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Del Mauro G, Li Y, Wang Z. Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables. J Neurosci Methods 2024; 409:110205. [PMID: 38914376 DOI: 10.1016/j.jneumeth.2024.110205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
BACKGROUND Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels. NEW METHOD We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC). RESULTS Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions. COMPARISON WITH EXISTING METHOD Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately. CONCLUSIONS Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Yiran Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States.
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2
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Wang Y, Li J, Jin S, Wang J, Lv Y, Zou Q, Wang J. Mapping morphological cortical networks with joint probability distributions from multiple morphological features. Neuroimage 2024; 296:120673. [PMID: 38851550 DOI: 10.1016/j.neuroimage.2024.120673] [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: 01/09/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/10/2024] Open
Abstract
Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.
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Affiliation(s)
- Yuqi Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jing Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 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.
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3
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Li J, Jin S, Li Z, Zeng X, Yang Y, Luo Z, Xu X, Cui Z, Liu Y, Wang J. Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400061. [PMID: 39005232 DOI: 10.1002/advs.202400061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.
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Affiliation(s)
- Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Zhen Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Xiangli Zeng
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Zhenzhen Luo
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Beijing, 100070, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
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4
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van der Wijk G, Zamyadi M, Bray S, Hassel S, Arnott SR, Frey BN, Kennedy SH, Davis AD, Hall GB, Lam RW, Milev R, Müller DJ, Parikh S, Soares C, Macqueen GM, Strother SC, Protzner AB. Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy. eNeuro 2024; 11:ENEURO.0286-23.2024. [PMID: 38830756 PMCID: PMC11163402 DOI: 10.1523/eneuro.0286-23.2024] [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: 07/31/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/05/2024] Open
Abstract
Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.
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Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Mojdeh Zamyadi
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
| | - Signe Bray
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
| | - Stephen R Arnott
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8S 4L8, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario L8N 4A6, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario M5G 2C4, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Krembil Research Institute, Toronto Western Hospital, Toronto, Ontario M5T 2S8, Canada
| | - Andrew D Davis
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
- Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 4A6, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 2A1, Canada
| | - Roumen Milev
- Department of Psychiatry and Psychology, and Providence Care Hospital, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Sagar Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan 48109
| | - Claudio Soares
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario K7L 3N6, Canada
| | - Glenda M Macqueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
| | - Stephen C Strother
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Andrea B Protzner
- Department of Psychology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
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5
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Hakonen M, Dahmani L, Lankinen K, Ren J, Barbaro J, Blazejewska A, Cui W, Kotlarz P, Li M, Polimeni JR, Turpin T, Uluç I, Wang D, Liu H, Ahveninen J. Individual connectivity-based parcellations reflect functional properties of human auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576475. [PMID: 38293021 PMCID: PMC10827228 DOI: 10.1101/2024.01.20.576475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.
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Affiliation(s)
- M Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - L Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - K Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - J Ren
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J Barbaro
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - A Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - W Cui
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - P Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - M Li
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - T Turpin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - I Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - D Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - H Liu
- Division of Brain Sciences, Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - J Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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6
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Han L, Chan MY, Agres PF, Winter-Nelson E, Zhang Z, Wig GS. Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cereb Cortex 2024; 34:bhad506. [PMID: 38385891 PMCID: PMC10883417 DOI: 10.1093/cercor/bhad506] [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: 12/05/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
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7
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Hu L, Katz ES, Stamoulis C. Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference. Neuroimage 2023; 284:120459. [PMID: 37977408 DOI: 10.1016/j.neuroimage.2023.120459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.
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Affiliation(s)
- Linfeng Hu
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard School of Public Health, Department of Biostatistics, Boston, MA 02115, USA
| | - Eliot S Katz
- Johns Hopkins All Children's Hospital, St. Petersburg, FL 33701, USA
| | - Catherine Stamoulis
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA.
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8
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [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: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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9
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Lew BJ, McCusker MC, O'Neill J, Bares SH, Wilson TW, Doucet GE. Resting state network connectivity alterations in HIV: Parallels with aging. Hum Brain Mapp 2023; 44:4679-4691. [PMID: 37417797 PMCID: PMC10400792 DOI: 10.1002/hbm.26409] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 05/10/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
The increasing incidence of age-related comorbidities in people with HIV (PWH) has led to accelerated aging theories. Functional neuroimaging research, including functional connectivity (FC) using resting-state functional magnetic resonance imaging (rs-fMRI), has identified neural aberrations related to HIV infection. Yet little is known about the relationship between aging and resting-state FC in PWH. This study included 86 virally suppressed PWH and 99 demographically matched controls spanning 22-72 years old who underwent rs-fMRI. The independent and interactive effects of HIV and aging on FC were investigated both within- and between-network using a 7-network atlas. The relationship between HIV-related cognitive deficits and FC was also examined. We also conducted network-based statistical analyses using a brain anatomical atlas (n = 512 regions) to ensure similar results across independent approaches. We found independent effects of age and HIV in between-network FC. The age-related increases in FC were widespread, while PWH displayed further increases above and beyond aging, particularly between-network FC of the default-mode and executive control networks. The results were overall similar using the regional approach. Since both HIV infection and aging are associated with independent increases in between-network FC, HIV infection may be associated with a reorganization of the major brain networks and their functional interactions in a manner similar to aging.
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Affiliation(s)
- Brandon J. Lew
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
| | - Marie C. McCusker
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- Interdepartmental Neuroscience ProgramYale University School of MedicineNew HavenConnecticutUSA
| | - Jennifer O'Neill
- Department of Internal Medicine, Division of Infectious DiseasesUNMCOmahaNebraskaUSA
| | - Sara H. Bares
- Department of Internal Medicine, Division of Infectious DiseasesUNMCOmahaNebraskaUSA
| | - Tony W. Wilson
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
- Department of Pharmacology & NeuroscienceCreighton UniversityOmahaNebraskaUSA
| | - Gaelle E. Doucet
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- Department of Pharmacology & NeuroscienceCreighton UniversityOmahaNebraskaUSA
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10
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Haines N, Sullivan-Toole H, Olino T. From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:822-831. [PMID: 36997406 PMCID: PMC10333448 DOI: 10.1016/j.bpsc.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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Affiliation(s)
- Nathaniel Haines
- Department of Data Science, Bayesian Beginnings LLC, Columbus, Ohio.
| | | | - Thomas Olino
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
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11
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Pommy J, Smart CM, Bryant AM, Wang Y. Three potential neurovascular pathways driving the benefits of mindfulness meditation for older adults. Front Aging Neurosci 2023; 15:1207012. [PMID: 37455940 PMCID: PMC10340530 DOI: 10.3389/fnagi.2023.1207012] [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: 04/16/2023] [Accepted: 06/06/2023] [Indexed: 07/18/2023] Open
Abstract
Mindfulness meditation has been shown to be beneficial for a range of different health conditions, impacts brain function and structure relatively quickly, and has shown promise with aging samples. Functional magnetic resonance imaging metrics provide insight into neurovascular health which plays a key role in both normal and pathological aging processes. Experimental mindfulness meditation studies that included functional magnetic resonance metrics as an outcome measure may point to potential neurovascular mechanisms of action relevant for aging adults that have not yet been previously examined. We first review the resting-state magnetic resonance studies conducted in exclusively older adult age samples. Findings from older adult-only samples are then used to frame the findings of task magnetic resonance imaging studies conducted in both clinical and healthy adult samples. Based on the resting-state studies in older adults and the task magnetic resonance studies in adult samples, we propose three potential mechanisms by which mindfulness meditation may offer a neurovascular therapeutic benefit for older adults: (1) a direct neurovascular mechanism via increased resting-state cerebral blood flow; (2) an indirect anti-neuroinflammatory mechanism via increased functional connectivity within the default mode network, and (3) a top-down control mechanism that likely reflects both a direct and an indirect neurovascular pathway.
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Affiliation(s)
- Jessica Pommy
- Department of Neurology, Division of Neuropsychology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Colette M. Smart
- Department of Psychology, University of Victoria, Victoria, BC, Canada
| | - Andrew M. Bryant
- Department of Neurology, The Ohio State University, Columbus, OH, United States
| | - Yang Wang
- Department of Neurology, Division of Neuropsychology, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
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12
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Scale-Free Dynamics in Instantaneous Alpha Frequency Fluctuations: Validation, Test-Retest Reliability and Its Relationship with Task Manipulations. Brain Topogr 2023; 36:230-242. [PMID: 36611116 DOI: 10.1007/s10548-022-00936-7] [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: 03/07/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023]
Abstract
Previous studies showed that scale-free structures and long-range temporal correlations are ubiquitous in physiological signals (e.g., electroencephalography). This is supposed to be associated with optimized information processing in human brain. The instantaneous alpha frequency (IAF) (i.e., the instantaneous frequency of alpha band of human EEG signals) may dictate the resolution at which information is sampled and/or processed by cortical neurons. To the best of our knowledge, no research has examined the scale-free dynamics and potential functional significance of IAF. Here, through three studies (Study 1: 25 participants; Study 2: 82 participants; Study 3: 26 participants), we investigated the possibility that time series of IAF exhibit scale-free property through maximum likelihood based detrended fluctuation analysis (ML-DFA). This technique could provide the scaling exponent (i.e., DFA exponent) on the basis of presence of scale-freeness being validated. Then the test-retest reliability (Study 1) and potential influencing factors (Study 2 and Study 3) of DFA exponent of IAF fluctuations were investigated. Firstly, the scale-free property was found to be inherent in IAF fluctuations with fairly high test-retest reliability over the parietal-occipital region. Moreover, the task manipulations could potentially modulate the DFA exponent of IAF fluctuations. Specifically, in Study 2, we found that the DFA exponent of IAF fluctuations in eye-closed resting-state condition was significantly larger than that in eye-open resting-state condition. In Study 3, we found that the DFA exponent of IAF fluctuations in eye-open resting-state condition was significantly larger than that in visual n-back tasks. The DFA exponent of IAF fluctuations in the 0-back task was significantly larger than in the 2-back and 3-back tasks. The results in studies 2 and 3 indicated that: (1) a smaller DFA exponent of IAF fluctuations should signify more efficient online visual information processing; (2) the scaling property of IAF fluctuations could reflect the physiological arousal level of participants.
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13
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Ge J, Yang G, Han M, Zhou S, Men W, Qin L, Lyu B, Li H, Wang H, Rao H, Cui Z, Liu H, Zuo XN, Gao JH. Increasing diversity in connectomics with the Chinese Human Connectome Project. Nat Neurosci 2023; 26:163-172. [PMID: 36536245 DOI: 10.1038/s41593-022-01215-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 10/25/2022] [Indexed: 12/24/2022]
Abstract
Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain-mind studies.
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Affiliation(s)
- Jianqiao Ge
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Meizhen Han
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sizhong Zhou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | | | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Haobo Wang
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | | | - Xi-Nian Zuo
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- McGovern Institute for Brain Research, Peking University, Beijing, China.
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
- Changping Laboratory, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
- National Biomedical Imaging Center, Peking University, Beijing, China.
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14
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Hu Y, Li Q, Qiao K, Zhang X, Chen B, Yang Z. PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments. Hum Brain Mapp 2022; 44:2062-2084. [PMID: 36583399 PMCID: PMC9980895 DOI: 10.1002/hbm.26194] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 11/20/2022] [Accepted: 12/11/2022] [Indexed: 12/31/2022] Open
Abstract
Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.
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Affiliation(s)
- Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Qingfeng Li
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Kaini Qiao
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Xiaochen Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Bing Chen
- Jing Hengyi School of EducationHangzhou Normal UniversityZhejiangChina
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina,Institute of Psychological and Behavioral SciencesShanghai Jiao Tong UniversityShanghaiChina,Brain Science and Technology Research CenterShanghai Jiao Tong UniversityShanghaiChina,Beijing University of Posts and TelecommunicationsBeijingChina
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15
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Wen X, Yang M, Hsu L, Zhang D. Test-retest reliability of modular-relevant analysis in brain functional network. Front Neurosci 2022; 16:1000863. [PMID: 36570835 PMCID: PMC9770801 DOI: 10.3389/fnins.2022.1000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials. Methods To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection. Results The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results. Discussion This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Liming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
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16
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Lee JK, Cho ACB, Andrews DS, Ozonoff S, Rogers SJ, Amaral DG, Solomon M, Nordahl CW. Default mode and fronto-parietal network associations with IQ development across childhood in autism. J Neurodev Disord 2022; 14:51. [PMID: 36109700 PMCID: PMC9479280 DOI: 10.1186/s11689-022-09460-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Intellectual disability affects approximately one third of individuals with autism spectrum disorder (autism). Yet, a major unresolved neurobiological question is what differentiates autistic individuals with and without intellectual disability. Intelligence quotients (IQs) are highly variable during childhood. We previously identified three subgroups of autistic children with different trajectories of intellectual development from early (2–3½ years) to middle childhood (9–12 years): (a) persistently high: individuals whose IQs remained in the normal range; (b) persistently low: individuals whose IQs remained in the range of intellectual disability (IQ < 70); and (c) changers: individuals whose IQs began in the range of intellectual disability but increased to the normal IQ range. The frontoparietal (FPN) and default mode (DMN) networks have established links to intellectual functioning. Here, we tested whether brain regions within the FPN and DMN differed volumetrically between these IQ trajectory groups in early childhood. Methods We conducted multivariate distance matrix regression to examine the brain regions within the FPN (11 regions x 2 hemispheres) and the DMN (12 regions x 2 hemispheres) in 48 persistently high (18 female), 108 persistently low (32 female), and 109 changers (39 female) using structural MRI acquired at baseline. FPN and DMN regions were defined using networks identified in Smith et al. (Proc Natl Acad Sci U S A 106:13040–5, 2009). IQ trajectory groups were defined by IQ measurements from up to three time points spanning early to middle childhood (mean age time 1: 3.2 years; time 2: 5.4 years; time 3: 11.3 years). Results The changers group exhibited volumetric differences in the DMN compared to both the persistently low and persistently high groups at time 1. However, the persistently high group did not differ from the persistently low group, suggesting that DMN structure may be an early predictor for change in IQ trajectory. In contrast, the persistently high group exhibited differences in the FPN compared to both the persistently low and changers groups, suggesting differences related more to concurrent IQ and the absence of intellectual disability. Conclusions Within autism, volumetric differences of brain regions within the DMN in early childhood may differentiate individuals with persistently low IQ from those with low IQ that improves through childhood. Structural differences in brain networks between these three IQ-based subgroups highlight distinct neural underpinnings of these autism sub-phenotypes. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-022-09460-y.
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17
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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: 16] [Impact Index Per Article: 8.0] [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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18
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Hlinka J, Děchtěrenko F, Rydlo J, Androvičová R, Vejmelka M, Jajcay L, Tintěra J, Lukavský J, Horáček J. The intra-session reliability of functional connectivity during naturalistic viewing conditions. Psychophysiology 2022; 59:e14075. [PMID: 35460523 DOI: 10.1111/psyp.14075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/11/2022] [Indexed: 11/30/2022]
Abstract
Functional connectivity analysis is a common approach to the characterization of brain function. While studies of functional connectivity have predominantly focused on resting-state fMRI, naturalistic paradigms, such as movie watching, are increasingly being used. This ecologically valid, yet relatively unconstrained acquisition state has been shown to improve subject compliance and, potentially, enhance individual differences. However, unlike the reliability of resting-state functional connectivity, the reliability of functional connectivity during naturalistic viewing has not yet been fully established. The current study investigates the intra-session reliability of functional connectivity during naturalistic viewing sessions to extend its understanding. Using fMRI data of 24 subjects measured at rest as well as during six naturalistic viewing conditions, we quantified the split-half reliability of each condition, as well as cross-condition reliabilities. We find that intra-session reliability is relatively high for all conditions. While cross-condition reliabilities are higher for pairings of two naturalistic viewing conditions, split-half reliability is highest for the resting state. Potential sources of variability across the conditions, as well as the strengths and limitations of using intra-session reliability as a measure in naturalistic viewing, are discussed.
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Affiliation(s)
- Jaroslav Hlinka
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
| | - Filip Děchtěrenko
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.,Institute of Psychology, Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Rydlo
- National Institute of Mental Health, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | | | - Martin Vejmelka
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
| | - Lucia Jajcay
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jiří Lukavský
- Institute of Psychology, Czech Academy of Sciences, Prague, Czech Republic
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Medicine, Charles University, Prague, Czech Republic
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19
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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20
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Yang L, Wei J, Li Y, Wang B, Guo H, Yang Y, Xiang J. Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI. Brain Sci 2021; 12:brainsci12010066. [PMID: 35053813 PMCID: PMC8773904 DOI: 10.3390/brainsci12010066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 11/16/2022] Open
Abstract
In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.
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Affiliation(s)
| | | | | | | | | | | | - Jie Xiang
- Correspondence: ; Tel.: +86-186-0351-1178
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21
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Xing XX. Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI. Front Aging Neurosci 2021; 13:803436. [PMID: 35027890 PMCID: PMC8748263 DOI: 10.3389/fnagi.2021.803436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02-0.03 Hz, slow-4: 0.03-0.08 Hz, and slow-3 band: 0.08-0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
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22
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Zhao F, Zhang X, Thung KH, Mao N, Lee SW, Shen D. Constructing Multi-view High-order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder. IEEE Trans Biomed Eng 2021; 69:1237-1250. [PMID: 34705632 DOI: 10.1109/tbme.2021.3122813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain functional connectivity network (FCN) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders such as autism spectrum disorder (ASD). Most existing FCN-based methods only estimate the correlation between brain regions of in terest (ROIs), without exploring more informative higher-level inter actions among multiple ROIs which could be beneficial to disease diagnosis. To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs. In addition, to address the phase mismatch issue in conventional FCNs, we also adopt the central moments of the correlation time series as the temporal-invariance features to capture the dynamic characteristics of low-order dynamic FCN (Lo-D-FCN). Experimental results on the ABIDE dataset validate that: 1) the proposed multi-view Ho-FCNs is able to explore rich discriminative information for ASD diagnosis; 2) the phase mismatch issue can be well circumvented by using central moments; and 3) the combination of different types of FCNs can significantly improve the diagnostic accuracy of ASD (86.2%).
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23
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Su Q, Zhao R, Wang S, Tu H, Guo X, Yang F. Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study. Front Neurol 2021; 12:711880. [PMID: 34690912 PMCID: PMC8531403 DOI: 10.3389/fneur.2021.711880] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/19/2021] [Indexed: 11/21/2022] Open
Abstract
Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.
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Affiliation(s)
- Qian Su
- Tianjin Key Laboratory of Cancer Prevention and Therapy, Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Rui Zhao
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - ShuoWen Wang
- School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - HaoYang Tu
- School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China
| | - Xing Guo
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fan Yang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
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24
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Hsu LM, Lane TJ, Wu CW, Lin CY, Yeh CB, Kao HW, Lin CP. Spontaneous thought-related network connectivity predicts sertraline effect on major depressive disorder. Brain Imaging Behav 2021; 15:1705-1717. [PMID: 32710339 DOI: 10.1007/s11682-020-00364-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect-a "malignant sadness". Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naïve MDD patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naïve MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DNMTL) and mood regulation pathway-the fronto-parietal control network (FPCN), the thalamus, and the salience network (SN)-were restored to normal connectivity, relative to the pre-treatment condition; however, the altered connections of FPCN-core DN (DNCORE), FPCN-SN, and intra-FPCN among MDD patients remained impaired; (2) thalamo-prefrontal connectivity provides moderate predictive power (r2 = 0.63) for the effectiveness of sertraline treatment. In summary, our findings contribute to a body of evidence that suggests salubrious effects of sertraline treatment primarily involve the FPCN-thalamus-SN pathway. The pre-treatment rsFC in this pathway could serve as a predictor of sertraline treatment outcome.
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Affiliation(s)
- Li-Ming Hsu
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Timothy Joseph Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
- Institute of European and American Studies, Academia Sinica, Taipei, Taiwan
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
| | | | - Chi-Bin Yeh
- Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
| | - Hung-Wen Kao
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Section 2, Chenggong Road, Neihu District, Taipei City, 114, Taiwan.
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
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25
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Sato JR, Biazoli CE, Zugman A, Pan PM, Bueno APA, Moura LM, Gadelha A, Picon FA, Amaro E, Salum GA, Miguel EC, Rohde LA, Bressan RA, Jackowski AP. Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence. Eur J Neurosci 2021; 54:6187-6201. [PMID: 34460993 DOI: 10.1111/ejn.15435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/18/2021] [Accepted: 08/28/2021] [Indexed: 11/26/2022]
Abstract
There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.
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Affiliation(s)
- João Ricardo Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Claudinei Eduardo Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
| | - André Zugman
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Pedro Mario Pan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Ana Paula Arantes Bueno
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Luciana Monteiro Moura
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Ary Gadelha
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Felipe Almeida Picon
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Edson Amaro
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,Department of Radiology, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Euripedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,Department of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Rodrigo Affonseca Bressan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Andrea Parolin Jackowski
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
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26
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Kong R, Yang Q, Gordon E, Xue A, Yan X, Orban C, Zuo XN, Spreng N, Ge T, Holmes A, Eickhoff S, Yeo BTT. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cereb Cortex 2021; 31:4477-4500. [PMID: 33942058 PMCID: PMC8757323 DOI: 10.1093/cercor/bhab101] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/03/2021] [Accepted: 03/12/2021] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).
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Affiliation(s)
- Ru Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Qing Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Evan Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Aihuiping Xue
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Xiaoxuan Yan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- National Basic Public Science Data Center, Chinese Academy of Sciences, Beijing 100101, China
| | - Nathan Spreng
- Laboratory of Brain and Cognition, Department of Neurology and Neurosurgery, McGill University, Montreal QC H3A 2B4, Canada
- Departments of Psychiatry and Psychology, Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal QC H3A 2B4, Canada
| | - Tian Ge
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Simon Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich 52425, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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27
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Fedorenko E. The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.02.023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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28
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Internal manipulation of perceptual representations in human flexible cognition: A computational model. Neural Netw 2021; 143:572-594. [PMID: 34332343 DOI: 10.1016/j.neunet.2021.07.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 06/30/2021] [Accepted: 07/09/2021] [Indexed: 11/24/2022]
Abstract
Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.
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29
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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30
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Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA. Neuroimage 2021; 237:118114. [PMID: 33933594 DOI: 10.1016/j.neuroimage.2021.118114] [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: 12/04/2020] [Revised: 04/10/2021] [Accepted: 04/24/2021] [Indexed: 11/21/2022] Open
Abstract
Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the "DMN-splitting" in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.
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31
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Resting-State Functional Connectivity in Mathematical Expertise. Brain Sci 2021; 11:brainsci11040430. [PMID: 33800679 PMCID: PMC8065786 DOI: 10.3390/brainsci11040430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 11/17/2022] Open
Abstract
To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal-thalamic-temporal connections for mathematicians and the medial-frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections' characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts' successful performance.
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32
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Zinyakov NG, Sosipatorova VY, Andriyasov AV, Ovchinnikova EV, Nikonova ZB, Kozlov AA, Altunin DA, Osipova OS, Akshalova PB, Andreychuk DB, Chvala IA. Genetic analysis of genotype G57 H9N2 avian influenza virus isolate A/chicken/Tajikistan/2379/2018 recovered in Central Asia. Arch Virol 2021; 166:1591-1597. [PMID: 33740120 DOI: 10.1007/s00705-021-05011-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/08/2021] [Indexed: 12/18/2022]
Abstract
This paper presents genetic data on the full genome analysis of A/chicken/Tajikistan/2379/2018 H9N2 influenza virus isolated in September 2018 from chicken pathological material received from poultry farms of the Republic of Tajikistan and subtyped as H9N2 by serological and molecular methods. According to the results of hemagglutinin gene sequencing, the amino acid sequence of the cleavage site was RSSR/GLF, which is typical for low-virulent avian influenza virus. Phylogenetic analysis of the nucleotide sequence of a hemagglutinin gene fragment (nt 1-1539 of the open reading frame) showed that the A/chicken/Tajikistan/2379/2018 H9N2 isolate belongs to the Y280 genetic group of low-virulent A/H9 influenza virus, which is widespread in Southeast Asia. The complete nucleotide sequence of the viral genome was determined. Comparative analysis of all genomic segments revealed that the A/chicken/Tajikistan/2379/2018 H9N2 virus is closely related to an A/H9 influenza virus isolated in the Far East of the Russian Federation in 2018. Genetic similarity (97.1-99% identity in four out of eight viral genes) was found to isolates of an H7N9 subtype virus recovered in the Inner Mongolia and Hebei regions of China in 2017. According to the analysis of the predicted amino acid sequence of the studied isolate, the positions of some molecular markers indicate possible adaptation of the virus to mammals. Further genetic analysis showed that this virus belongs to genotype G57.
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Affiliation(s)
- N G Zinyakov
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia.
| | | | - A V Andriyasov
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
| | - E V Ovchinnikova
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
| | - Z B Nikonova
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
| | - A A Kozlov
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
| | - D A Altunin
- Veterinary Physician, FGBI "ARRIAH", Vladimir, Russia
| | - O S Osipova
- Veterinary Physician, FGBI "ARRIAH", Vladimir, Russia
| | - P B Akshalova
- Veterinary Physician, FGBI "ARRIAH", Vladimir, Russia
| | - D B Andreychuk
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
| | - I A Chvala
- Candidate of Sciences (Biology), FGBI "ARRIAH", Vladimir, Russia
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Canario E, Chen D, Biswal B. A review of resting-state fMRI and its use to examine psychiatric disorders. PSYCHORADIOLOGY 2021; 1:42-53. [PMID: 38665309 PMCID: PMC10917160 DOI: 10.1093/psyrad/kkab003] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 04/28/2024]
Abstract
Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.
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Affiliation(s)
- Edgar Canario
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Donna Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
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Teeuw J, Hulshoff Pol HE, Boomsma DI, Brouwer RM. Reliability modelling of resting-state functional connectivity. Neuroimage 2021; 231:117842. [PMID: 33581291 DOI: 10.1016/j.neuroimage.2021.117842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.
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Affiliation(s)
- Jalmar Teeuw
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rachel M Brouwer
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
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Mueller JM, Pritschet L, Santander T, Taylor CM, Grafton ST, Jacobs EG, Carlson JM. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle. Netw Neurosci 2021; 5:125-144. [PMID: 33688609 PMCID: PMC7935041 DOI: 10.1162/netn_a_00169] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization. Sex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood whether day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network connectivity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the course of a menstrual cycle, temporary reorganization of several large-scale functional brain networks occurs during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with nodes belonging to the temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.
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Affiliation(s)
- Joshua M Mueller
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Laura Pritschet
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Tyler Santander
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Caitlin M Taylor
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Scott T Grafton
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily Goard Jacobs
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jean M Carlson
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
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Cho JW, Korchmaros A, Vogelstein JT, Milham MP, Xu T. Impact of concatenating fMRI data on reliability for functional connectomics. Neuroimage 2021; 226:117549. [PMID: 33248255 PMCID: PMC7983579 DOI: 10.1016/j.neuroimage.2020.117549] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 11/30/2022] Open
Abstract
Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that 'more data is better' emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of cortical functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.
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Affiliation(s)
- Jae Wook Cho
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | | | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400N. Charles St Baltimore, MD 21218, United States
| | - Michael P Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
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Schneider I, Schmitgen MM, Bach C, Listunova L, Kienzle J, Sambataro F, Depping MS, Kubera KM, Roesch-Ely D, Wolf RC. Cognitive remediation therapy modulates intrinsic neural activity in patients with major depression. Psychol Med 2020; 50:2335-2345. [PMID: 31524112 DOI: 10.1017/s003329171900240x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Cognitive impairment is a core feature of major depressive disorder (MDD). Cognitive remediation may improve cognition in MDD, yet so far, the underlying neural mechanisms are unclear. This study investigated changes in intrinsic neural activity in MDD after a cognitive remediation trial. METHODS In a longitudinal design, 20 patients with MDD and pronounced cognitive deficits and 18 healthy controls (HC) were examined using resting-state functional magnetic resonance imaging. MDD patients received structured cognitive remediation therapy (CRT) over 5 weeks. The whole-brain fractional amplitude of low-frequency fluctuations was computed before the first and after the last training session. Univariate methods were used to address regionally-specific effects, and a multivariate data analysis strategy was employed to investigate functional network strength (FNS). RESULTS MDD patients significantly improved in cognitive function after CRT. Baseline comparisons revealed increased right caudate activity and reduced activity in the left frontal cortex, parietal lobule, insula, and precuneus in MDD compared to HC. In patients, reduced FNS was found in a bilateral prefrontal system at baseline (p < 0.05, uncorrected). In MDD, intrinsic neural activity increased in right inferior frontal gyrus after CRT (p < 0.05, small volume corrected). Left inferior parietal lobule, left insula, left precuneus, and right caudate activity showed associations with cognitive improvement (p < 0.05, uncorrected). Prefrontal network strength increased in patients after CRT, but this increase was not associated with improved cognitive performance. CONCLUSIONS Our findings support the role of intrinsic neural activity of the prefrontal cortex as a possible mediator of cognitive improvement following CRT in MDD.
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Affiliation(s)
- Isabella Schneider
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Mike M Schmitgen
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Claudia Bach
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Lena Listunova
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Johanna Kienzle
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Malte S Depping
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Katharina M Kubera
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Daniela Roesch-Ely
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
| | - Robert C Wolf
- Department of General Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Heidelberg Germany, Voßstr. 4, 69115Heidelberg, Germany
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38
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Alderson TH, Bokde ALW, Kelso JAS, Maguire L, Coyle D. Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms. Hum Brain Mapp 2020; 41:3212-3234. [PMID: 32301561 PMCID: PMC7375112 DOI: 10.1002/hbm.25009] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 01/20/2020] [Accepted: 03/31/2020] [Indexed: 12/24/2022] Open
Abstract
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large-scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large-scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well-defined collective variable capturing the level of 'phase-locking' between large-scale networks over time. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre-configured' to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large-scale networks in the cerebral cortex and cognition.
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Affiliation(s)
- Thomas H. Alderson
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUnited States
| | - Arun L. W. Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - J. A. Scott Kelso
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Center for Complex Systems and Brain SciencesFlorida Atlantic UniversityBoca RatonFloridaUnited States
| | - Liam Maguire
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
| | - Damien Coyle
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
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39
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Jin W, Zhu H, Shu P, Tong S, Sun J. Extracting Individual Neural Fingerprint Encoded in Functional Connectivity by Silencing Indirect Effects. IEEE Trans Biomed Eng 2020; 67:2253-2265. [DOI: 10.1109/tbme.2019.2958333] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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40
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Zhang J, Huang Z, Tumati S, Northoff G. Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. PLoS Biol 2020; 18:e3000733. [PMID: 32649707 PMCID: PMC7375654 DOI: 10.1371/journal.pbio.3000733] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/22/2020] [Accepted: 06/23/2020] [Indexed: 12/26/2022] Open
Abstract
Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states. Recent resting-state fMRI studies have shown that the global signal exhibits a nonuniform spatial distribution across gray matter, but is this informative? This neuroimaging study reveals novel insights into the informative nature of global signal by rest-task modulation of the global signal topography.
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Affiliation(s)
- Jianfeng Zhang
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China
| | - Zirui Huang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Shankar Tumati
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
- Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
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41
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Iterative consensus spectral clustering improves detection of subject and group level brain functional modules. Sci Rep 2020; 10:7590. [PMID: 32371990 PMCID: PMC7200822 DOI: 10.1038/s41598-020-63552-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 03/31/2020] [Indexed: 11/29/2022] Open
Abstract
Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC.
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42
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Li X, Wang Y, Wang W, Huang W, Chen K, Xu K, Zhang J, Chen Y, Li H, Wei D, Shu N, Zhang Z. Age-Related Decline in the Topological Efficiency of the Brain Structural Connectome and Cognitive Aging. Cereb Cortex 2020; 30:4651-4661. [PMID: 32219315 DOI: 10.1093/cercor/bhaa066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 02/14/2020] [Accepted: 02/28/2020] [Indexed: 12/12/2022] Open
Abstract
Brain disconnection model has been proposed as a possible neural mechanism for cognitive aging. However, the relationship between structural connectivity degeneration and cognitive decline with normal aging remains unclear. In the present study, using diffusion MRI and tractography techniques, we report graph theory-based analyses of the brain structural connectome in a cross-sectional, community-based cohort of 633 cognitively healthy elderly individuals. Comprehensive neuropsychological assessment of the elderly subjects was performed. The association between age, brain structural connectome, and cognition across elderly individuals was examined. We found that the topological efficiency, modularity, and hub integration of the brain structural connectome exhibited a significant decline with normal aging, especially in the frontal, parietal, and superior temporal regions. Importantly, network efficiency was positively correlated with attention and executive function in elderly subjects and had a significant mediation effect on the age-related decline in these cognitive functions. Moreover, nodal efficiency of the brain structural connectome showed good performance for the prediction of attention and executive function in elderly individuals. Together, our findings revealed topological alterations of the brain structural connectome with normal aging, which provides possible structural substrates underlying cognitive aging and sensitive imaging markers for the individual prediction of cognitive functions in elderly subjects.
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Affiliation(s)
- Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Yezhou Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Wenxiao Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Kewei Chen
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Junying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - He Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Dongfeng Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
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43
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Vink JJT, Klooster DCW, Ozdemir RA, Westover MB, Pascual-Leone A, Shafi MM. EEG Functional Connectivity is a Weak Predictor of Causal Brain Interactions. Brain Topogr 2020; 33:221-237. [PMID: 32090281 DOI: 10.1007/s10548-020-00757-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 02/17/2020] [Indexed: 10/24/2022]
Abstract
In recent years there has been an explosion of research evaluating resting-state brain functional connectivity (FC) using different modalities. However, the relationship between such measures of FC and the underlying causal brain interactions has not been well characterized. To further characterize this relationship, we assessed the relationship between electroencephalography (EEG) resting state FC and propagation of transcranial magnetic stimulation (TMS) evoked potentials (TEPs) at the sensor and source level in healthy participants. TMS was applied to six different cortical regions in ten healthy individuals (9 male; 1 female), and effects on brain activity were measured using simultaneous EEG. Pre-stimulus FC was assessed using five different FC measures (Pearson's correlation, mutual information, weighted phase lag index, coherence and phase locking value). Propagation of the TEPs was quantified as the root mean square (RMS) of the TEP voltage and current source density (CSD) at the sensor and source level, respectively. The relationship between pre-stimulus FC and the spatial distribution of TEP activity was determined using a generalized linear model (GLM) analysis. On the group level, all FC measures correlated significantly with TEP activity over the early (15-75 ms) and full range (15-400 ms) of the TEP at the sensor and source level. However, the predictive value of all FC measures is quite limited, accounting for less than 10% of the variance of TEP activity, and varies substantially across participants and stimulation sites. Taken together, these results suggest that EEG functional connectivity studies in sensor and source space should be interpreted with caution.
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Affiliation(s)
- Jord J T Vink
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Heidelberglaan 100, 3584CM, Utrecht, The Netherlands.
| | - Deborah C W Klooster
- Department of Electrical Engineering, Eindhoven University of Technology, 5612AZ, Eindhoven, The Netherlands.,Deparment of Neurology, University Hospital Ghent, Corneel Heymanslaan 10, 9000, Ghent, Belgium
| | - Recep A Ozdemir
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA
| | | | - Alvaro Pascual-Leone
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research and the Center for Memory Health, Roslindale, USA.,Institut Guttman, Universitat Autonoma de Barcelona, Camí Can Ruti, s/n, 08916, Badalona, Barcelona, Spain.,Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA
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44
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Vohryzek J, Griffa A, Mullier E, Friedrichs-Maeder C, Sandini C, Schaer M, Eliez S, Hagmann P. Dynamic spatiotemporal patterns of brain connectivity reorganize across development. Netw Neurosci 2020; 4:115-133. [PMID: 32043046 PMCID: PMC7006876 DOI: 10.1162/netn_a_00111] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/16/2019] [Indexed: 01/21/2023] Open
Abstract
Late human development is characterized by the maturation of high-level functional processes, which rely on reshaping of white matter connections, as well as synaptic density. However, the relationship between the whole-brain dynamics and the underlying white matter networks in neurodevelopment is largely unknown. In this study, we focused on how the structural connectome shapes the emerging dynamics of cerebral development between the ages of 6 and 33 years, using functional and diffusion magnetic resonance imaging combined into a spatiotemporal connectivity framework. We defined two new measures of brain dynamics, namely the system diversity and the spatiotemporal diversity, which quantify the level of integration/segregation between functional systems and the level of temporal self-similarity of the functional patterns of brain dynamics, respectively. We observed a global increase in system diversity and a global decrease and local refinement in spatiotemporal diversity values with age. In support of these findings, we further found an increase in the usage of long-range and inter-system white matter connectivity and a decrease in the usage of short-range connectivity with age. These findings suggest that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age. These functional changes are supported by a greater involvement of long-range and inter-system axonal pathways.
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Affiliation(s)
- Jakub Vohryzek
- Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland
| | - Alessandra Griffa
- Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland
- Dutch Connectome Lab, Department of Complex Trait Genetics, Centre for Neuroscience and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands
| | - Emeline Mullier
- Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland
| | - Cecilia Friedrichs-Maeder
- Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland
- Department of Neurology, Bern University Hospital, University of Bern, Switzerland
| | - Corrado Sandini
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Marie Schaer
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Stephan Eliez
- Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Patric Hagmann
- Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland
- Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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45
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Bilingualism as a gradient measure modulates functional connectivity of language and control networks. Neuroimage 2020; 205:116306. [DOI: 10.1016/j.neuroimage.2019.116306] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 11/18/2022] Open
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Li X, Pan Y, Fang Z, Lei H, Zhang X, Shi H, Ma N, Raine P, Wetherill R, Kim JJ, Wan Y, Rao H. Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task. Neuroimage 2019; 209:116495. [PMID: 31887425 PMCID: PMC7061333 DOI: 10.1016/j.neuroimage.2019.116495] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/20/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice’s similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC = 0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC = 0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.
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Affiliation(s)
- Xiong Li
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yu Pan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China; Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Zhuo Fang
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Lei
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Xiaocui Zhang
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Shi
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ning Ma
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip Raine
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Reagan Wetherill
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Junghoon J Kim
- Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York, New York, NY, USA
| | - Yan Wan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hengyi Rao
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Plourde V, Rohr CS, Virani S, Bray S, Yeates KO, Brooks BL. Default mode network functional connectivity after multiple concussions in children and adolescents. Arch Clin Neuropsychol 2019. [DOI: 10.1093/arclin/acz073] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
The default mode network (DMN), a set of brain regions, has been shown to be affected post-concussion.
Objective
This cross-sectional study aims to elucidate if children and adolescents with multiple concussions demonstrate long-term alterations in DMN functional connectivity (FC).
Method
Participants (N = 57, 27 girls and 30 boys; 8-19 years old, M age = 14.7, SD = 2.8) were divided into three groups (orthopedic injury [OI] n = 20; one concussion n = 16; multiple concussions n = 21, M = 3.2 concussions, SD = 1.7) and seen on average 31.6 months post-injury (range 4.3-130.7 months; SD = 19.4). They underwent a resting-state functional magnetic resonance imaging scan. Parents completed the ADHD rating scale-5 for children and adolescents. Children and parents completed the post-concussion symptom inventory (PCSI).
Results
Anterior and posterior DMN components were extracted from the fMRI data for each participant using FSL’s MELODIC and dual regression. We tested for pairwise group differences within each DMN component in FSL’s Randomize (5000 permutations) using threshold-free cluster enhancement to estimate cluster activation, controlling for age, sex, and symptoms of inattention. FC of the anterior DMN was significantly reduced in the group with multiple concussions compared to the two other groups, whereas there were no significant group differences on FC of the posterior DMN. There were no significant associations between DMN FC and PCSI scores.
Conclusions
These results suggest reduced FC in the anterior DMN in youth with multiple concussions, but no linear association with post-concussive symptoms.
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Affiliation(s)
- Vickie Plourde
- School of Psychology, Université de Moncton, Moncton, Canada; Faculty Saint-Jean, University of Alberta, Edmonton, Canada
| | - Christiane S Rohr
- Department of Radiology, University of Calgary; Child and Adolescent Imaging Research Program, University of Calgary; Alberta Children’s Hospital Research Institute, University of Calgary; Hotchkiss Brain Institute, Calgary, Canada
| | - Shane Virani
- Alberta Children’s Hospital Research Institute, University of Calgary; Faculty of Kinesiology, University of Calgary, Calgary, Canada
| | - Signe Bray
- Department of Radiology, University of Calgary; Child and Adolescent Imaging Research Program, University of Calgary; Alberta Children’s Hospital Research Institute, University of Calgary; Hotchkiss Brain Institute, Calgary, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary; Alberta Children’s Hospital Research Institute, University of Calgary; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Brian L Brooks
- Neurosciences Program, Alberta Children’s Hospital; Alberta Children’s Hospital Research Institute, University of Calgary; Hotchkiss Brain Institute, University of Calgary; Departments of Pediatrics, Clinical Neurosciences, and Psychology, University of Calgary, Calgary, Canada
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48
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Building functional connectivity neuromarkers of behavioral self-regulation across children with and without Autism Spectrum Disorder. Dev Cogn Neurosci 2019; 41:100747. [PMID: 31826838 PMCID: PMC6994646 DOI: 10.1016/j.dcn.2019.100747] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/25/2019] [Accepted: 12/03/2019] [Indexed: 01/10/2023] Open
Abstract
Behavioral self-regulation develops rapidly during childhood and struggles in this area can have lifelong negative outcomes. Challenges with self-regulation are common to several neurodevelopmental conditions, including Autism Spectrum Disorder (ASD). Little is known about the neural expression of behavioral regulation in children with and without neurodevelopmental conditions. We examined whole-brain brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM). CPM is a data-driven protocol for developing predictive models of brain–behavior relationships and assessing their potential as ‘neuromarkers’ using cross-validation. The data stems from the ABIDE II and comprises 276 children with and without ASD (8–13 years). We identified networks whose FC predicted individual differences in behavioral regulation. These network models predicted novel individuals’ inhibition and shifting from FC data in both a leave-one-out, and split halves, cross-validation. We observed commonalities and differences, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN. Our findings substantially add to our knowledge on the neural expressions of inhibition and shifting across children with and without a neurodevelopmental condition. Given the numerous behavioral issues that can be quantified dimensionally, refinement of whole-brain neuromarker techniques may prove useful in the future.
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Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage 2019; 203:116157. [PMID: 31494250 PMCID: PMC6907736 DOI: 10.1016/j.neuroimage.2019.116157] [Citation(s) in RCA: 287] [Impact Index Per Article: 57.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/30/2019] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. OBJECTIVES We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. METHODS A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. RESULTS From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a "poor" ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. CONCLUSION This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA.
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, USA; Child Study Center, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
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50
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Kaiser RH, Treadway MT, Wooten DW, Kumar P, Goer F, Murray L, Beltzer M, Pechtel P, Whitton A, Cohen AL, Alpert NM, El Fakhri G, Normandin MD, Pizzagalli DA. Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation. Cereb Cortex 2019; 28:4281-4290. [PMID: 29121332 DOI: 10.1093/cercor/bhx281] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Indexed: 01/07/2023] Open
Abstract
Prior studies have shown that dopamine (DA) functioning in frontostriatal circuits supports reinforcement learning (RL), as phasic DA activity in ventral striatum signals unexpected reward and may drive coordinated activity of striatal and orbitofrontal regions that support updating of action plans. However, the nature of DA functioning in RL is complex, in particular regarding the role of DA clearance in RL behavior. Here, in a multi-modal neuroimaging study with healthy adults, we took an individual differences approach to the examination of RL behavior and DA clearance mechanisms in frontostriatal learning networks. We predicted that better RL would be associated with decreased striatal DA transporter (DAT) availability and increased intrinsic functional connectivity among DA-rich frontostriatal regions. In support of these predictions, individual differences in RL behavior were related to DAT binding potential in ventral striatum and resting-state functional connectivity between ventral striatum and orbitofrontal cortex. Critically, DAT binding potential had an indirect effect on reinforcement learning behavior through frontostriatal connectivity, suggesting potential causal relationships across levels of neurocognitive functioning. These data suggest that individual differences in DA clearance and frontostriatal coordination may serve as markers for RL, and suggest directions for research on psychopathologies characterized by altered RL.
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Affiliation(s)
- Roselinde H Kaiser
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.,Department of Psychology, University of California Los Angeles, CA, USA
| | - Michael T Treadway
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.,Department of Psychology, Emory University, Atlanta, GA, USA
| | - Dustin W Wooten
- Department of Radiology, Gordon Center for Medical Imaging, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Poornima Kumar
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Franziska Goer
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Laura Murray
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Miranda Beltzer
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Pia Pechtel
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Alexis Whitton
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Andrew L Cohen
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | | | | | | | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
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