Psychiatry - Research Publications
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ItemNo Preview AvailablePlasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives(SAGE PUBLICATIONS LTD, 2022-10)OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuroprogressive or neurodegenerative component to schizophrenia, remain largely unknown. Examining fluid biomarkers of diverse types of neuronal damage could increase our understanding of these processes, as well as potentially provide clinically useful biomarkers, for example with assisting with differentiation from progressive neurodegenerative disorders such as Alzheimer and frontotemporal dementias. METHODS: This study measured plasma neurofilament light chain protein (NfL) using ultrasensitive Simoa technology, to investigate the degree of neuronal injury in a well-characterised cohort of people with treatment-resistant schizophrenia on clozapine (n = 82), compared to first-degree relatives (an at-risk group, n = 37), people with schizophrenia not treated with clozapine (n = 13), and age- and sex-matched controls (n = 59). RESULTS: We found no differences in NfL levels between treatment-resistant schizophrenia (mean NfL, M = 6.3 pg/mL, 95% confidence interval: [5.5, 7.2]), first-degree relatives (siblings, M = 6.7 pg/mL, 95% confidence interval: [5.2, 8.2]; parents, M after adjusting for age = 6.7 pg/mL, 95% confidence interval: [4.7, 8.8]), controls (M = 5.8 pg/mL, 95% confidence interval: [5.3, 6.3]) and not treated with clozapine (M = 4.9 pg/mL, 95% confidence interval: [4.0, 5.8]). Exploratory, hypothesis-generating analyses found weak correlations in treatment-resistant schizophrenia, between NfL and clozapine levels (Spearman's r = 0.258, 95% confidence interval: [0.034, 0.457]), dyslipidaemia (r = 0.280, 95% confidence interval: [0.064, 0.470]) and a negative correlation with weight (r = -0.305, 95% confidence interval: [-0.504, -0.076]). CONCLUSION: Treatment-resistant schizophrenia does not appear to be associated with neuronal, particularly axonal degeneration. Further studies are warranted to investigate the utility of NfL to differentiate treatment-resistant schizophrenia from neurodegenerative disorders such as behavioural variant frontotemporal dementia, and to explore NfL in other stages of schizophrenia such as the prodome and first episode.
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ItemNo Preview AvailableNetwork communication models narrow the gap between the modular organization of structural and functional brain networks(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-08-15)Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.
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ItemNo Preview AvailableAccommodating site variation in neuroimaging data using normative and hierarchical Bayesian models(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-10-29)The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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ItemNo Preview AvailableDisruptions in white matter microstructure associated with impaired visual associative memory in schizophrenia-spectrum illness(SPRINGER HEIDELBERG, 2022-09-01)Episodic memory ability relies on hippocampal-prefrontal connectivity. However, few studies have examined relationships between memory performance and white matter (WM) microstructure in hippocampal-prefrontal pathways in schizophrenia-spectrum disorder (SSDs). Here, we investigated these relationships in individuals with first-episode psychosis (FEP) and chronic schizophrenia-spectrum disorders (SSDs) using tractography analysis designed to interrogate the microstructure of WM tracts in the hippocampal-prefrontal pathway. Measures of WM microstructure (fractional anisotropy [FA], radial diffusivity [RD], and axial diffusivity [AD]) were obtained for 47 individuals with chronic SSDs, 28 FEP individuals, 52 older healthy controls, and 27 younger healthy controls. Tractography analysis was performed between the hippocampus and three targets involved in hippocampal-prefrontal connectivity (thalamus, amygdala, nucleus accumbens). Measures of WM microstructure were then examined in relation to episodic memory performance separately across each group. Both those with FEP and chronic SSDs demonstrated impaired episodic memory performance. However, abnormal WM microstructure was only observed in individuals with chronic SSDs. Abnormal WM microstructure in the hippocampal-thalamic pathway in the right hemisphere was associated with poorer memory performance in individuals with chronic SSDs. These findings suggest that disruptions in WM microstructure in the hippocampal-prefrontal pathway may contribute to memory impairments in individuals with chronic SSDs but not FEP.
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ItemPersonalized brain stimulation of memory networks.(Elsevier BV, 2022)BACKGROUND: The finding that transcranial magnetic stimulation (TMS) can enhance memory performance via stimulation of parietal sites within the Cortical-Hippocampal Network counts as one of the most exciting findings in this field in the past decade. However, the first independent effort aiming to fully replicate this finding found no discernible influence of TMS on memory performance. OBJECTIVE: We examined whether this might relate to interindividual spatial variation in brain connectivity architecture, and the capacity of personalisation methodologies to overcome the noise inherent across independent scanners and cohorts. METHODS: We implemented recently detailed personalisation methodology to retrospectively compute individual-specific parietal targets and then examined relation to TMS outcomes. RESULTS: Closer proximity between actual and novel fMRI-personalized targets associated with greater improvement in memory performance. CONCLUSION: These findings demonstrate the potential importance of aligning brain stimulation targets according to individual-specific differences in brain connectivity, and extend upon recent findings in prefrontal cortex.
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ItemAssessment of Parent Income and Education, Neighborhood Disadvantage, and Child Brain Structure(AMER MEDICAL ASSOC, 2022-08-18)IMPORTANCE: Although different aspects of socioeconomic status (SES) may represent distinct risk factors for poor mental health in children, knowledge of their differential and synergistic associations with the brain is limited. OBJECTIVE: To examine the independent associations between distinct SES factors and child brain structure. DESIGN, SETTING, AND PARTICIPANTS: We used baseline data from participants aged 9 to 10 years in the Adolescent Brain Cognitive Development (ABCD) study. These data were collected from 21 US sites between September 2017 and August 2018. Study participants were recruited from schools to create a participant sample that closely reflects the US population. EXPOSURES: Neighborhood disadvantage was measured using the area deprivation index. We also used data on total parent or caregiver educational attainment (in years) and household income-to-needs ratio. MAIN OUTCOMES AND MEASURES: T1-weighted magnetic resonance imaging was used to assess measures of cortical thickness, surface area, and subcortical volume. RESULTS: Data from 8862 ABCD participants aged 9 to 10 years were analyzed. The mean (SD) age was 119.1 (7.5) months; there were 4243 girls (47.9%) and 4619 boys (52.1%). Data on race or ethnicity were available for 8857 of 8862 participants: 173 (2.0%) were Asian, 1099 (12.4%) were Black or African American, 1688 (19.1%) were Hispanic, 4967 (56.1%) were White, and 930 (10.5%) reported multiple races or ethnicities. Using 10-fold, within-sample split-half replication, we found that neighborhood disadvantage was associated with lower cortical thickness in the following brain regions (η2 = 0.004-0.009): cuneus (B [SE] = -0.099 [0.013]; P < .001), lateral occipital (B [SE] = -0.088 [0.011]; P < .001), lateral orbitofrontal (B [SE] = -0.072 [0.012]; P < .001), lingual (B [SE] = -0.104 [0.012]; P < .001), paracentral (B [SE] = -0.086 [0.012]; P < .001), pericalcarine (B [SE] = -0.077 [0.012]; P < .001), postcentral (B [SE] = -0.069 [0.012]; P < .001), precentral (B [SE] = -0.059 [0.011]; P < .001), rostral middle frontal (B [SE] = -0.076 [0.011]; P < .001), and superior parietal (B [SE] = -0.060 [0.011]; P < .001). Exploratory analyses showed that the associations of low educational attainment or neighborhood disadvantage and low cortical thickness were attenuated in the presence of a high income-to-needs ratio (η2 = 0.003-0.007). CONCLUSIONS AND RELEVANCE: The findings of this cross-sectional study suggest that different SES indicators have distinct associations with children's brain structure. A high income-to-needs ratio may play a protective role in the context of neighborhood disadvantage and low parent or caregiver educational attainment. This study highlights the importance of considering the joint associations of different SES indicators in future work.
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ItemBrain charts for the human lifespan (vol 604, pg 525, 2022)(NATURE PORTFOLIO, 2022-10-13)
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ItemOn the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI(MDPI, 2022-08)Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
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ItemImproving power in functional magnetic resonance imaging by moving beyond cluster-level inference.(Proceedings of the National Academy of Sciences, 2022-08-09)Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.
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ItemNo Preview AvailableConnectome spatial smoothing (CSS): Concepts, methods, and evaluation(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-04-15)Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.
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