Biology subjects

Mishra, J.

Publications and source records attributed to Mishra, J..

23 records · Page 2Linked to original sources

bioRxiv2022

Tyrosine phosphorylation of mitofusin 2 regulates endoplasmic reticulum-mitochondria tethering

Contact sites between the mitochondria and endoplasmic reticulum (ER) regulate the exchange of lipids, Ca2+, and reactive oxygen species (ROS) across the two organelles. Mitofusin 2 (Mfn2) has been identified as one of the major components tethering these two organelles together. Several post-translational modifications (PTMs) of Mfn2 have been shown to modulate canonical (i.e., mitochondrial fusion) and non-canonical Mfn2 functions, such as mitophagy and activation of ER stress signaling. However, there is little information about whether any PTMs can regulate mitochondrial and ER tethering. Basal tyrosine phosphorylation of Mfn2 was detected by mass spectroscopy, but it is unknown whether Mfn2 is a substrate of mitochondria-localized tyrosine kinases. Here, we show that mitochondria-localized c-Src can phosphorylate the C-terminal tail of Mfn2, which decreases the distance between the mitochondria and ER and facilitates Ca2+ transfer from the ER to mitochondria, followed by changes in ROS generation and mitochondrial bioenergetics. Our findings suggest that tyrosine phosphorylation of Mfn2 may uniquely work to fine-tune ER-mitochondrial Ca2+ transport under physiological and pathological conditions.

cell biology↗

bioRxiv2021

Disentangling reward processes underlying payoff maximization from individual differences in gain frequency bias and reinforcement learning

Choice selection strategies and decision making are typically investigated using multiple-choice gambling paradigms that require participants to maximize reward payoff. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains to choose smaller local gains over larger longer term gain, also referred to as melioration. Here, we developed a simple two-choice reward task, implemented in 186 healthy human adult subjects across the adult lifespan to understand the behavioral, computational, and neural bases of payoff maximization versus melioration. The observed reward choice behavior on this task was best explained by a reinforcement learning model of differential future reward prediction. Simultaneously recorded and source-localized electroencephalography (EEG) showed that diminished theta-band activations in the right rostral anterior cingulate cortex (rACC) correspond to greater reward payoff maximization, specifically during the presentation of cumulative reward information at the end of each task trial. Notably, these activations (greater rACC theta) predicted depressed mood symptoms, thereby showcasing a reward processing marker of potential clinical utility. Significance StatementThis study presents cognitive, computational and neural (EEG-based) analyses of a rapid reward-based decision-making task. The research has the following three highlights. 1) It teases apart two core aspects of reward processing, i.e. long term expected value maximization versus immediate gain frequency melioration based choice behavior. 2) It models reinforcement learning based behavioral differences between individuals showing that observed performance is best explained by differential extents of reward prediction. 3) It investigates neural correlates in 186 healthy human subjects across the adult lifespan, revealing specific theta band cortical source activations in right rostral anterior cingulate as correlates for maximization that further predict depressed mood across subjects.

neuroscience↗

bioRxiv2020

Mapping Cognitive Brain Functions at Scale

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion. HighlightsO_LIRapid and scalable EEG recordings reveal common and distinct cortical activations across five core cognitive tasks. C_LIO_LIData acquired across visits one-week-apart show high test-retest reliability for both cognitive and neural measurements. C_LIO_LIEvoked neural responses during emotion interference processing demonstrate specific short-term plasticity driven by type of neurostimulation. C_LIO_LICognitively evoked neural responses are sensitive to variations in mental health symptoms. C_LI

neuroscience↗

bioRxiv2019

SimBSI: An open-source Simulink library for developingclosed-loop brain signal interfaces in animals and humans

ObjectiveA promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans, is still lacking. The objective of this paper is to develop such a neurotechnology software framework.\n\nApproachHere we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.\n\nMain resultsWe used several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment.\n\nSignificanceWith the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.

bioengineering↗

bioRxiv2019

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust EEG source estimates on a wide range of experimental paradigms. In this paper, we propose a unifying Bayesian framework in which these apparently dissimilar problems can be understood and solved in a principled manner using a single algorithm. We explicitly model the effect of non-brain sources by augmenting the lead field matrix with a dictionary of stereotypical artifact scalp projections. We propose to populate the artifact dictionary with non-brain scalp projections obtained by running Independent Component Analysis (ICA) on an EEG database. Within a parametric empirical Bayes (PEB) framework, we use an anatomical brain atlas to parameterize a source prior distribution that encourages sparsity in the number of cortical regions. We show that, in our inversion algorithm, PEB+ (PEB with the addition of artifact modeling), the sparsity prior has the property of inducing the segregation of the cortical activity into a few maximally independent components with known anatomical support. Artifacts produced by electrooculographic and electromyographic activity as well as single-channel spikes are also segregated into their respective components. Of theoretical relevance, we use our framework to point out the connections between Infomax ICA and distributed source imaging. We use real data to demonstrate that PEB+ outperforms Infomax for source separation on short segments of data and, unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations. In this example, we run PEB+ followed by the spectral analysis of the activity in the retrosplenial cortex, largely replicating the findings of previous experimental literature.

neuroscience↗