Biology subjects

Cornblath, E. J.

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

8 recordsLinked to original sources

bioRxiv2025

Alcohol impacts an fMRI marker of neural inhibition in humans and rodents.

Acute alcohol consumption leads to cognitive and behavioral disinhibition that increase health and social risks, such as traffic fatalities and violence. Although rodent studies have shown that alcohol affects inhibitory neuronal activity, its relevance to humans remains largely unexplored. Here, we used the Hurst exponent--an fMRI-based marker of neural inhibition--to examine alcohols effects in both rats and humans. In rats, acute alcohol administration significantly reduced the cortical Hurst exponent, suggesting a decrease in inhibitory neuronal activity. This reduction was strongly correlated with the spatial distribution of GABAA receptor expression, highlighting the key role of these receptors in mediating alcohols effects. Similarly, in humans, acute alcohol consumption reduced the cortical Hurst exponent, especially in brain regions with high GABAA receptor expression. Our findings provide cross-species in vivo evidence that acute alcohol consumption modulates neural inhibition, offering new insights into the neural mechanisms underlying alcohol-induced behavioral modulation.

neuroscience↗

bioRxiv2024

Hyperactive neuronal networks facilitate tau spread in an Alzheimer's disease mouse model

Pathological tau spreads via neuronal connections in Alzheimers disease (AD). Given the high incidence and deleterious consequences of epileptiform activity in AD, we hypothesized that neuronal hyperactivity and seizures exacerbate tau spread. To examine the impacts of brain-wide network and population hyperactivity on tau spread, we created a novel mouse model involving the cross of targeted recombination in active populations (TRAP) and the 5 times familial AD mice (5X-TRAP) that allows for the permanent labelling of seizure-activated neurons. To explore the effects of seizures on tau spread, we injected these mice with human AD brain-derived tau to induce pathological tau spread, and induced seizures with pentylenetetrazol (PTZ) kindling. Brain mapping revealed that seizures increased tau spread in 5X-TRAP mice, which correlated extensively with memory deficits in PTZ kindled 5X-TRAP mice. Using computational models, we found data supportive of increased anterograde tau spread in 5X-TRAP mice and that regional neuronal activity levels were predictive of tau pathology. On a cellular level, we found that hyperactive neurons drive elevated tau propagation in 5X-TRAP mice. We also found corroborating evidence of increased tau spread in AD patients with a seizure history compared to those without. Our study identifies neuronal hyperactivity and seizures as key, targetable factors underlying AD progression.

neuroscience↗

bioRxiv2021

Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

neuroscience↗

bioRxiv2020

Is the brain macroscopically linear? A system identification of resting state dynamics

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to exhibit an even larger repertoire of nonlinear behaviors. An implicit assumption has thus formed that an "accurate" computational model of whole-brain dynamics must inevitably be non-linear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide new evidence that challenges this assumption at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

neuroscience↗

bioRxiv2020

Altered functional brain dynamics in chromosome 22q11.2 deletion syndrome during facial affect processing

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a multisystem disorder associated with multiple congenital anomalies, variable medical features, and neurodevelopmental differences resulting in diverse psychiatric phenotypes, including marked deficits in facial memory and social cognition. Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks, but time-varying interactions between brain areas during facial affect processing have not yet been studied in 22q11.2DS. We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with 22q11.2DS and 58 age-, race-, and sex-matched healthy controls. Delayed frontal-motor feedback signals were diminished in individuals with 22q11.2DS, as were delayed emotional memory signals engaging amygdala, hippocampus, and entorhinal cortex. Early task-related engagement of motor and visual cortices and salience-related insular activation were relatively preserved in 22q11.2DS. Insular activation was associated with task performance within the 22q11.2DS sample. Differences in cortical surface area, but not cortical thickness, showed spatial alignment with an activation pattern associated with face processing. These findings suggest that relative to matched controls, primary visual processing and insular function are relatively intact in individuals with 22q11.22DS, while motor feedback, face processing, and emotional memory processes are more affected. Such insights may help inform potential interventional targets and enhance the specificity of neuroimaging indices of cognitive dysfunction in 22q11.2DS.

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bioRxiv2020

Tau pathology spreads between anatomically-connected regions of the brain and is modulated by a LRRK2 mutation

Tau pathology is a diagnostic feature of Alzheimers disease (AD) but is also a prominent feature of Parkinsons disease (PD), including genetic forms of PD with mutations in leucine-rich repeat kinase 2 (LRRK2). In both diseases, tau pathology is progressive and correlates with cognitive decline. Neuropathological staging studies in humans and mouse models have suggested that tau spreads through the brain, but it is unclear how neuroanatomical connections, spatial proximity, and regional vulnerability contribute to pathology spread. Further, it is unknown how mutations in the LRRK2 gene may modulate susceptibility to tau pathologys initiation or spread. In this study, we used seed-based models of tauopathy to capture spatiotemporal patterns of pathology in mice. Following the injection of AD brain-derived tau into the brains of non-transgenic mice, tau pathology spreads progressively through the brain in a spatiotemporal pattern that is well-explained by anatomical connectivity. We validated and compared network models based on diffusion along anatomical connections to predict tau spread, estimate regional vulnerability to tau pathology, and investigate gene expression patterns related to regional vulnerability. We further investigated tau pathology spread in mice harboring a mutation in LRRK2 and found that while tau pathology spread is still constrained by anatomical connectivity, it spreads preferentially in a retrograde direction to regions that are otherwise resilient in wildtype mice. This study provides a quantitative demonstration that tau pathology spreads along anatomical connections, explores the kinetics of this spread, and provides a platform for investigating the effect of genetic risk factors and treatments on the progression of tauopathies.

neuroscience↗

bioRxiv2019

Defining and predicting transdiagnostic categories of neurodegenerative disease

In current models of neurodegeneration, individual diseases are defined by the presence of one or two pathogenic protein species. Yet, it is the rule rather than the exception that a patient meets criteria for more than one disease. This fact often remains hidden until autopsy, when neuropathological evaluation can assign disease labels based on gold-standard criteria. Ultimately, the prevalence of concomitant diagnoses and the inability to infer an underlying neuropathological syndrome from clinical variables hinders the identification of patients who might be good candidates for a particular intervention. Here, by applying graph-based clustering to post-mortem histopathological data from 1389 patients with degeneration in the central nervous system, we generate 4 non-overlapping, data-driven disease categories that simultaneously account for amyloid-{beta} plaques, tau neurofibrillary tangles, -synuclein inclusions, neuritic plaques, TDP-43 inclusions, angiopathy, neuron loss, and gliosis. The resulting disease clusters are transdiagnostic in the sense that each cluster contains patients belonging to multiple different existing disease diagnoses, who colocalize in clusters according to the pathogenic protein aggregates known to drive each disease. We show that our disease clusters, defined solely by histopathology, separate patients in terms of cognitive phenotypes, cerebrospinal fluid (CSF) protein levels, and genotype in a manner that is not trivially explained by the representation of individual diseases within each cluster. Finally, we use cross-validated multiple logistic regression to generate high accuracy predictions (AUC > 0.9) of membership to both existing disease categories and transdiagnostic clusters based on CSF protein levels and genotype, both accessible in vivo. Broadly, our approach parses phenotypic and genotypic heterogeneity in neurodegenerative disease, and represents a general framework for identifying otherwise-fuzzy disease subtypes in other areas of medicine, such as epilepsy, vascular disease, and cancer. In clinical neurology, the statistical models we generate may be useful for repurposing drugs by comparing efficacy to probabilistic estimates of disease cluster membership, as well as for future trials that could be targeted towards an algorithmically defined family of diseases.

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bioRxiv2018

Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control

A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we deploy an unsupervised machine learning algorithm to define brain states as time point level activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task. We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics. Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brain states. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to support states of high activity in default mode areas, while requiring relatively low energy. Finally, we show that brain state dynamics change throughout development and explain working memory performance. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.

neuroscience↗