1
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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [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: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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2
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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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3
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An L, Zhang C, Wulan N, Zhang S, Chen P, Ji F, Ng KK, Chen C, Zhou JH, Thomas Yeo BT. DeepResBat: deep residual batch harmonization accounting for covariate distribution differences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.574145. [PMID: 38293022 PMCID: PMC10827218 DOI: 10.1101/2024.01.18.574145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization outcomes. We propose two DNN-based harmonization approaches that explicitly account for covariate distribution differences across datasets: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three different continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Therefore, future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat.
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Affiliation(s)
- Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Chen Zhang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Naren Wulan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Shaoshi Zhang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Pansheng Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Fang Ji
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Christopher Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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4
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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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5
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Ji JL, Lencz T, Gallego J, Neufeld N, Voineskos A, Malhotra A, Anticevic A. Informing individualized multi-scale neural signatures of clozapine response in patients with treatment-refractory schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.10.23286854. [PMID: 36993630 PMCID: PMC10055439 DOI: 10.1101/2023.03.10.23286854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Clozapine is currently the only antipsychotic with demonstrated efficacy in treatment-refractory schizophrenia (TRS). However, response to clozapine differs widely between TRS patients, and there are no available clinical or neural predictive indicators that could be used to increase or accelerate the use of clozapine in patients who stand to benefit. Furthermore, it remains unclear how the neuropharmacology of clozapine contributes to its therapeutic effects. Identifying the mechanisms underlying clozapine's therapeutic effects across domains of symptomatology could be crucial for development of new optimized therapies for TRS. Here, we present results from a prospective neuroimaging study that quantitatively related heterogeneous patterns of clinical clozapine response to neural functional connectivity at baseline. We show that we can reliably capture specific dimensions of clozapine clinical response by quantifying the full variation across item-level clinical scales, and that these dimensions can be mapped to neural features that are sensitive to clozapine-induced symptom change. Thus, these features may act as "failure modes" that can provide an early indication of treatment (non-)responsiveness. Lastly, we related the response-relevant neural maps to spatial expression profiles of genes coding for receptors implicated in clozapine's pharmacology, demonstrating that distinct dimensions of clozapine symptom-informed neural features may be associated with specific receptor targets. Collectively, this study informs prognostic neuro-behavioral measures for clozapine as a more optimal treatment for selected patients with TRS. We provide support for the identification of neuro-behavioral targets linked to pharmacological efficacy that can be further developed to inform optimal early treatment decisions in schizophrenia.
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6
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Hawco C, Steeves JKE, Voineskos AN, Blumberger DM, Daskalakis ZJ. Within-subject reliability of concurrent TMS-fMRI during a single session. Psychophysiology 2023:e14252. [PMID: 36694109 DOI: 10.1111/psyp.14252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/03/2022] [Accepted: 12/06/2022] [Indexed: 01/26/2023]
Abstract
Concurrent transcranial magnetic stimulation with functional MRI (concurrent TMS-fMRI) allows real-time causative probing of brain connectivity. However, technical challenges, safety, and tolerability may limit the number of trials employed during a concurrent TMS-fMRI experiment. We leveraged an existing data set with 100 trials of active TMS compared to a sub-threshold control condition to assess the reliability of the evoked BOLD response during concurrent TMS-fMRI. This data will permit an analysis of the minimum number of trials that should be employed in a concurrent TMS-fMRI protocol in order to achieve reliable spatial changes in activity. Single-subject maps of brain activity were created by splitting the trials within the same experimental session into groups of 50, 40, 30, 25, 20, 15, or 10 trials, correlations (R) between t-maps derived from paired subsets of trials within the same individual were calculated as reliability. R was moderate-high for 50 trials (mean R = .695) and decreased as the number of trials decreased. Consistent with previous findings of high individual variability in the spatial patterns of evoked neuronal changes following a TMS pulse, the spatial pattern of Rs differed across participants, but regional R was correlated with the magnitude of TMS-evoked activity. These results demonstrate concurrent TMS-fMRI produces a reliable pattern of activity at the individual level at higher trial numbers, particularly within localized regions. The spatial pattern of reliability is individually idiosyncratic and related to the individual pattern of evoked changes.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer K E Steeves
- Centre for Vision Research and Department of Psychology, York University, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Toronto, Ontario, Canada.,Department of Psychiatry, University of California, San Diego, California, USA
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7
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An L, Chen J, Chen P, Zhang C, He T, Chen C, Zhou JH, Yeo BTT. Goal-specific brain MRI harmonization. Neuroimage 2022; 263:119570. [PMID: 35987490 DOI: 10.1016/j.neuroimage.2022.119570] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/05/2022] [Accepted: 08/15/2022] [Indexed: 11/19/2022] Open
Abstract
There is significant interest in pooling magnetic resonance image (MRI) data from multiple datasets to enable mega-analysis. Harmonization is typically performed to reduce heterogeneity when pooling MRI data across datasets. Most MRI harmonization algorithms do not explicitly consider downstream application performance during harmonization. However, the choice of downstream application might influence what might be considered as study-specific confounds. Therefore, ignoring downstream applications during harmonization might potentially limit downstream performance. Here we propose a goal-specific harmonization framework that utilizes downstream application performance to regularize the harmonization procedure. Our framework can be integrated with a wide variety of harmonization models based on deep neural networks, such as the recently proposed conditional variational autoencoder (cVAE) harmonization model. Three datasets from three different continents with a total of 2787 participants and 10,085 anatomical T1 scans were used for evaluation. We found that cVAE removed more dataset differences than the widely used ComBat model, but at the expense of removing desirable biological information as measured by downstream prediction of mini mental state examination (MMSE) scores and clinical diagnoses. On the other hand, our goal-specific cVAE (gcVAE) was able to remove as much dataset differences as cVAE, while improving downstream cross-sectional prediction of MMSE scores and clinical diagnoses.
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Affiliation(s)
- Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Pansheng Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Chen Zhang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Tong He
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Christopher Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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8
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Hawco C, Dickie EW, Herman G, Turner JA, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. A longitudinal multi-scanner multimodal human neuroimaging dataset. Sci Data 2022; 9:332. [PMID: 35701471 PMCID: PMC9198098 DOI: 10.1038/s41597-022-01386-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Erin W Dickie
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Gabrielle Herman
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychology & Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Miklos Argyelan
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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9
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Zhukovsky P, Wainberg M, Milic M, Tripathy SJ, Mulsant BH, Felsky D, Voineskos AN. Multiscale neural signatures of major depressive, anxiety, and stress-related disorders. Proc Natl Acad Sci U S A 2022; 119:e2204433119. [PMID: 35648832 PMCID: PMC9191681 DOI: 10.1073/pnas.2204433119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The extent of shared and distinct neural mechanisms underlying major depressive disorder (MDD), anxiety, and stress-related disorders is still unclear. We compared the neural signatures of these disorders in 5,405 UK Biobank patients and 21,727 healthy controls. We found the greatest case–control differences in resting-state functional connectivity and cortical thickness in MDD, followed by anxiety and stress-related disorders. Neural signatures for MDD and anxiety disorders were highly concordant, whereas stress-related disorders showed a distinct pattern. Controlling for cross-disorder genetic risk somewhat decreased the similarity between functional neural signatures of stress-related disorders and both MDD and anxiety disorders. Among cases and healthy controls, reduced within-network and increased between-network frontoparietal and default mode connectivity were associated with poorer cognitive performance (processing speed, attention, associative learning, and fluid intelligence). These results provide evidence for distinct neural circuit function impairments in MDD and anxiety disorders compared to stress disorders, yet cognitive impairment appears unrelated to diagnosis and varies with circuit function.
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Affiliation(s)
- Peter Zhukovsky
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Michael Wainberg
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Milos Milic
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Shreejoy J. Tripathy
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- cDepartment of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Benoit H. Mulsant
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- dInstitute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Daniel Felsky
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- dInstitute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- eDalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Aristotle N. Voineskos
- aCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- bDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- 2To whom correspondence may be addressed.
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10
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Treit S, Stolz E, Rickard JN, McCreary CR, Bagshawe M, Frayne R, Lebel C, Emery D, Beaulieu C. Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms. Front Neurol 2022; 13:826564. [PMID: 35614930 PMCID: PMC9124864 DOI: 10.3389/fneur.2022.826564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.
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Affiliation(s)
- Sarah Treit
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Sarah Treit
| | - Emily Stolz
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Julia N. Rickard
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Cheryl R. McCreary
- Departments of Radiology and Clinical Neurosciences, Foothills Medical Centre, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Mercedes Bagshawe
- Department of Radiology, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Foothills Medical Centre, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
| | - Derek Emery
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
- Christian Beaulieu
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11
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Aceves-Serrano L, Neva JL, Doudet DJ. Insight Into the Effects of Clinical Repetitive Transcranial Magnetic Stimulation on the Brain From Positron Emission Tomography and Magnetic Resonance Imaging Studies: A Narrative Review. Front Neurosci 2022; 16:787403. [PMID: 35264923 PMCID: PMC8899094 DOI: 10.3389/fnins.2022.787403] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/01/2022] [Indexed: 12/14/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a therapeutic tool to alleviate symptoms for neurological and psychiatric diseases such as chronic pain, stroke, Parkinson’s disease, major depressive disorder, and others. Although the therapeutic potential of rTMS has been widely explored, the neurological basis of its effects is still not fully understood. Fortunately, the continuous development of imaging techniques has advanced our understanding of rTMS neurobiological underpinnings on the healthy and diseased brain. The objective of the current work is to summarize relevant findings from positron emission tomography (PET) and magnetic resonance imaging (MRI) techniques evaluating rTMS effects. We included studies that investigated the modulation of neurotransmission (evaluated with PET and magnetic resonance spectroscopy), brain activity (evaluated with PET), resting-state connectivity (evaluated with resting-state functional MRI), and microstructure (diffusion tensor imaging). Overall, results from imaging studies suggest that the effects of rTMS are complex and involve multiple neurotransmission systems, regions, and networks. The effects of stimulation seem to not only be dependent in the frequency used, but also in the participants characteristics such as disease progression. In patient populations, pre-stimulation evaluation was reported to predict responsiveness to stimulation, while post-stimulation neuroimaging measurements showed to be correlated with symptomatic improvement. These studies demonstrate the complexity of rTMS effects and highlight the relevance of imaging techniques.
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Affiliation(s)
- Lucero Aceves-Serrano
- Department of Medicine/Neurology, University of British Columbia, Vancouver, BC, Canada
- *Correspondence: Lucero Aceves-Serrano,
| | - Jason L. Neva
- École de Kinésiologie et des Sciences de l’Activité Physique, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Doris J. Doudet
- Department of Medicine/Neurology, University of British Columbia, Vancouver, BC, Canada
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12
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Hawco C, Dickie EW, Jacobs G, Daskalakis ZJ, Voineskos AN. Moving beyond the mean: Subgroups and dimensions of brain activity and cognitive performance across domains. Neuroimage 2021; 231:117823. [PMID: 33549760 DOI: 10.1016/j.neuroimage.2021.117823] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/29/2021] [Accepted: 01/31/2021] [Indexed: 01/08/2023] Open
Abstract
Human neuroimaging during cognitive tasks has provided unique and important insights into the neurobiology of cognition. However, the vast majority of research relies on group aggregate or average statistical maps of activity, which do not fully capture the rich intersubject variability in brain function. In order to fully understand the neurobiology of cognitive processes, it is necessary to explore the range of variability in activation patterns across individuals. To better characterize individual variability, hierarchical clustering was performed separately on six fMRI tasks in 822 participants from the Human Connectome Project. Across all tasks, clusters ranged from a predominantly 'deactivating' pattern towards a more 'activating' pattern of brain activity, with significant differences in out-of-scanner cognitive test scores between clusters. Cluster stability was assessed via a resampling approach; a cluster probability matrix was generated, as the probability of any pair of participants clustering together when both were present in a random subsample. Rather than forming distinct clusters, participants fell along a spectrum or into pseudo-clusters without clear boundaries. A principal components analysis of the cluster probability matrix revealed three components explaining over 90% of the variance in clustering. Plotting participants in this lower-dimensional 'similarity space' revealed manifolds of variations along an S 'snake' shaped spectrum or a folded circle or 'tortilla' shape. The 'snake' shape was present in tasks where individual variability related to activity along covarying networks, while the 'tortilla' shape represented multiple networks which varied independently.
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Affiliation(s)
- Colin Hawco
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Grace Jacobs
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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13
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Menara T, Lisi G, Pasqualetti F, Cortese A. Brain network dynamics fingerprints are resilient to data heterogeneity. J Neural Eng 2020; 18:026004. [PMID: 33361552 DOI: 10.1088/1741-2552/abd684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
CONTEXT Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. OBJECTIVE We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models. APPROACH Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions. MAIN RESULTS Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data. SIGNIFICANCE These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.
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Affiliation(s)
- Tommaso Menara
- Bourns College of Engineering, University of California Riverside, 900 University Ave, Riverside, California, 92521, UNITED STATES
| | - Giuseppe Lisi
- Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, JAPAN
| | - Fabio Pasqualetti
- Bourns College of Engineering, University of California Riverside, 900 University Ave, Riverside, California, 92521, UNITED STATES
| | - Aurelio Cortese
- Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, JAPAN
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14
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Oliver LD, Hawco C, Homan P, Lee J, Green MF, Gold JM, DeRosse P, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. Social Cognitive Networks and Social Cognitive Performance Across Individuals With Schizophrenia Spectrum Disorders and Healthy Control Participants. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:1202-1214. [PMID: 33579663 DOI: 10.1016/j.bpsc.2020.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/17/2020] [Accepted: 11/30/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Schizophrenia spectrum disorders (SSDs) feature social cognitive deficits, although their neural basis remains unclear. Social cognitive performance may relate to neural circuit activation patterns more than to diagnosis, which would have important prognostic and therapeutic implications. The current study aimed to determine how functional connectivity within and between social cognitive networks relates to social cognitive performance across individuals with SSDs and healthy control participants. METHODS Participants with SSDs (n = 164) and healthy control participants (n = 117) completed the Empathic Accuracy task during functional magnetic resonance imaging as well as lower-level (e.g., emotion recognition) and higher-level (e.g., theory of mind) social cognitive measures outside the scanner. Functional connectivity during the Empathic Accuracy task was analyzed using background connectivity and graph theory. Data-driven social cognitive networks were identified across participants. Regression analyses were used to examine network connectivity-performance relationships across individuals. Positive and negative within- and between-network connectivity strengths were also compared in poor versus good social cognitive performers and in SSD versus control groups. RESULTS Three social cognitive networks were identified: motor resonance, affect sharing, and mentalizing. Regression and group-based analyses demonstrated reduced between-network negative connectivity, or segregation, and greater within- and between-network positive connectivity in worse social cognitive performers. There were no significant effects of diagnostic group on within- or between-network connectivity. CONCLUSIONS These findings suggest that the neural circuitry of social cognitive performance may exist dimensionally. Across participants, better social cognitive performance was associated with greater segregation between social cognitive networks, whereas poor versus good performers may compensate via hyperconnectivity within and between social cognitive networks.
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Affiliation(s)
- Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Philipp Homan
- University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland; Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Junghee Lee
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Department of Veterans Affairs, Desert Pacific Mental Illness Research, Education, and Clinical Center, Los Angeles, California; Department of Psychiatry and Behavioral Neurobiology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Department of Veterans Affairs, Desert Pacific Mental Illness Research, Education, and Clinical Center, Los Angeles, California
| | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Pamela DeRosse
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Miklos Argyelan
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Anil K Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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15
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Structural brain networks in remitted psychotic depression. Neuropsychopharmacology 2020; 45:1223-1231. [PMID: 32109935 PMCID: PMC7235256 DOI: 10.1038/s41386-020-0646-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 02/18/2020] [Indexed: 12/12/2022]
Abstract
Major depressive disorder with psychotic features (psychotic depression) is a severe disorder. Compared with other psychotic disorders such as schizophrenia, relatively few studies on the neurobiology of psychotic depression have been pursued. Neuroimaging studies investigating psychotic depression have provided evidence for distributed structural brain abnormalities implicating the insular cortex and limbic system. We examined structural brain networks in participants (N = 245) using magnetic resonance imaging. This sample included healthy controls (n = 159) and the largest cross-sectional sample of patients with remitted psychotic depression (n = 86) collected to date. All patients participated in the Study of Pharmacotherapy of Psychotic Depression II randomized controlled trial. We used a novel, whole-brain, data-driven parcellation technique-non-negative matrix factorization-and applied it to cortical thickness data to derive structural covariance networks. We compared patients with remitted psychotic depression to healthy controls and found that patients had significantly thinner cortex in five structural covariance networks (insular-limbic, occipito-temporal, temporal, parahippocampal-limbic, and inferior fronto-temporal), confirming our hypothesis that affected brain networks would incorporate cortico-limbic regions. We also found that cross-sectional depression and severity scores at the time of scanning were associated with the insular-limbic network. Furthermore, the insular-limbic network predicted future severity scores that were collected at the time of recurrence of psychotic depression or sustained remission. Overall, decreased cortical thickness was found in five structural brain networks in patients with remitted psychotic depression and brain-behavior relationships were observed, particularly between the insular-limbic network and illness severity.
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16
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Hawco C, Yoganathan L, Voineskos AN, Lyon R, Tan T, Daskalakis ZJ, Blumberger DM, Croarkin PE, Lai MC, Szatmari P, Ameis SH. Greater Individual Variability in Functional Brain Activity during Working Memory Performance in young people with Autism and Executive Function Impairment. Neuroimage Clin 2020; 27:102260. [PMID: 32388347 PMCID: PMC7218076 DOI: 10.1016/j.nicl.2020.102260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 03/12/2020] [Accepted: 04/02/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Individuals with autism spectrum disorder (ASD) often present with executive functioning (EF) deficits, including spatial working memory (SWM) impairment, which impedes real-world functioning. The present study examined task-related brain activity, connectivity and individual variability in fMRI-measured neural response during an SWM task in older youth and young adults with autism and clinically significant EF impairment. METHODS Neuroimaging was analyzed in 29 individuals with ASD without intellectual disability who had clinically significant EF impairment on the Behavior Rating Inventory of Executive Function, and 20 typically developing controls (participant age range=16-34). An SWM N-Back task was performed during fMRI. SWM activity (2-Back vs. 0-Back) and task-related dorsolateral prefrontal cortex (DLPFC) connectivity was examined within and between groups. Variability of neural response during SWM was also examined. RESULTS During SWM performance both groups activated the expected networks, and no group differences in network activation or task-related DLPFC-connectivity were found. However, greater individual variability in the pattern of SWM activity was found in the ASD versus the typically developing control group. CONCLUSIONS While there were no group differences in SWM task-evoked activity or connectivity, fronto-parietal network engagement was found to be more variable/idiosyncratic in ASD. Our results suggest that the fronto-parietal network may be shifted or sub-optimally engaged during SWM performance in participants with ASD with clinically significant EF impairment, with implications for developing targeted interventions for this subgroup.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Laagishan Yoganathan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Rachael Lyon
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada
| | - Thomas Tan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Paul E Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Peter Szatmari
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada.
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17
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Moyer D, Ver Steeg G, Tax CMW, Thompson PM. Scanner invariant representations for diffusion MRI harmonization. Magn Reson Med 2020; 84:2174-2189. [PMID: 32250475 PMCID: PMC7384065 DOI: 10.1002/mrm.28243] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 12/23/2022]
Abstract
Purpose In the present work, we describe the correction of diffusion‐weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi‐site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory‐based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto‐encoders (VAE) to construct scanner invariant encodings of the imaging data. Results To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusions As imaging studies continue to grow, the use of pooled multi‐site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.
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Affiliation(s)
- Daniel Moyer
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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18
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Suh JS, Minuzzi L, Raamana PR, Davis A, Hall GB, Harris J, Hassel S, Zamyadi M, Arnott SR, Alders GL, Sassi RB, Milev R, Lam RW, MacQueen GM, Strother SC, Kennedy SH, Frey BN. An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report. NEUROIMAGE-CLINICAL 2020; 25:102178. [PMID: 32036277 PMCID: PMC7011077 DOI: 10.1016/j.nicl.2020.102178] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/25/2019] [Accepted: 01/10/2020] [Indexed: 11/28/2022]
Abstract
Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10-20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2-10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=-29,9,54.5,-7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.
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Affiliation(s)
- Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Luciano Minuzzi
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Pradeep Reddy Raamana
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Andrew Davis
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Jacqueline Harris
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Roberto B Sassi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University and Providence Care Hospital, Kingston, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Canadian Biomarker Integration Network for Depression, St. Michael's Hospital, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Benicio N Frey
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
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19
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Badhwar A, Collin-Verreault Y, Orban P, Urchs S, Chouinard I, Vogel J, Potvin O, Duchesne S, Bellec P. Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors. Neuroimage 2019; 205:116210. [PMID: 31593793 DOI: 10.1016/j.neuroimage.2019.116210] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 11/26/2022] Open
Abstract
Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to 10 min of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearson's correlation) of rsfMRI connectivity maps for seven canonical networks ranged from 0.3 to 0.8, with a negligible effect of time, but significant site and vendor effects. We noted systematic differences in data quality (i.e. head motion, number of useable time frames, temporal signal-to-noise ratio) across vendors, which may also confound some of these results, and could not be disentangled in this sample. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this "fingerprinting" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, even falling outside of the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet our results demonstrate that new methods for harmonizing multisite analysis represent an important area for future work.
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Affiliation(s)
- AmanPreet Badhwar
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada.
| | - Yannik Collin-Verreault
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada; Université de Montréal, Montréal, Canada
| | - Sebastian Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; McGill University, Montréal, Canada
| | | | | | - Olivier Potvin
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada; Department of Radiology, Faculty of Medicine, Université Laval, Quebec, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada
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20
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Viviano JD, Buchanan RW, Calarco N, Gold JM, Foussias G, Bhagwat N, Stefanik L, Hawco C, DeRosse P, Argyelan M, Turner J, Chavez S, Kochunov P, Kingsley P, Zhou X, Malhotra AK, Voineskos AN. Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects. Biol Psychiatry 2018; 84:665-674. [PMID: 29779671 PMCID: PMC6177285 DOI: 10.1016/j.biopsych.2018.03.013] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/12/2018] [Accepted: 03/31/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). METHODS We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. RESULTS Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). CONCLUSIONS A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.
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Affiliation(s)
- Joseph D Viviano
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - Robert W Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - George Foussias
- Department of Psychiatry, University of Toronto, Toronto, Ontario
| | - Nikhil Bhagwat
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario; Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Laura Stefanik
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - Colin Hawco
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Department of Psychiatry, University of Toronto, Toronto, Ontario
| | - Pamela DeRosse
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Miklos Argyelan
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Sofia Chavez
- Department of Psychiatry, University of Toronto, Toronto, Ontario; MRI Unit, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - Peter Kingsley
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Xiangzhi Zhou
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Anil K Malhotra
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Department of Psychiatry, University of Toronto, Toronto, Ontario.
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21
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Neufeld NH, Mulsant BH, Dickie EW, Meyers BS, Alexopoulos GS, Rothschild AJ, Whyte EM, Hoptman MJ, Nazeri A, Downar J, Flint AJ, Voineskos AN. Resting state functional connectivity in patients with remitted psychotic depression: A multi-centre STOP-PD study. EBioMedicine 2018; 36:446-453. [PMID: 30287158 PMCID: PMC6197617 DOI: 10.1016/j.ebiom.2018.09.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 09/13/2018] [Indexed: 11/23/2022] Open
Abstract
Background There is paucity of neurobiological knowledge about major depressive disorder with psychotic features (“psychotic depression”). This study addresses this knowledge gap by using resting state functional magnetic resonance imaging (R-fMRI) to compare functional connectivity in patients with psychotic depression and healthy controls. Methods We scanned patients who participated in a randomized controlled trial as well as healthy controls. All patients achieved remission from depressive and psychotic symptoms with sertraline and olanzapine. We employed Independent Component Analysis in independent samples to isolate the default mode network (DMN) and compared patients and controls. Findings The Toronto sample included 28 patients (mean [SD], age 56·2 [13·7]) and 39 controls (age 55·1 [13·5]). The Replication sample included 29 patients (age 56·1 [17·7]) and 36 controls (age 48·3 [17·9]). Patients in the Toronto sample demonstrated decreased between-network functional connectivity between the DMN and bilateral insular, somatosensory/motor, and auditory cortices with peak activity in the right planum polare (t = 4·831; p = 0·001, Family Wise Error (FWE) corrected). A similar pattern of between-network functional connectivity was present in our Replication sample with peak activity in the right precentral gyrus (t = 4·144; p = 0·003, FWE corrected). Interpretation Remission from psychotic depression is consistently associated with an absence of increased DMN-related functional connectivity and presence of decreased between-network functional connectivity. Future research will evaluate this abnormal DMN-related functional connectivity as a potential biomarker for treatment trajectories. Funding National Institute of Mental Health.
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Affiliation(s)
- Nicholas H Neufeld
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, 250 College Street, Toronto, ON M5T 1R8, Canada; Campbell Family Mental Health Research Institute, 250 College Street, Toronto, ON M5T 1R8, Canada; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, 250 College Street, Toronto, ON M5T 1R8, Canada; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, 250 College Street, Toronto, ON M5T 1R8, Canada; Campbell Family Mental Health Research Institute, 250 College Street, Toronto, ON M5T 1R8, Canada; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Barnett S Meyers
- Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA
| | - George S Alexopoulos
- Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA
| | - Anthony J Rothschild
- University of Massachusetts Medical School and UMass Memorial Medical Centre, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Ellen M Whyte
- University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15213, USA
| | - Matthew J Hoptman
- Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA; Department of Psychiatry, NYU School of Medicine, 550 First Avenue, New York, NY 10016, USA; Department of Psychology, CUNY Graduate Centre, 365 Fifth Avenue, New York, NY 10016, USA
| | - Arash Nazeri
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, 250 College Street, Toronto, ON M5T 1R8, Canada; Campbell Family Mental Health Research Institute, 250 College Street, Toronto, ON M5T 1R8, Canada; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 South Kingshighway Boulevard, Saint Louis, MO 63110, USA
| | - Jonathan Downar
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada; University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Alastair J Flint
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada; University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, 250 College Street, Toronto, ON M5T 1R8, Canada; Campbell Family Mental Health Research Institute, 250 College Street, Toronto, ON M5T 1R8, Canada; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada.
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