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Hu J, Luo J, Xu Z, Liao B, Dong S, Peng B, Hou G. Spatio-temporal learning and explaining for dynamic functional connectivity analysis: Application to depression. J Affect Disord 2024; 364:266-273. [PMID: 39137835 DOI: 10.1016/j.jad.2024.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/26/2024] [Accepted: 08/09/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology. METHODS The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified. RESULTS We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified. LIMITATIONS The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population. CONCLUSIONS The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression.
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Affiliation(s)
- Jinlong Hu
- Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jianmiao Luo
- Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Ziyun Xu
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Bin Liao
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
| | - Shoubin Dong
- Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Bo Peng
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Gangqiang Hou
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China.
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Peng L, Su J, Hu D, Yu Y, Wei H, Li M. Measuring functional connectivity in frequency-domain helps to better characterize brain function. Hum Brain Mapp 2024; 45:e26726. [PMID: 38949487 PMCID: PMC11215841 DOI: 10.1002/hbm.26726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 03/25/2024] [Accepted: 05/09/2024] [Indexed: 07/02/2024] Open
Abstract
Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Jianpo Su
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Dewen Hu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Yang Yu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Huilin Wei
- Systems Engineering InstituteAcademy of Military SciencesBeijingChina
| | - Ming Li
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
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3
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Gregorich M, Simpson SL, Heinze G. Flexible parametrization of graph-theoretical features from individual-specific networks for prediction. Stat Med 2024; 43:2592-2606. [PMID: 38664934 DOI: 10.1002/sim.10091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/24/2024]
Abstract
Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.
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Affiliation(s)
- Mariella Gregorich
- Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics, Vienna, Austria
| | - Sean L Simpson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Georg Heinze
- Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics, Vienna, Austria
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4
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Kozhemiako N, Buckley AW, Chervin RD, Redline S, Purcell SM. Mapping neurodevelopment with sleep macro- and micro-architecture across multiple pediatric populations. Neuroimage Clin 2023; 41:103552. [PMID: 38150746 PMCID: PMC10788305 DOI: 10.1016/j.nicl.2023.103552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/30/2023] [Accepted: 12/12/2023] [Indexed: 12/29/2023]
Abstract
Profiles of sleep duration and timing and corresponding electroencephalographic activity reflect brain changes that support cognitive and behavioral maturation and may provide practical markers for tracking typical and atypical neurodevelopment. To build and evaluate a sleep-based, quantitative metric of brain maturation, we used whole-night polysomnography data, initially from two large National Sleep Research Resource samples, spanning childhood and adolescence (total N = 4,013, aged 2.5 to 17.5 years): the Childhood Adenotonsillectomy Trial (CHAT), a research study of children with snoring without neurodevelopmental delay, and Nationwide Children's Hospital (NCH) Sleep Databank, a pediatric sleep clinic cohort. Among children without neurodevelopmental disorders (NDD), sleep metrics derived from the electroencephalogram (EEG) displayed robust age-related changes consistently across datasets. During non-rapid eye movement (NREM) sleep, spindles and slow oscillations further exhibited characteristic developmental patterns, with respect to their rate of occurrence, temporal coupling and morphology. Based on these metrics in NCH, we constructed a model to predict an individual's chronological age. The model performed with high accuracy (r = 0.93 in the held-out NCH sample and r = 0.85 in a second independent replication sample - the Pediatric Adenotonsillectomy Trial for Snoring (PATS)). EEG-based age predictions reflected clinically meaningful neurodevelopmental differences; for example, children with NDD showed greater variability in predicted age, and children with Down syndrome or intellectual disability had significantly younger brain age predictions (respectively, 2.1 and 0.8 years less than their chronological age) compared to age-matched non-NDD children. Overall, our results indicate that sleep architectureoffers a sensitive window for characterizing brain maturation, suggesting the potential for scalable, objective sleep-based biomarkers to measure neurodevelopment.
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Affiliation(s)
- N Kozhemiako
- Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA
| | - A W Buckley
- Sleep & Neurodevelopment Core, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - R D Chervin
- Sleep Disorders Center and Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - S Redline
- Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA; Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - S M Purcell
- Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
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5
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Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
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Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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6
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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7
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Dall'Aglio L, Estévez-López F, López-Vicente M, Xu B, Agcaoglu O, Boroda E, Lim KO, Calhoun VD, Tiemeier H, Muetzel RL. Exploring the longitudinal associations of functional network connectivity and psychiatric symptom changes in youth. Neuroimage Clin 2023; 38:103382. [PMID: 36965455 PMCID: PMC10074199 DOI: 10.1016/j.nicl.2023.103382] [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: 12/09/2022] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/22/2023]
Abstract
BACKGROUND Functional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs. METHODS We adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC. RESULTS Small longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing. CONCLUSION We found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.
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Affiliation(s)
- Lorenza Dall'Aglio
- Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC, Rotterdam, The Netherlands; The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
| | - Fernando Estévez-López
- Department of Social and Behavioral Sciences, Harvard T. Chan School of Public Health, Boston, USA
| | - Mónica López-Vicente
- Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC, Rotterdam, The Netherlands; The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
| | - Bing Xu
- Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC, Rotterdam, The Netherlands; The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
| | - Oktay Agcaoglu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Elias Boroda
- Department of Psychiatry and Behavioral Science, University of Minnesota, Minneapolis, USA
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Science, University of Minnesota, Minneapolis, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Henning Tiemeier
- Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC, Rotterdam, The Netherlands; Department of Social and Behavioral Sciences, Harvard T. Chan School of Public Health, Boston, USA.
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC, Rotterdam, The Netherlands
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8
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Long Y, Liu X, Liu Z. Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives. Brain Sci 2023; 13:brainsci13030429. [PMID: 36979239 PMCID: PMC10046056 DOI: 10.3390/brainsci13030429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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10
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Zhong S, Chen P, Lai S, Chen G, Zhang Y, Lv S, He J, Tang G, Pan Y, Wang Y, Jia Y. Aberrant dynamic functional connectivity in corticostriatal circuitry in depressed bipolar II disorder with recent suicide attempt. J Affect Disord 2022; 319:538-548. [PMID: 36155235 DOI: 10.1016/j.jad.2022.09.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The underlying neurobiological mechanisms on suicidal behavior in bipolar disorder remain unclear. We aim to explore the mechanisms of suicide by detecting dynamic functional connectivity (dFC) of corticostriatal circuitry and cognition in depressed bipolar II disorder (BD II) with recent suicide attempt (SA). METHODS We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 68 depressed patients with BD-II (30 with SA and 38 without SA) and 35 healthy controls (HCs). The whole-brain dFC variability of corticostriatal circuitry was calculated using a sliding-window analysis. Their correlations with cognitive dysfunction were further detected. Support vector machine (SVM) classification tested the potential of dFC to differentiate BD-II with SA from HCs. RESULTS Increased dFC variability between the right vCa and the right insula was found in SA compared to non-SA and HCs, and negatively correlated with speed of processing. Decreased dFC variability between the left dlPu and the right postcentral gyrus was found in non-SA compared to SA and HCs, and positively correlated with reasoning problem-solving. Both SA and non-SA exhibited decreased dFC variability between the right dCa and the left MTG, and between the right dlPu and the right calcarine when compared to HCs. SVM classification achieved an accuracy of 75.24 % and AUC of 0.835 to differentiate SA from non-SA, while combining the abnormal dFC features between SA and non-SA. CONCLUSIONS Aberrant dFC variability of corticostriatal circuitry may serve as potential neuromarker for SA in BD-II, which might help to discriminate suicidal BD-II patients from non-suicidal patients and HCs.
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Affiliation(s)
- Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Shunkai Lai
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Sihui Lv
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Jiali He
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Guixian Tang
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Youling Pan
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Ying Wang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
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El Damaty S, Darcey VL, McQuaid GA, Picci G, Stoianova M, Mucciarone V, Chun Y, Laws ML, Campano V, Van Hecke K, Ryan M, Rose EJ, Fishbein DH, VanMeter AS. Introducing an adolescent cognitive maturity index. Front Psychol 2022; 13:1017317. [PMID: 36571021 PMCID: PMC9771453 DOI: 10.3389/fpsyg.2022.1017317] [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: 08/11/2022] [Accepted: 10/25/2022] [Indexed: 12/12/2022] Open
Abstract
Children show substantial variation in the rate of physical, cognitive, and social maturation as they traverse adolescence and enter adulthood. Differences in developmental paths are thought to underlie individual differences in later life outcomes, however, there remains a lack of consensus on the normative trajectory of cognitive maturation in adolescence. To address this problem, we derive a Cognitive Maturity Index (CMI), to estimate the difference between chronological and cognitive age predicted with latent factor estimates of inhibitory control, risky decision-making and emotional processing measured with standard neuropsychological instruments. One hundred and forty-one children from the Adolescent Development Study (ADS) were followed longitudinally across three time points from ages 11-14, 13-16, and 14-18. Age prediction with latent factor estimates of cognitive skills approximated age within ±10 months (r = 0.71). Males in advanced puberty displayed lower cognitive maturity relative to peers of the same age; manifesting as weaker inhibitory control, greater risk-taking, desensitization to negative affect, and poor recognition of positive affect.
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Affiliation(s)
- Shady El Damaty
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Valerie L. Darcey
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Goldie A. McQuaid
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Maria Stoianova
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Veronica Mucciarone
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Yewon Chun
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Marissa L. Laws
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
| | - Victor Campano
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Kinney Van Hecke
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Mary Ryan
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
| | - Emma Jane Rose
- Department of Psychology, The Pennsylvania State University, University Park, PA, United States
| | - Diana H. Fishbein
- Translational Neuro-Prevention Research, Frank Porter Graham Child Development Institute, University of Northern Carolina, Chapel Hill, NC, United States
| | - Ashley S. VanMeter
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
- Center for Functional & Molecular Imaging, Georgetown University Medical Center, Department of Neurology, Washington, DC, United States
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12
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Jusseaume K, Valova I. Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects. SENSORS (BASEL, SWITZERLAND) 2022; 22:8112. [PMID: 36365809 PMCID: PMC9655329 DOI: 10.3390/s22218112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer's and other neurodegenerative diseases later in life. Utilizing recordings of patients' brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients' brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients' brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.
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13
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Schlemm E, Frey BM, Mayer C, Petersen M, Fiehler J, Hanning U, Kühn S, Twerenbold R, Gallinat J, Gerloff C, Thomalla G, Cheng B. Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease. Biol Psychiatry 2022; 92:592-602. [PMID: 35691727 DOI: 10.1016/j.biopsych.2022.03.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 01/02/2023]
Abstract
BACKGROUND Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment. METHODS We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level-dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function. RESULTS Data were available for 979 participants (ages 45-74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (-) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN- = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN-. Time spent in DMN-related brain states was associated with executive function. CONCLUSIONS Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.
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Affiliation(s)
- Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
| | - Benedikt M Frey
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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14
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Peng L, Luo Z, Zeng LL, Hou C, Shen H, Zhou Z, Hu D. Parcellating the human brain using resting-state dynamic functional connectivity. Cereb Cortex 2022; 33:3575-3590. [PMID: 35965076 DOI: 10.1093/cercor/bhac293] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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15
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White matter substrates of functional connectivity dynamics in the human brain. Neuroimage 2022; 258:119391. [PMID: 35716842 DOI: 10.1016/j.neuroimage.2022.119391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.
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16
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A. Markovics J. Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback. ARTIF INTELL 2022. [DOI: 10.5772/intechopen.98343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.
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17
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Huang J, Ke P, Chen X, Li S, Zhou J, Xiong D, Huang Y, Li H, Ning Y, Duan X, Li X, Zhang W, Wu F, Wu K. Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients. Front Aging Neurosci 2022; 14:823502. [PMID: 35309897 PMCID: PMC8929292 DOI: 10.3389/fnagi.2022.823502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
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Affiliation(s)
- Jiayuan Huang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoyi Chen
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Shijia Li
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xujun Duan
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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18
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Li Y, Zeng W, Shi Y, Deng J, Nie W, Luo S, Yang J. A Novel Constrained Non-negative Matrix Factorization Method for Group Functional Magnetic Resonance Imaging Data Analysis of Adult Attention-Deficit/Hyperactivity Disorder. Front Neurosci 2022; 16:756938. [PMID: 35250441 PMCID: PMC8891574 DOI: 10.3389/fnins.2022.756938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder that often persists into adulthood. Extracting brain networks from functional magnetic resonance imaging (fMRI) data can help explore neurocognitive disorders in adult ADHD. However, there is still a lack of effective methods to extract large-scale brain networks to identify disease-related brain network changes. Hence, this study proposed a spatial constrained non-negative matrix factorization (SCNMF) method based on the fMRI real reference signal. First, non-negative matrix factorization analysis was carried out on each subject to select the brain network components of interest. Subsequently, the available spatial prior information was mined by integrating the interested components of all subjects. This prior constraint was then incorporated into the NMF objective function to improve its efficiency. For the sake of verifying the effectiveness and feasibility of the proposed method, we quantitatively compared the SCNMF method with other classical algorithms and applied it to the dynamic functional connectivity analysis framework. The algorithm successfully extracted ten resting-state brain functional networks from fMRI data of adult ADHD and healthy controls and found large-scale brain network changes in adult ADHD patients, such as enhanced connectivity between executive control network and right frontoparietal network. In addition, we found that older ADHD spent more time in the pattern of relatively weak connectivity. These findings indicate that the method can effectively extract large-scale functional networks and provide new insights into understanding the neurobiological mechanisms of adult ADHD from the perspective of brain networks.
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Affiliation(s)
- Ying Li
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
- *Correspondence: Weiming Zeng,
| | - Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Jin Deng
- College of Mathematics and Information, South China Agricultural University, Guangzhou, China
| | - Weifang Nie
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Sizhe Luo
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Jiajun Yang
- Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
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19
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Cai B, Zhou Z, Zhang A, Zhang G, Xiao L, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences. J Neurosci Methods 2022; 372:109539. [PMID: 35219769 DOI: 10.1016/j.jneumeth.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
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20
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Wei Y, Zhang W, Li Y, Liu X, Zha B, Hu S, Wang Y, Wang X, Yu X, Yang J, Qiu B. Acupuncture Treatment Decreased Temporal Variability of Dynamic Functional Connectivity in Chronic Tinnitus. Front Neurosci 2022; 15:737993. [PMID: 35153654 PMCID: PMC8835346 DOI: 10.3389/fnins.2021.737993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Acupuncture is recommended for the relief of chronic tinnitus in traditional Chinese medicine, but the underlying neural mechanism remains unclear. The human brain is a dynamic system, and it’s unclear about acupuncture’s effects on the dynamic functional connectivity (DFC) of chronic tinnitus. Therefore, this study based on resting-state functional magnetic resonance imaging (fMRI) investigates abnormal DFC in chronic tinnitus patients and the neural activity change evoked by acupuncture treatment for tinnitus. In this study, 17 chronic tinnitus patients and 22 age- and sex-matched normal subjects were recruited, and their tinnitus-related scales and hearing levels were collected. The fMRI data were measured before and after acupuncture, and then sliding-window and k-means clustering methods were used to calculate DFC and perform clustering analysis, respectively. We found that, compared with the normal subjects, chronic tinnitus patients had higher temporal variability of DFC between the supplementary motor area and medial part of the superior frontal gyrus, and it positively correlated with hearing loss. Clustering analysis showed higher transition probability (TP) between connection states in chronic tinnitus patients, and it was positively correlated with tinnitus severity. Furthermore, the findings showed that acupuncture treatment might improve tinnitus. DFC between the posterior cingulate gyrus and angular gyrus in chronic tinnitus patients after acupuncture showed significantly decreased, and it positively correlated with the improvement of tinnitus. Clustering analysis showed that acupuncture treatment might promote chronic tinnitus patients under lower DFC state, and it also positively correlated with the improvement of tinnitus. This study suggests that acupuncture as an alternative therapy method might decrease the tinnitus severity by decreasing the time variability of DFC in chronic tinnitus patients.
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Affiliation(s)
- Yarui Wei
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wanlin Zhang
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Yu Li
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiangwei Liu
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Bixiang Zha
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Sheng Hu
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- School of Medical Information Engineering, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Yanming Wang
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaoxiao Wang
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaochun Yu
- Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China
- Xiaochun Yu,
| | - Jun Yang
- Department of Acupuncture and Rehabilitation, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
- Jun Yang,
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and the Center for Biomedical Engineering, University of Science and Technology of China, Hefei, China
- *Correspondence: Bensheng Qiu,
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21
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Distinct networks of periaqueductal gray columns in pain and threat processing. Neuroimage 2022; 250:118936. [PMID: 35093518 DOI: 10.1016/j.neuroimage.2022.118936] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 12/02/2021] [Accepted: 01/24/2022] [Indexed: 01/21/2023] Open
Abstract
Noxious events that can cause physical damage to the body are perceived as threats. In the brainstem, the periaqueductal gray (PAG) ensures survival by generating an appropriate response to these threats. Hence, the experience of pain is coupled with threat signaling and interfaces in the dl/l and vlPAG columns. In this study, we triangulate the functional circuits of the dl/l and vlPAG by using static and time-varying functional connectivity (FC) in multiple fMRI scans in healthy participants (n=37, 21 female). The dl/l and vlPAG were activated during cue, heat, and rating periods when the cue signaled a high threat of experiencing heat pain and the incoming intensity of heat pain was low; responses were significantly lower after low threat cues. The two regions responded similarly to the cued conditions but showed prominent distinctions in the extent of FC with other brain regions. Thus, both static and time-varying FC showed significant differences in the functional circuits of dl/l and vlPAG in rest and task scans. The dl/lPAG consistently synchronized with the salience network, suggesting a role in threat detection, while the vlPAG exhibited more widespread synchronization and frequently connected with memory/language and sensory regions. Hence, these two PAG regions process heat pain when stronger pain is expected or when it is uncertain, and preferentially synchronize with distinct brain circuits in a reproducible manner. The dl/lPAG seems more directly involved in salience detection, while the vlPAG seems engaged in contextualizing threats.
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22
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Liu Y, Ren X, Zeng M, Li J, Zhao X, Zhang X, Yang J. Resting-state dynamic functional connectivity predicts the psychosocial stress response. Behav Brain Res 2022; 417:113618. [PMID: 34610370 DOI: 10.1016/j.bbr.2021.113618] [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: 05/06/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/18/2022]
Abstract
Acute stress triggers a complex cascade of psychological, physiological, and neural responses, which show large and enduring individual differences. Although previous studies have examined the relationship between the stress response and dynamic features of the brain's resting state, no study has used the brain's dynamic activity in the resting state to predict individual differences in the psychosocial stress response. In the current study, resting-state scans of forty-eight healthy participants were collected, and then their individual acute stress responses during the Montreal Imaging Stress Test (MIST) paradigm were recorded. Results defined a connectivity state (CS) characterized by positive correlations across the whole brain during resting-state that could negatively predict participants' feelings of social evaluative threat during stress tasks. Another CS characterized by negative correlations between the frontal-parietal network (FPN) and almost all other networks, except the dorsal attentional network (DAN), could predict participants' subjective stress, feelings of uncontrollability, and feelings of social evaluative threat. However, no CS could predict participants' salivary cortisol stress response. Overall, these results suggested that the brain state characterized as attentional regulation, linking self-control, and top-down regulation ability, could predict the psychosocial stress response. This study also developed an objective indicator for predicting human stress responses.
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Affiliation(s)
- Yadong Liu
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xi Ren
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Mei Zeng
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Jiwen Li
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xiaolin Zhao
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xuehan Zhang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China.
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23
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Tang S, Wu Z, Cao H, Chen X, Wu G, Tan W, Liu D, Yang J, Long Y, Liu Z. Age-Related Decrease in Default-Mode Network Functional Connectivity Is Accelerated in Patients With Major Depressive Disorder. Front Aging Neurosci 2022; 13:809853. [PMID: 35082661 PMCID: PMC8785895 DOI: 10.3389/fnagi.2021.809853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/20/2021] [Indexed: 12/14/2022] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder which is associated with an accelerated biological aging. However, little is known whether such process would be reflected by a more rapid aging of the brain function. In this study, we tested the hypothesis that MDD would be characterized by accelerated aging of the brain's default-mode network (DMN) functions. Resting-state functional magnetic resonance imaging data of 971 MDD patients and 902 healthy controls (HCs) was analyzed, which was drawn from a publicly accessible, multicenter dataset in China. Strength of functional connectivity (FC) and temporal variability of dynamic functional connectivity (dFC) within the DMN were calculated. Age-related effects on FC/dFC were estimated by linear regression models with age, diagnosis, and diagnosis-by-age interaction as variables of interest, controlling for sex, education, site, and head motion effects. The regression models revealed (1) a significant main effect of age in the predictions of both FC strength and dFC variability; and (2) a significant main effect of diagnosis and a significant diagnosis-by-age interaction in the prediction of FC strength, which was driven by stronger negative correlation between age and FC strength in MDD patients. Our results suggest that (1) both healthy participants and MDD patients experience decrease in DMN FC strength and increase in DMN dFC variability along age; and (2) age-related decrease in DMN FC strength may occur at a faster rate in MDD patients than in HCs. However, further longitudinal studies are still needed to understand the causation between MDD and accelerated aging of brain.
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Affiliation(s)
- Shixiong Tang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China
| | - Zhipeng Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
| | - Xudong Chen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Guowei Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Wenjian Tan
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Dayi Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jie Yang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yicheng Long
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
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24
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Ramos-Loyo J, Olguín-Rodríguez PV, Espinosa-Denenea SE, Llamas-Alonso LA, Rivera-Tello S, Müller MF. EEG functional brain connectivity strengthens with age during attentional processing to faces in children. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:890906. [PMID: 36926063 PMCID: PMC10013043 DOI: 10.3389/fnetp.2022.890906] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 09/15/2022] [Indexed: 03/18/2023]
Abstract
Studying functional connectivity may generate clues to the maturational changes that occur in children, expressed by the dynamical organization of the functional network assessed by electroencephalographic recordings (EEG). In the present study, we compared the EEG functional connectivity pattern estimated by linear cross-correlations of the electrical brain activity of three groups of children (6, 8, and 10 years of age) while performing odd-ball tasks containing facial stimuli that are chosen considering their importance in socioemotional contexts in everyday life. On the first task, the children were asked to identify the sex of faces, on the second, the instruction was to identify the happy expressions of the faces. We estimated the stable correlation pattern (SCP) by the average cross-correlation matrix obtained separately for the resting state and the task conditions and quantified the similarity of these average matrices comparing the different conditions. The accuracy improved with higher age. Although the topology of the SCPs showed high similarity across all ages, the two older groups showed a higher correlation between regions associated with the attentional and face processing networks compared to the youngest group. Only in the youngest group, the similarity metric decreased during the sex condition. In general, correlation values strengthened with age and during task performance compared to rest. Our findings indicate that there is a spatially extended stable brain network organization in children like that reported in adults. Lower similarity scores between several regions in the youngest children might indicate a lesser ability to cope with tasks. The brain regions associated with the attention and face networks presented higher synchronization across regions with increasing age, modulated by task demands.
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Affiliation(s)
- Julieta Ramos-Loyo
- Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Paola V Olguín-Rodríguez
- Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México, México.,Centro de Ciencias de La Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | | | | | - Sergio Rivera-Tello
- Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Markus F Müller
- Centro de Ciencias de La Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México.,Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México.,Centro Internacional de Ciencias A. C., Cuernavaca, Morelos, México
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25
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Fan L, Xu H, Su J, Qin J, Gao K, Ou M, Peng S, Shen H, Li N. Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics. Brain Behav 2021; 11:e2414. [PMID: 34775693 PMCID: PMC8671791 DOI: 10.1002/brb3.2414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 09/23/2021] [Accepted: 10/13/2021] [Indexed: 11/06/2022] Open
Abstract
Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject-specific time-varying evolution of a set of overlapping group-level sparse connectivity components (SCCs) based on the resting-state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter-group differences in connectivity patterns. Nevertheless, subject-specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave-one-out cross-validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time-resolved functional connectivity.
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Affiliation(s)
- Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Huaze Xu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Jian Qin
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Kai Gao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Min Ou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Song Peng
- Radiology Department, Xiangya 3rd Hospital, Central South University, Changsha, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Na Li
- Radiology Department, Xiangya 3rd Hospital, Central South University, Changsha, China
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26
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López-Vicente M, Agcaoglu O, Pérez-Crespo L, Estévez-López F, Heredia-Genestar JM, Mulder RH, Flournoy JC, van Duijvenvoorde ACK, Güroğlu B, White T, Calhoun V, Tiemeier H, Muetzel RL. Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence. Front Syst Neurosci 2021; 15:724805. [PMID: 34880732 PMCID: PMC8645798 DOI: 10.3389/fnsys.2021.724805] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
The longitudinal study of typical neurodevelopment is key for understanding deviations due to specific factors, such as psychopathology. However, research utilizing repeated measurements remains scarce. Resting-state functional magnetic resonance imaging (MRI) studies have traditionally examined connectivity as 'static' during the measurement period. In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. Resting-state MRI data were acquired at the ages of 10 (range 8-to-12, n = 3,327) and 14 (range 13-to-15, n = 2,404) years old using a single, study-dedicated 3 Tesla scanner. A fully-automated spatially constrained group-independent component analysis (ICA) was applied to decompose multi-subject resting-state data into functionally homogeneous regions. Dynamic functional network connectivity (FNC) between all ICA time courses were computed using a tapered sliding window approach. We used a k-means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. We examined age and sex associations using linear mixed-effects models. First, independent from the dynamic states, we found a general increase in the temporal variability of the connections between intrinsic connectivity networks with increasing age. Second, when examining the clusters of dynamic FNC windows, we observed that the time spent in less modularized states, with low intra- and inter-network connectivity, decreased with age. Third, the number of transitions between states also decreased with age. Finally, compared to boys, girls showed a more mature pattern of dynamic brain connectivity, indicated by more time spent in a highly modularized state, less time spent in specific states that are frequently observed at a younger age, and a lower number of transitions between states. This longitudinal population-based study demonstrates age-related maturation in dynamic intrinsic neural activity from childhood into adolescence and offers a meaningful baseline for comparison with deviations from typical development. Given that several behavioral and cognitive processes also show marked changes through childhood and adolescence, dynamic functional connectivity should also be explored as a potential neurobiological determinant of such changes.
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Affiliation(s)
- Mónica López-Vicente
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | | | - Fernando Estévez-López
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | | | - Rosa H. Mulder
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - John C. Flournoy
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Anna C. K. van Duijvenvoorde
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
- Department of Developmental and Educational Psychology, Leiden University, Leiden, Netherlands
| | - Berna Güroğlu
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
- Department of Developmental and Educational Psychology, Leiden University, Leiden, Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ryan L. Muetzel
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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27
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Kupis L, Goodman ZT, Kornfeld S, Hoang S, Romero C, Dirks B, Dehoney J, Chang C, Spreng RN, Nomi JS, Uddin LQ. Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan. Cereb Cortex 2021; 31:5263-5274. [PMID: 34145442 PMCID: PMC8491685 DOI: 10.1093/cercor/bhab156] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 11/14/2022] Open
Abstract
The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Zachary T Goodman
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Salome Kornfeld
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Stephanie Hoang
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Joseph Dehoney
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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28
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Gonneaud J, Baria AT, Pichet Binette A, Gordon BA, Chhatwal JP, Cruchaga C, Jucker M, Levin J, Salloway S, Farlow M, Gauthier S, Benzinger TLS, Morris JC, Bateman RJ, Breitner JCS, Poirier J, Vachon-Presseau E, Villeneuve S. Accelerated functional brain aging in pre-clinical familial Alzheimer's disease. Nat Commun 2021; 12:5346. [PMID: 34504080 PMCID: PMC8429427 DOI: 10.1038/s41467-021-25492-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/06/2021] [Indexed: 01/02/2023] Open
Abstract
Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18-94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
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Affiliation(s)
- Julie Gonneaud
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Alex T Baria
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Alexa Pichet Binette
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Brian A Gordon
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jasmeer P Chhatwal
- Brigham and Women's Hospital-Massachusetts General Hospital, Boston, MA, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Mathias Jucker
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Johannes Levin
- Ludwig-Maximilians-Universität München, German Center for Neurodegenerative Diseases and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Martin Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Serge Gauthier
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C S Breitner
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Judes Poirier
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Etienne Vachon-Presseau
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Faculty of Dentistry, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Sylvia Villeneuve
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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29
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Rabuffo G, Fousek J, Bernard C, Jirsa V. Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest. eNeuro 2021; 8:ENEURO.0283-21.2021. [PMID: 34583933 PMCID: PMC8555887 DOI: 10.1523/eneuro.0283-21.2021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/11/2021] [Accepted: 09/15/2021] [Indexed: 01/20/2023] Open
Abstract
At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent functional connectivity (FC) states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathologic brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks (RSNs). The ignition and subsequent propagation of cascades depend on the brain state and connectivity of each region. The largest cascades produce bursts of blood oxygen level-dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated RSN dynamics. We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of FC comprising BOLD co-activation (CA) bursts in mice and human functional magnetic resonance imaging (fMRI). We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest.
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Affiliation(s)
- Giovanni Rabuffo
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Jan Fousek
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Christophe Bernard
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Viktor Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
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30
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Lei T, Liao X, Chen X, Zhao T, Xu Y, Xia M, Zhang J, Xia Y, Sun X, Wei Y, Men W, Wang Y, Hu M, Zhao G, Du B, Peng S, Chen M, Wu Q, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence. Cereb Cortex 2021; 32:1024-1039. [PMID: 34378030 DOI: 10.1093/cercor/bhab263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/14/2022] Open
Abstract
Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.
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Affiliation(s)
- Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaochen Sun
- Department of Linguistics, Beijing Language and Culture University, Beijing 100083, China
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Bin Du
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
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31
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Coblentz A, Elias GJB, Boutet A, Germann J, Algarni M, Oliveira LM, Neudorfer C, Widjaja E, Ibrahim GM, Kalia SK, Jain M, Lozano AM, Fasano A. Mapping efficacious deep brain stimulation for pediatric dystonia. J Neurosurg Pediatr 2021; 27:346-356. [PMID: 33385998 DOI: 10.3171/2020.7.peds20322] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/21/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The objective of this study was to report the authors' experience with deep brain stimulation (DBS) of the internal globus pallidus (GPi) as a treatment for pediatric dystonia, and to elucidate substrates underlying clinical outcome using state-of-the-art neuroimaging techniques. METHODS A retrospective analysis was conducted in 11 pediatric patients (6 girls and 5 boys, mean age 12 ± 4 years) with medically refractory dystonia who underwent GPi-DBS implantation between June 2009 and September 2017. Using pre- and postoperative MRI, volumes of tissue activated were modeled and weighted by clinical outcome to identify brain regions associated with clinical outcome. Functional and structural networks associated with clinical benefits were also determined using large-scale normative data sets. RESULTS A total of 21 implanted leads were analyzed in 11 patients. The average follow-up duration was 19 ± 20 months (median 5 months). Using a 7-point clinical rating scale, 10 patients showed response to treatment, as defined by scores < 3. The mean improvement in the Burke-Fahn-Marsden Dystonia Rating Scale motor score was 40% ± 23%. The probabilistic map of efficacy showed that the voxel cluster most associated with clinical improvement was located at the posterior aspect of the GPi, comparatively posterior and superior to the coordinates of the classic GPi target. Strong functional and structural connectivity was evident between the probabilistic map and areas such as the precentral and postcentral gyri, parietooccipital cortex, and brainstem. CONCLUSIONS This study reported on a series of pediatric patients with dystonia in whom GPi-DBS resulted in variable clinical benefit and described a clinically favorable stimulation site for this cohort, as well as its structural and functional connectivity. This information could be valuable for improving surgical planning, simplifying programming, and further informing disease pathophysiology.
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Affiliation(s)
- Ailish Coblentz
- 1Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto
| | | | - Alexandre Boutet
- 2University Health Network, Toronto
- 3Joint Department of Medical Imaging, University of Toronto
| | | | - Musleh Algarni
- 4Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Division of Neurology, University of Toronto
| | - Lais M Oliveira
- 4Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Division of Neurology, University of Toronto
| | | | - Elysa Widjaja
- 1Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto
| | - George M Ibrahim
- 5Department of Neurosurgery, The Hospital for Sick Children, Toronto
| | - Suneil K Kalia
- 3Joint Department of Medical Imaging, University of Toronto
- 7Krembil Brain Institute, Toronto; and
- 8Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, Ontario, Canada
| | - Mehr Jain
- 6Faculty of Medicine, University of Ottawa
| | | | - Alfonso Fasano
- 4Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Division of Neurology, University of Toronto
- 7Krembil Brain Institute, Toronto; and
- 8Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, Ontario, Canada
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32
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Fan L, Zhong Q, Qin J, Li N, Su J, Zeng LL, Hu D, Shen H. Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics. Hum Brain Mapp 2020; 42:1416-1433. [PMID: 33283954 PMCID: PMC7927310 DOI: 10.1002/hbm.25303] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 01/04/2023] Open
Abstract
Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.
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Affiliation(s)
- Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Qi Zhong
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Jian Qin
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
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33
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Nenning KH, Xu T, Schwartz E, Arroyo J, Woehrer A, Franco AR, Vogelstein JT, Margulies DS, Liu H, Smallwood J, Milham MP, Langs G. Joint embedding: A scalable alignment to compare individuals in a connectivity space. Neuroimage 2020; 222:117232. [PMID: 32771618 PMCID: PMC7779372 DOI: 10.1016/j.neuroimage.2020.117232] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/31/2020] [Accepted: 08/02/2020] [Indexed: 11/15/2022] Open
Abstract
A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.
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Affiliation(s)
- Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jesus Arroyo
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Hesheng Liu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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34
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Wen X, Wang R, Yin W, Lin W, Zhang H, Shen D. Development of Dynamic Functional Architecture during Early Infancy. Cereb Cortex 2020; 30:5626-5638. [PMID: 32537641 DOI: 10.1093/cercor/bhaa128] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open
Abstract
Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the "global" (entire brain), "mesoscale" (functional system), and "local" (brain region) levels in the first 2 years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamic is linearly increased; 2) the high-order functional systems tend to display increased FC dynamics for both within- and between-network connections, while the primary systems show the opposite trajectories; and 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured toward a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first 2 postnatal years, explaining why infants have rapidly developing high-order cognitive functions and complex behaviors.
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Affiliation(s)
- Xuyun Wen
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rifeng Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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35
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Fan L, Su J, Qin J, Hu D, Shen H. A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction. Front Neurosci 2020; 14:881. [PMID: 33013292 PMCID: PMC7461846 DOI: 10.3389/fnins.2020.00881] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/28/2020] [Indexed: 02/01/2023] Open
Abstract
Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.
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Affiliation(s)
| | | | | | | | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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36
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Iraji A, Faghiri A, Lewis N, Fu Z, Rachakonda S, Calhoun VD. Tools of the trade: estimating time-varying connectivity patterns from fMRI data. Soc Cogn Affect Neurosci 2020; 16:849-874. [PMID: 32785604 PMCID: PMC8343585 DOI: 10.1093/scan/nsaa114] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/24/2020] [Accepted: 08/05/2020] [Indexed: 01/04/2023] Open
Abstract
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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37
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Northoff G, Lamme V. Neural signs and mechanisms of consciousness: Is there a potential convergence of theories of consciousness in sight? Neurosci Biobehav Rev 2020; 118:568-587. [PMID: 32783969 DOI: 10.1016/j.neubiorev.2020.07.019] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/03/2020] [Accepted: 07/16/2020] [Indexed: 11/18/2022]
Abstract
Various theories for the neural basis of consciousness have been proposed, suggesting a diversity of neural signs and mechanisms. We ask to what extent this diversity is real, or whether many theories share the same basic ideas with a potential for convergence towards a more unified theory of the neural basis of consciousness. For that purpose, we review and compare the various neural signs, measures, and mechanisms proposed in the different theories. We demonstrate that different theories focus on neural signs and measures of distinct aspects of neural activity including stimulus-related, prestimulus, and resting state activity as well as on distinct features of consciousness. Therefore, the various mechanisms proposed in the different theories may, in part, complement each other. Together, we provide insight into the shared basis and convergences (and, in part, discrepancies) of the different theories of consciousness. We conclude that the different theories concern distinct aspects of both neural activity and consciousness which, as we suppose, may be integrated and nested within the brain's overall temporo-spatial dynamics.
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Affiliation(s)
- Georg Northoff
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Canada; Centre for Research Ethics & Bioethics, University of Uppsala, Uppsala, Sweden.
| | - Victor Lamme
- Amsterdam Brain and Cognition (ABC), Department of Psychology, University of Amsterdam, the Netherlands
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38
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Battaglia D, Boudou T, Hansen ECA, Lombardo D, Chettouf S, Daffertshofer A, McIntosh AR, Zimmermann J, Ritter P, Jirsa V. Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan. Neuroimage 2020; 222:117156. [PMID: 32698027 DOI: 10.1016/j.neuroimage.2020.117156] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 05/25/2020] [Accepted: 07/07/2020] [Indexed: 12/14/2022] Open
Abstract
Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.
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Affiliation(s)
- Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Thomas Boudou
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; ENSTA ParisTech, F-91762, Palaiseau, France.
| | - Enrique C A Hansen
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; Institut de biologie de l'Ecole normale supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Université Paris, F-75005, Paris, France.
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Sabrina Chettouf
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany; Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Joelle Zimmermann
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany.
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
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39
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Zhao L, Zeng W, Shi Y, Nie W, Yang J. Dynamic visual cortical connectivity analysis based on functional magnetic resonance imaging. Brain Behav 2020; 10:e01698. [PMID: 32506636 PMCID: PMC7375061 DOI: 10.1002/brb3.1698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 04/10/2020] [Accepted: 05/09/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Studies of brain functional connectivity (FC) and effective connectivity (EC) using the functional magnetic resonance imaging (fMRI) have advanced our understanding of functional organization on visual cortex of human brain. The current studies mainly focus on static or dynamic connectivity, while the relationships between them have not been well characterized especially for static EC (sEC) and dynamic EC (dEC), as well as the consistency characteristics of changing trend of dFCs and dECs, which is of great importance to reveal the neural information processing mechanism in visual cortex region. METHOD In this study, we explore these relationships among several subareas of human visual cortex (V1-V5) by calculating the connection intensity and information flow among them over time by sliding window method, which are defined by Pearson correlation coefficient and Granger causality analysis, respectively, in each window. RESULTS The results demonstrate that there are extensive connections existing in human visual network, which are time-varying both in resting and task-related states. sFC intensity is negatively correlated with the variance of dFC, while sEC intensity is positively correlated with the variance of dEC. Furthermore, we also find that dFC within visual cortex at rest shows more consistency, while dEC shows less compared with task state in changing trend. CONCLUSION Therefore, this study provides novel findings about dynamics of connectivity in human visual cortex from the perspective of functional and effective connectivity.
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Affiliation(s)
- Le Zhao
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.,Department of Neurology, Shanghai Sixth People's Hospital East Affiliated to Shanghai University of Medicine & Health Science, Shanghai, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.,Department of Neurology, Shanghai Sixth People's Hospital East Affiliated to Shanghai University of Medicine & Health Science, Shanghai, China
| | - Yuhu Shi
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.,Department of Neurology, Shanghai Sixth People's Hospital East Affiliated to Shanghai University of Medicine & Health Science, Shanghai, China
| | - Weifang Nie
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.,Department of Neurology, Shanghai Sixth People's Hospital East Affiliated to Shanghai University of Medicine & Health Science, Shanghai, China
| | - Jiajun Yang
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.,Department of Neurology, Shanghai Sixth People's Hospital East Affiliated to Shanghai University of Medicine & Health Science, Shanghai, China
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40
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Huang SY, Hsu JL, Lin KJ, Hsiao IT. A Novel Individual Metabolic Brain Network for 18F-FDG PET Imaging. Front Neurosci 2020; 14:344. [PMID: 32477042 PMCID: PMC7235322 DOI: 10.3389/fnins.2020.00344] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/23/2020] [Indexed: 02/06/2023] Open
Abstract
Introduction Metabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer’s disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities. Method First, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD. Results Our results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters. Conclusion We proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.
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Affiliation(s)
- Sheng-Yao Huang
- Department of Medical Imaging and Radiological Sciences, Healthy Aging Research Center, Taoyuan, Taiwan.,Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Jung-Lung Hsu
- Department of Neurology, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan.,Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Neuroscience Research Center, Chang-Gung University, Taoyuan, Taiwan.,Graduate Institute of Humanities in Medicine and Research Center for Brain and Consciousness, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Kun-Ju Lin
- Department of Medical Imaging and Radiological Sciences, Healthy Aging Research Center, Taoyuan, Taiwan.,Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Ing-Tsung Hsiao
- Department of Medical Imaging and Radiological Sciences, Healthy Aging Research Center, Taoyuan, Taiwan.,Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
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41
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Zhang G, Cai B, Zhang A, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:488-498. [PMID: 31329112 DOI: 10.1109/tmi.2019.2929959] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task since it reflects the spontaneous brain activity without an external stimulus. The sliding window method has been successfully used to extract dFNC but typically assumes a fixed window size. The hidden Markov model (HMM) based method is an alternative approach for estimating time-varying connectivity. In this paper, we propose a sparse HMM based on Gaussian HMM and Gaussian graphical model (GGM). In this model, the time-varying neural processes are represented as discrete brain states which are described with functional connectivity networks. By enforcing the sparsity on the precision matrix, we can get interpretable connectivity between different functional regions. The optimization of our model can be realized with the expectation maximization (EM) and graphical least absolute shrinkage and selection operator (glasso) algorithms. The proposed model is validated on both simulated blood oxygenation-level dependent (BOLD) time series and rs-fMRI data. Results indicate that the proposed model can capture both stationary and abrupt brain activity fluctuations. We also compare dFNC patterns between children and young adults from the Philadelphia Neurodevelopmental Cohort (PNC) study. Both spatial and temporal behavior of the dFNC are analyzed and compared. The results provide insight into the developmental trajectory across childhood and motivate further research on brain connectivity.
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42
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Cai B, Zhang G, Zhang A, Hu W, Stephen JM, Wilson TW, Calhoun VD, Wang YP. A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity. J Neurosci Methods 2020; 332:108531. [PMID: 31830544 PMCID: PMC10187053 DOI: 10.1016/j.jneumeth.2019.108531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/18/2019] [Accepted: 11/21/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings. METHODS We propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using both the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. RESULTS Our results indicate that females and males in young adult group possess substantial difference related to visual network. In addition, some other consistent conclusions have been reached by using these two datasets. Furthermore, the GICA-TVGL model indicated that females had a higher probability to stay in a stable state. Males had a higher tendency to remain in a globally disconnected mode. COMPARISON WITH EXISTING METHOD The performance of sliding window approach is largely affected by the window size selection. In addition, it also assumes temporal locality hypothesis. CONCLUSION Our proposed framework provides a feasible method to investigate brain dynamics and has the potential to become a widely used tool in neuroimaging studies.
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43
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Dimitriadis SI, Simos PG, Fletcher JΜ, Papanicolaou AC. Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia. Brain Sci 2019; 9:E380. [PMID: 31888230 PMCID: PMC6956162 DOI: 10.3390/brainsci9120380] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/22/2019] [Accepted: 12/12/2019] [Indexed: 12/03/2022] Open
Abstract
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8-60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity.
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Affiliation(s)
- Stavros I. Dimitriadis
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
- School of Psychology, Cardiff University, Cardiff CF10 3AT, UK
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
| | - Panagiotis G. Simos
- School of Medicine, University of Crete, Herakleion 70013, Greece;
- Institute of Computer Science, Foundation for Research and Technology, Herakleion 70013, Greece
| | - Jack Μ. Fletcher
- Department of Psychology, University of Houston, Houston, Texas, TX 77204-5022, USA;
| | - Andrew C. Papanicolaou
- Division of Clinical Neurosciences, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA;
- Le Bonheur Neuroscience Institute, Le Bonheur Children’s Hospital, Memphis, TN 38103, USA
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44
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Long X, Kar P, Gibbard B, Tortorelli C, Lebel C. The brain's functional connectome in young children with prenatal alcohol exposure. Neuroimage Clin 2019; 24:102082. [PMID: 31795047 PMCID: PMC6889793 DOI: 10.1016/j.nicl.2019.102082] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 10/30/2019] [Accepted: 11/06/2019] [Indexed: 11/18/2022]
Abstract
Prenatal alcohol exposure (PAE) can lead to altered brain function and structure, as well as lifelong cognitive, behavioral, and mental health difficulties. Previous research has shown reduced brain network efficiency in older children and adolescents with PAE, but no imaging studies have examined brain differences in young children with PAE, at an age when cognitive and behavioral problems often first become apparent. The present study aimed to investigate the brain's functional connectome in young children with PAE using passive viewing fMRI. We analyzed 34 datasets from 26 children with PAE aged 2-7 years and 215 datasets from 87 unexposed typically-developing children in the same age range. The whole brain functional connectome was constructed using functional connectivity analysis across 90 regions for each dataset. We examined intra- and inter-participant stability of the functional connectome, graph theoretical measurements, and their correlations with age. Children with PAE had similar inter- and intra-participant stability to controls. However, children with PAE, but not controls, showed increasing intra-participant stability with age, suggesting a lack of variability of intrinsic brain activity over time. Inter-participant stability increased with age in controls but not in children with PAE, indicating more variability of brain function across the PAE population. Global graph metrics were similar between children with PAE and controls, in line with previous studies in older children. This study characterizes the functional connectome in young children with PAE for the first time, suggesting that the increased brain variability seen in older children develops early in childhood, when participants with PAE fail to show the expected age-related increases in inter-individual stability.
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Affiliation(s)
- Xiangyu Long
- Alberta Children's Hospital, 28 Oki Drive NW, Calgary T3B6A8, AB, Canada; Alberta Children's Hospital Research Institute, Canada; Hotchkiss Brain Institute, Canada
| | - Preeti Kar
- Alberta Children's Hospital Research Institute, Canada; Hotchkiss Brain Institute, Canada
| | - Ben Gibbard
- Alberta Children's Hospital Research Institute, Canada; Department of Pediatrics, University of Calgary, Canada
| | | | - Catherine Lebel
- Alberta Children's Hospital, 28 Oki Drive NW, Calgary T3B6A8, AB, Canada; Alberta Children's Hospital Research Institute, Canada; Hotchkiss Brain Institute, Canada.
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45
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Cao B, Chen Y, Yu R, Chen L, Chen P, Weng Y, Chen Q, Song J, Xie Q, Huang R. Abnormal dynamic properties of functional connectivity in disorders of consciousness. Neuroimage Clin 2019; 24:102071. [PMID: 31795053 PMCID: PMC6881656 DOI: 10.1016/j.nicl.2019.102071] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/09/2019] [Accepted: 11/04/2019] [Indexed: 01/01/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to research abnormal functional connectivity (FC) in patients with disorders of consciousness (DOC). However, most studies assumed steady spatial-temporal signal interactions between distinct brain regions during the scan period. The aim of this study was to explore abnormal dynamic functional connectivity (dFC) in DOC patients. After excluding 26 patients' data that failed to meet the requirements of imaging quality, we retained 19 DOC patients (12 with unresponsive wakefulness syndrome and 7 in a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised [CRS-R]) for the dFC analysis. We used the sliding windows approach to construct dFC matrices. Then these matrices were clustered into distinct states using the k-means clustering algorithm. We found that the DOC patients showed decreased dFC in the sensory and somatomotor networks compared with the healthy controls. There were also significant differences in temporal properties, the mean dwell time (MDT) and the number of transitions (NT), between the DOC patients and the healthy controls. In addition, we also used a hidden Markov model (HMM) to test the robustness of the results. With the connectome-based predictive modeling (CPM) approach, we found that the properties of abnormal dynamic network can be used to predict the CRS-R scores of the patients after severe brain injury. These findings may contribute to a better understanding of the abnormal brain networks in DOC patients.
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Affiliation(s)
- Bolin Cao
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Yan Chen
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Liuhuaqiao Hospital, Guangzhou 510010, China
| | - Ronghao Yu
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Liuhuaqiao Hospital, Guangzhou 510010, China
| | - Lixiang Chen
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Ping Chen
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Yihe Weng
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qinyuan Chen
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Jie Song
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiuyou Xie
- Department of Rehabilitation Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou 510280, China.
| | - Ruiwang Huang
- Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China.
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46
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Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest. Neuroimage 2019; 202:116129. [PMID: 31461679 DOI: 10.1016/j.neuroimage.2019.116129] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 08/16/2019] [Accepted: 08/23/2019] [Indexed: 01/26/2023] Open
Abstract
Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.
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47
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Cai B, Zhang G, Hu W, Zhang A, Zille P, Zhang Y, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Refined measure of functional connectomes for improved identifiability and prediction. Hum Brain Mapp 2019; 40:4843-4858. [PMID: 31355994 DOI: 10.1002/hbm.24741] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/26/2019] [Accepted: 07/13/2019] [Indexed: 11/08/2022] Open
Abstract
Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.
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Affiliation(s)
- Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Wenxing Hu
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Pascal Zille
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Yipu Zhang
- School of Electronics and Control Engineering, Chang'an University, Xi'an, Shaanxi, China
| | - Julia M Stephen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, Nebraska
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
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48
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Valsasina P, Hidalgo de la Cruz M, Filippi M, Rocca MA. Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis. Front Neurosci 2019; 13:618. [PMID: 31354402 PMCID: PMC6636554 DOI: 10.3389/fnins.2019.00618] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 05/29/2019] [Indexed: 01/27/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.
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Affiliation(s)
- Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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49
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Corps J, Rekik I. Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants. Sci Rep 2019; 9:9676. [PMID: 31273275 PMCID: PMC6609705 DOI: 10.1038/s41598-019-46145-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 06/24/2019] [Indexed: 11/16/2022] Open
Abstract
Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the ‘data-driven’ brain age using functional MRI (fMRI) and diffusion MRI (dMRI). Particularly, predicting the ‘brain age’ from connectomic data might help identify relevant connectional biomarkers of neurological disorders that emerge early or late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional connectomic data, here we unprecedentedly propose to predict the morphological age of the brain by solely using morphological brain networks (derived from T1-weighted images) in both healthy and disordered populations. Besides, although T1-weighted MRI was widely used for brain age prediction, it was leveraged from an image-based analysis perspective not from a connectomic perspective. Our method includes the following steps: (i) building multi-view morphological brain networks (M-MBN), (ii) feature extraction and selection, (iii) training a machine-learning regression model to predict age from M-MBN data, and (iv) utilizing our model to identify connectional brain features related to age in both autistic and healthy populations. We demonstrate that our method significantly outperforms existing approaches and discovered brain connectional morphological features that fingerprint the age of brain cortical morphology in both autistic and healthy individuals. In particular, we discovered that the connectional cortical thickness best predicts the morphological age of the autistic brain.
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Affiliation(s)
- Joshua Corps
- BASIRA lab, School of Science and Engineering, Computing, University of Dundee, Dundee, UK
| | - Islem Rekik
- BASIRA lab, School of Science and Engineering, Computing, University of Dundee, Dundee, UK. .,Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
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50
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Chen Y, Liu YN, Zhou P, Zhang X, Wu Q, Zhao X, Ming D. The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization. Front Physiol 2019; 9:1852. [PMID: 30662409 PMCID: PMC6328489 DOI: 10.3389/fphys.2018.01852] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Accepted: 12/07/2018] [Indexed: 01/23/2023] Open
Abstract
Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21-32 years), the adult (age 41-54 years), and the old (age 60-86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.
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Affiliation(s)
- Yuanyuan Chen
- College of Microelectronics, Tianjin University, Tianjin, China
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ya-nan Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Peng Zhou
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xiong Zhang
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Qiong Wu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xin Zhao
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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