Psychiatry - Research Publications
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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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ItemPublisher Correction: Brain charts for the human lifespan.(Springer Science and Business Media LLC, 2022-10)
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ItemOn the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI(MDPI, 2022-08-01)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, 2022-02-02)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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ItemNo Preview AvailableMachine learning prediction of cognition from functional connectivity: Are feature weights reliable?(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-10-22)Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
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ItemNo Preview AvailableNBS-Predict: A prediction-based extension of the network-based statistic(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-10-06)Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects' functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.
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ItemNo Preview AvailableBrain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease(NATURE PORTFOLIO, 2021-07-08)Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions (n = 461, five datasets), transcranial magnetic stimulation (n = 151, four datasets) and deep brain stimulation (n = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets (P < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar (P < 0.0005), as were circuits derived from patients with major depression versus other diagnoses (P < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites (P < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson's disease (P < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders.
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ItemNo Preview AvailableTemporal complexity of fMRI is reproducible and correlates with higher order cognition(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-02-06)It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.
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