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Sorooshyari, S.

Publications and source records attributed to Sorooshyari, S..

3 recordsLinked to original sources

Transcriptomic profiles of stress susceptibility and resilience in the amygdala and hippocampus

A single, severe episode of stress can bring about myriad responses amongst individuals, ranging from cognitive enhancement to debilitating and persistent anxiety; however, the biological mechanisms that contribute to resilience versus susceptibility to stress are poorly understood. The dentate gyrus (DG) of the hippocampus and the basolateral nucleus of the amygdala (BLA) are key limbic regions that are susceptible to the neural and hormonal effects of stress. Previous work has also shown that these regions contribute to individual variability in stress responses; however, the molecular mechanisms underlying the role of these regions in susceptibility and resilience are unknown. In this study, we profiled the transcriptomic signatures of the DG and BLA of rats with divergent behavioral outcomes after a single, severe stressor. We subjected rats to three hours of immobilization with exposure to fox urine and conducted a behavioral battery one week after stress to identify animals that showed persistent, high anxiety-like behavior. We then conducted bulk RNA sequencing of the DG and BLA from susceptible, resilient, and unexposed control rats. Differential gene expression analyses revealed that the molecular signatures separating each of the three groups were distinct and non-overlapping between the DG and BLA. In the amygdala, key genes associated with insulin and hormonal signaling corresponded with vulnerability. Specifically, Inhbb, Rab31, and Ncoa3 were upregulated in the amygdala of stress-susceptible animals compared to resilient animals. In the hippocampus, increased expression of Cartpt - which encodes a key neuropeptide involved in reward, reinforcement, and stress responses - was strongly correlated with vulnerability to anxiety-like behavior. However, few other genes distinguished stress-susceptible animals from control animals, while a larger number of genes separated stress-resilient animals from control and stress-susceptible animals. Of these, Rnf112, Tbx19, and UBALD1 distinguished resilient animals from both control and susceptible animals and were downregulated in resilience, suggesting that an active molecular response in the hippocampus facilitates protection from the long-term consequences of severe stress. These results provide novel insight into the mechanisms that bring about individual variability in the behavioral responses to stress and provide new targets for the advancement of therapies for stress-induced neuropsychiatric disorders.

neuroscience↗

Identifying Neural Signatures of Dopamine Signaling with Machine Learning

The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to determine whether recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy yet note that integrated neuromodulator amount is the conventional metric currently used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly-utilized tool to differentiate between neuroanatomical regions, or between neurotypical and disease states, with features not detectable by conventional statistical analysis.

neuroscience↗

Correlates of Sleep and Arousal via Matrix Methods

Conventional computational modeling of sleep and arousal are primarily brain-centric in restricting attention to data from the nervous system. While such a view is warranted, the importance of considering the coupling of peripheral systems in the causes and effects of sleep are being increasingly recognized. An analysis is presented that has the capability to incorporate neural recordings of different modalities as well as data from the metabolic and immune systems. We formulate a matrix-based approach for inference of the arousal state based on the activity level of cell types that will comprise the matrix components. While the presented computations are intended to predict sleep/arousal, it is anticipated that a scrutiny of the structure of the matrices will provide insight into the dynamics of the constituent systems. A model is also proposed to consider the interaction of the signals recorded across the neural, metabolic, and immune systems in leading to the arousal state.

neuroscience↗