Publications by authors named "Erin W Dickie"

32 Publications

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 17. Epub 2021 Feb 17.

Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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http://dx.doi.org/10.1002/hbm.25364DOI Listing
February 2021

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 11. Epub 2021 Feb 11.

Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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http://dx.doi.org/10.1002/hbm.25320DOI Listing
February 2021

Effects of Repetitive Transcranial Magnetic Stimulation on Working Memory Performance and Brain Structure in People With Schizophrenia Spectrum Disorders: A Double-Blind, Randomized, Sham-Controlled Trial.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Apr 28;6(4):449-458. Epub 2020 Nov 28.

Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, California.

Background: There are currently no approved treatments for working memory deficits in schizophrenia spectrum disorders (SSDs). The objective of the present study was to assess whether repetitive transcranial magnetic stimulation (rTMS) to the bilateral dorsolateral prefrontal cortex (DLPFC) in people with SSDs 1) improves working memory deficits and 2) changes brain structure.

Methods: We conducted a double-blind, parallel, randomized, sham-controlled study at the Centre for Addiction and Mental Health in Toronto, Canada. We randomized 83 participants with SSDs to receive either active 20 Hz rTMS applied to the bilateral DLPFC or sham rTMS for 4 weeks. The participants also completed pre/posttreatment magnetic resonance imaging. Clinical and cognitive assessments were performed at baseline, treatment end, and 1 month later. The primary outcome was change in verbal n-back working memory performance accuracy (d-prime). The secondary outcome measures were change in DLPFC thickness and fractional anisotropy of white matter tracts connecting to the DLPFC. Prespecified exploratory outcome measures were changes in general cognition; positive, negative, and depressive symptoms.

Results: Compared with sham treatment, active rTMS did not lead to significant change in working memory performance; it was associated with an increase in right DLPFC thickness but not fractional anisotropy. Prespecified exploratory analysis showed a significant decrease in depressive symptoms in the active group; the decrease in depressive symptoms was correlated with an increase in right DLPFC thickness.

Conclusions: Although rTMS applied to the bilateral DLPFC was not efficacious in treating working memory deficits in SSDs, it did increase right DLPFC thickness and decrease depressive symptoms. These findings deserve further study given the lack of efficacy of antidepressant medications in SSDs.
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http://dx.doi.org/10.1016/j.bpsc.2020.11.011DOI Listing
April 2021

Moving beyond the mean: Subgroups and dimensions of brain activity and cognitive performance across domains.

Neuroimage 2021 Feb 4;231:117823. Epub 2021 Feb 4.

Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada. Electronic address:

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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http://dx.doi.org/10.1016/j.neuroimage.2021.117823DOI Listing
February 2021

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.

Gigascience 2021 Jan;10(1)

Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada.

Background: The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance.

Methods: Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction.

Results: Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks.

Conclusions: This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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http://dx.doi.org/10.1093/gigascience/giaa155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821710PMC
January 2021

Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD.

Neuropsychopharmacology 2021 02 9;46(3):643-653. Epub 2020 Nov 9.

Department of Psychiatry, University of Toronto, Toronto, ON, Canada.

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.
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http://dx.doi.org/10.1038/s41386-020-00902-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027842PMC
February 2021

Greater male than female variability in regional brain structure across the lifespan.

Hum Brain Mapp 2020 Oct 12. Epub 2020 Oct 12.

FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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http://dx.doi.org/10.1002/hbm.25204DOI Listing
October 2020

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

JAMA Psychiatry 2021 Jan;78(1):47-63

Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, the Netherlands.

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

Design, Setting, And Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

Main Outcomes And Measures: Interregional profiles of group difference in cortical thickness between cases and controls.

Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

Conclusions And Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.2694DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450410PMC
January 2021

Brain Volume Changes Under Treatment With Antipsychotics-Reply.

JAMA Psychiatry 2020 08;77(8):877-878

Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1001/jamapsychiatry.2020.1364DOI Listing
August 2020

Variability in the analysis of a single neuroimaging dataset by many teams.

Nature 2020 06 20;582(7810):84-88. Epub 2020 May 20.

Department of Psychology, Rutgers University-Newark, Newark, NJ, USA.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
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http://dx.doi.org/10.1038/s41586-020-2314-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771346PMC
June 2020

Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition.

Neuropsychopharmacology 2020 08 18;45(9):1567-1578. Epub 2020 May 18.

Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

A history of depression is a risk factor for dementia. Despite strong epidemiologic evidence, the pathways linking depression and dementia remain unclear. We assessed structural brain alterations in white and gray matter of frontal-executive and corticolimbic circuitries in five groups of older adults putatively at-risk for developing dementia- remitted depression (MDD), non-amnestic MCI (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. We also examined two other groups: non-psychiatric ("healthy") controls (HC) and individuals with Alzheimer's dementia (AD). Magnetic resonance imaging (MRI) data were acquired on the same 3T scanner. Following quality control in these seven groups, from diffusion-weighted imaging (n = 300), we compared white matter fractional anisotropy (FA), mean diffusivity (MD), and from T1-weighted imaging (n = 333), subcortical volumes and cortical thickness in frontal-executive and corticolimbic regions of interest (ROIs). We also used exploratory graph theory analysis to compare topological properties of structural covariance networks and hub regions. We found main effects for diagnostic group in FA, MD, subcortical volume, and cortical thickness. These differences were largely due to greater deficits in the AD group and to a lesser extent aMCI compared with other groups. Graph theory analysis revealed differences in several global measures among several groups. Older individuals with remitted MDD and naMCI did not have the same white or gray matter changes in the frontal-executive and corticolimbic circuitries as those with aMCI or AD, suggesting distinct neural mechanisms in these disorders. Structural covariance global metrics suggested a potential difference in brain reserve among groups.
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http://dx.doi.org/10.1038/s41386-020-0715-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360554PMC
August 2020

Structural brain networks in remitted psychotic depression.

Neuropsychopharmacology 2020 06 28;45(7):1223-1231. Epub 2020 Feb 28.

Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Toronto, ON, M5T 1R8, Canada.

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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http://dx.doi.org/10.1038/s41386-020-0646-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235256PMC
June 2020

Effects of Antipsychotic Medication on Brain Structure in Patients With Major Depressive Disorder and Psychotic Features: Neuroimaging Findings in the Context of a Randomized Placebo-Controlled Clinical Trial.

JAMA Psychiatry 2020 07;77(7):674-683

University Health Network, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

Importance: Prescriptions for antipsychotic medications continue to increase across many brain disorders, including off-label use in children and elderly individuals. Concerning animal and uncontrolled human data suggest antipsychotics are associated with change in brain structure, but to our knowledge, there are no controlled human studies that have yet addressed this question.

Objective: To assess the effects of antipsychotics on brain structure in humans.

Design, Setting, And Participants: Prespecified secondary analysis of a double-blind, randomized, placebo-controlled trial over a 36-week period at 5 academic centers. All participants, aged 18 to 85 years, were recruited from the multicenter Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II). All participants had major depressive disorder with psychotic features (psychotic depression) and were prescribed olanzapine and sertraline for a period of 12 to 20 weeks, which included 8 weeks of remission of psychosis and remission/near remission of depression. Participants were then were randomized to continue receiving this regimen or to be switched to placebo and sertraline for a subsequent 36-week period. Data were analyzed between October 2018 and February 2019.

Interventions: Those who consented to the imaging study completed a magnetic resonance imaging (MRI) scan at the time of randomization and a second MRI scan at the end of the 36-week period or at time of relapse.

Main Outcomes And Measures: The primary outcome measure was cortical thickness in gray matter and the secondary outcome measure was microstructural integrity of white matter.

Results: Eighty-eight participants (age range, 18-85 years) completed a baseline scan; 75 completed a follow-up scan, of which 72 (32 men and 40 women) were useable for final analyses. There was a significant treatment-group by time interaction in cortical thickness (left, t = 3.3; P = .001; right, t = 3.6; P < .001) but not surface area. No significant interaction was found for fractional anisotropy, but one for mean diffusivity of the white matter skeleton was present (t = -2.6, P = .01). When the analysis was restricted to those who sustained remission, exposure to olanzapine compared with placebo was associated with significant decreases in cortical thickness in the left hemisphere (β [SE], 0.04 [0.009]; t34.4 = 4.7; P <.001), and the right hemisphere (β [SE], 0.03 [0.009]; t35.1 = 3.6; P <.001). Post hoc analyses showed that those who relapsed receiving placebo experienced decreases in cortical thickness compared with those who sustained remission.

Conclusions And Relevance: In this secondary analysis of a randomized clinical trial, antipsychotic medication was shown to change brain structure. This information is important for prescribing in psychiatric conditions where alternatives are present. However, adverse effects of relapse on brain structure support antipsychotic treatment during active illness.

Trial Registration: ClinicalTrials.gov Identifier: NCT01427608.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.0036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330722PMC
July 2020

An overlapping pattern of cerebral cortical thinning is associated with both positive symptoms and aggression in schizophrenia via the ENIGMA consortium.

Psychol Med 2020 09 16;50(12):2034-2045. Epub 2019 Oct 16.

Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.

Background: Positive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.

Method: To study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.

Results: The result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.

Conclusion: These findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.
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http://dx.doi.org/10.1017/S0033291719002149DOI Listing
September 2020

Ciftify: A framework for surface-based analysis of legacy MR acquisitions.

Neuroimage 2019 08 12;197:818-826. Epub 2019 May 12.

Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.

The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.
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http://dx.doi.org/10.1016/j.neuroimage.2019.04.078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6675413PMC
August 2019

Developmentally divergent sexual dimorphism in the cortico-striatal-thalamic-cortical psychosis risk pathway.

Neuropsychopharmacology 2019 08 6;44(9):1649-1658. Epub 2019 May 6.

Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada.

Structural and functional cortico-striatal-thalamic-cortical (CSTC) circuit abnormalities have been observed in schizophrenia and the clinical high-risk state. However, this circuit is sexually dimorphic and changes across neurodevelopment. We examined effects of sex and age on structural and functional properties of the CSTC circuit in a large sample of youth with and without psychosis spectrum symptoms (PSS) from the Philadelphia Neurodevelopmental Cohort. T1-weighted and resting-state functional MRI scans were collected on a 3T Siemens scanner, in addition to participants' cognitive and psychopathology data. After quality control, the total sample (aged 11-21) was n = 1095 (males = 485, females = 610). Structural subdivisions of the striatum and thalamus were identified using the MAGeT Brain segmentation tool. Functional seeds were segmented based on brain network connectivity. Interaction effects among PSS group, sex, and age on striatum, thalamus, and subdivision volumes were examined. A similar model was used to test effects on functional connectivity of the CSTC circuit. A sex by PSS group interaction was identified, whereby PSS males had higher volumes and PSS females had lower volumes in striatal and thalamic subdivisions. Reduced functional striato-cortical connectivity was found in PSS youth, primarily driven by males, whereby younger male PSS youth also exhibited thalamo-cortical hypo-connectivity (compared to non-PSS youth), vs. striato-cortical hyper-connectivity in older male PSS youth (compared to non-PSS youth). Youth with PSS demonstrate sex and age-dependent differences in striatal and thalamic subdivision structure and functional connectivity. Further efforts at biomarker discovery and early therapeutic intervention targeting the CSTC circuit in psychosis should consider effects of sex and age.
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http://dx.doi.org/10.1038/s41386-019-0408-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785143PMC
August 2019

Separable and Replicable Neural Strategies During Social Brain Function in People With and Without Severe Mental Illness.

Am J Psychiatry 2019 07 4;176(7):521-530. Epub 2019 Jan 4.

Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto (Hawco, Calarco, Mulsant, Viviano, Dickie, Foussias, Voineskos); the Department of Psychiatry, University of Toronto, Toronto (Hawco, Foussias, Mulsant, Voineskos); Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Md. (Buchanan, Gold); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (Argyelan, DeRosse, Malhotra); Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (Argyelan, DeRosse, Malhotra); the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California, Los Angeles (Iacoboni).

Objective: Case-control study design and disease heterogeneity may impede biomarker discovery in brain disorders, including serious mental illnesses. To identify biologically and/or behaviorally driven as opposed to diagnostically driven subgroups of individuals, the authors used hierarchical clustering to identify individuals with similar patterns of brain activity during a facial imitate/observe functional MRI task.

Methods: Participants in the Social Processes Initiative in Neurobiology of the Schizophrenia(s) study (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy control participants) underwent MRI scanning at three sites. Hierarchical clustering was used to identify new data-driven groups of participants; differences on social and neurocognitive tests completed outside the scanner were compared among the new groups.

Results: Three clusters with distinct patterns of neural activity were found. Cluster membership was not related to diagnosis or scan site. The largest cluster consisted of "typical activators," with activity in the canonical "simulation" circuit. The other clusters represented a "hyperactivating" group and a "deactivating" group. Between-participants Euclidean distances were smaller within clusters than within site or diagnostics groups. The deactivating group had the highest social cognitive and neurocognitive test scores. The hierarchical clustering analysis was repeated on a replication sample (N=108; 32 schizophrenia spectrum disorder, 37 euthymic bipolar disorder, and 39 healthy control participants), which exhibited the same three cluster patterns.

Conclusions: The study findings demonstrate replicable differing patterns of neural activity among individuals during a socio-emotional task, independent of DSM diagnosis or scan site. The findings may provide objective neuroimaging endpoints (biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.
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http://dx.doi.org/10.1176/appi.ajp.2018.17091020DOI Listing
July 2019

Resting state functional connectivity in patients with remitted psychotic depression: A multi-centre STOP-PD study.

EBioMedicine 2018 Oct 1;36:446-453. Epub 2018 Oct 1.

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. Electronic address:

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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http://dx.doi.org/10.1016/j.ebiom.2018.09.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197617PMC
October 2018

Spread of activity following TMS is related to intrinsic resting connectivity to the salience network: A concurrent TMS-fMRI study.

Cortex 2018 11 30;108:160-172. Epub 2018 Jul 30.

Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.

Transcranial magnetic stimulation (TMS) modulates activity at local and regions distal to the site of simulation. TMS has also been found to modulate brain networks, and it has been hypothesized that functional connectivity may predict the neuronal changes at local and distal sites in response to a TMS pulse. However, a direct relationship between resting connectivity and change in TMS-induced brain activation has yet to be demonstrated. Concurrent TMS-fMRI is a technique to directly measure this spread activity following TMS in real time. In twenty-two participants, resting-state fMRI scans were acquired, followed by four ten minute sessions of concurrent TMS-fMRI over the left dorsolateral prefrontal cortex (DLPFC). Seed-based functional connectivity to the individualized TMS target was examined using the baseline resting fMRI scan data, and the change of activity resulting from TMS was determined using a general linear model (High vs Low intensity TMS). While at the group level the spatial pattern of resting connectivity related to the pattern of TMS-induced cortical changes, there was substantial variability across individuals. This variability was further probed by examining individual's connectivity from the TMS target to six resting state networks. Only connectivity between the salience network (SN) and the TMS target site correlated with the RSC-TMS score. This suggests that resting state connectivity is correlated with TMS-induced changes in activity following DLPFC stimulation, particularly when the DLPFC target interacts with the SN. These results highlight the importance of examining such relationships at the individual level and may help to guide individual treatment in clinical populations.
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http://dx.doi.org/10.1016/j.cortex.2018.07.010DOI Listing
November 2018

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.

Mach Learn Med Imaging 2017 Sep 7;10541:371-378. Epub 2017 Sep 7.

Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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http://dx.doi.org/10.1007/978-3-319-67389-9_43DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6049825PMC
September 2017

Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium.

Biol Psychiatry 2018 11 14;84(9):644-654. Epub 2018 May 14.

Division of Mental Health and Addiction, NORMENT, K.G. Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway.

Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.

Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.

Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.

Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.
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http://dx.doi.org/10.1016/j.biopsych.2018.04.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6177304PMC
November 2018

A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data.

Psychiatry Res Neuroimaging 2018 12 9;282:134-142. Epub 2018 Jun 9.

Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada. Electronic address:

Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders. Here, we sought to establish the utility of a clustering algorithm as an alternative to more traditional intra-class correlation coefficient approaches in a longitudinal multi-center human phantom study. We completed annual reliability scans on 'travelling human phantoms'. Acquisitions across sites were harmonized prospectively. Twenty-seven MRI sessions were available across four participants, scanned on five scanners, across three years. For each scan, three metrics were extracted: cortical thickness (CT), white matter fractional anisotropy (FA), and resting state functional connectivity (FC). For each metric, hierarchical clustering (Ward's method) was performed. The cluster solutions were compared to participant and scanner using the adjusted Rand index (ARI). For all metrics, data clustered by participant rather than by scanner (ARI > 0.8 comparing clusters to participants, ARI < 0.2 comparing clusters to scanners). These results demonstrate that hierarchical clustering can reliably identify structural and functional scans from different participants imaged on different scanners across time. With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups of participants based on brain structure or function.
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http://dx.doi.org/10.1016/j.pscychresns.2018.06.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482446PMC
December 2018

Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder.

Biol Psychiatry 2018 08 17;84(4):278-286. Epub 2018 Mar 17.

Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada. Electronic address:

Background: Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported.

Methods: We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TD persons remain after PINT application.

Results: We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TD persons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD.

Conclusions: Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders.
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http://dx.doi.org/10.1016/j.biopsych.2018.02.1174DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6076333PMC
August 2018

Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies.

Hum Brain Mapp 2018 02 27;39(2):1015-1023. Epub 2017 Nov 27.

Centre for Addiction and Mental Health, Toronto, Canada.

A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.
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http://dx.doi.org/10.1002/hbm.23900DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5764798PMC
February 2018

The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project.

Schizophr Res 2018 05 3;195:306-317. Epub 2017 Oct 3.

The Cognitive Genetics and Cognitive Therapy Group, Department of Psychology, National University of Ireland, Galway, Ireland.

Background: Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection.

Methods: We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site.

Results: Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other.

Conclusions: The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.
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http://dx.doi.org/10.1016/j.schres.2017.09.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882601PMC
May 2018

Effect of Early Adversity and Childhood Internalizing Symptoms on Brain Structure in Young Men.

JAMA Pediatr 2015 Oct;169(10):938-46

Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College, London, England.

Importance: Early adversity is an important risk factor that relates to internalizing symptoms and altered brain structure.

Objective: To assess the direct effects of early adversity and child internalizing symptoms (ie, depression, anxiety) on cortical gray matter (GM) volume, as well as the extent to which early adversity associates with variation in cortical GM volume indirectly via increased levels of internalizing symptoms.

Design, Setting, And Participants: A prospective investigation of associations between adversity within the first 6 years of life, internalizing symptoms during childhood and early adolescence, and altered brain structure in late adolescence (age, 18-21 years) was conducted in a community-based birth cohort in England (Avon Longitudinal Study of Parents and Children). Participants from the cohort included 494 mother-son pairs monitored since the mothers were pregnant (estimated date of delivery between April 1, 1991, and December 31, 1992). Data collection for the present study was conducted between April 1, 1991, and November 30, 2010; the neuroimaging data were collected between September 1, 2010, and November 30, 2012, and data analyses for the present study occurred between January 25, 2013, and February 15, 2015. Risk factors were adversity within the first 6 years of the child's life (including prenatal exposure) and the child's internalizing symptoms between age 7 and 13 years.

Exposures: Early childhood adversity.

Main Outcomes And Measures: The main outcome was GM volume of cortical regions previously associated with major depression measured through T1-weighted magnetic resonance images collected in late adolescence.

Results: Among 494 young men included in this analysis, early adversity was directly associated with lower GM volumes in the anterior cingulate cortex (β = -.18; P = .01) and higher GM volume in the precuneus (β = .18; P = .009). Childhood internalizing symptoms were associated with lower GM volume in the right superior frontal gyrus (β = -.20; P = .002). Early adversity was also associated with higher levels of internalizing symptoms (β = .37; P < .001), which, in turn, were associated with lower superior frontal gyrus volume (ie, an indirect effect) (β = -.08; 95% CI, -0.14 to -0.01; P = .02).

Conclusions And Relevance: Adversity early in life was associated with higher levels of internalizing symptoms as well as with altered brain structure. Early adversity was related to variation in brain structure both directly and via increased levels of internalizing symptoms. These findings may suggest that some of the structural variation often attributed to depression might be associated with early adversity in addition to the effect of depression.
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http://dx.doi.org/10.1001/jamapediatrics.2015.1486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5137198PMC
October 2015

Global genetic variations predict brain response to faces.

PLoS Genet 2014 Aug 14;10(8):e1004523. Epub 2014 Aug 14.

Rotman Research Institute, University of Toronto, Toronto, Ontario, Canada; School of Psychology, University of Nottingham, Nottingham, United Kindom.

Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.
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http://dx.doi.org/10.1371/journal.pgen.1004523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133042PMC
August 2014

Estimating volumes of the pituitary gland from T1-weighted magnetic-resonance images: effects of age, puberty, testosterone, and estradiol.

Neuroimage 2014 Jul 12;94:216-221. Epub 2014 Mar 12.

Department of Psychology, University of Toronto, Toronto, Canada; Rotman Research Institute, Baycrest, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada. Electronic address:

The pituitary gland is a key structure in the hypothalamic-pituitary-gonadal (HPG) axis--it plays an important role in sexual maturation during puberty. Despite its small size, its volume can be quantified using magnetic resonance imaging (MRI). Here, we study a cohort of 962 typically developing adolescents from the Saguenay Youth Study and estimate pituitary volumes using a newly developed multi-atlas segmentation method known as the MAGeT Brain algorithm. We found that age and puberty stage (controlled for age) each predicts adjusted pituitary volumes (controlled for total brain volume) in both males and females. Controlling for the effects of age and puberty stage, total testosterone and estradiol levels also predict adjusted pituitary volumes in males and pre-menarche females, respectively. These findings demonstrate that the pituitary gland grows during adolescence, and its volume relates to circulating plasma-levels of sex steroids in both males and females.
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http://dx.doi.org/10.1016/j.neuroimage.2014.02.030DOI Listing
July 2014

Neural correlates of recovery from post-traumatic stress disorder: a longitudinal fMRI investigation of memory encoding.

Neuropsychologia 2011 Jun 5;49(7):1771-8. Epub 2011 Mar 5.

Douglas Mental Health University Institute, 6875 LaSalle Boulevard, FBC Pavillon, Verdun, QC H4H 1R3, Canada.

Post-traumatic stress disorder (PTSD) is characterized by a failure of psychological recovery from a traumatic experience. At a neural level, it is associated with abnormalities of the areas of the neural system that process threatening information, including the amygdala and medial-prefrontal cortex, as well as of that involved in episodic memory, including the hippocampus. However, little is known about how the function of these regions may change as one recovers from the disorder. In this investigation, PTSD patients underwent two functional magnetic resonance imaging (fMRI) scans, 6-9 months apart, while viewing fearful and neutral faces in preparation for a memory test (administered outside the scanner). At Time 2, 65% of patients were in remission. Current symptom levels correlated positively with memory-related fMRI activity in the amygdala and ventral-medial prefrontal cortex (vmPFC). In addition, the change in activity within the hippocampus and the subgenual anterior cingulate cortex (sgACC) was associated with the degree of symptom improvement (n=18). These results suggest differential involvement of structures within the fear network in symptom manifestation and in recovery from PTSD: whereas activity within the amygdala and vmPFC appeared to be a marker of current symptom severity, functional changes in the hippocampus and sgACC reflected recovery. These results underscore the importance of longitudinal investigations for the identification of the differential neural structures associated with the expression and remission of anxiety disorders.
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http://dx.doi.org/10.1016/j.neuropsychologia.2011.02.055DOI Listing
June 2011

An fMRI investigation of memory encoding in PTSD: influence of symptom severity.

Neuropsychologia 2008 Apr 19;46(5):1522-31. Epub 2008 Jan 19.

Douglas Mental Health University Institute, Canada.

Previous studies have shown memory deficits in Post-Traumatic Stress Disorder (PTSD) patients, as well as abnormal patterns of brain activity, especially when retrieving trauma-related information. This study extended previous findings by investigating the neural correlates of successful memory encoding of trauma-unrelated stimuli and their relationship with PTSD symptom severity. We used the subsequent memory paradigm, in the context of event-related functional magnetic resonance imaging, in 27 PTSD patients to identify the brain regions involved in the encoding of fearful and neutral faces. Symptom severity was assessed by the Clinically Administered PTSD Scale (CAPS) scores. It was found that memory performance was negatively correlated with CAPS scores. Furthermore, a negative correlation was observed between CAPS scores and ventral medial prefrontal cortex (vmPFC) activity elicited by the subsequently forgotten faces. Finally, symptom severity predicted the contribution of the amygdala to the successful encoding of fearful faces. These results confirm the roles of the vmPFC and the amygdala in PTSD and highlight the importance of taking into account individual differences when assessing the behavioural and neural correlates of the disorder.
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http://dx.doi.org/10.1016/j.neuropsychologia.2008.01.007DOI Listing
April 2008