Psychiatry - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 18
-
ItemGenetic Influences on Cost-Efficient Organization of Human Cortical Functional Networks(SOC NEUROSCIENCE, 2011-03-02)The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interindividual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09-0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization.
-
Item
-
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.
-
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.
-
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?.
-
ItemDisruption of structure-function coupling in the schizophrenia connectome(ELSEVIER SCI LTD, 2014)Neuroimaging studies have demonstrated that the phenomenology of schizophrenia maps onto diffuse alterations in large-scale functional and structural brain networks. However, the relationship between structural and functional deficits remains unclear. To answer this question, patients with established schizophrenia and matched healthy controls underwent resting-state functional and diffusion weighted imaging. The network-based statistic was used to characterize between-group differences in whole-brain functional connectivity. Indices of white matter integrity were then estimated to assess the structural correlates of the functional alterations observed in patients. Finally, group differences in the relationship between indices of functional and structural brain connectivity were determined. Compared to controls, patients with schizophrenia showed decreased functional connectivity and impaired white matter integrity in a distributed network encompassing frontal, temporal, thalamic, and striatal regions. In controls, strong interregional coupling in neural activity was associated with well-myelinated white matter pathways in this network. This correspondence between structure and function appeared to be absent in patients with schizophrenia. In two additional disrupted functional networks, encompassing parietal, occipital, and temporal cortices, the relationship between function and structure was not affected. Overall, results from this study highlight the importance of considering not only the separable impact of functional and structural connectivity deficits on the pathoaetiology of schizophrenia, but also the implications of the complex nature of their interaction. More specifically, our findings support the core nature of fronto-striatal, fronto-thalamic, and fronto-temporal abnormalities in the schizophrenia connectome.
-
ItemThe impact of premorbid and current intellect in schizophrenia: cognitive, symptom, and functional outcomes(SPRINGERNATURE, 2015)BACKGROUND: Cognitive heterogeneity among people with schizophrenia has been defined on the basis of premorbid and current intelligence quotient (IQ) estimates. In a relatively large, community cohort, we aimed to independently replicate and extend cognitive subtyping work by determining the extent of symptom severity and functional deficits in each group. METHODS: A total of 635 healthy controls and 534 patients with a diagnosis of schizophrenia or schizoaffective disorder were recruited through the Australian Schizophrenia Research Bank. Patients were classified into cognitive subgroups on the basis of the Wechsler Test of Adult Reading (a premorbid IQ estimate) and current overall cognitive abilities into preserved, deteriorated, and compromised groups using both clinical and empirical (k-means clustering) methods. Additional cognitive, functional, and symptom outcomes were compared among the resulting groups. RESULTS: A total of 157 patients (29%) classified as 'preserved' performed within one s.d. of control means in all cognitive domains. Patients classified as 'deteriorated' (n=239, 44%) performed more than one s.d. below control means in all cognitive domains except estimated premorbid IQ and current visuospatial abilities. A separate 138 patients (26%), classified as 'compromised,' performed more than one s.d. below control means in all cognitive domains and displayed greater impairment than other groups on symptom and functional measures. CONCLUSIONS: In the present study, we independently replicated our previous cognitive classifications of people with schizophrenia. In addition, we extended previous work by demonstrating worse functional outcomes and symptom severity in the compromised group.
-
ItemConnectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution(ELSEVIER SCI LTD, 2018)Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico-basal ganglia-thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico-basal ganglia-thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.
-
ItemWidespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group(SPRINGERNATURE, 2018-05)The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
-
ItemPET imaging of putative microglial activation in individuals at ultra-high risk for psychosis, recently diagnosed and chronically ill with schizophrenia(NATURE PUBLISHING GROUP, 2017-08-29)We examined putative microglial activation as a function of illness course in schizophrenia. Microglial activity was quantified using [11C](R)-(1-[2-chrorophynyl]-N-methyl-N-[1-methylpropyl]-3 isoquinoline carboxamide (11C-(R)-PK11195) positron emission tomography (PET) in: (i) 10 individuals at ultra-high risk (UHR) of psychosis; (ii) 18 patients recently diagnosed with schizophrenia; (iii) 15 patients chronically ill with schizophrenia; and, (iv) 27 age-matched healthy controls. Regional-binding potential (BPND) was calculated using the simplified reference-tissue model with four alternative reference inputs. The UHR, recent-onset and chronic patient groups were compared to age-matched healthy control groups to examine between-group BPND differences in 6 regions: dorsal frontal, orbital frontal, anterior cingulate, medial temporal, thalamus and insula. Correlation analysis tested for BPND associations with gray matter volume, peripheral cytokines and clinical variables. The null hypothesis of equality in BPND between patients (UHR, recent-onset and chronic) and respective healthy control groups (younger and older) was not rejected for any group comparison or region. Across all subjects, BPND was positively correlated to age in the thalamus (r=0.43, P=0.008, false discovery rate). No correlations with regional gray matter, peripheral cytokine levels or clinical symptoms were detected. We therefore found no evidence of microglial activation in groups of individuals at high risk, recently diagnosed or chronically ill with schizophrenia. While the possibility of 11C-(R)-PK11195-binding differences in certain patient subgroups remains, the patient cohorts in our study, who also displayed normal peripheral cytokine profiles, do not substantiate the assumption of microglial activation in schizophrenia as a regular and defining feature, as measured by 11C-(R)-PK11195 BPND.