Psychiatry - Research Publications
Permanent URI for this collection
Search Results
1 - 7 of 7
-
ItemNo Preview AvailableImpaired olfactory ability associated with larger left hippocampus and rectus volumes at earliest stages of schizophrenia: A sign of neuroinflammation?(ELSEVIER IRELAND LTD, 2020-07)Impaired olfactory identification has been reported as a first sign of schizophrenia during the earliest stages of illness, including before illness onset. The aim of this study was to examine the relationship between volumes of these regions (amygdala, hippocampus, gyrus rectus and orbitofrontal cortex) and olfactory ability in three groups of participants: healthy control participants (Ctls), patients with first-episode schizophrenia (FE-Scz) and chronic schizophrenia patients (Scz). Exploratory analyses were performed in a sample of individuals at ultra-high risk (UHR) for psychosis in a co-submission paper (Masaoka et al., 2020). The relationship to brain structural measures was not apparent prior to psychosis onset, but was only evident following illness onset, with a different pattern of relationships apparent across illness stages (FE-Scz vs Scz). Path analysis found that lower olfactory ability was related to larger volumes of the left hippocampus and gyrus rectus in the FE-Scz group. We speculate that larger hippocampus and rectus in early schizophrenia are indicative of swelling, potentially caused by an active neurochemical or immunological process, such as inflammation or neurotoxicity, which is associated with impaired olfactory ability. The volumetric decreases in the chronic stage of Scz may be due to degeneration resulting from an active immune process and its resolution.
-
ItemS166. EFFECTIVE CONNECTIVITY OF FRONTOSTRIATAL SYSTEMS IN FIRST-EPISODE PSYCHOSIS(Oxford University Press (OUP), 2020-05-18)Abstract Background Neuroimaging studies have found dysconnectivity of frontostriatal circuits across a broad spectrum of psychotic symptoms. However, it is unknown whether dysconnectivity within frontostriatal circuits originates from disrupted bottom-up or top-down control signaling within these systems. Here, we used dynamic causal modelling (DCM) to examine the effective connectivity of frontostriatal systems in first-episode psychosis (FEP). Methods A total of 55 FEP patients (26 males; mean [SD] age = 19.24 [2.89]) and 24 healthy controls (15 males; mean [SD] age = 21.83 [1.93]) underwent a resting-state functional magnetic resonance imaging protocol. Biologically plausible connections between eight left hemisphere regions encompassing the dorsal and ventral frontostriatal systems were modelled using spectral DCM. The regions comprise dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior hippocampus, amygdala, dorsal caudate, nucleus accumbens, thalamus, and the midbrain. Effective connectivity between groups were assessed using a parametric Bayesian model. Associations between effective connectivity parameters and positive symptoms, measured by the Brief Psychiatric Rating Scale positive subscale, was assessed in the patient group in a separate Bayesian general linear model. Results DCM shows evidence for differences in effective connectivity between patients and healthy controls, namely in the bottom-down connections distributed in the frontostriatal system encompassing the hippocampus, amygdala, striatum, and midbrain. Compared to healthy controls, patients also demonstrated increased disinhibition of the midbrain. In patients, positive symptoms are associated with increased top-down connections to the midbrain. Outgoing connection from the midbrain to the nucleus accumbens is also increased in association with positive symptoms. Discussion Aberrant top-down connectivity in the frontostriatal system in patients is consistent with top-down dysregulation of dopamine function in FEP, as dopaminergic activity in the midbrain is proposed to be under the control of higher brain areas. In patients, increased self-inhibition of the midbrain, as well as symptom associations in both ingoing and outgoing connections of this region, are congruous with hyperactivity of the midbrain as proposed by the dopamine dysregulation hypothesis. Here, we demonstrate that mathematical models of brain imaging signals can be used to identify the key disruptions driving brain circuit dysfunction, identifying new targets for treatment.
-
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 AvailableImaging of neuroinflammation in adult Niemann-Pick type C disease: a cross-sectional study(American Academy of Neurology, 2020-04-21)Objective: To test the hypothesis that neuroinflammation is a key process in adult Niemann-Pick type C (NPC) disease, we undertook PET scanning utilizing a ligand binding activated microglia on 9 patients and 9 age- and sex-matched controls. Method: We scanned all participants with the PET radioligand 11C-(R)-PK-11195 and undertook structural MRI to measure gray matter volume and white matter fractional anisotropy (FA). Results: We found increased binding of 11C-(R)-PK-11195 in total white matter compared to controls (p < 0.01), but not in gray matter regions, and this did not correlate with illness severity or duration. Gray matter was reduced in the thalamus (p < 0.0001) in patients, who also showed widespread reductions in FA across the brain compared to controls (p < 0.001). A significant correlation between 11C-(R)-PK11195 binding and FA was shown (p = 0.002), driven by the NPC patient group. Conclusions: Our findings suggest that neuroinflammation—particularly in white matter—may underpin some structural and degenerative changes in patients with NPC.
-
ItemIncreased power by harmonizing structural MRI site differences with the ComBat batch method in ENIGMA(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2020-09)A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).
-
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.