Biology subjects

Kapanaiah, S. K. T.

Publications and source records attributed to Kapanaiah, S. K. T..

4 recordsLinked to original sources

bioRxiv2024

Excitatory neurons of the anterior cingulate cortex encode chosen actions and their outcomes rather than cognitive state

The anterior cingulate cortex (ACC) causally influences cognitive control of goal-directed behaviour. However, it is unclear whether ACC directly encodes cognitive variables like attention or impulsivity, or implements goal-directed action selection mechanisms that are modulated by them. We recorded ACC activity with miniature endoscopic microscopes in mice performing the 5-choice-serial-reaction time task, and applied decoding and encoding analyses. ACC pyramidal cells represented specific actions before and during the behavioural response, whereas the response type (e.g. correct/incorrect/premature) - indicating the state of attentional and impulse control - could only be decoded during and after the response with high reliability. Devaluation and extinction experiments further revealed that action encoding depended on reward expectation. Our findings support a role for ACC in goal-directed action selection and monitoring, that is modulated by cognitive state, rather than in tracking levels of attention or impulsivity directly in individual trials.

neuroscience↗

bioRxiv2021

Highly task-specific and distributed neural connectivity in working memory revealed by single-trial decoding in mice and humans

Working memory (WM), the capacity to briefly and intentionally maintain mental items, is key to successful goal-directed behaviour and impaired in a range of psychiatric disorders. To date, several brain regions, connections, and types of neural activity have been correlatively associated with WM performance. However, no unifying framework to integrate these findings exits, as the degree of their species- and task-specificity remains unclear. Here, we investigate WM correlates in three task paradigms each in mice and humans, with simultaneous multi-site electrophysiological recordings. We developed a machine learning-based approach to decode WM-mediated choices in individual trials across subjects from hundreds of electrophysiological measures of neural connectivity with up to 90% prediction accuracy. Relying on predictive power as indicator of correlates of psychological functions, we unveiled a large number of task phase-specific WM-related connectivity from analysis of predictor weights in an unbiased manner. Only a few common connectivity patterns emerged across tasks. In rodents, these were thalamus-prefrontal cortex delta- and beta-frequency connectivity during memory encoding and maintenance, respectively, and hippocampal-prefrontal delta- and theta-range coupling during retrieval, in rodents. In humans, task-independent WM correlates were exclusively in the gamma-band. Mostly, however, the predictive activity patterns were unexpectedly specific to each task and always widely distributed across brain regions. Our results suggest that individual tasks cannot be used to uncover generic physiological correlates of the psychological construct termed WM and call for a new conceptualization of this cognitive domain in translational psychiatry.

neuroscience↗

bioRxiv2021

pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends we developed pyControl, a system of open source hardware and software for controlling behavioural experiments comprising; a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. ResourcesDocumentation: https://pycontrol.readthedocs.io Repositories: https://github.com/pyControl User support: https://groups.google.com/g/pycontrol

neuroscience↗

bioRxiv2020

Lack of redundancy between electrophysiological measures of long-range neuronal communication

Communication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various functional connectivity metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed. In order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, weighted Phase-Lag-Index (wPLI), Granger causality (GC), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations, and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. We find that across such measures there is a considerable lack of functional redundancy. While coherence and GC yielded similar results, other measures, especially wPLI and SPC, often produced deviating conclusions. Bivariate correlations within animals revealed that virtually none of the metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Parametric GC showed the qualitatively highest degree of redundancy with other metrics and would be most suitable for connectivity analysis. We conclude that analysis of multiple metrics is necessary to characterise functional connectivity.

neuroscience↗