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Del Mauro G, Li Y, Wang Z. Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables. J Neurosci Methods 2024; 409:110205. [PMID: 38914376 DOI: 10.1016/j.jneumeth.2024.110205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
BACKGROUND Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels. NEW METHOD We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC). RESULTS Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions. COMPARISON WITH EXISTING METHOD Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately. CONCLUSIONS Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Yiran Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States.
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Lahnakoski JM, Nolte T, Solway A, Vilares I, Hula A, Feigenbaum J, Lohrenz T, King-Casas B, Fonagy P, Montague PR, Schilbach L. A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity. J Affect Disord 2024; 360:345-353. [PMID: 38806064 DOI: 10.1016/j.jad.2024.05.125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AIMS Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. METHOD Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data. RESULTS Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LIMITATIONS The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. CONCLUSIONS Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.
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Affiliation(s)
- Juha M Lahnakoski
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany.
| | - Tobias Nolte
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Anna Freud National Centre for Children and Families, London, United Kingdom
| | - Alec Solway
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Andreas Hula
- Austrian Institute of Technology, Vienna, Austria
| | - Janet Feigenbaum
- Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - Terry Lohrenz
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Brooks King-Casas
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Peter Fonagy
- Anna Freud National Centre for Children and Families, London, United Kingdom; Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - P Read Montague
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Physics, Virginia Tech, Blacksburg, VA, USA; Department of Psychiatry and Behavioral Medicine, Virginia Tech Carilion School of Medicine, Virginia Tech, Roanoke, VA, USA
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
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Kong Y, Roser M, Bègue I, Elandaloussi Y, Neu N, Grigis A, Duchesnay E, Leboyer M, Houenou J, Laidi C. Cerebellum and social abilities: A structural and functional connectivity study in a transdiagnostic sample. Hum Brain Mapp 2024; 45:e26749. [PMID: 38989605 PMCID: PMC11237877 DOI: 10.1002/hbm.26749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 07/12/2024] Open
Abstract
The cerebellum has been involved in social abilities and autism. Given that the cerebellum is connected to the cortex via the cerebello-thalamo-cortical loop, the connectivity between the cerebellum and cortical regions involved in social interactions, that is, the right temporo-parietal junction (rTPJ) has been studied in individuals with autism, who suffer from prototypical deficits in social abilities. However, existing studies with small samples of categorical, case-control comparisons have yielded inconsistent results due to the inherent heterogeneity of autism, suggesting that investigating how clinical dimensions are related to cerebellar-rTPJ functional connectivity might be more relevant. Therefore, our objective was to study the functional connectivity between the cerebellum and rTPJ, focusing on its association with social abilities from a dimensional perspective in a transdiagnostic sample. We analyzed structural magnetic resonance imaging (MRI) and functional MRI (fMRI) scans obtained during naturalistic films watching from a large transdiagnostic dataset, the Healthy Brain Network (HBN), and examined the association between cerebellum-rTPJ functional connectivity and social abilities measured with the social responsiveness scale (SRS). We conducted univariate seed-to-voxel analysis, multivariate canonical correlation analysis (CCA), and predictive support vector regression (SVR). We included 1404 subjects in the structural analysis (age: 10.516 ± 3.034, range: 5.822-21.820, 506 females) and 414 subjects in the functional analysis (age: 11.260 ± 3.318 years, range: 6.020-21.820, 161 females). Our CCA model revealed a significant association between cerebellum-rTPJ functional connectivity, full-scale IQ (FSIQ) and SRS scores. However, this effect was primarily driven by FSIQ as suggested by SVR and univariate seed-to-voxel analysis. We also demonstrated the specificity of the rTPJ and the influence of structural anatomy in this association. Our results suggest that there is a complex relationship between cerebellum-rTPJ connectivity, social performance and IQ. This relationship is specific to the cerebellum-rTPJ connectivity, and is largely related to structural anatomy in these two regions. PRACTITIONER POINTS: We analyzed cerebellum-right temporoparietal junction (rTPJ) connectivity in a pediatric transdiagnostic sample. We found a complex relationship between cerebellum and rTPJ connectivity, social performance and IQ. Cerebellum and rTPJ functional connectivity is related to structural anatomy in these two regions.
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Affiliation(s)
- Yue Kong
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Mathilde Roser
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
| | - Indrit Bègue
- Department of Psychiatry, Beth Israel Deaconess Medical School and Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Neuroimaging and Translational Psychiatry lab, Synapsy Center for Neuroscience and Mental Health Research, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Yannis Elandaloussi
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Nathan Neu
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Antoine Grigis
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | | | - Marion Leboyer
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- Fondation Fondamental, Créteil, France
| | - Josselin Houenou
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
| | - Charles Laidi
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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Lu J, Yan T, Yang L, Zhang X, Li J, Li D, Xiang J, Wang B. Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability. Neuroimage 2024; 295:120651. [PMID: 38788914 DOI: 10.1016/j.neuroimage.2024.120651] [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: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024] Open
Abstract
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
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Affiliation(s)
- Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 100081, China
| | - Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xi Zhang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaxin Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
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Treves IN, Kucyi A, Park M, Kral TRA, Goldberg SB, Davidson RJ, Rosenkranz M, Whitfield-Gabrieli S, Gabrieli JDE. Connectome predictive modeling of trait mindfulness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602725. [PMID: 39026870 PMCID: PMC11257611 DOI: 10.1101/2024.07.09.602725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Introduction Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. Methods To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. Results In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. Conclusions We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.
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Affiliation(s)
- Isaac N Treves
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
| | - Aaron Kucyi
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA
| | - Madelynn Park
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
| | - Tammi R A Kral
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI
| | - Simon B Goldberg
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI
- Department of Counseling Psychology, University of Wisconsin-Madison, Madison, WI
| | - Richard J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI
- Department of Psychology, University of Wisconsin-Madison, Madison, WI
| | - Melissa Rosenkranz
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - John D E Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
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Ramduny J, Uddin LQ, Vanderwal T, Feczko E, Fair DA, Kelly C, Baskin-Sommers A. Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.22.600221. [PMID: 38979302 PMCID: PMC11230295 DOI: 10.1101/2024.06.22.600221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive Development Study ( N = 5,733 ). Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Both methods retained more than 99% of Black and Hispanic youth. They produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and fulfill the promise of consortia datasets to produce generalizable effect sizes across diverse populations.
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Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA
| | - Lucina Q. Uddin
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Damien A. Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Arielle Baskin-Sommers
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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Pollatou A, Holland CM, Stockton TJ, Peterson BS, Scheinost D, Monk C, Spann MN. Mapping Early Brain-Body Interactions: Associations of Fetal Heart Rate Variation with Newborn Brainstem, Hypothalamic, and Dorsal Anterior Cingulate Cortex Functional Connectivity. J Neurosci 2024; 44:e2363232024. [PMID: 38604780 PMCID: PMC11140686 DOI: 10.1523/jneurosci.2363-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024] Open
Abstract
The autonomic nervous system (ANS) regulates the body's physiology, including cardiovascular function. As the ANS develops during the second to third trimester, fetal heart rate variability (HRV) increases while fetal heart rate (HR) decreases. In this way, fetal HR and HRV provide an index of fetal ANS development and future neurobehavioral regulation. Fetal HR and HRV have been associated with child language ability and psychomotor development behavior in toddlerhood. However, their associations with postbirth autonomic brain systems, such as the brainstem, hypothalamus, and dorsal anterior cingulate cortex (dACC), have yet to be investigated even though brain pathways involved in autonomic regulation are well established in older individuals. We assessed whether fetal HR and HRV were associated with the brainstem, hypothalamic, and dACC functional connectivity in newborns. Data were obtained from 60 pregnant individuals (ages 14-42) at 24-27 and 34-37 weeks of gestation using a fetal actocardiograph to generate fetal HR and HRV. During natural sleep, their infants (38 males and 22 females) underwent a fMRI scan between 40 and 46 weeks of postmenstrual age. Our findings relate fetal heart indices to brainstem, hypothalamic, and dACC connectivity and reveal connections with widespread brain regions that may support behavioral and emotional regulation. We demonstrated the basic physiologic association between fetal HR indices and lower- and higher-order brain regions involved in regulatory processes. This work provides the foundation for future behavioral or physiological regulation research in fetuses and infants.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Cristin M Holland
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Thirsten J Stockton
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Bradley S Peterson
- Institute for the Developing Mind, Children's Hospital Los Angeles, Los Angeles, California 90027
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Dustin Scheinost
- Departments of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520
- Child Study Center, Yale School of Medicine, New Haven, Connecticut 06520
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, Connecticut 06520
- Department of Statistics and Data Science, Yale University, New Haven, Connecticut 06511
- Wu Tsai Institute, Yale University, New Haven, Connecticut 06506
| | - Catherine Monk
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
- Department of Obstetrics and Gynecology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York 10032
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
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Wang G, Jiang N, Ma Y, Suo D, Liu T, Funahashi S, Yan T. Using a deep generation network reveals neuroanatomical specificity in hemispheres. PATTERNS (NEW YORK, N.Y.) 2024; 5:100930. [PMID: 38645770 PMCID: PMC11026975 DOI: 10.1016/j.patter.2024.100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 04/23/2024]
Abstract
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
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10
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Rodriguez RX, Noble S, Camp CC, Scheinost D. Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588578. [PMID: 38645002 PMCID: PMC11030410 DOI: 10.1101/2024.04.08.588578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity 1-5 . Further, they resemble task activation patterns and are well-studied 3,5-10 . However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP) 11 and the UCLA Consortium for Neuropsychiatric Phenomics 12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest 13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification 14 . They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.
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11
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane JM, Malhotra AK. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol Psychiatry 2024; 29:929-938. [PMID: 38177349 PMCID: PMC11176002 DOI: 10.1038/s41380-023-02381-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA.
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John M Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anil K Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
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12
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Mizzi S, Pedersen M, Rossell SL, Rendell P, Terrett G, Heinrichs M, Labuschagne I. Resting-state amygdala subregion and precuneus connectivity provide evidence for a dimensional approach to studying social anxiety disorder. Transl Psychiatry 2024; 14:147. [PMID: 38485930 PMCID: PMC10940725 DOI: 10.1038/s41398-024-02844-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/12/2024] [Accepted: 02/20/2024] [Indexed: 03/18/2024] Open
Abstract
Social anxiety disorder (SAD) is a prevalent and disabling mental health condition, characterized by excessive fear and anxiety in social situations. Resting-state functional magnetic resonance imaging (fMRI) paradigms have been increasingly used to understand the neurobiological underpinnings of SAD in the absence of threat-related stimuli. Previous studies have primarily focused on the role of the amygdala in SAD. However, the amygdala consists of functionally and structurally distinct subregions, and recent studies have highlighted the importance of investigating the role of these subregions independently. Using multiband fMRI, we analyzed resting-state data from 135 participants (42 SAD, 93 healthy controls). By employing voxel-wise permutation testing, we examined group differences of fMRI connectivity and associations between fMRI connectivity and social anxiety symptoms to further investigate the classification of SAD as a categorical or dimensional construct. Seed-to-whole brain functional connectivity analysis using multiple 'seeds' including the amygdala and its subregions and the precuneus, revealed no statistically significant group differences. However, social anxiety severity was significantly negatively correlated with functional connectivity of the precuneus - perigenual anterior cingulate cortex and positively correlated with functional connectivity of the amygdala (specifically the superficial subregion) - parietal/cerebellar areas. Our findings demonstrate clear links between symptomatology and brain connectivity in the absence of diagnostic differences, with evidence of amygdala subregion-specific alterations. The observed brain-symptom associations did not include disturbances in the brain's fear circuitry (i.e., disturbances in connectivity between amygdala - prefrontal regions) likely due to the absence of threat-related stimuli.
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Affiliation(s)
- Simone Mizzi
- School of Health and Biomedical Science, RMIT University, Melbourne, VIC, Australia.
| | - Mangor Pedersen
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand
| | - Susan L Rossell
- Centre for Mental Health, School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
- Psychiatry, St Vincent's Hospital, Fitzroy, Australia
| | - Peter Rendell
- Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, Australia
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Gill Terrett
- Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, Australia
| | - Markus Heinrichs
- Department of Psychology, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
- Freiburg Brain Imaging Center, University Medical Center, Albert-Ludwigs University of Freiburg, Freiburg, Germany
| | - Izelle Labuschagne
- Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, Australia.
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia.
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13
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Rai S, Graff K, Tansey R, Bray S. How do tasks impact the reliability of fMRI functional connectivity? Hum Brain Mapp 2024; 45:e26535. [PMID: 38348730 PMCID: PMC10884875 DOI: 10.1002/hbm.26535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 02/24/2024] Open
Abstract
While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.
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Affiliation(s)
- Shefali Rai
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Kirk Graff
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Ryann Tansey
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Signe Bray
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
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14
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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15
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Kowalczyk OS, Medina S, Tsivaka D, McMahon SB, Williams SCR, Brooks JCW, Lythgoe DJ, Howard MA. Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study. Hum Brain Mapp 2024; 45:e26600. [PMID: 38339896 PMCID: PMC10831202 DOI: 10.1002/hbm.26600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.
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Affiliation(s)
- Olivia S. Kowalczyk
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- The Wellcome Centre for Human Neuroimaging, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sonia Medina
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Dimitra Tsivaka
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- Medical Physics Department, Medical SchoolUniversity of ThessalyLarisaGreece
| | | | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | | | - David J. Lythgoe
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Matthew A. Howard
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
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16
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Butler ER, Samia N, White S, Gratton C, Nusslock R. Neuroimmune mechanisms connecting violence with internalizing symptoms: A high-dimensional multimodal mediation analysis. Hum Brain Mapp 2024; 45:e26615. [PMID: 38339956 DOI: 10.1002/hbm.26615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/27/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms (ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ ,95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.
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Affiliation(s)
- Ellyn R Butler
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - Noelle Samia
- Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA
| | - Stuart White
- Nebraska Children and Families Foundation, Lincoln, Nebraska, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Institute for Policy Research, Northwestern University, Evanston, Illinois, USA
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17
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Zhang L, Feng J, Liu C, Hu H, Zhou Y, Yang G, Peng X, Li T, Chen C, Xue G. Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks. Cereb Cortex 2024; 34:bhad510. [PMID: 38183183 DOI: 10.1093/cercor/bhad510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024] Open
Abstract
Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.
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Affiliation(s)
- Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Junjiao Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Chuqi Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Huinan Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Yu Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Gangyao Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Xiaojing Peng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Tong Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, USA
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
- Chinese Institute for Brain Research, Beijing 102206, PR China
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18
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Camp CC, Noble S, Scheinost D, Stringaris A, Nielson DM. Test-Retest Reliability of Functional Connectivity in Adolescents With Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:21-29. [PMID: 37734478 PMCID: PMC10843837 DOI: 10.1016/j.bpsc.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND The test-retest reliability of functional magnetic resonance imaging is critical to identifying reproducible biomarkers for psychiatric illness. Recent work has shown how reliability limits the observable effect size of brain-behavior associations, hindering detection of these effects. However, while a fast-growing literature has explored both univariate and multivariate reliability in healthy individuals, relatively few studies have explored reliability in populations with psychiatric illnesses or how this interacts with age. METHODS Here, we investigated functional connectivity reliability over the course of 1 year in a longitudinal cohort of 88 adolescents (age at baseline = 15.63 ± 1.29 years; 64 female) with major depressive disorder (MDD) and without MDD (healthy volunteers [HVs]). We compared a univariate metric, intraclass correlation coefficient, and 2 multivariate metrics, fingerprinting and discriminability. RESULTS Adolescents with MDD had marginally higher mean intraclass correlation coefficient (μMDD = 0.34, 95% CI, 0.12-0.54; μHV = 0.27, 95% CI, 0.05-0.52), but both groups had poor average intraclass correlation coefficients (<0.4). Fingerprinting index was greater than chance and did not differ between groups (fingerprinting indexMDD = 0.75; fingerprinting indexHV = 0.91; Poisson tests p < .001). Discriminability indicated high multivariate reliability in both groups (discriminabilityMDD = 0.80; discriminabilityHV = 0.82; permutation tests p < .01). Neither univariate nor multivariate reliability was associated with symptom severity or edge-level effect size of group differences. CONCLUSIONS Overall, we found little evidence for a relationship between depression and reliability of functional connectivity during adolescence. These findings suggest that biomarker identification in depression is not limited due to reliability compared with healthy samples and support the shift toward multivariate analysis for improved power and reliability.
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Affiliation(s)
- Chris C Camp
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut.
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, Massachusetts; Department of Bioengineering, Northeastern University, Boston, Massachusetts; Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics & Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut
| | - Argyris Stringaris
- Faculty of Brain Sciences, Division of Psychiatry and Psychology and Language Sciences, University College London, London, United Kingdom; 1st Department of Psychiatry, National and Kapodistrian University of Athens, Aiginition Hospital, Athens, Greece
| | - Dylan M Nielson
- Machine Learning Team, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
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19
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O’Brien MP, Pryzhkova MV, Lake EMR, Mandino F, Shen X, Karnik R, Atkins A, Xu MJ, Ji W, Konstantino M, Brueckner M, Ment LR, Khokha MK, Jordan PW. SMC5 Plays Independent Roles in Congenital Heart Disease and Neurodevelopmental Disability. Int J Mol Sci 2023; 25:430. [PMID: 38203602 PMCID: PMC10779392 DOI: 10.3390/ijms25010430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Up to 50% of patients with severe congenital heart disease (CHD) develop life-altering neurodevelopmental disability (NDD). It has been presumed that NDD arises in CHD cases because of hypoxia before, during, or after cardiac surgery. Recent studies detected an enrichment in de novo mutations in CHD and NDD, as well as significant overlap between CHD and NDD candidate genes. However, there is limited evidence demonstrating that genes causing CHD can produce NDD independent of hypoxia. A patient with hypoplastic left heart syndrome and gross motor delay presented with a de novo mutation in SMC5. Modeling mutation of smc5 in Xenopus tropicalis embryos resulted in reduced heart size, decreased brain length, and disrupted pax6 patterning. To evaluate the cardiac development, we induced the conditional knockout (cKO) of Smc5 in mouse cardiomyocytes, which led to the depletion of mature cardiomyocytes and abnormal contractility. To test a role for Smc5 specifically in the brain, we induced cKO in the mouse central nervous system, which resulted in decreased brain volume, and diminished connectivity between areas related to motor function but did not affect vascular or brain ventricular volume. We propose that genetic factors, rather than hypoxia alone, can contribute when NDD and CHD cases occur concurrently.
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Affiliation(s)
- Matthew P. O’Brien
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Marina V. Pryzhkova
- Biochemistry and Molecular Biology Department, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Biochemistry and Molecular Biology, Uniformed Services, University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA
| | - Evelyn M. R. Lake
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Ruchika Karnik
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Alisa Atkins
- Biochemistry and Molecular Biology Department, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Michelle J. Xu
- Biochemistry and Molecular Biology Department, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Weizhen Ji
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Pediatric Genomics Discovery Program, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Monica Konstantino
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Pediatric Genomics Discovery Program, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Martina Brueckner
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Department of Genetics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Laura R. Ment
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Mustafa K. Khokha
- Department of Pediatrics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Pediatric Genomics Discovery Program, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
- Department of Genetics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Philip W. Jordan
- Biochemistry and Molecular Biology Department, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Biochemistry and Molecular Biology, Uniformed Services, University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA
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20
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Mellema CJ, Montillo AA. Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI. J Neural Eng 2023; 20:066023. [PMID: 37963396 DOI: 10.1088/1741-2552/ad0c5f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.
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Affiliation(s)
- Cooper J Mellema
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- Advanced Imaging Research Center, Dallas, TX, United States of America
- Radiology Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
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21
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Kim M, Choi KS, Hyun RC, Hwang I, Kwon YN, Sung JJ, Kim SM, Kim JH. Structural disconnection is associated with disability in the neuromyelitis optica spectrum disorder. Brain Imaging Behav 2023; 17:664-673. [PMID: 37676409 DOI: 10.1007/s11682-023-00792-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVES Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune inflammatory disease of the central nervous system. Accumulating evidence suggests there is a distinct pattern of brain lesions characteristic of NMOSD, and brain MRI has potential prognostic implications. However, the question of how the brain lesions in NMOSD are associated with its distinct clinical course remains incompletely understood. Here, we aimed to investigate the association between neurological impairment and brain lesions via brain structural disconnection. METHODS Twenty patients were diagnosed with NMOSD according to the 2015 International Panel for NMO Diagnosis criteria. The white matter lesions were manually drawn section by section. Whole-brain structural disconnection was estimated, and connectome-based predictive modeling (CPM) was used to estimate the patient's Expanded Disability Status Scale score (EDSS) from their disconnection severity matrix. Furthermore, correlational tractography was performed to assess the fractional anisotropy (FA) and axial diffusivity (AD) of white matter fibers, which negatively correlated with the EDSS score. RESULTS CPM successfully predicted the EDSS using the disconnection severity matrix (r = 0.506, p = 0.028; q2 = 0.274). Among the important edges in the prediction process, the majority of edges connected the motor to the frontoparietal network. Correlational tractography identified a decreased FA and AD value according to EDSS scores in periependymal white matter tracts. DISCUSSION Structural disconnection-based predictive modeling and local connectome analysis showed that frontoparietal and periependymal white matter disconnection is predictive and associated with the EDSS score of NMOSD patients.
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Affiliation(s)
- Minchul Kim
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea
| | - Ryoo Chang Hyun
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea
| | - Young Nam Kwon
- Department of Neurology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea
| | - Jung-Joon Sung
- Department of Neurology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea
| | - Sung Min Kim
- Department of Neurology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea.
| | - Ji-Hoon Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Seoul, 110-744, Republic of Korea.
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22
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Roy JC, Desmidt T, Dam S, Mirea-Grivel I, Weyl L, Bannier E, Barantin L, Drapier D, Batail JM, David R, Coloigner J, Robert GH. Connectivity patterns of the core resting-state networks associated with apathy in late-life depression. J Psychiatry Neurosci 2023; 48:E404-E413. [PMID: 37914222 PMCID: PMC10620011 DOI: 10.1503/jpn.230008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/28/2023] [Accepted: 08/03/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Apathy is associated with reduced antidepressant response and dementia in late-life depression (LLD). However, the functional cerebral basis of apathy is understudied in LLD. We investigated the functional connectivity of 5 resting-state networks (RSN) hypothesized to underlie apathy in LLD. METHODS Resting-state functional MRI data were collected from individuals with LLD who did not have dementia as well as healthy older adults between October 2019 and April 2022. Apathy was evaluated using the diagnostic criteria for apathy (DCA), the Apathy Evaluation Scale (AES) and the Apathy Motivation Index (AMI). Subnetworks whose connectivity was significantly associated with each apathy measure were identified via the threshold-free network-based statistics. Regions that were consistently associated with apathy across the measures were reported as robust findings. RESULTS Our sample included 39 individuals with LLD who did not have dementia and 26 healthy older adults. Compared with healthy controls, individuals with LLD had an altered intra-RSN and inter-RNS connectivity in the default mode, the cingulo-opercular and the frontoparietal networks. All 3 apathy measurements showed associations with modified intra-RSN connectivity in these networks, except for the DCA in the cingulo-opercular network. The AMI scores showed stronger associations with the cingulo-opercular and frontoparietal networks, whereas the AES had stronger associations with the default mode network and the goal-oriented behaviour network. LIMITATIONS The study was limited by the small number of participants without apathy according to the DCA, which may have reduced the statistical power of between-group comparisons. Additionally, the reliance on specific apathy measures may have influenced the observed overlap in brain regions. CONCLUSION Our findings indicate that apathy in LLD is consistently associated with changes in both intra-RSN and inter-RSN connectivity of brain regions implicated in goal-oriented behaviours. These results corroborate previous findings of altered functional RSN connectivity in severe LLD.
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Affiliation(s)
- Jean-Charles Roy
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Thomas Desmidt
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Sébastien Dam
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Iris Mirea-Grivel
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Louise Weyl
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Elise Bannier
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Laurent Barantin
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Dominique Drapier
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Jean-Marie Batail
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Renaud David
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Julie Coloigner
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
| | - Gabriel H Robert
- From the Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France (Roy, Mirea-Grivel, Louise, Drapier, Batail, Robert); the Centre d'investigation clinique (CIC) de Rennes 1414, CHU de Rennes, Institut national de la santé et de la recherche médicale (INSERM), Rennes, France (Roy, Drapier, Batail, Robert); l'Université de Rennes, Inria Centre, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France (Roy, Dam, Bannier, Coloigner, Robert); the Service de Radiologie, CHU Rennes, Rennes, France (Bannier); the CHU de Tours, Tours, France (Desmidt, Barantin); the UMR 1253, iBrain, Université de Tours, INSERM, Tours, France (Desmidt, Barantin); the CIC 1415, CHU de Tours, INSERM, Tours, France (Desmidt); the CoBTeK (Cognition Behaviour Technology) Lab, University Côte d'Azur, Nice, France (David)
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23
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Wang G, Zeng M, Li J, Liu Y, Wei D, Long Z, Chen H, Zang X, Yang J. Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality. Neuroscience 2023; 530:66-78. [PMID: 37619767 DOI: 10.1016/j.neuroscience.2023.08.017] [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: 02/14/2023] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear. METHODS We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset. RESULTS SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control. CONCLUSION Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.
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Affiliation(s)
- Guangtong Wang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Mei Zeng
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Jiwen Li
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Yadong Liu
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Dongtao Wei
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Zhiliang Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Haopeng Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xinlei Zang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China.
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24
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Park S, Park D, Kim MJ. Similarity in functional connectome architecture predicts teenage grit. Soc Cogn Affect Neurosci 2023; 18:nsad047. [PMID: 37700673 PMCID: PMC10549957 DOI: 10.1093/scan/nsad047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/14/2023] [Accepted: 09/05/2023] [Indexed: 09/14/2023] Open
Abstract
Grit is a personality trait that encapsulates the tendency to persevere and maintain consistent interest for long-term goals. While prior studies found that grit predicts positive behavioral outcomes, there is a paucity of work providing explanatory evidence from a neurodevelopmental perspective. Based on previous research suggesting the utility of the functional connectome (FC) as a developmental measure, we tested the idea that individual differences in grit might be, in part, rooted in brain development in adolescence and emerging adulthood (N = 64, 11-19 years of age). Our analysis showed that grit was associated with connectome stability across conditions and connectome similarity across individuals. Notably, inter-subject representational similarity analysis revealed that teenagers who were grittier shared similar FC architecture with each other, more so than those with lower grit. Our findings suggest that individuals with high levels of grit are more likely to exhibit a converging pattern of whole-brain functional connectivity, which may underpin subsequent beneficial behavioral outcomes.
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Affiliation(s)
- Sujin Park
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - Daeun Park
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
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25
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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26
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Bottenhorn KL, Cardenas-Iniguez C, Mills KL, Laird AR, Herting MM. Profiling intra- and inter-individual differences in brain development across early adolescence. Neuroimage 2023; 279:120287. [PMID: 37536527 PMCID: PMC10833064 DOI: 10.1016/j.neuroimage.2023.120287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/27/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.
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Affiliation(s)
- Katherine L Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA; Department of Psychology, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA.
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, 1227 University St, Eugene, OR 97403, USA
| | - Angela R Laird
- Department of Physics, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA.
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27
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Betzel RF, Cutts SA, Tanner J, Greenwell SA, Varley T, Faskowitz J, Sporns O. Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI. Netw Neurosci 2023; 7:926-949. [PMID: 37781150 PMCID: PMC10473297 DOI: 10.1162/netn_a_00321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/03/2023] [Indexed: 10/03/2023] Open
Abstract
Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.
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Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Sarah A. Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Thomas Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
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28
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Sheehan AE, Bounoua N, Stumps A, Miglin R, Huerta W, Sadeh N. Neurobiological metric of cortical delay discounting differentiates risk for self- and other-directed violence among trauma-exposed individuals. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2023; 132:897-907. [PMID: 37676141 PMCID: PMC10592155 DOI: 10.1037/abn0000857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Self- and other-directed violence (SDV/ODV) contribute to elevated rates of mortality. Early trauma exposure shows robust positive associations with these forms of violence but alone does not distinguish those at heightened risk for later engagement in SDV/ODV. Novel assessment metrics could aid early identification efforts for individuals with vulnerabilities to violence perpetration. This study examined a novel neurobiological measure of impulsive choice for reward as a potential moderator of associations between childhood trauma exposure and lifetime SDV/ODV. A high-risk community sample of 177 adults (89 men; 50.3%) were assessed for childhood trauma exposure, engagement in SDV (e.g., suicide attempts), and ODV (e.g., assault). A cortical delay discounting (C-DD) measure was created using a multivariate additive model of gray matter thickness across both hemispheres, previously found to be positively associated with susceptibility to impulsivity and externalizing disorders. Childhood trauma exposure was positively associated with ODV and SDV; however, these relationships differed as a function of C-DD. Engagement in ODV increased as scores on C-DD increased, and SDV increased as scores on C-DD decreased. Furthermore, moderation revealed biological sex differences, as the association between childhood trauma and SDV depended on C-DD for women but not for men. Findings from the present work demonstrate that risk conferred by childhood trauma exposure to violence varied as a function of a C-DD. Together, these findings point to the utility of neurobiological markers of impulsive decision-making for differentiating risk for violence among individuals with a history of trauma exposure. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Ana E Sheehan
- Department of Psychological and Brain Sciences, University of Delaware
| | - Nadia Bounoua
- Department of Psychological and Brain Sciences, University of Delaware
| | - Anna Stumps
- Department of Psychological and Brain Sciences, University of Delaware
| | - Rickie Miglin
- Department of Psychological and Brain Sciences, University of Delaware
| | - Wendy Huerta
- Department of Psychological and Brain Sciences, University of Delaware
| | - Naomi Sadeh
- Department of Psychological and Brain Sciences, University of Delaware
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29
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Ye J, Sun H, Gao S, Dadashkarimi J, Rosenblatt M, Rodriguez RX, Mehta S, Jiang R, Noble S, Westwater ML, Scheinost D. Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States. Biol Psychiatry 2023; 94:580-590. [PMID: 37031780 PMCID: PMC10524212 DOI: 10.1016/j.biopsych.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.
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Affiliation(s)
- Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Saloni Mehta
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut
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30
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Bounoua N, Church LD, Matyi MA, Rudoler J, Wieand K, Spielberg JM. Assessing the utility of a novel cortical marker of delay discounting (C-DD) in two independent samples of early adolescents: Links with externalizing pathology. PLoS One 2023; 18:e0291868. [PMID: 37756262 PMCID: PMC10529595 DOI: 10.1371/journal.pone.0291868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Delay discounting is a well-established risk factor for risky behaviors and the development of externalizing spectrum disorders. Building upon recent work that developed a novel cortical marker of delay discounting (C-DD) in adult samples, the objective of this study was to test whether the C-DD relates to delay discounting and subsequently externalizing pathology in adolescent samples. The current study used two samples: 9992 early adolescents participating in the ABCD study (Mage = 9.93 years old, 48.7% female), and 56 early adolescents recruited from the community (Mage = 12.27 years old, 55.4% female). Cortical thickness was estimated using the FreeSurfer standard pipeline, and the cortical marker of delay discounting (C-DD) was calculated based on procedures outlined by the initial validation study. All data are cross-sectional in nature. As expected, C-DD was positively related to delay discounting in the ABCD sample, even after accounting for age, biological sex, collection site and data quality indicators. Moreover, results showed that C-DD was discriminately associated with externalizing, but not internalizing, symptoms in both samples of young adolescents. Findings replicate those found in adult samples, suggestive that C-DD may be a useful neuroanatomical marker of youth delay discounting. Replication of findings in other samples will be needed to determine whether C-DD has translational relevance to understanding externalizing psychopathology in adolescent samples.
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Affiliation(s)
- Nadia Bounoua
- Department of Psychology, University of Maryland, College Park, Maryland, United States of America
| | - Leah D. Church
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Melanie A. Matyi
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Jeremy Rudoler
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Kaleigh Wieand
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Jeffrey M. Spielberg
- Department of Psychological & Brain Sciences, University of Delaware, Newark, Delaware, United States of America
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31
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Wilcox RR, Barbey AK. Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks. Cereb Cortex 2023; 33:10322-10331. [PMID: 37526284 DOI: 10.1093/cercor/bhad284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.
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Affiliation(s)
- Ramsey R Wilcox
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, United States
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Abbas K, Liu M, Wang M, Duong-Tran D, Tipnis U, Amico E, Kaplan AD, Dzemidzic M, Kareken D, Ances BM, Harezlak J, Goñi J. Tangent functional connectomes uncover more unique phenotypic traits. iScience 2023; 26:107624. [PMID: 37694156 PMCID: PMC10483051 DOI: 10.1016/j.isci.2023.107624] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Michael Wang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Duong-Tran
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Uttara Tipnis
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Alan D. Kaplan
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - Beau M. Ances
- Department of Neurology, Washington University in Saint Louis, School of Medicine, St Louis, MO, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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Rabini G, Pierotti E, Meli C, Dodich A, Papagno C, Turella L. Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease. Cereb Cortex 2023; 33:9896-9907. [PMID: 37455441 DOI: 10.1093/cercor/bhad252] [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: 04/21/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.
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Affiliation(s)
- Giuseppe Rabini
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Enrica Pierotti
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Claudia Meli
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Alessandra Dodich
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Costanza Papagno
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Luca Turella
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
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Cao H, Barber AD, Rubio JM, Argyelan M, Gallego JA, Lencz T, Malhotra AK. Effects of phase encoding direction on test-retest reliability of human functional connectome. Neuroimage 2023; 277:120238. [PMID: 37364743 PMCID: PMC10529794 DOI: 10.1016/j.neuroimage.2023.120238] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Miklos Argyelan
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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Shafee R, Moraczewski D, Liu S, Mallard T, Thomas A, Raznahan A. A sex-stratified analysis of the genetic architecture of human brain anatomy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.09.23293881. [PMID: 37609186 PMCID: PMC10441503 DOI: 10.1101/2023.08.09.23293881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared males and females - despite theoretical grounds for potential sex differences. By systematically screening for sex differences in the common genetic architecture of > 1000 neuroanatomical phenotypes in the UK Biobank, we establish a general concordance between males and females in heritability estimates, genetic correlations and variant-level effects. Notable exceptions include: higher mean h 2 in females for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and, a male-specific effect common variant mapping to RBFOX1 - a gene linked to multiple male-biased neuropsychiatric disorders. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research as biobanks continue to grow in size.
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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Rubio J, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal D, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Maholtra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. RESEARCH SQUARE 2023:rs.3.rs-3185688. [PMID: 37609149 PMCID: PMC10441472 DOI: 10.21203/rs.3.rs-3185688/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Northwell Health
| | - Todd Lencz
- Zucker School of Medicine at Hofstra/Northwell
| | - Hengyi Cao
- The Feinstein Institute for Medical Research
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Kan S, Fujita N, Shibata M, Miki K, Yukioka M, Senba E. Three weeks of exercise therapy altered brain functional connectivity in fibromyalgia inpatients. NEUROBIOLOGY OF PAIN (CAMBRIDGE, MASS.) 2023; 14:100132. [PMID: 38099286 PMCID: PMC10719530 DOI: 10.1016/j.ynpai.2023.100132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 04/26/2023] [Accepted: 05/16/2023] [Indexed: 12/17/2023]
Abstract
Background Fibromyalgia (FM) is a chronic pain syndrome characterized by widespread pain, tenderness, and fatigue. Patients with FM have no effective medication so far, and their activity of daily living and quality of life are remarkably impaired. Therefore, new therapeutic approaches are awaited. Recently, exercise therapy has been gathering much attention as a promising treatment for FM. However, the underlying mechanisms are not fully understood, particularly, in the central nervous system, including the brain. Therefore, we investigated functional connectivity changes and their relationship with clinical improvement in patients with FM after exercise therapy to investigate the underlying mechanisms in the brain using resting-state fMRI (rs-fMRI) and functional connectivity (FC) analysis. Methods Seventeen patients with FM participated in this study. They underwent a 3-week exercise therapy on in-patient basis and a 5-min rs-fMRI scan before and after the exercise therapy. We compared the FC strength of sensorimotor regions and the mesocortico-limbic system between two scans. We also performed a multiple regression analysis to examine the relationship between pre-post differences in FC strength and improvement of patients' clinical symptoms or motor abilities. Results Patients with FM showed significant improvement in clinical symptoms and motor abilities. They also showed a significant pre-post difference in FC of the anterior cingulate cortex and a significant correlation between pre-post FC changes and improvement of clinical symptoms and motor abilities. Although sensorimotor regions tended to be related to the improvement of general disease severity and depression, brain regions belonging to the mesocortico-limbic system tended to be related to the improvement of motor abilities. Conclusion Our 3-week exercise therapy could ameliorate clinical symptoms and motor abilities of patients with FM, and lead to FC changes in sensorimotor regions and brain regions belonging to the mesocortico-limbic system. Furthermore, these changes were related to improvement of clinical symptoms and motor abilities. Our findings suggest that, as predicted by previous animal studies, spontaneous brain activities modified by exercise therapy, including the mesocortico-limbic system, improve clinical symptoms in patients with FM.
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Affiliation(s)
- Shigeyuki Kan
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima Hiroshima 734-8551, Japan
- Department of Anesthesiology and Intensive Care Medicine, 2-2 Yamadaoka, Suita, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
| | - Nobuko Fujita
- Department of Rehabilitation, Faculty of Health Sciences, Naragakuen University, 3-15-1 Nakatomigaoka, Nara, Nara 631-8524, Japan
| | - Masahiko Shibata
- Department of Rehabilitation, Faculty of Health Sciences, Naragakuen University, 3-15-1 Nakatomigaoka, Nara, Nara 631-8524, Japan
| | - Kenji Miki
- Hayaishi Hospital, 2-75 Fudegasakicho, Tennoji-ku, Osaka, Osaka 543-0027, Japan
- Department of Physical Therapy, Osaka Yukioka College of Health Science, 1-1-41 Sojiji, Ibaraki, Osaka 567-0801, Japan
| | - Masao Yukioka
- Department of Rheumatology, Yukioka Hospital, 2-2-3 Ukita, Kita-ku, Osaka 530-0021, Japan
| | - Emiko Senba
- Department of Physical Therapy, Osaka Yukioka College of Health Science, 1-1-41 Sojiji, Ibaraki, Osaka 567-0801, Japan
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Liu S, Abdellaoui A, Verweij KJH, van Wingen GA. Replicable brain-phenotype associations require large-scale neuroimaging data. Nat Hum Behav 2023; 7:1344-1356. [PMID: 37365408 DOI: 10.1038/s41562-023-01642-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 05/25/2023] [Indexed: 06/28/2023]
Abstract
Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.
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Affiliation(s)
- Shu Liu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Guido A van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Amsterdam, the Netherlands.
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Haines N, Sullivan-Toole H, Olino T. From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:822-831. [PMID: 36997406 PMCID: PMC10333448 DOI: 10.1016/j.bpsc.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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Affiliation(s)
- Nathaniel Haines
- Department of Data Science, Bayesian Beginnings LLC, Columbus, Ohio.
| | | | - Thomas Olino
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Malhotra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.17.23292779. [PMID: 37503088 PMCID: PMC10371185 DOI: 10.1101/2023.07.17.23292779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anil Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
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Rosenblatt M, Rodriguez RX, Westwater ML, Dai W, Horien C, Greene AS, Constable RT, Noble S, Scheinost D. Connectome-based machine learning models are vulnerable to subtle data manipulations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100756. [PMID: 37521052 PMCID: PMC10382940 DOI: 10.1016/j.patter.2023.100756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/10/2023] [Accepted: 04/24/2023] [Indexed: 08/01/2023]
Abstract
Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
| | - Raimundo X. Rodriguez
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Abigail S. Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - R. Todd Constable
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT 06510, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06510, USA
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Tan V, Jeyachandra J, Ge R, Dickie EW, Gregory E, Vanderwal T, Vila-Rodriguez F, Hawco C. Subgenual cingulate connectivity as a treatment predictor during low-frequency right dorsolateral prefrontal rTMS: A concurrent TMS-fMRI study. Brain Stimul 2023; 16:1165-1172. [PMID: 37543171 DOI: 10.1016/j.brs.2023.07.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
INTRODUCTION Repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) is effective in alleviating treatment-resistant depression (TRD). It has been proposed that regions within the left DLPFC that are anti-correlated with the right subgenual anterior cingulate cortex (sgACC) may represent optimal individualized target sites for high-frequency left rTMS (HFL). OBJECTIVE/HYPOTHESIS This study aimed to explore the effects of low-frequency right rTMS (LFR) on left sgACC connectivity during concurrent TMS-fMRI. METHODS 34 TRD patients underwent an imaging session that included both a resting-state fMRI run (rs-fMRI0) and a run during which LFR was applied to the right DLPFC (TMS-fMRI). Participants subsequently completed four weeks of LFR treatment. The left sgACC functional connectivity was compared between the rs-fMRI0 run and TMS-fMRI run. Personalized e-fields and a region-of-interest approach were used to calculate overlap of left sgACC functional connectivity at the TMS target and to assess for a relationship with treatment effects. RESULTS TMS-fMRI increased left sgACC functional connectivity to parietal regions within the ventral attention network; differences were not significantly associated with clinical improvements. Personalized e-fields were not significant in predicting treatment outcomes (p = 0.18). CONCLUSION This was the first study to examine left sgACC anti-correlation with the right DLPFC during an LFR rTMS protocol. In contrast to studies that targeted the left DLPFC, we did not find that higher anti-correlation was associated with clinical outcomes. Our results suggest that the antidepressant mechanism of action of LFR to the right DLPFC may be different than for HFL.
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Affiliation(s)
- Vinh Tan
- Kimel Family Translational Imaging Genetics Research Laboratory, Centre for Addiction and Mental Health, Toronto, Canada
| | - Jerrold Jeyachandra
- Kimel Family Translational Imaging Genetics Research Laboratory, Centre for Addiction and Mental Health, Toronto, Canada
| | - Ruiyang Ge
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging Genetics Research Laboratory, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Elizabeth Gregory
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Colin Hawco
- Kimel Family Translational Imaging Genetics Research Laboratory, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
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Kotoula V, Evans JW, Punturieri CE, Zarate CA. Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression. FRONTIERS IN NEUROIMAGING 2023; 2:1110258. [PMID: 37554642 PMCID: PMC10406217 DOI: 10.3389/fnimg.2023.1110258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/12/2023] [Indexed: 08/10/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.
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Li MT, Sun JW, Zhan LL, Antwi CO, Lv YT, Jia XZ, Ren J. The effect of seed location on functional connectivity: evidence from an image-based meta-analysis. Front Neurosci 2023; 17:1120741. [PMID: 37325032 PMCID: PMC10264592 DOI: 10.3389/fnins.2023.1120741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
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Affiliation(s)
- Meng-Ting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jia-Wei Sun
- Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden
| | - Lin-Lin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | | | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Xi-Ze Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
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Horien C, Greene AS, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O’Connor D, Lake EMR, McPartland JC, Volkmar FR, Chun M, Chawarska K, Rosenberg MD, Scheinost D, Constable RT. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cereb Cortex 2023; 33:6320-6334. [PMID: 36573438 PMCID: PMC10183743 DOI: 10.1093/cercor/bhac506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/29/2022] Open
Abstract
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Diogo Fortes
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Rachel Foster
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Kelly Powell
- Yale Child Study Center, New Haven, CT, United States
| | | | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - James C McPartland
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Fred R Volkmar
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Marvin Chun
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Katarzyna Chawarska
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, United States
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
- Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, United States
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Ficek-Tani B, Horien C, Ju S, Xu W, Li N, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in default mode network connectivity in healthy aging adults. Cereb Cortex 2023; 33:6139-6151. [PMID: 36563018 PMCID: PMC10183749 DOI: 10.1093/cercor/bhac491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022] Open
Abstract
Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.
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Affiliation(s)
- Bronte Ficek-Tani
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, United States
| | - Suyeon Ju
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Wanwan Xu
- Department of Biostatistics, Yale School of Medicine, New Haven, CT 06520, United States
| | - Nancy Li
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Carolyn Fredericks
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
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Kaptan M, Horn U, Vannesjo SJ, Mildner T, Weiskopf N, Finsterbusch J, Brooks JCW, Eippert F. Reliability of resting-state functional connectivity in the human spinal cord: assessing the impact of distinct noise sources. Neuroimage 2023; 275:120152. [PMID: 37142169 DOI: 10.1016/j.neuroimage.2023.120152] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/06/2023] Open
Abstract
The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability - due to the removal of stable and participant-specific noise patterns - whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.
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Affiliation(s)
- Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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Wu C, Wu H, Zhou C, Guan X, Guo T, Cao Z, Wu J, Liu X, Chen J, Wen J, Qin J, Tan S, Duanmu X, Zhang B, Huang P, Xu X, Zhang M. Normalization effect of dopamine replacement therapy on brain functional connectome in Parkinson's disease. Hum Brain Mapp 2023; 44:3845-3858. [PMID: 37126590 DOI: 10.1002/hbm.26316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/06/2023] [Accepted: 04/09/2023] [Indexed: 05/03/2023] Open
Abstract
Dopamine replacement therapy (DRT) represents the standard treatment for Parkinson's disease (PD), however, instant and long-term medication influence on patients' brain function have not been delineated. Here, a total of 97 drug-naïve patients, 43 patients under long-term DRT, and 94 normal control (NC) were, retrospectively, enrolled. Resting-state functional magnetic resonance imaging data and motor symptom assessments were conducted before and after levodopa challenge test. Whole-brain functional connectivity (FC) matrices were constructed. Network-based statistics were performed to assess FC difference between drug-naïve patients and NC, and these significant FCs were defined as disease-related connectomes, which were used for further statistical analyses. Patients showed better motor performances after both long-term DRT and levodopa challenge test. Two disease-related connectomes were observed with distinct patterns. The FC of the increased connectome, which mainly consisted of the motor, visual, subcortical, and cerebellum networks, was higher in drug-naïve patients than that in NC and was normalized after long-term DRT (p-value <.050). The decreased connectome was mainly composed of the motor, medial frontal, and salience networks and showed significantly lower FC in all patients than NC (p-value <.050). The global FC of both increased and decreased connectome was significantly enhanced after levodopa challenge test (q-value <0.050, false discovery rate-corrected). The global FC of increased connectome in ON-state was negatively associated with levodopa equivalency dose (r = -.496, q-value = 0.007). Higher global FC of the decreased connectome was related to better motor performances (r = -.310, q-value = 0.022). Our findings provided insights into brain functional alterations under dopaminergic medication and its benefit on motor symptoms.
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Affiliation(s)
- Chenqing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sijia Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojie Duanmu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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