Psychiatry - Research Publications
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ItemA cautionary note on the use of SIFT in pathological connectomes(WILEY, 2020-03)
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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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ItemS187. EXPLORING NEURODEVELOPMENTAL AND FAMILIAL ORIGINS OF NEUROLOGICAL SOFT SIGNS IN SCHIZOPHRENIA(Oxford University Press (OUP), 2020-05-18)Abstract Background The neurodevelopmental hypothesis is the most widely regarded framework for understanding the development of schizophrenia. One of the most commonly cited pieces of evidence for this theory is the presence of neurological soft signs (NSS) in individuals prior to the onset of psychosis. Increased NSS is also reported in unaffected individuals with a family history of schizophrenia, suggesting that NSS may also have a familial component. Although much research has implicated reduced grey matter volume (GMV) in association with these signs, a subcomponent of volume, known as gyrification, has been poorly researched. Given that gyrification develops predominantly in prenatal life it may be particularly susceptible to a neurodevelopmental abnormality. The aims of this study were to investigate the neurodevelopmental and familial underpinnings of NSS in schizophrenia. Specifically, we examined the brain structural correlates, at both the level of GMV and gyrification, of NSS in individuals with schizophrenia, their unaffected relatives and healthy controls. We aimed to determine whether gyrification better predicted NSS severity than GMV, and whether the relationship between brain structure and NSS were present in a step-wise manner across the diagnostic groups. Methods The sample consisted of individuals with schizophrenia (N=66), their unaffected relatives (N=27) and healthy controls (N=53). NSS was assessed with the Neurological Evaluation Scale (NES), and GMV and gyrification were extracted from MRI using the FreeSurfer imaging suite. A series of analysis of covariance were used to compare NES scores and brain measures between the groups. Separate linear regression analyses were used to assess whether whole-brain GMV and gyrification predicted NES above a covariate-only model. Moderation analyses were used to assess whether the relationship between NES and brain structure were different between the diagnostic groups. Error control was achieved with a false discovery rate of 5%. Results NES was significantly higher in schizophrenia patients than relatives (p<.0001), who were in turn significantly higher than controls (p=.034). With the groups combined, lower GMV (p<.0001), as well as lower gyrification (p=.004), predicted higher NES above a covariate-only model. GMV predicted greater variance in NSS in comparison to gyrification, explaining an additional 20.3% of the variance in NES, in comparison to the additional 5.5% of variance in NES explained by gyrification. Diagnostic group moderated the association between GMV and NES (p=.019), but not between gyrification and NES (p=.245). Follow-up tests revealed that lower GMV was associated with higher NES in schizophrenia (t=-4.5, p<.0001) and relatives (t=-2.5, p=.015) but not controls (t=-1.9, p=.055). Discussion Our findings indicate that NSS is heritable, being present in patients with established schizophrenia, and to a lesser extent, in unaffected relatives. Consistent with previous research, we revealed that GMV predicted NSS severity, suggesting that abnormalities in volume may underlie these signs. We additionally found that gyrification predicted, although to a lesser extent than volume, NSS severity, providing some support for schizophrenia being of possible neurodevelopmental origin. Evidence for an association between volume and NSS in relatives, whom are not confounded by illness-related factors such as medication and symptom severity, indicates a familial contribution to the neural underpinnings of NSS. Together, our study suggests that there may be various aetiological pathways underlying soft signs across the schizophrenia diathesis, some that may be of familial or neurodevelopmental origin.
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ItemAberrant Metabolic Patterns Networks in Insular Epilepsy(FRONTIERS MEDIA SA, 2020-12-23)Introduction: Insular epilepsy is clinically challenging. This study aimed to map cerebral metabolic networks in insular epilepsy and investigate their graph-theoretic properties, with the goal of elucidating altered metabolic network architectures that underlie interictal hypometabolism. Aims: Fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) imaging was performed in 17 individuals with a stereoelectroencephalography (SEEG) confirmed diagnosis of insula epilepsy and 14 age- and sex-matched healthy comparison individuals. Metabolic covariance networks were mapped for each group and graph theoretical analyses of these networks were undertaken. For each pair of regions comprising a whole-brain parcellation, regionally-averaged FDG uptake values were correlated across individuals to estimate connection weights. Results: Correlation in regionally-averaged FDG uptake values in the insular epilepsy group was substantially increased for several pairs of regions compared to the healthy comparison group, particularly for the opercular cortex and subcortical structures. This effect was less prominent in brainstem structures. Metabolic covariance networks in the epilepsy group showed reduced small-worldness as well as altered nodal properties in the ipsilateral hemisphere, compared to the healthy comparison group. Conclusions: Cerebral glucose metabolism in insular epilepsy is marked by a lack of normal regional heterogeneity in metabolic patterns, resulting in metabolic covariance networks that are more tightly coupled between regions than healthy comparison individuals. Metabolic networks in insular epilepsy exhibit altered topological properties and evidence of potentially compensatory formation of aberrant local connections. Taken together, these results demonstrate that insular epilepsy is a systemic neurological disorder with widespread disruption to cerebral metabolic networks.
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ItemNo Preview AvailableWhite matter pathology in schizophrenia(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?.
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ItemAdvanced Diffusion Imaging in Psychosis Risk: a cross-sectional and longitudinal study of white matter development(Oxford University Press, 2020-04-01)Background: Studies in individuals at clinical high risk (CHR) for psychosis provide a powerful means to predict outcomes and inform putative mechanisms underlying conversion to psychosis. In previous work, we applied advanced diffusion imaging methods to reveal that white matter pathology in a CHR population is characterized by cellular-specific changes in white matter, suggesting a preexisting neurodevelopmental anomaly. However, it remains unknown whether these deficits relate to clinical symptoms and/or conversion to frank psychosis. To address this gap, we examined cross-sectional and longitudinal white matter maturation in the largest imaging population of CHR individuals to date, obtained from the North American Prodrome Longitudinal Study (NAPLS-3). Methods: Multi-shell diffusion magnetic resonance imaging (MRI) data were collected across multiple timepoints (1–6 at ~2 month intervals) in 286 subjects (age range=12–32 years). These were 230 unmedicated CHR subjects, including 11% (n=25) who transitioned to psychosis (CHR-converters), as well as 56 age and sex-matched healthy controls. Raw diffusion signals were harmonized to remove scanner/site-induced effects, yielding a unified imaging dataset. Fractional anisotropy of cellular tissue (FAt) and the volume fraction of extracellular free-water (FW) were assessed in 12 major tracts from the IIT Human Brain Atlas (v.5.0). Linear mixed effects (LME) models were fitted to infer developmental trajectories of FAt and FW across age for CHR-converters, CHR-nonconverters and control groups, while accounting for the repeated measurements on each individual. Results: The rate at which FAt changed with age significantly differed between the three groups across commissural and association tracts (5 in total; p<0.05). In these tracts, FAt increased with age in controls (0.002% change per year) and in CHR-nonconverters, albeit at a slower rate (0.00074% per year). In contrast, FAt declined with age in CHR-converters at a rate that was significantly faster (-3.944% per year) than the rate of increase in the other two groups. By 25 years of age, FAt was significantly lower in both CHR groups compared to controls (p<0.05). With regard to FW, the rate of change significantly differed between CHR-converters and controls across the forceps major and the left inferior longitudinal and fronto‐occipital fasciculi (IFOF; 3 tracts in total; p<0.05). This was due to increased FW with age in the CHR-converters (0.0024% change per year) relative to controls (-0.0002% per year). Consequently, FW was significantly higher in CHR-converters compared to controls by 20 years of age (p<.05). With regard to symptoms, there was a significant impact of IFOF FW on positive symptom severity across CHR subjects, regardless of conversion status (t=2.37, p<0.05). Discussion: Our results revealed that clinical high-risk for psychosis is associated with cellular-specific alterations in white matter, regardless of conversion status. Only converters showed excess extracellular free-water, which involved tracts connecting occipital, posterior temporal, and orbito-frontal areas. We also demonstrate a direct impact of free-water on positive symptomatology, collectively, suggesting that excess free-water may signal acute psychosis and its onset. This marker may be useful for patient selection for clinical trials and assessment of individuals with prodromal psychosis.
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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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ItemPrediction of sleep side effects following methylphenidate treatment in ADHD youth(ELSEVIER SCI LTD, 2020)OBJECTIVE: Sleep problems is the most common side effect of methylphenidate (MPH) treatment in ADHD youth and carry potential to negatively impact long-term self-regulatory functioning. This study aimed to examine whether applying machine learning approaches to pre-treatment demographic, clinical questionnaire, environmental, neuropsychological, genetic, and neuroimaging features can predict sleep side effects following MPH administration. METHOD: The present study included 83 ADHD subjects as a training dataset. The participants were enrolled in an 8-week, open-label trial of MPH. The Barkley Stimulant Side Effects Rating Scale was used to determine the presence/absence of sleep problems at the 2nd week of treatment. Prediction of sleep side effects were performed with step-wise addition of variables measured at baseline: demographics (age, gender, IQ, height/weight) and clinical variables (ADHD Rating Scale-IV (ADHD-RS) and Disruptive Behavior Disorder rating scale) at stage 1, neuropsychological test (continuous performance test (CPT), Stroop color word test) and genetic/environmental variables (dopamine and norepinephrine receptor gene (DAT1, DRD4, ADRA2A, and SLC6A2) polymorphisms, blood lead, and urine cotinine level) at stage 2, and structural connectivities of frontostriatal circuits at stage 3. Three different machine learning algorithms ((Logistic Ridge Regression (LR), support vector machine (SVM), J48) were used for data analysis. Robustness of classifier model was validated in the independent dataset of 36 ADHD subjects. RESULTS: Classification accuracy of LR was 95.5% (area under receiver operating characteristic curve (AUC) 0.99), followed by SVM (91.0%, AUC 0.85) and J48 (90.0%, AUC 0.87) at stage 3 for predicting sleep problems. The inattention symptoms of ADHD-RS, CPT response time variability, the DAT1, ADRA2A DraI, and SLC6A2 A-3081T polymorphisms, and the structural connectivities between frontal and striatal brain regions were identified as the most differentiating subset of features. Validation analysis achieved accuracy of 86.1% (AUC 0.92) at stage 3 with J48. CONCLUSIONS: Our results provide preliminary support to the combination of multimodal classifier, in particular, neuroimaging features, as an informative method that can assist in predicting MPH side effects in ADHD.