Psychiatry - Research Publications
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ItemO2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMSCropley, V ; Tian, Y ; Fernando, K ; Mansour, S ; Pantelis, C ; Cocchi, L ; Zalesky, A (Oxford University Press (OUP), 2020-05-18)Abstract Background Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. Methods Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. Results Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p<0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. Discussion We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions.
ItemS187. EXPLORING NEURODEVELOPMENTAL AND FAMILIAL ORIGINS OF NEUROLOGICAL SOFT SIGNS IN SCHIZOPHRENIACooper, R ; Van Rheenen, T ; Zalesky, A ; Wannan, C ; Wang, Y ; Bousman, C ; Everall, I ; Pantelis, C ; Cropley, V (Oxford University Press (OUP), 2020-05-18)
ItemNo Preview AvailableWhite matter pathology in schizophreniaDi Biase, MA ; Pantelis, C ; Zalesky, A ; Kubicki, M ; Shenton, ME (Springer Nature, 2020-01-01)Significant effort has been devoted to characterizing white matter pathology in patients with schizophrenia and its impact on brain connectivity (Samartzis et al., J Neuroimaging 24(2):101-10, 2014; Fusar-Poli et al., Neurosci Biobehav Rev 37(8):1680-91, 2013; Bora et al., Schizophr Res 127(1):46-57, 2011). This is particularly important in light of the disconnection hypothesis-a key etiological theory of schizophrenia suggesting that symptoms arise from a failure of integration between distinct brain regions (Friston, Schizophr Res 30(2):115-25, 1998). In this chapter, we focus on neuroimaging evidence demonstrating structural white matter alterations in schizophrenia. Key questions addressed include: what methods are sensitive to the pathophysiology of schizophrenia? What is the evidence that white matter pathology emerges prior to or near to the onset of psychosis? Is the trajectory of white matter pathology stable or, alternatively, a dynamic process, with progressive changes evident over the course of illness? What are the limitations of these studies? How does neuroimaging evidence relate to micro- and meso-structural white matter findings?.
ItemIndividual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohortLv, J ; Di Biase, M ; Cash, RFH ; Cocchi, L ; Cropley, VL ; Klauser, P ; Tian, Y ; Bayer, J ; Schmaal, L ; Cetin-Karayumak, S ; Rathi, Y ; Pasternak, O ; Bousman, C ; Pantelis, C ; Calamante, F ; Zalesky, A (SPRINGERNATURE, 2020-09-22)The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.
ItemPredicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictorsKottaram, A ; Johnston, LA ; Tian, Y ; Ganella, EP ; Laskaris, L ; Cocchi, L ; McGorry, P ; Pantelis, C ; Kotagiri, R ; Cropley, V ; Zalesky, A (Wiley, 2020-08-15)In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1‐year follow‐up was assessed in 30 individuals with a schizophrenia‐spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting‐state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1‐year follow‐up varied markedly among individuals (interquartile range: 55%). Dynamic resting‐state connectivity measured within the default‐mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow‐up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1‐year follow‐up were predicted by hyper‐connectivity and hypo‐dynamism within the default‐mode network at baseline assessment, while hypo‐connectivity and hyper‐dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.