Biology subjects

Barron, D. S.

Publications and source records attributed to Barron, D. S..

5 recordsLinked to original sources

bioRxiv2023

Ketamine Effects on Energy Metabolism, Functional Connectivity and Working Memory in Healthy Humans

Working memory (WM) is a crucial resource for temporary memory storage and the guiding of ongoing behavior. N-methyl-D-aspartate glutamate receptors (NMDARs) are thought to support the neural underpinnings of WM. Ketamine is an NMDAR antagonist that has cognitive and behavioral effects at subanesthetic doses. To shed light on subanesthetic ketamine effects on brain function, we employed a multimodal imaging design, combining gas-free calibrated functional magnetic resonance imaging (fMRI) measurement of oxidative metabolism (CMRO2), resting-state cortical functional connectivity assessed with fMRI, and WM-related fMRI. Healthy subjects participated in two scan sessions in a randomized, double-blind, placebo-controlled design. Ketamine increased CMRO2 and cerebral blood flow (CBF) in prefrontal cortex (PFC) and other cortical regions. However, resting-state cortical functional connectivity was not affected. Ketamine did not alter CBF-CMRO2 coupling brain-wide. Higher levels of basal CMRO2 were associated with lower task-related PFC activation and WM accuracy impairment under both saline and ketamine conditions. These observations suggest that CMRO2 and resting-state functional connectivity index distinct dimensions of neural activity. Ketamines impairment of WM-related neural activity and performance appears to be related to its ability to produce cortical metabolic activation. This work illustrates the utility of direct measurement of CMRO2 via calibrated fMRI in studies of drugs that potentially affect neurovascular and neurometabolic coupling.

neuroscience↗

bioRxiv2019

Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

ImportanceWe show that three common approaches to clinical deficits (cognitive phenotype, disease group, disease severity) each offer useful and perhaps complimentary explanations for the brains underlying functional architecture as affected by psychiatric disease.\n\nObjectiveTo understand how different clinical frameworks are represented in the brains functional connectome.\n\nDesignWe use an openly available dataset to create predictive models based on multiple connectomes built from task-based functional MRI data. We use these models to predict individual traits corresponding to multiple cognitive constructs across disease category. We also show that these same connectomes statistically differ depending on disease category and symptom burden.\n\nSettingThis was a population-based study with data collected in UCLA.\n\nParticipantsHealthy adults were recruited by community advertisements from the Los Angeles area. Participants with adult ADHD, bipolar disorder, and schizophrenia were recruited using a patient-oriented strategy involving outreach to local clinics and online portals (separate from the methods used to recruit healthy volunteers)

neuroscience↗

bioRxiv2019

Altered brain function in anorexia nervosa and bulimia nervosa: A hierarchical series of task-based fMRI meta-analyses

BackgroundMany structural and functional magnetic resonance imaging (MRI) studies have reported differences in brain functional anatomy associated with eating disorders (EDs). We aimed to quantitatively synthesize the current literature of anorexia nervosa (AN) and bulimia nervosa (BN) with a goal of deriving a consensus across these studies. MethodsWe performed a hierarchical series of 49 activation likelihood estimation meta-analyses of 101 experiments from 63 studies at the disorder (AN+BN), diagnosis (AN or BN), and task (food-, body-, emotion- or cognitive function-related tasks) levels. We further performed sub-analyses at the diagnosis level to assess the influence of disease stage (current or recovered AN), subtype (restrictive AN, rAN), psychiatric comorbidity, medication, and data processing. ResultsWe did not observe consistent differences in brain activity across all hierarchies. Rather, we observed differences in brain activity in the right fusiform, left inferior parietal lobule and left precuneus that were primarily related to food (representing 19 experiments) and emotion (25 experiments) tasks performed by current AN patients (56 experiments). ConclusionsThe task-based functional MRI literature in AN and BN represents a heterogenous set of tasks and patients. Given this heterogeneity, we found very limited convergence across a rather large literature. Such limited convergence suggests individual task-based studies of EDs should be interpreted cautiously. We recommend that future studies of EDs carefully characterize patients based on nutritional status and that, beyond clinical diagnosis, studies utilize a trait- or cognitive-domain-based approach to define their populations of interest. Meta-ananlysis Registration"Meta-analysis of functional magnetic resonance imaging studies of eating disorders", https://www.crd.york.ac.uk/prospero (Registration Number: CRD42018086497).

neuroscience↗

bioRxiv2018

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

BackgroundClinically approved antidepressants modulate the brains emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n=306) exploring the effect of antidepressant administration on emotional face processing.\n\nMethodsWe created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies.\n\nResultsWe found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50-87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another.\n\nConclusionsWe found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development.

neuroscience↗

bioRxiv2018

State-specific individualized functional networks form a predictive signature of brain state

There is extensive evidence that human brain functional organization is dynamic, varying within a subject as the brain switches between tasks demands. This functional organization also varies across subjects, even when they are all engaged in similar tasks. Currently, we lack a comprehensive model that unifies the two dimensions of variation (brain state and subject). Using fMRI data obtained across multiple task-evoked and rest conditions (which we operationally define as brain states) and across multiple subjects, we develop a state-and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brains functional networks are not spatially fixed, but reconfigure with brain state. This reconfiguration is robust and reliable to such an extent that it can be used to predict brain state with accuracies up to 97%.

neuroscience↗