Biology subjects

Chumin, E. J.

Publications and source records attributed to Chumin, E. J..

6 recordsLinked to original sources

bioRxiv2024

Intra-Striatal Dopaminergic Inter-Subject Covariance in Social Drinkers and Nontreatment-Seeking Alcohol Use Disorder Participants

One of the neurobiological correlates of alcohol use disorder (AUD) is the disruption of striatal dopaminergic function. While regional differences in dopamine (DA) function have been well studied, inter-regional relationships (represented as inter-subject covariance) have not been investigated and may offer a novel avenue for understanding DA function. Positron emission tomography (PET) data with [11C]raclopride in 22 social drinking controls and 17 AUD participants were used to generate group-level striatal covariance (partial Pearson correlation) networks, which were compared edgewise, also comparing global network metrics and community structure. An exploratory analysis examined the impact of tobacco cigarette use status. Striatal covariance was validated in an independent publicly available [18F]fallypride PET sample of healthy volunteers. Striatal covariance of control participants from both datasets showed a clear bipartition of the network into two distinct communities, one in the anterior and another in the posterior striatum. This organization was disrupted in the AUD participant network, with significantly lower network metrics in AUD compared to the control network. Stratification by cigarette use suggests differential consequences on group covariance networks. This work demonstrates that network neuroscience can quantify group differences in striatal DA and that its inter-regional interactions offer new insight into the consequences of AUD.

neuroscience↗

bioRxiv2023

Levetiracetam Modulates Brain Metabolic Networks and Transcriptomic Signatures in the 5XFAD Mouse Model of Alzheimer's disease.

INTRODUCTIONSubcritical epileptiform activity is associated with impaired cognitive function and is commonly seen in patients with Alzheimers disease (AD). The anti-convulsant, levetiracetam (LEV), is currently being evaluated in clinical trials for its ability to reduce epileptiform activity and improve cognitive function in AD. The purpose of the current study was to apply pharmacokinetics (PK), network analysis of medical imaging, gene transcriptomics, and PK/PD modeling to a cohort of amyloidogenic mice to establish how LEV restores or drives alterations in the brain networks of mice in a dose-dependent basis using the rigorous preclinical pipeline of the MODEL-AD Preclinical Testing Core. METHODSChronic LEV was administered to 5XFAD mice of both sexes for 3 months based on allometrically scaled clinical dose levels from PK models. Data collection and analysis consisted of a multi-modal approach utilizing 18F-FDG PET/MRI imaging and analysis, transcriptomic analyses, and PK/PD modeling. RESULTSPharmacokinetics of LEV showed a sex and dose dependence in Cmax, CL/F, and AUC0-{infty}, with simulations used to estimate dose regimens. Chronic dosing at 10, 30, and 56 mg/kg, showed 18F-FDG specific regional differences in brain uptake, and in whole brain covariance measures such as clustering coefficient, degree, network density, and connection strength (i.e. positive and negative). In addition, transcriptomic analysis via nanoString showed dose-dependent changes in gene expression in pathways consistent 18F-FDG uptake and network changes, and PK/PD modeling showed a concentration dependence for key genes, but not for network covariance modeling. DISCUSSIONThis study represents the first report detailing the relationships of metabolic covariance and transcriptomic network changes resulting from LEV administration in 5XFAD mice. Overall, our results highlight non-linear kinetics based on dose and sex, where gene expression analysis demonstrated LEV dose- and concentration-dependent changes, along with cerebral metabolism, and/or cerebral homeostatic mechanisms relevant to human AD, which aligned closely with network covariance analysis of 18F-FDG images. Collectively, this study show cases the value of a multimodal connectomic, transcriptomic, and pharmacokinetic approach to further investigate dose dependent relationships in preclinical studies, with translational value towards informing clinical study design.

neuroscience↗

bioRxiv2023

Temporal Variability of Brain-Behavior Relationships in Fine-Scale Dynamics of Edge Time Series

Most work on functional connectivity (FC) in neuroimaging data prefers longer scan sessions or greater subject count to improve reliability of brain-behavior relationships or predictive models. Here, we investigate whether systematically isolating moments in time can improve brain-behavior relationships and outperform full scan data. We perform optimizations using a temporal filtering strategy to identify time points that improve brain-behavior relationships across 58 different behaviors. We analyzed functional brain networks from resting state fMRI data of 352 healthy subjects from the Human Connectome Project. Templates were created to select time points with similar patterns of brain activity. Optimizations were performed to produce templates for each behavior that maximize brain-behavior relationships from reconstructed functional networks. With 10% of scan data, optimized templates of select behavioral measures achieved greater strength of brain-behavior correlations and greater transfer between groups of subjects than full FC across multiple cross validation splits of the dataset. Therefore, selectively filtering time points may allow for development of more targeted FC analyses and increased understanding of how specific moments in time contribute to behavioral prediction. Significance StatementIndividuals exhibit significant variations in brain functional connectivity, and these individual differences relate to variations in behavioral and cognitive measures. Here we show that the strength and similarity of brain-behavior associations across groups vary over time and that these relations can be improved by selecting time points that maximize brain-behavior correlations. By employing an optimization strategy for 58 distinct behavioral variables we find that different behaviors load onto different moments in time. Our work suggests new strategies for revealing brain signatures of behavior.

neuroscience↗

bioRxiv2023

Brain Metabolic Network Covariance and Aging in a Mouse Model of Alzheimer's Disease

INTRODUCTIONAlzheimers disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage. METHODSWe performed 18F-FDG Positron Emission Tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis. RESULTS5XFAD model mice showed age related changes glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model. DISCUSSIONThe current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models. RESEARCH IN CONTEXTO_ST_ABSSYSTEMATIC REVIEWC_ST_ABSThe authors extensively reviewed literature (e.g., PubMed), meeting abstracts, and presentations on approaches to evaluate brain network analysis in animal models. Based on the available data, there were clear gaps in our understanding of how metabolic networks change with disease progression at the preclinical phase, thus limiting the utility of these measures for clinical comparison in Alzheimers disease (AD). INTERPRETATIONOur findings indicate that employing metabolic covariance modeling in mouse models of AD and littermate controls of both sexes with age provides a mechanism to evaluate brain changes in network function which align closely with previous clinical stages of AD. Moreover, utilizing open-source clinical tools from the Brain Connectivity Toolbox (BCT), we demonstrated that brain networks reorganize with AD progression at multiple levels, and these changes are consistent with previous reports in human AD studies. FUTURE DIRECTIONSThe open-source framework developed in the current work provides valuable tools for brain metabolic covariance modeling. Such tools can be used in both preclinical and clinical settings and they enable more direct translation of preclinical imaging studies to those in the clinic. When matched with an appropriate animal model, genetics, and/or treatments, this study will enable assessment of in vivo target engagement, translational pharmacodynamics, and insight into potential treatments of AD.

neuroscience↗

bioRxiv2022

System-level high-amplitude co-fluctuations

Edge time series decompose interregional correlations (functional connectivity; FC) into their time-varying contributions. Previous studies have revealed that brief, high-amplitude, and globally-defined "events" contribute disproportionately to the time-averaged FC pattern. This whole-brain view prioritizes systems that occupy vast neocortical territory, possibly obscuring extremely high-amplitude co-fluctuations that are localized to smaller brain systems. Here, we investigate local events detected at the system level, assessing their independent contributions to global events and characterizing their repertoire during resting-state and movie-watching scans. We find that, as expected, global events are more likely to occur when large brain systems exhibit events. Next, we study the co-fluctuation patterns that coincide with system events-i.e. events detected locally based on the behavior of individual brain systems. We find that although each system exhibits a distinct co-fluctuation pattern that is dissimilar from those associated with global events, the patterns can nonetheless be grouped into two broad categories, corresponding to events that coincide with sensorimotor and attention systems and, separately, association systems. We then investigate system-level events during movie-watching, discovering that the timing of events in sensorimotor and attention systems decouple, yielding reductions in co-fluctuation amplitude. Next, we show that by associating each edge with its most similar system-averaged edge time series, we recover overlapping community structure, obviating the need for applying clustering algorithms to high-dimensional edge time series. Finally, we focus on cortical responses to system-level events in subcortical areas and the cerebellum. We show that these structures coincide with spatially distributed cortical co-fluctuations, centered on prefrontal and somatosensory systems. Collectively, the findings presented here help clarify the relative contributions of large and small systems to global events, as well as their independent behavior.

neuroscience↗

bioRxiv2021

Cortico-Subcortical Interactions in Overlapping Communities of Edge Functional Connectivity

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

neuroscience↗