201
|
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.
Collapse
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
| |
Collapse
|
202
|
Noble S, Mejia AF, Zalesky A, Scheinost D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc Natl Acad Sci U S A 2022; 119:e2203020119. [PMID: 35925887 PMCID: PMC9371642 DOI: 10.1073/pnas.2203020119] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.
Collapse
Affiliation(s)
- Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519
| | - Amanda F. Mejia
- Department of Statistics, Indiana University Bloomington, Bloomington, IN 47408
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519
- Department of Biomedical Engineering, Yale School of Medicine, New Haven, CT 06520
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520
- Department of Statistics and Data Science, Yale University, New Haven, CT 06511
- Child Study Center, Yale School of Medicine, New Haven, CT 06519
| |
Collapse
|
203
|
Kim E, Kim S, Kim Y, Cha H, Lee HJ, Lee T, Chang Y. Connectome-based predictive models using resting-state fMRI for studying brain aging. Exp Brain Res 2022; 240:2389-2400. [PMID: 35922524 DOI: 10.1007/s00221-022-06430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
Abstract
Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.
Collapse
Affiliation(s)
- Eunji Kim
- Department of Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Seungho Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Yunheung Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hyunsil Cha
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hui Joong Lee
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea
| | - Taekwan Lee
- Korea Brain Research Institute, Chumdanro 61, Dong-gu, Daegu, 41021, Republic of Korea.
| | - Yongmin Chang
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea.
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea.
- The Department of Molecular Medicine and Radiology, Kyungpook National University School of Medicine, 200 Dongduk-Ro Jung-Gu, Daegu, Korea.
| |
Collapse
|
204
|
Panda R, Thibaut A, Lopez-Gonzalez A, Escrichs A, Bahri MA, Hillebrand A, Deco G, Laureys S, Gosseries O, Annen J, Tewarie P. Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 2022; 11:e77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
Collapse
Affiliation(s)
- Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Ane Lopez-Gonzalez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of LiègeLiègeBelgium
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
- CERVO Research Center, Laval UniversityQuébecCanada
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of NottinghamNottinghamUnited Kingdom
| |
Collapse
|
205
|
Litwińczuk MC, Trujillo-Barreto N, Muhlert N, Cloutman L, Woollams A. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. Neuroimage 2022; 262:119531. [PMID: 35931312 DOI: 10.1016/j.neuroimage.2022.119531] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022] Open
Abstract
The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).
Collapse
Affiliation(s)
| | | | - Nils Muhlert
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Lauren Cloutman
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Anna Woollams
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| |
Collapse
|
206
|
Manglani HR, Fountain-Zaragoza S, Shankar A, Nicholas JA, Prakash RS. Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis. Brain Connect 2022; 12:502-514. [PMID: 34309408 PMCID: PMC10039278 DOI: 10.1089/brain.2021.0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (ninternal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (nvalidation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: rs = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (rs = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R2 = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: rs = 0.33 p = 0.049, MSE = 0.009, R2 = 13.4%; validation cohort: rs = 0.46, p = 0.005, MSE = 0.005, R2 = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.
Collapse
Affiliation(s)
- Heena R Manglani
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Stephanie Fountain-Zaragoza
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Anita Shankar
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | | | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| |
Collapse
|
207
|
Wang M, Wang L, Yang B, Yuan L, Wang X, Potenza MN, Dong GH. Disrupted dynamic network reconfiguration of the brain functional networks of individuals with autism spectrum disorder. Brain Commun 2022; 4:fcac177. [PMID: 35950094 PMCID: PMC9356733 DOI: 10.1093/braincomms/fcac177] [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: 12/17/2021] [Revised: 04/06/2022] [Accepted: 07/31/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Human and animal studies on brain functions in subjects with autism spectrum disorder have confirmed the aberrant organization of functional networks. However, little is known about the neural features underlying these impairments.
Using community structure analyses (recruitment and integration), the current study explored the functional network features of individuals with autism spectrum disorder from one database (101 individuals with autism spectrum disorder and 120 healthy controls) and tested the replicability in an independent database (50 individuals with autism spectrum disorder and 74 healthy controls). Additionally, the study divided subjects into different age groups and tested the features in different subgroups.
As for recruitment, subjects with autism spectrum disorder had lower coefficients in the default mode network and basal ganglia network than healthy controls. The integration results showed that subjects with autism spectrum disorder had a lower coefficient than healthy controls in the default mode network -medial frontal network and basal ganglia network -limbic networks. The results for the default mode network were mostly replicated in the independent database, but the results for the basal ganglia network were not. The results for different age groups were also analyzed, and the replicability was tested in different databases.
The lower recruitment in subjects with autism spectrum disorder suggests that they are less efficient at engaging these networks when performing relevant tasks. The lower integration results suggest impaired flexibility in cognitive functions in individuals with autism spectrum disorder. All these findings might explain why subjects with autism spectrum disorder show impaired brain networks and have important therapeutic implications for developing potentially effective interventions.
Collapse
Affiliation(s)
- Min Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Bo Yang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Lixia Yuan
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
| | - Xiuqin Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
| | - Marc N Potenza
- Department of Psychiatry and Child Study Center, Yale University School of Medicine , New Haven, CT , USA
- Connecticut Mental Health Center , New Haven, CT , USA
- Connecticut Council on Problem Gambling , Wethersfield, CT , USA
- Department of Neuroscience and Wu Tsai Institute, Yale University , New Haven, CT , USA
| | - Guang Heng Dong
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
| |
Collapse
|
208
|
Farahani FV, Karwowski W, D’Esposito M, Betzel RF, Douglas PK, Sobczak AM, Bohaterewicz B, Marek T, Fafrowicz M. Diurnal variations of resting-state fMRI data: A graph-based analysis. Neuroimage 2022; 256:119246. [PMID: 35477020 PMCID: PMC9799965 DOI: 10.1016/j.neuroimage.2022.119246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 02/18/2022] [Accepted: 04/22/2022] [Indexed: 12/31/2022] Open
Abstract
Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.
Collapse
Affiliation(s)
- Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA,Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA,Corresponding author: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA. (F.V. Farahani)
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA
| | - Mark D’Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA,Department of Psychology, University of California, Berkeley, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Pamela K. Douglas
- Institute for Simulation and Training, University of Central Florida, Orlando, FL, USA,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Anna Maria Sobczak
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland,Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, Warsaw, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland,Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland,Corresponding author. Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland. (M. Fafrowicz)
| |
Collapse
|
209
|
Fang X, Davis X, Flack KD, Duncan C, Li F, White M, Grilo C, Small DM. Dietary adaptation for weight loss maintenance at Yale (DAWLY): Protocol and predictions for a randomized controlled trial. Front Nutr 2022; 9:940064. [PMID: 35967820 PMCID: PMC9369668 DOI: 10.3389/fnut.2022.940064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Current therapies for obesity treatment are effective at producing short-term weight loss, but weight loss maintenance remains a significant challenge. Here we investigate the impact of pre-intervention dietary fat intake on the efficacy of a dietary supplement to support weight loss maintenance. Preclinical work demonstrates that a vagal afferent pathway critical for sensing dietary lipids is blunted by a high-fat diet (HFD), resulting in a reduced preference for a low-fat emulsion and severe blunting of the dopamine (DA) response to the gastric infusion of lipids. Infusion of the gut lipid messenger oleoylethanolamide (OEA), which is also depleted by HFD, immediately reverses this DA blunting and restores preference for the low-fat emulsion. Studies of OEA supplementation for weight loss in humans have had limited success. Given the strong effect of HFD on this pathway, we designed a study to test whether the efficacy of OEA as a weight loss treatment is related to pre-intervention habitual intake of dietary fat. Methods/Design We employed a randomized, double-blind, placebo-controlled trial in which 100 adults with overweight/obesity (OW/OB) were randomized to receive either OEA or placebo daily for 16 months. Following a baseline evaluation of diet, metabolic health, adiposity, and brain response to a palatable an energy dense food, participants in both groups underwent a 4-month behavioral weight loss intervention (LEARN®) followed by a 1-year maintenance period. The study aims are to (1) determine if pre-intervention dietary fat intake moderates the ability of OEA to improve weight loss and weight loss maintenance after a gold standard behavioral weight loss treatment; (2) identify biomarkers that predict outcome and optimize a stratification strategy; and (3) test a model underlying OEA's effectiveness. Discussion Focusing on interventions that target the gut-brain axis is supported by mounting evidence for the role of gut-brain signaling in food choice and the modulation of this circuit by diet. If successful, this work will provide support for targeting the gut-brain pathway for weight loss maintenance using a precision medicine approach that is easy and inexpensive to implement. Clinical Trial Registration [www.ClinicalTrials.gov], identifier [NCT04614233].
Collapse
Affiliation(s)
- Xi Fang
- Modern Diet and Physiology Research Center, New Haven, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Xue Davis
- Modern Diet and Physiology Research Center, New Haven, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Kyle D. Flack
- Department of Dietetics and Human Nutrition, College of Agriculture, Foods, and Environment, University of Kentucky, Lexington, KY, United States
| | - Chavonn Duncan
- Modern Diet and Physiology Research Center, New Haven, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Fangyong Li
- Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, United States
| | - Marney White
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, United States
| | - Carlos Grilo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Dana M. Small
- Modern Diet and Physiology Research Center, New Haven, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| |
Collapse
|
210
|
Kühnel A, Czisch M, Sämann PG, Binder EB, Kroemer NB. Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity. Biol Psychiatry 2022; 92:158-169. [PMID: 35260225 DOI: 10.1016/j.biopsych.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/09/2022] [Accepted: 01/13/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. METHODS Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. RESULTS We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder. CONCLUSIONS Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
Collapse
Affiliation(s)
- Anne Kühnel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | | | | | -
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
| | - Nils B Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| |
Collapse
|
211
|
Suo X, Zuo C, Lan H, Pan N, Zhang X, Kemp GJ, Wang S, Gong Q. COVID-19 vicarious traumatization links functional connectome to general distress. Neuroimage 2022; 255:119185. [PMID: 35398284 PMCID: PMC8986542 DOI: 10.1016/j.neuroimage.2022.119185] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 02/08/2023] Open
Abstract
As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.
Collapse
Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, PR China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, PR China.
| |
Collapse
|
212
|
DeMarco AT, van der Stelt C, Paul S, Dvorak E, Lacey E, Snider S, Turkeltaub PE. Absence of Perilesional Neuroplastic Recruitment in Chronic Poststroke Aphasia. Neurology 2022; 99:e119-e128. [PMID: 35508398 PMCID: PMC9280993 DOI: 10.1212/wnl.0000000000200382] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/25/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND AND OBJECTIVES A prominent theory proposes that neuroplastic recruitment of perilesional tissue supports aphasia recovery, especially when language-capable cortex is spared by smaller lesions. This theory has rarely been tested directly and findings have been inconclusive. We tested the perilesional plasticity hypothesis using 2 fMRI tasks in 2 groups of patients with previous aphasia diagnosis. METHODS Two cohorts totaling 82 patients with chronic left-hemisphere stroke with previous aphasia diagnosis and 82 control participants underwent fMRI using either a naming task or a reliable semantic decision task. Individualized perilesional tissue was defined by dilating anatomical lesions and language regions were defined using meta-analyses. Mixed modeling examined differences in activity between groups. Relationships with lesion size and aphasia severity were examined. RESULTS Patients exhibited reduced activity in perilesional language tissue relative to controls in both tasks. Although a few cortical regions exhibited greater activity irrespective of distance from the lesion, or only when distant from the lesion, no regions exhibited increased activity only when near the lesion. Larger lesions were associated with reduced language activity irrespective of distance from the lesion. Using the reliable fMRI task, reduced language activity was related to aphasia severity independent of lesion size. DISCUSSION We found no evidence for neuroplastic recruitment of perilesional tissue in aphasia beyond its typical role in language. Rather, our findings are consistent with alternative hypotheses that changes in left-hemisphere activation during recovery relate to normalization of language network dysfunction and possibly recruitment of alternate cortical processors. These findings clarify left-hemisphere neuroplastic mechanisms supporting language recovery after stroke.
Collapse
Affiliation(s)
- Andrew Tesla DeMarco
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Candace van der Stelt
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Sachi Paul
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Elizabeth Dvorak
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Elizabeth Lacey
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Sarah Snider
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC
| | - Peter E Turkeltaub
- From the Departments of Rehabilitation Medicine (A.T.D., P.E.T.) and Neurology (A.T.D., C.v.d.S., S.P., E.D., E.L., S.S., P.E.T.), Georgetown University; and MedStar National Rehabilitation Hospital (E.L., P.E.T.), Washington, DC.
| |
Collapse
|
213
|
Kwon H, Kim JI, Son SY, Jang YH, Kim BN, Lee HJ, Lee JM. Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels. Front Neurosci 2022; 16:935431. [PMID: 35873817 PMCID: PMC9301472 DOI: 10.3389/fnins.2022.935431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.
Collapse
Affiliation(s)
- Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, South Korea
| | - Seung-Yeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Yong Hun Jang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| |
Collapse
|
214
|
Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
Collapse
Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| |
Collapse
|
215
|
McCulloch DEW, Knudsen GM, Barrett FS, Doss MK, Carhart-Harris RL, Rosas FE, Deco G, Kringelbach ML, Preller KH, Ramaekers JG, Mason NL, Müller F, Fisher PM. Psychedelic resting-state neuroimaging: A review and perspective on balancing replication and novel analyses. Neurosci Biobehav Rev 2022; 138:104689. [PMID: 35588933 DOI: 10.1016/j.neubiorev.2022.104689] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/20/2022]
Abstract
Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.
Collapse
Affiliation(s)
| | - Gitte Moos Knudsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark; Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Frederick Streeter Barrett
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neuroscience and Department of Psychological and Brain Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Manoj K Doss
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robin Lester Carhart-Harris
- Neuroscape, Weill Institute for Neurosciences, University of California San Francisco, CA, USA; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark
| | - Katrin H Preller
- Pharmaco-Neuroimaging and Cognitive-Emotional Processing, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
| | | | - Natasha L Mason
- Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Felix Müller
- University of Basel, Department of Psychiatry (UPK), Basel, Switzerland
| | | |
Collapse
|
216
|
Charquero‐Ballester M, Kleim B, Vidaurre D, Ruff C, Stark E, Tuulari JJ, McManners H, Bar‐Haim Y, Bouquillon L, Moseley A, Williams SCR, Woolrich MW, Kringelbach ML, Ehlers A. Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks. Hum Brain Mapp 2022; 43:3207-3220. [PMID: 35393717 PMCID: PMC9188968 DOI: 10.1002/hbm.25846] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 12/03/2022] Open
Abstract
In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+ ) and healthy trauma-exposed controls (only mtDMN+ ). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function.
Collapse
Affiliation(s)
| | - Birgit Kleim
- Experimental Psychopathology and Psychotherapy, Department of PsychologyUniversity of ZurichZurichSwitzerland
- Department of Psychiatry, Psychotherapy and PsychosomaticsUniversity of ZurichZurichSwitzerland
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Christian Ruff
- Zurich Center for Neuroeconomics (ZNE), Department of EconomicsUniversity of ZurichZurichSwitzerland
| | - Eloise Stark
- Department of PsychiatryUniversity of OxfordOxfordUK
| | | | | | - Yair Bar‐Haim
- School of Psychological SciencesTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Linda Bouquillon
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Allison Moseley
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Mark W. Woolrich
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUK
- Scars of War FoundationThe Queen's CollegeOxfordUK
- Centre for Music in the BrainAarhus UniversityAarhusDenmark
| | - Anke Ehlers
- Oxford Centre for Anxiety Disorders and Trauma, Department of Experimental PsychologyUniversity of OxfordOxfordUK
- Oxford Health NHS Foundation TrustOxfordUK
| |
Collapse
|
217
|
Han S, Xu Y, Guo HR, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information. Hum Brain Mapp 2022; 43:4254-4265. [PMID: 35726798 PMCID: PMC9435007 DOI: 10.1002/hbm.25951] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.
Collapse
Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yinhuan Xu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Hui-Rong Guo
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Junying Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| |
Collapse
|
218
|
Zhu T, Becquey C, Chen Y, Lejuez CW, Li CSR, Bi J. Identifying alcohol misuse biotypes from neural connectivity markers and concurrent genetic associations. Transl Psychiatry 2022; 12:253. [PMID: 35710901 PMCID: PMC9203552 DOI: 10.1038/s41398-022-01983-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/18/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022] Open
Abstract
Alcohol use behaviors are highly heterogeneous, posing significant challenges to etiologic research of alcohol use disorder (AUD). Magnetic resonance imaging (MRI) provides intermediate endophenotypes in characterizing problem alcohol use and assessing the genetic architecture of addictive behavior. We used connectivity features derived from resting state functional MRI to subtype alcohol misuse (AM) behavior. With a machine learning pipeline of feature selection, dimension reduction, clustering, and classification we identified three AM biotypes-mild, comorbid, and moderate AM biotypes (MIA, COA, and MOA)-from a Human Connectome Project (HCP) discovery sample (194 drinkers). The three groups and controls (397 non-drinkers) demonstrated significant differences in alcohol use frequency during the heaviest 12-month drinking period (MOA > MIA; COA > non-drinkers) and were distinguished by connectivity features involving the frontal, parietal, subcortical and default mode networks. Further, COA relative to MIA, MOA and controls endorsed significantly higher scores in antisocial personality. A genetic association study identified that an alcohol use and antisocial behavior related variant rs16930842 from LINC01414 was significantly associated with COA. Using a replication HCP sample (28 drinkers and 46 non-drinkers), we found that subtyping helped in classifying AM from controls (area under the curve or AUC = 0.70, P < 0.005) in comparison to classifiers without subtyping (AUC = 0.60, not significant) and successfully reproduced the genetic association. Together, the results suggest functional connectivities as important features in classifying AM subgroups and the utility of reducing the heterogeneity in connectivity features among AM subgroups in advancing the research of etiological neural markers of AUD.
Collapse
Affiliation(s)
- Tan Zhu
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Chloe Becquey
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Yu Chen
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Carl W Lejuez
- Department of Psychological Sciences, College of Liberal Arts and Sciences, University of Connecticut, Storrs, CT, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
- Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jinbo Bi
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA.
| |
Collapse
|
219
|
Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Sheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nat Hum Behav 2022; 6:782-795. [PMID: 35241793 PMCID: PMC9232838 DOI: 10.1038/s41562-022-01301-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/14/2022] [Indexed: 11/15/2022]
Abstract
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
Collapse
Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Qi Lin
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Sheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA. .,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
| |
Collapse
|
220
|
He T, An L, Chen P, Chen J, Feng J, Bzdok D, Holmes AJ, Eickhoff SB, Yeo BTT. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nat Neurosci 2022; 25:795-804. [PMID: 35578132 PMCID: PMC9202200 DOI: 10.1038/s41593-022-01059-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/23/2022] [Indexed: 12/12/2022]
Abstract
We propose a simple framework-meta-matching-to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = -0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.
Collapse
Affiliation(s)
- Tong He
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Lijun An
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Pansheng Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal QC, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| |
Collapse
|
221
|
Ly MT, Scarneo-Miller SE, Lepley AS, Coleman K, Hirschhorn R, Yeargin S, Casa DJ, Chen CM. Combining MRI and cognitive evaluation to classify concussion in university athletes. Brain Imaging Behav 2022; 16:2175-2187. [PMID: 35639240 DOI: 10.1007/s11682-022-00687-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 11/26/2022]
Abstract
Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R2 = 0.32, p < .001), and performed worse on tests of concentration (∆R2 = 0.07, p < .05) and delayed memory (∆R2 = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.
Collapse
Affiliation(s)
- Monica T Ly
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA.
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA.
- Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA.
| | - Samantha E Scarneo-Miller
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- Division of Athletic Training, School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Adam S Lepley
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- School of Kinesiology, Exercise and Sport Science Initiative, University of Michigan, Ann Arbor, MI, USA
| | - Kelly Coleman
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
- Department of Health & Movement Sciences, Southern Connecticut State University, New Haven, CT, USA
| | - Rebecca Hirschhorn
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA
| | - Susan Yeargin
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Douglas J Casa
- Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA
| | - Chi-Ming Chen
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| |
Collapse
|
222
|
Cohesive parcellation of the human brain using resting-state fMRI. J Neurosci Methods 2022; 377:109629. [PMID: 35618164 DOI: 10.1016/j.jneumeth.2022.109629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/14/2022] [Accepted: 05/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
Collapse
|
223
|
Tinaz S, Kamel S, Aravala SS, Elfil M, Bayoumi A, Patel A, Scheinost D, Sinha R, Hampson M. Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial. Neuroimage Clin 2022; 34:102980. [PMID: 35247729 PMCID: PMC8897714 DOI: 10.1016/j.nicl.2022.102980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/23/2022] [Accepted: 02/28/2022] [Indexed: 11/06/2022]
Abstract
Parkinson’s disease causes difficulty with sustained motor performance. Insula and dorsomedial frontal cortex (dmFC) are implicated in motivating movement. Regulation of insula-dmFC functional connectivity with neurofeedback (NF) failed. Motor imagery practice regardless of NF improved motor function and body awareness. Visual imagery practice without NF also improved motor function.
Background Parkinson’s disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD. Methods We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10–12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively. Results The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome). Conclusions The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.
Collapse
Affiliation(s)
- Sule Tinaz
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA.
| | - Serageldin Kamel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Sai S Aravala
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Mohamed Elfil
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Ahmed Bayoumi
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Amar Patel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA
| | - Rajita Sinha
- Yale School of Medicine, Yale Stress Center, 2 Church St South, Suite 209, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA; Yale School of Medicine, Department of Neuroscience, 333 Cedar St, SHM-L-200, New Haven, CT 06510, USA
| | - Michelle Hampson
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA
| |
Collapse
|
224
|
Tejavibulya L, Peterson H, Greene A, Gao S, Rolison M, Noble S, Scheinost D. Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. Neuroimage 2022; 252:119040. [PMID: 35272202 PMCID: PMC9013515 DOI: 10.1016/j.neuroimage.2022.119040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 11/15/2022] Open
Abstract
Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole-brain functional connectivity (i.e., functional connectomes) due to handedness have been relatively understudied beyond pre-specified networks of interest. Here, we compared functional connectomes of left- and right-handed individuals at the whole brain level. We explored differences in functional connectivity of previously established regions of interest, and showed differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional connectivity of left- and right-handed individuals are not specific to networks of interest, but extend across every region of the brain. In particular, we found that connections between and within the cerebellum show distinct patterns of connectivity. To put these effects into context, we show that the effect sizes associated with handedness differences account for a similar amount of individual differences in the connectome as sex differences. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals and underscore that handedness effects are neurobiologically meaningful in addition to being statistically significant.
Collapse
Affiliation(s)
- Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Abigail Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD PhD Program, Yale School of Medicine, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| |
Collapse
|
225
|
The effect of perceived stress on cognition is mediated by personality and the underlying neural mechanism. Transl Psychiatry 2022; 12:199. [PMID: 35550503 PMCID: PMC9098451 DOI: 10.1038/s41398-022-01929-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 11/30/2022] Open
Abstract
Perceived stress impairs cognitive function across the adult lifespan, but the extent to which cognition decline is variable across individuals. Individual differences in the stress response are described as personality traits. Substantial individual differences in the magnitude of cognitive impairment that is induced by short-term perceived stress are poorly understood. The present study tested the hypothesis that the relationship between short-term perceived stress and different aspects of cognition is mediated by personality traits. The study included 1066 participants with behavior and neuroimaging data from the Human Connectome Project after excluding individuals with missing variables. In the result, the parallel multiple mediation model demonstrated that the influence of perceived stress on the total and crystalized cognition is mainly mediated by neuroticism (indirect effect = -0.04, p < 0.05) and conscientiousness (indirect effect = 0.05, p < 0.05) in adults. Cortical thickness value (n = 1066) of the right superior frontal gyrus (SFG) showed not only positive correlations with short-term perceived stress and neuroticism, but negative associations with cognition. The chain mediation model found that the right SFG and neuroticism play a small but significant chain mediating effect between stress and total cognition. The strength of the resting-state functional connectivity (n = 968) between the left orbitofrontal cortex versus the left superior medial frontal cortex was positively correlated with crystallized cognition and negatively associated with conscientiousness. These results extend previous findings by the impacts of short-term perceived stress on cognitive function is mediated by neuroticism and the right SFG was the underlying neural mechanism.
Collapse
|
226
|
Dan T, Huang Z, Cai H, Lyday RG, Laurienti PJ, Wu G. Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold. Hum Brain Mapp 2022; 43:3970-3986. [PMID: 35538672 PMCID: PMC9374896 DOI: 10.1002/hbm.25897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022] Open
Abstract
Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold‐based geometric neural network for functional brain networks (called “Geo‐Net4Net” for short) to learn the intrinsic low‐dimensional feature representations of resting‐state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low‐dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive‐definite (SPD) form of the correlation matrices. Due to the lack of well‐defined ground truth in the resting state, existing learning‐based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self‐supervise the feature representation learning of resting‐state functional networks by leveraging the task‐based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo‐Net4Net allows us to establish a more reasonable understanding of resting‐state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task‐based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo‐Net4Net not only achieves more accurate change detection results than other state‐of‐the‐art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function.
Collapse
Affiliation(s)
- Tingting Dan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zhuobin Huang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Robert G Lyday
- Department of Radiology, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Carolina Institute for Developmental Disabilities (CIDD), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,UNC NeuroScience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| |
Collapse
|
227
|
Sun J, Zhao R, He Z, Chang M, Wang F, Wei W, Zhang X, Zhu Y, Xi Y, Yang X, Qin W. Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective. Hum Brain Mapp 2022; 43:3824-3839. [PMID: 35524680 PMCID: PMC9294309 DOI: 10.1002/hbm.25886] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 12/25/2022] Open
Abstract
Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.
Collapse
Affiliation(s)
- Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Zhaoyang He
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Mengying Chang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Fumin Wang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Wei Wei
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Xiaodan Zhang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| |
Collapse
|
228
|
Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
Collapse
Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| |
Collapse
|
229
|
Lettieri G, Handjaras G, Setti F, Cappello EM, Bruno V, Diano M, Leo A, Ricciardi E, Pietrini P, Cecchetti L. Default and control network connectivity dynamics track the stream of affect at multiple timescales. Soc Cogn Affect Neurosci 2022; 17:461-469. [PMID: 34673987 PMCID: PMC9071410 DOI: 10.1093/scan/nsab112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/24/2021] [Accepted: 10/21/2021] [Indexed: 11/14/2022] Open
Abstract
In everyday life, the stream of affect results from the interaction between past experiences, expectations and the unfolding of events. How the brain represents the relationship between time and affect has been hardly explored, as it requires modeling the complexity of everyday life in the laboratory setting. Movies condense into hours a multitude of emotional responses, synchronized across subjects and characterized by temporal dynamics alike real-world experiences. Here, we use time-varying intersubject brain synchronization and real-time behavioral reports to test whether connectivity dynamics track changes in affect during movie watching. The results show that polarity and intensity of experiences relate to the connectivity of the default mode and control networks and converge in the right temporoparietal cortex. We validate these results in two experiments including four independent samples, two movies and alternative analysis workflows. Finally, we reveal chronotopic connectivity maps within the temporoparietal and prefrontal cortex, where adjacent areas preferentially encode affect at specific timescales.
Collapse
Affiliation(s)
- Giada Lettieri
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Giacomo Handjaras
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Francesca Setti
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Elisa Morgana Cappello
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Valentina Bruno
- MANIBUS Lab, Department of Psychology, University of Turin, Turin 10124, Italy
| | - Matteo Diano
- Department of Psychology, University of Turin, Turin 10124, Italy
| | - Andrea Leo
- Department of Translational Research and Advanced Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy
| | - Emiliano Ricciardi
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Pietro Pietrini
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| |
Collapse
|
230
|
Yeung AWK, More S, Wu J, Eickhoff SB. Reporting details of neuroimaging studies on individual traits prediction: A literature survey. Neuroimage 2022; 256:119275. [PMID: 35513295 DOI: 10.1016/j.neuroimage.2022.119275] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] Open
Abstract
Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.
Collapse
Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.
| | - Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
231
|
Brain functional connectivities that mediate the association between childhood traumatic events, and adult mental health and cognition. EBioMedicine 2022; 79:104002. [PMID: 35472671 PMCID: PMC9058958 DOI: 10.1016/j.ebiom.2022.104002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/16/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Childhood traumatic events are risk factors for psychopathology, but large-scale studies of how childhood traumatic events relate to mental health and cognition in adulthood, and how the brain is involved, are needed. METHODS The associations between childhood traumatic events (such as abuse and neglect, and defined by the 'Childhood Trauma' questions in the UK Biobank database) and brain functional connectivity, mental health problems, and cognitive performance were investigated by a univariate correlation analysis with 19,535 participants aged 45-79 from the UK Biobank dataset. The results were replicated with 17,747 independent participants in the second release from the same dataset. FINDINGS Childhood traumatic events were significantly associated with mental health problems in adulthood including anxiety (r=0.19, p<1.0 × 10-323), depression (r=0.21, p<1.0 × 10-323), and self-harm (r=0.24, p<1.0 × 10-323), and with adult cognitive performance including fluid intelligence (r=-0.05, p=2.8 × 10-10) and prospective memory (r=-0.04, p=6.8 × 10-8). Functional connectivities of the medial and lateral temporal cortex, the precuneus, the medial orbitofrontal cortex; and the superior, middle and inferior prefrontal cortex extending back to precentral regions were negatively correlated with the childhood traumatic events (FDR corrected, p<0.01). These lower functional connectivities significantly mediated the associations between childhood traumatic events and addiction, anxiety, depression and well-being (all p<1.0 × 10-3), and cognitive performance. The association between childhood traumatic events and behavioural measures and functional connectivity were confirmed in a replication with different participants in the second release of the UK Biobank dataset. INTERPRETATION Childhood traumatic events are strongly associated with adult mental health problems mediated by brain functional connectivities in brain areas involved in executive function, emotion, face processing, and memory. This understanding may help with prevention and treatment. FUNDING Funding was provided by the National Key R&D Program of China (No. 2018YFC1312900 and No. 2019YFA0709502).
Collapse
|
232
|
Wang X, Yoo K, Chen H, Zou T, Wang H, Gao Q, Meng L, Hu X, Li R. Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling. NPJ Parkinsons Dis 2022; 8:49. [PMID: 35459232 PMCID: PMC9033778 DOI: 10.1038/s41531-022-00315-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
Motor impairment is a core clinical feature of Parkinson’s disease (PD). Although the decoupled brain connectivity has been widely reported in previous neuroimaging studies, how the functional connectome is involved in motor dysfunction has not been well elucidated in PD patients. Here we developed a distributed brain signature by predicting clinical motor scores of PD patients across multicenter datasets (total n = 236). We decomposed the Pearson’s correlation into accordance and discordance via a temporal discrete procedure, which can capture coupling and anti-coupling respectively. Using different profiles of functional connectivity, we trained candidate predictive models and tested them on independent and heterogeneous PD samples. We showed that the antagonistic model measured by discordance had the best sensitivity and generalizability in all validations and it was dubbed as Parkinson’s antagonistic motor signature (PAMS). The PAMS was dominated by the subcortical, somatomotor, visual, cerebellum, default-mode, and frontoparietal networks, and the motor-visual stream accounted for the most part of predictive weights among network pairs. Additional stage-specific analysis showed that the predicted scores generated from the antagonistic model tended to be higher than the observed scores in the early course of PD, indicating that the functional signature may vary more sensitively with the neurodegenerative process than clinical behaviors. Together, these findings suggest that motor dysfunction of PD is represented as antagonistic interactions within multi-level brain systems. The signature shows great potential in the early motor evaluation and developing new therapeutic approaches for PD in the clinical realm.
Collapse
Affiliation(s)
- Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 610054, People's Republic of China.
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| |
Collapse
|
233
|
Zhi D, King M, Hernandez-Castillo CR, Diedrichsen J. Evaluating brain parcellations using the distance-controlled boundary coefficient. Hum Brain Mapp 2022; 43:3706-3720. [PMID: 35451538 PMCID: PMC9294308 DOI: 10.1002/hbm.25878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/13/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.
Collapse
Affiliation(s)
- Da Zhi
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada
| | - Maedbh King
- Department of Psychology, University of California, Berkeley, California, USA
| | | | - Jörn Diedrichsen
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada.,Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
| |
Collapse
|
234
|
Gbadeyan O, Teng J, Prakash RS. Predicting response time variability from task and resting-state functional connectivity in the aging brain. Neuroimage 2022; 250:118890. [PMID: 35007719 PMCID: PMC9063711 DOI: 10.1016/j.neuroimage.2022.118890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/22/2022] Open
Abstract
Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.
Collapse
Affiliation(s)
- Oyetunde Gbadeyan
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - James Teng
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA.
| |
Collapse
|
235
|
Bahrami M, Simpson SL, Burdette JH, Lyday RG, Quandt SA, Chen H, Arcury TA, Laurienti PJ. Altered Default Mode Network Associated with Pesticide Exposure in Latinx Children from Rural Farmworker Families. Neuroimage 2022; 256:119179. [PMID: 35429626 PMCID: PMC9251855 DOI: 10.1016/j.neuroimage.2022.119179] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/03/2022] [Accepted: 04/03/2022] [Indexed: 01/21/2023] Open
Abstract
Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.
Collapse
Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jonathan H Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert G Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sara A Quandt
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Haiying Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Thomas A Arcury
- Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| |
Collapse
|
236
|
Han S, Xu Y, Guo H, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis. Hum Brain Mapp 2022; 43:3037-3046. [PMID: 35384125 PMCID: PMC9188970 DOI: 10.1002/hbm.25833] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 01/31/2023] Open
Abstract
Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.
Collapse
|
237
|
Burdette JH, Bahrami M, Laurienti PJ, Simpson S, Nicklas BJ, Fanning J, Rejeski WJ. Longitudinal relationship of baseline functional brain networks with intentional weight loss in older adults. Obesity (Silver Spring) 2022; 30:902-910. [PMID: 35333443 PMCID: PMC8969753 DOI: 10.1002/oby.23396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The goal of this study was to determine whether the degree of weight loss after 6 months of a behavior-based intervention is related to baseline connectivity within two functional networks (FNs) of interest, FN1 and FN2, in a group of older adults with obesity. METHODS Baseline functional magnetic resonance imaging data were collected following an overnight fast in 71 older adults with obesity involved in a weight-loss intervention. Functional brain networks in a resting state and during a food-cue task were analyzed using a mixed-regression framework to examine the relationships between baseline networks and 6-month change in weight. RESULTS During the resting condition, the relationship of baseline brain functional connectivity and network clustering in FN1, which includes the visual cortex and sensorimotor areas, was significantly associated with 6-month weight loss. During the food-cue condition, 6-month weight loss was significantly associated with the relationship between baseline brain connectivity and network global efficiency in FN2, which includes executive control, attention, and limbic regions. CONCLUSION These findings provide further insight into complex functional circuits in the brain related to successful weight loss and may ultimately aid in developing tailored behavior-based treatment regimens that target specific brain circuitry.
Collapse
Affiliation(s)
- Jonathan H. Burdette
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Mohsen Bahrami
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Biomedical EngineeringVirginia Tech‐Wake Forest School of Biomedical Engineering and SciencesWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Paul J. Laurienti
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Sean L. Simpson
- Laboratory for Complex Brain NetworksWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Biostatistics and Data ScienceWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Barbara J. Nicklas
- Section on Geriatric MedicineDepartment of Internal MedicineWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Jason Fanning
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - W. Jack Rejeski
- Section on Geriatric MedicineDepartment of Internal MedicineWake Forest School of MedicineWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| |
Collapse
|
238
|
Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome. Commun Biol 2022; 5:261. [PMID: 35332230 PMCID: PMC8948277 DOI: 10.1038/s42003-022-03185-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 02/08/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints. Mantwill et al. analysed the relationship between functional connectivity markers that are specific to individuals (i.e. brain fingerprints) and those predictive of behaviour. They reveal that these markers are unrelated and suggest an alternative perspective on the basis of individual signatures.
Collapse
|
239
|
Zhang D, Zhou L, Yang A, Li S, Chang C, Liu J, Zhou K. A connectome-based neuromarker of nonverbal number acuity and arithmetic skills. Cereb Cortex 2022; 33:881-894. [PMID: 35254408 PMCID: PMC9890459 DOI: 10.1093/cercor/bhac108] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 02/04/2023] Open
Abstract
The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual's ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.
Collapse
Affiliation(s)
- Dai Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Anmin Yang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Shanshan Li
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Chunqi Chang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518060, China
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, No. 30, Shuangqing Street, Haidian District, Beijing 100084, China
| | - Ke Zhou
- Corresponding author: Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China.
| |
Collapse
|
240
|
Parelman JM, Doré BP, Cooper N, O’Donnell MB, Chan HY, Falk EB. Overlapping Functional Representations of Self- and Other-Related Thought are Separable Through Multivoxel Pattern Classification. Cereb Cortex 2022; 32:1131-1141. [PMID: 34398230 PMCID: PMC8924429 DOI: 10.1093/cercor/bhab272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 12/13/2022] Open
Abstract
Self-reflection and thinking about the thoughts and behaviors of others are important skills for humans to function in the social world. These two processes overlap in terms of the component processes involved, and share overlapping functional organizations within the human brain, in particular within the medial prefrontal cortex (MPFC). Several functional models have been proposed to explain these two processes, but none has directly explored the extent to which they are distinctly represented within different parts of the brain. This study used multivoxel pattern classification to quantify the separability of self- and other-related thought in the MPFC and expanded this question to the entire brain. Using a large-scale mega-analytic dataset, spanning three separate studies (n = 142), we find that self- and other-related thought can be reliably distinguished above chance within the MPFC, posterior cingulate cortex and temporal lobes. We highlight subcomponents of the ventral MPFC that are particularly important in representing self-related thought, and subcomponents of the orbitofrontal cortex robustly involved in representing other-related thought. Our findings indicate that representations of self- and other-related thought in the human brain are described best by a distributed pattern rather than stark localization or a purely ventral to dorsal linear gradient in the MPFC.
Collapse
Affiliation(s)
- Jacob M Parelman
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bruce P Doré
- Desautels Faculty of Management, McGill University, H3A 1G5, Montreal, Canada
| | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Hang-Yee Chan
- Amsterdam School of Communication Research, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
241
|
Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
Collapse
Affiliation(s)
- Sage Hahn
- Corresponding author: Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, 100 South Prospect Street Burlington, Vermont 05401, United States.
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| |
Collapse
|
242
|
Friedrich P, Patil KR, Mochalski LN, Li X, Camilleri JA, Kröll JP, Wiersch L, Eickhoff SB, Weis S. Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality. Brain Struct Funct 2022; 227:425-440. [PMID: 34882263 PMCID: PMC8844166 DOI: 10.1007/s00429-021-02418-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022]
Abstract
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework-based on machine learning-based classification-for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.
Collapse
Affiliation(s)
- Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| |
Collapse
|
243
|
Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
Collapse
Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
| |
Collapse
|
244
|
Han S, Zheng R, Li S, Zhou B, Jiang Y, Wang C, Wei Y, Pang J, Li H, Zhang Y, Chen Y, Cheng J. Integrative Functional, Molecular, and Transcriptomic Analyses of Altered Intrinsic Timescale Gradient in Depression. Front Neurosci 2022; 16:826609. [PMID: 35250462 PMCID: PMC8891525 DOI: 10.3389/fnins.2022.826609] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
The pathophysiology and pharmacology of depression are hypothesized to be related to the imbalance of excitation–inhibition that gives rise to hierarchical dynamics (or intrinsic timescale gradient), further supporting a hierarchy of cortical functions. On this assumption, intrinsic timescale gradient is theoretically altered in depression. However, it remains unknown. We investigated altered intrinsic timescale gradient recently developed to measure hierarchical brain dynamics gradient and its underlying molecular architecture and brain-wide gene expression in depression. We first presented replicable intrinsic timescale gradient in two independent Chinese Han datasets and then investigated altered intrinsic timescale gradient and its possible underlying molecular and transcriptional bases in patients with depression. As a result, patients with depression showed stage-specifically shorter timescales compared with healthy controls according to illness duration. The shorter timescales were spatially correlated with monoamine receptor/transporter densities, suggesting the underlying molecular basis of timescale aberrance and providing clues to treatment. In addition, we identified that timescale aberrance-related genes ontologically enriched for synapse-related and neurotransmitter (receptor) terms, elaborating the underlying transcriptional basis of timescale aberrance. These findings revealed atypical timescale gradient in depression and built a link between neuroimaging, transcriptome, and neurotransmitter information, facilitating an integrative understanding of depression.
Collapse
Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- *Correspondence: Shaoqiang Han,
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jianyue Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Yuan Chen,
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Jingliang Cheng,
| |
Collapse
|
245
|
Bahrami M, Laurienti PJ, Shappell HM, Dagenbach D, Simpson SL. A mixed-modeling framework for whole-brain dynamic network
analysis. Netw Neurosci 2022; 6:591-613. [PMID: 35733427 PMCID: PMC9208000 DOI: 10.1162/netn_a_00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/09/2022] [Indexed: 11/15/2022] Open
Abstract
The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.
Collapse
Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather M. Shappell
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| |
Collapse
|
246
|
Functional brain connectomes reflect acute and chronic cannabis use. Sci Rep 2022; 12:2449. [PMID: 35165360 PMCID: PMC8844352 DOI: 10.1038/s41598-022-06509-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
AbstractResting state fMRI has been employed to identify alterations in functional connectivity within or between brain regions following acute and chronic exposure to Δ9-tetrahydrocannabinol (THC), the psychoactive component in cannabis. Most studies focused a priori on a limited number of local brain areas or circuits, without considering the impact of cannabis on whole-brain network organization. The present study attempted to identify changes in the whole-brain human functional connectome as assessed with ultra-high field (7T) resting state scans of cannabis users (N = 26) during placebo and following vaporization of cannabis. Two distinct data-driven methodologies, i.e. network-based statistics (NBS) and connICA, were used to identify changes in functional connectomes associated with acute cannabis intoxication and history of cannabis use. Both methodologies revealed a broad state of hyperconnectivity within the entire range of major brain networks in chronic cannabis users compared to occasional cannabis users, which might be reflective of an adaptive network reorganization following prolonged cannabis exposure. The connICA methodology also extracted a distinct spatial connectivity pattern of hypoconnectivity involving the dorsal attention, limbic, subcortical and cerebellum networks and of hyperconnectivity between the default mode and ventral attention network, that was associated with the feeling of subjective high during THC intoxication. Whole-brain network approaches identified spatial patterns in functional brain connectomes that distinguished acute from chronic cannabis use, and offer an important utility for probing the interplay between short and long-term alterations in functional brain dynamics when progressing from occasional to chronic use of cannabis.
Collapse
|
247
|
Fallahi A, Pooyan M, Habibabadi JM, Hashemi-Fesharaki SS, Tabatabaei NH, Ay M, Nazem-Zadeh MR. A novel approach for extracting functional brain networks involved in mesial temporal lobe epilepsy based on self organizing maps. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
|
248
|
Tomlinson CE, Laurienti PJ, Lyday RG, Simpson SL. A regression framework for brain network distance metrics. Netw Neurosci 2022; 6:49-68. [PMID: 35350586 PMCID: PMC8942614 DOI: 10.1162/netn_a_00214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.
Collapse
Affiliation(s)
- Chal E. Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| |
Collapse
|
249
|
Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 2022; 246:118774. [PMID: 34861391 PMCID: PMC10569447 DOI: 10.1016/j.neuroimage.2021.118774] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/26/2021] [Accepted: 11/29/2021] [Indexed: 12/23/2022] Open
Abstract
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
Collapse
Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Boris Duka
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
| |
Collapse
|
250
|
Miranda M, Campo CG, Birba A, Neely A, Hernandez FDT, Faure E, Costa GR, Ibáñez A, García A. An action-concept processing advantage in a patient with a double motor cortex. Brain Cogn 2022; 156:105831. [PMID: 34922210 PMCID: PMC9944406 DOI: 10.1016/j.bandc.2021.105831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 02/08/2023]
Abstract
Patients with atrophy in motor brain regions exhibit selective deficits in processing action-related meanings, suggesting a link between movement conceptualization and the amount of regional tissue. Here we examine such a relation in a unique opposite model: a rare patient with a double cortex (due to subcortical band heterotopia) in primary/supplementary motor regions, and no double cortex in multimodal semantic regions. We measured behavioral performance in action- and object-concept processing as well and resting-state functional connectivity. Both dimensions involved comparisons with healthy controls. Results revealed preserved accuracy in action and object categories for the patient. However, unlike controls, the patient exhibited faster performance for action than object concepts, a difference that was uninfluenced by general cognitive abilities. Moreover, this pattern was accompanied by heightened functional connectivity between the bilateral primary motor cortices. This suggests that a functionally active double motor cortex may entail action-processing advantages. Our findings offer new constraints for models of action semantics and motor-region function at large.
Collapse
Affiliation(s)
- Magdalena Miranda
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Instituto de Neurociencia Cognitiva y Traslacional (INCyT), Buenos Aires, Argentina
| | - Cecilia Gonzalez Campo
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
| | - Agustina Birba
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina,Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Alejandra Neely
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | | | - Evelyng Faure
- Department of Radiology, Clínica las Condes, Santiago, Chile,Advanced Epilepsy Center, Clínica las Condes, Santiago, Chile
| | - Gonzalo Rojas Costa
- Department of Radiology, Clínica las Condes, Santiago, Chile,Advanced Epilepsy Center, Clínica las Condes, Santiago, Chile,Health Innovation Center, Clínica las Condes, Santiago, Chile
| | - Agustín Ibáñez
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina,Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile,Global Brain Health Institute, University of California-San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland
| | - Adolfo García
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute, University of California-San Francisco, San Francisco, CA, United States; and Trinity College Dublin, Dublin, Ireland; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
| |
Collapse
|