Leila Kushan

Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms.

BACKGROUND No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data. METHODS Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects. RESULTS Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation. CONCLUSIONS These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.

Categorical Versus Dimensional Approaches to Autism-Associated Intermediate Phenotypes in 22q11.2 Microdeletion Syndrome

BACKGROUND 22q11.2 Microdeletion syndrome (22q11DS) is associated with elevated rates of autism spectrum disorders (ASDs), although the diagnosis is controversial. In order to determine whether there is a biological substrate of ASD in 22q11DS, we examined neurocognitive and structural neuroanatomic differences between those with 22q11DS and an ASD diagnosis (22q11DS-ASD+) and those with 22q11DS without ASD (22q11DS-ASD-); we then determined whether these differences were better characterized within a categorical or dimensional framework. METHODS We collected multiple neurocognitive measures and high-resolution T1-weighted scans on 116 individuals (29 22q11DS-ASD+, 32 22q11DS-ASD-, 55 typically developing controls) between 6 and 26 years of age. Measures of subcortical volume, cortical thickness (CT), and surface area were extracted using the FreeSurfer image analysis suite. Group differences in neurocognitive and neuroanatomic measures were assessed; regression analyses were then performed to determine whether a categorical or dimensional measure of ASD was a better predictor of neurocognitive impairment and/or neuroanatomic abnormalities observed in 22q11DS-ASD+. RESULTS In comparison to 22q11DS-ASD-, 22q11DS-ASD+ participants exhibited decreased bilateral hippocampal CT and decreased right amygdala volumes. Those with 22q11DS-ASD+ also showed slowed processing speed and impairments in visuospatial and facial memory. Neurocognitive impairments fit a dimensional model of ASD, whereas reductions in parahippocampal CT were best explained by a categorical measure of ASD. CONCLUSIONS A combination of categorical and dimensional measures of ASD may provide the most comprehensive understanding of ASDs in 22q11DS.

Nested Inversion Polymorphisms Predispose Chromosome 22q11.2 to Meiotic Rearrangements.

Inversion polymorphisms between low-copy repeats (LCRs) might predispose chromosomes to meiotic non-allelic homologous recombination (NAHR) events and thus lead to genomic disorders. However, for the 22q11.2 deletion syndrome (22q11.2DS), the most common genomic disorder, no such inversions have been uncovered as of yet. Using fiber-FISH, we demonstrate that parents transmitting the de novo 3 Mb LCR22A-D 22q11.2 deletion, the reciprocal duplication, and the smaller 1.5 Mb LCR22A-B 22q11.2 deletion carry inversions of LCR22B-D or LCR22C-D. Hence, the inversions predispose chromosome 22q11.2 to meiotic rearrangements and increase the individual risk for transmitting rearrangements. Interestingly, the inversions are nested or flanking rather than coinciding with the deletion or duplication sizes. This finding raises the possibility that inversions are a prerequisite not only for 22q11.2 rearrangements but also for all NAHR-mediated genomic disorders.

Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth with 22q11.2 Deletions

22q11.2 Deletion syndrome (22q11DS) is a genetic disorder associated with numerous phenotypic consequences and is one of the greatest known risk factors for psychosis. We investigated intrinsic-connectivity-networks (ICNs) as potential biomarkers for patient and psychosis-risk status in 2 independent cohorts, UCLA (33 22q11DS-participants, 33 demographically matched controls), and Syracuse (28 22q11DS, 28 controls). After assessing group connectivity differences, ICNs from the UCLA cohort were used to train classifiers to distinguish cases from controls, and to predict psychosis risk status within 22q11DS; classifiers were subsequently tested on the Syracuse cohort. In both cohorts we observed significant hypoconnectivity in 22q11DS relative to controls within anterior cingulate (ACC)/precuneus, executive, default mode (DMN), posterior DMN, and salience networks. Of 12 ICN-derived classifiers tested in the Syracuse replication-cohort, the ACC/precuneus, DMN, and posterior DMN classifiers accurately distinguished between 22q11DS and controls. Within 22q11DS subjects, connectivity alterations within 4 networks predicted psychosis risk status for a given individual in both cohorts: the ACC/precuneus, DMN, left executive, and salience networks. Widespread within-network-hypoconnectivity in large-scale networks implicated in higher-order cognition may be a defining characteristic of 22q11DS during adolescence and early adulthood; furthermore, loss of coherence within these networks may be a valuable biomarker for individual prediction of psychosis-risk in 22q11DS.

Neural mechanisms of response inhibition and impulsivity in 22q11.2 deletion carriers and idiopathic attention deficit hyperactivity disorder

Highlights • 22q11DS offers a compelling model to understand the neural substrates of attentional dysfunction.• First study directly comparing neural function in 22q11DS vs. ADHD patients• 22q11DS and ADHD patients show a shared deficit in RI-related activation.• ADHD patients showed greater activity in the middle frontal gyrus than 22q11DS during RI.• Neural activity is inversely correlated with self-reported Cognitive Impulsivity in 22q11DS.

Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: Convergence with idiopathic psychosis and effects of deletion size

The 22q11.2 deletion (22q11DS) is a common chromosomal microdeletion and a potent risk factor for psychotic illness. Prior studies reported widespread cortical changes in 22q11DS, but were generally underpowered to characterize neuroanatomic abnormalities associated with psychosis in 22q11DS, and/or neuroanatomic effects of variability in deletion size. To address these issues, we developed the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta-Analysis) 22q11.2 Working Group, representing the largest analysis of brain structural alterations in 22q11DS to date. The imaging data were collected from 10 centers worldwide, including 474 subjects with 22q11DS (age = 18.2 ± 8.6; 46.9% female) and 315 typically developing, matched controls (age = 18.0 ± 9.2; 45.9% female). Compared to controls, 22q11DS individuals showed thicker cortical gray matter overall (left/right hemispheres: Cohen’s d = 0.61/0.65), but focal thickness reduction in temporal and cingulate cortex. Cortical surface area (SA), however, showed pervasive reductions in 22q11DS (left/right hemispheres: d = −1.01/−1.02). 22q11DS cases vs. controls were classified with 93.8% accuracy based on these neuroanatomic patterns. Comparison of 22q11DS-psychosis to idiopathic schizophrenia (ENIGMA-Schizophrenia Working Group) revealed significant convergence of affected brain regions, particularly in fronto-temporal cortex. Finally, cortical SA was significantly greater in 22q11DS cases with smaller 1.5 Mb deletions, relative to those with typical 3 Mb deletions. We found a robust neuroanatomic signature of 22q11DS, and the first evidence that deletion size impacts brain structure. Psychotic illness in this highly penetrant deletion was associated with similar neuroanatomic abnormalities to idiopathic schizophrenia. These consistent cross-site findings highlight the homogeneity of this single genetic etiology, and support the suitability of 22q11DS as a biological model of schizophrenia.

Altered Brain Structure-Function Relationships Underlie Executive Dysfunction in 22q11.2 Deletion Syndrome

22q11.2 deletion syndrome (22q11DS) is a neurogenetic disorder associated with elevated rates of developmental neuropsychiatric disorders and impaired executive function (EF). Disrupted brain structure-function relationships may underlie EF deficits in 22q11DS. We administered the Behavior Rating Inventory of Executive Function (BRIEF) to assess real-world EF in patients with 22q11DS and matched controls (n = 86; age 6-17 years), along with cognitive measures that tap behavioral regulation and metacognition aspects of EF. Using FreeSurfers whole-brain vertex cortical thickness pipeline, we investigated brain structure-EF relationships in patients with 22q11DS and controls. Behaviorally, patients with 22q11DS were impaired on multiple EF measures. Right orbitofrontal cortical thickness showed a differential relationship between real-world EF in patients with 22q11DS and controls. We also observed a group difference in the relationship between behavioral regulation and metacognition measures with thickness of ventral and dorsolateral prefrontal regions, respectively. Our findings suggest that executive dysfunction characteristic of 22q11DS is underscored by altered prefrontal cortical structure.

The Neuroanatomy of Autism Spectrum Disorder Symptomatology in 22q11.2 Deletion Syndrome

22q11.2 Deletion Syndrome (22q11.2DS) is a genetic condition associated with a high prevalence of neuropsychiatric conditions that include autism spectrum disorder (ASD). While evidence suggests that clinical phenotypes represent distinct neurodevelopmental outcomes, it remains unknown whether this translates to the level of neurobiology. To fractionate the 22q11.2DS phenotype on the level of neuroanatomy, we examined differences in vertex-wise estimates of cortical volume, surface area, and cortical thickness between 1) individuals with 22q11.2DS (n = 62) and neurotypical controls (n = 57) and 2) 22q11.2DS individuals with ASD symptomatology (n = 30) and those without (n = 25). We firstly observed significant differences in surface anatomy between 22q11.2DS individuals and controls for all 3 neuroanatomical features, predominantly in parietotemporal regions, cingulate and dorsolateral prefrontal cortices. We also established that 22q11.2DS individuals with ASD symptomatology were neuroanatomically distinct from 22q11.2DS individuals without ASD symptoms, particularly in brain regions that have previously been linked to ASD (e.g., dorsolateral prefrontal cortices and the entorhinal cortex). Our findings indicate that different clinical 22q11.2DS phenotypes, including those with ASD symptomatology, may represent different neurobiological subgroups. The spatially distributed patterns of neuroanatomical differences associated with ASD symptomatology in 22q11.2DS may thus provide useful information for patient stratification and the prediction of clinical outcomes.

Contents Vol. 1, 2015

Associate Editors Cathy L. Barr – Toronto Western Research Institute, Toronto, Canada Joel Gelernter – West Haven VA Medical Center, New Haven, USA Daniel H. Mathalon – University of Florida, Gainesville, USA Carol A. Mathews – University of California, San Francisco, USA Andrew McIntosh – University of Edinburgh, Edinburgh, United Kingdom Alexander B. Niculescu – Indiana University, Indianapolis, USA Tracey L. Petryshen – Center for Human Genetic Research, Boston, USA Marina R. Picciotto – Yale University, New Haven, USA Akira Sawa – Johns Hopkins University, Baltimore, USA