Biology subjects

Kashyap, A.

Publications and source records attributed to Kashyap, A..

22 records · Page 2Linked to original sources

bioRxiv2019

Resting brain fluctuations are intrinsically coupled to visual response dynamics

How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional (f)MRI to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) govern mouse resting brain dynamics. QPPs captured the temporal alignment of global brain fluctuations, anti-correlation of the Default Mode (DMN)- and Task Positive (TPN)-like networks, and activity in neuromodulatory nuclei of the reticular formation. While visual stimulation could trigger a transient spatiotemporal pattern highly similar to intrinsic QPPs, global signal fluctuations and QPPs during rest periods could explain variance in the following visual responses. QPPs and the global signal thus appeared to capture a common arousal-related brain-state fluctuation, orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

neuroscience↗

bioRxiv2018

Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

Brain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

neuroscience↗

bioRxiv2018

Quasi-periodic patterns contribute to functional connectivity in the brain

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.\n\nHighlightsO_LIQuasi-periodic patterns (QPPs) of low-frequency activity contribute to functional connectivity\nC_LIO_LIThe spatiotemporal pattern of QPPs differs between resting-state and task-performing individuals\nC_LIO_LIQPPs account for significant functional connectivity in the DMN and TPN during rest and task performance\nC_LIO_LIChanges in functional connectivity in these networks may reflect differences in QPPs\nC_LI

neuroscience↗

bioRxiv2017

Instantaneous Brain Dynamics Mapped to a Continuous State Space

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brains dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brains surface reveals the patterns of brain activity that support each experimental state.

neuroscience↗