Psychiatry - Research Publications
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ItemO2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS(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.
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ItemNetwork communication models improve the behavioral and functional predictive utility of the human structural connectome(MIT PRESS, 2020-11)The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
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ItemSparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions(IOP Publishing Ltd, 2020-12)Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of interest is first selected and dynamic analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or states are studied, more local regional rearrangements are then not described, potentially missing important neurobiological information. Here, we propose a novel mathematical framework that jointly estimates resting-state functional networks and spatially more localised cross-regional modulations. To do so, the changes in activity of each brain region are modelled by a logistic regression including co-activation coefficients (reflective of network assignment, as they highlight simultaneous activations across areas) and causal interplays (denoting finer regional cross-talks, when one region active at timetmodulates thettot + 1 transition likelihood of another area). A two-parameterℓ1regularisation scheme is used to make these two sets of coefficients sparse: one controls overall sparsity, while the other governs the trade-off between co-activations and causal interplays, enabling to properly fit the data despite the yet unknown balance between both types of couplings. Across a range of simulation settings, we show that the framework successfully retrieves the two types of cross-regional interactions at once. Performance across noise and sample size settings was globally on par with that of other existing methods, with the potential to reveal more precise information missed by alternative approaches. Preliminary application to experimental data revealed that in the resting brain, co-activations and causal modulations co-exist with a varying balance across regions. Our methodological pipeline offers a conceptually elegant alternative for the assessment of functional brain dynamics and can be downloaded athttps://c4science.ch/source/Sparse_logistic_regression.git.
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ItemPredicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors(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.