee TMC. Aberrant functional metastability and structural connectivity are associated with rumination in individuals with major depressive disorder. Neuroimage Clin 2022; 33:102916. [PMID: 34923200 PMCID: PMC8693354 DOI: 10.1016/j.nicl.2021.102916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 12/13/2021] [Indexed: 11/23/2022]
Abstract
Higher synchrony and lower metastability in adults with higher levels of rumination. Prefrontal white matter integrity deficits are also associated with rumination. Most prominent aberrations were found in the genu of the corpus callosum. Structural connectivity as the basis between dynamic connectivity and rumination. New outlook on altered structural integrity and metastability subserving rumination.
Rumination is a repetitive and compulsive thinking focusing on oneself, and the nature and consequences of distress. It is a core characteristic in psychiatric disorders characterized by affective dysregulation, and emerging evidence suggests that rumination is associated with aberrant dynamic functional connectivity and structural connectivity. However, the underlying neural mechanisms remain poorly understood. Here, we adopted a multimodal approach and tested the hypothesis that white matter connectivity forms the basis of the implications of temporal dynamics of functional connectivity in the rumination trait. Fifty-three depressed and ruminative individuals and a control group of 47 age- and gender-matched individuals with low levels of rumination underwent resting-state fMRI and diffusion tensor imaging. We found that lower global metastability and higher global synchrony of the dynamic functional connectivity were associated with higher levels of rumination. Specifically, the altered global synchrony and global metastability mediated the association between white matter integrity of the genu of the corpus callosum to rumination. Hence, our findings offered the first line of evidence for the intricate role of (sub)optimal transition of functional brain states in the connection of structural brain connectivity in ruminative thinking.
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Affiliation(s)
- Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Sammi-Kenzie T S Tam
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China
| | - Nichol M L Wong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China.
| | - Jingsong Wu
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jing Tao
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Lidian Chen
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China.
| | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China; Institute of Clinical Neuropsychology, The University of Hong Kong, Hong Kong, China.
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46
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Chiêm B, Abbas K, Amico E, Duong-Tran DA, Crevecoeur F, Goñi J. Improving Functional Connectome Fingerprinting with Degree-Normalization. Brain Connect 2022; 12:180-192. [PMID: 34015966 PMCID: PMC8978572 DOI: 10.1089/brain.2020.0968] [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] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Duy Anh Duong-Tran
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
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47
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Individualized event structure drives individual differences in whole-brain functional connectivity. Neuroimage 2022; 252:118993. [DOI: 10.1016/j.neuroimage.2022.118993] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/25/2021] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
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48
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Cai B, Zhou Z, Zhang A, Zhang G, Xiao L, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences. J Neurosci Methods 2022; 372:109539. [PMID: 35219769 DOI: 10.1016/j.jneumeth.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
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49
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Wei Y, Zhang W, Li Y, Liu X, Zha B, Hu S, Wang Y, Wang X, Yu X, Yang J, Qiu B. Acupuncture Treatment Decreased Temporal Variability of Dynamic Functional Connectivity in Chronic Tinnitus. Front Neurosci 2022; 15:737993. [PMID: 35153654 PMCID: PMC8835346 DOI: 10.3389/fnins.2021.737993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Acupuncture is recommended for the relief of chronic tinnitus in traditional Chinese medicine, but the underlying neural mechanism remains unclear. The human brain is a dynamic system, and it’s unclear about acupuncture’s effects on the dynamic functional connectivity (DFC) of chronic tinnitus. Therefore, this study based on resting-state functional magnetic resonance imaging (fMRI) investigates abnormal DFC in chronic tinnitus patients and the neural activity change evoked by acupuncture treatment for tinnitus. In this study, 17 chronic tinnitus patients and 22 age- and sex-matched normal subjects were recruited, and their tinnitus-related scales and hearing levels were collected. The fMRI data were measured before and after acupuncture, and then sliding-window and k-means clustering methods were used to calculate DFC and perform clustering analysis, respectively. We found that, compared with the normal subjects, chronic tinnitus patients had higher temporal variability of DFC between the supplementary motor area and medial part of the superior frontal gyrus, and it positively correlated with hearing loss. Clustering analysis showed higher transition probability (TP) between connection states in chronic tinnitus patients, and it was positively correlated with tinnitus severity. Furthermore, the findings showed that acupuncture treatment might improve tinnitus. DFC between the posterior cingulate gyrus and angular gyrus in chronic tinnitus patients after acupuncture showed significantly decreased, and it positively correlated with the improvement of tinnitus. Clustering analysis showed that acupuncture treatment might promote chronic tinnitus patients under lower DFC state, and it also positively correlated with the improvement of tinnitus. This study suggests that acupuncture as an alternative therapy method might decrease the tinnitus severity by decreasing the time variability of DFC in chronic tinnitus patients.
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Affiliation(s)
- Yarui Wei
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wanlin Zhang
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Yu Li
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiangwei Liu
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Bixiang Zha
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Sheng Hu
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- School of Medical Information Engineering, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Yanming Wang
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaoxiao Wang
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaochun Yu
- Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China
- Xiaochun Yu,
| | - Jun Yang
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
- Jun Yang,
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- *Correspondence: Bensheng Qiu,
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50
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Hassanzadeh R, Silva RF, Abrol A, Salman M, Bonkhoff A, Du Y, Fu Z, DeRamus T, Damaraju E, Baker B, Calhoun VD. Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. PLoS One 2022; 17:e0249502. [PMID: 35061657 PMCID: PMC8782493 DOI: 10.1371/journal.pone.0249502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 12/07/2021] [Indexed: 11/20/2022] Open
Abstract
Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.
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Affiliation(s)
- Reihaneh Hassanzadeh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States of America
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Rogers F. Silva
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Mustafa Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Anna Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Thomas DeRamus
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
| | - Bradley Baker
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Vince D. Calhoun
- Department of Computer Science, Georgia State University, Atlanta, GA, United States of America
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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