Research

Current research

The human brain shows complex connectivity patterns at multiple spatial and temporal scales which are fundamental to the emergence of cognition. My primary research goal is to develop innovative analysis tools and computational models (i) to capture and understand the patterns of structural and functional connectivity in the human brain, and (ii) to characterize specific changes in these patterns due to different neurological disorders.

I utilize a dynamical systems perspective and visualize the brain as an adaptive, complex network (graph) of sub-systems. Analysis tools I develop are (i) integrative, (ii) adaptable for the ‘big-data’ in neuroscience, and (iii) potentially beneficial for futuristic personalized therapeutics.

Here is a brief description of my research projects:

Structure-function coupling in the brain

Cortical and subcortical structural connectivity (connectome) provides fundamental basis for brain’s functional dynamics and subsequently, cognition. We build personalized brain network models to be able to assess the relationship between brain’s structural connectivity and emergent functional dynamics, and gain predictive knowledge of individual cognitive abilities.

Dissociation of cognitive states using high-amplitude spontaneous activity in the brain

Resting state or spontaneous activity represents the intrinsic functional organization of the brain. Spontaneous activity in resting state EEG data can be captured by the wide-spread spatiotemporal cascades of high-amplitude bursts or (macroscopic) neural avalanches.

  • Similar to the avalanches observed in the neural data recorded in vitro, macroscopic avalanches also show power-law probability distributions and scale-free organization.

  • We observed that the spatial avalanche distribution (which we call 'normalized engagement') changes with changing stimuli and can be predictive of the stimulus-driven, individualistic cognitive responses. [NeuroImage, 241, 118425 (2021)].

  • Currently, we are exploring the application of neural avalanches in cilinical neuroscience.


Dynamic reconfiguration of functional brain modules

Mounting evidence supports that modular organization is characteristic to functional brain networks. Brain modules are defined as mutually segregated, highly connected groups of brain regions.

  • My collaborators (Drs. Nina Lauharatanahirun and Javier Garcia) and I used dynamic community detection algorithms with hierarchical statistical modeling to show that the flexible reconfiguration of brain modules can compensate for having a poor night’s sleep during a visual working memory task . [Preprint: arXiv:2009.07233].

  • Currently, I am working (in collaboration with Drs. Erin Flynn-Evans and Cassie Hilditch) to compare the task-driven reconfiguration of functional modules between two cognitively distinct states when an individual is (i) fully awake and (ii) just woken up from sleep. Typically, the brain experiences a decrease in cognitive performance immediately after waking up from sleep, and this state is called ‘sleep inertia’.

Development of novel metrics to dissociate the dynamics in healthy versus diseased brains

  • Working in collaboration with Dr. Randy Auerbach, our recent findings suggest that the scale-free organization of macroscopic avalanches significantly differ between patients suffering from major depressive disorders and healthy controls. These findings also suggest that this difference might originate from a deficiency of functional deactivation in the frontal regions of the brain.

  • We used a network-based approach to model the functional dynamics of epilepsy patients during interictal recordings. Our recent findings (using more than 80 seizures across 10 patients, in collaboration with Dr. Sydney Cash) suggest that the neural dynamics associated with different seizure sub-types can be assessed and identified through a combination of graph network features such as degree, synchronizability, clustering coefficient, and spectral radius. [Preprint: biorxiv.org/content/10.1101/2021.06.12.448205v1].

Other ongoing research projects

  • Impact of inconsistent sleep on the functional organization of the brain during task performance.

  • Application of capacitance-based, ankle-worn devices in detecting sleep related disorders (in collaboration with Tanzen Medical Inc).

Previous research

Before making a transition into neuroscience, I studied optoelectronic devices and obsevred interesting emergent properties, both by investigating an individual device as well as a network of devices.

Network symmetries predict emergent patterns of synchronization

With Prof. Rajarshi Roy, University of Maryland, College Park.

Symmetries in a network’s structure can help predict the emergent patterns of synchronized clusters in a network of coupled oscillators. In a globally coupled system of opto-electronic chaotic oscillators, we showed that the symmetries and sub-symmetries of the network predict the formation of synchrony and partial synchrony states including chimera states, where system evolves into separate domains of synchronized and desynchronized populations. We further showed that the master stability equation can be significantly simplified by usind symmetry analysis with a group theoretical approach, and stability of chimera and cluster states can be successfully predicted [Chaos, 26 (9), 094801 (2016)].

PhD dissertation: Electrical and optical investigations of the condensed matter physics of junction diodes under charge carrier injection

With Dr. Shouvik Datta, Indian Institute of Science education and Research (IISER), Pune, India.

Lasers are complex systems where coherent light emission is achieved as the system is directed (by design) to enhance particular interactions. A diode laser is a special case where fermionic populations achieve a state analogous to population inversion by charge carrier injection. We demonstrated that the conventional electrostatic description of a junction diode breaks down with increasing injected charge density. We captured this departure as coupled, frequency dependent optical (modulated light emission) and electrical (capacitance) properties of the device. We observed the occurrence of inductive like ‘negative capacitance’ simultaneously with the onset of low frequency dependent modulated light output, and explained this electrical and optical correlation through the participation of defect energy levels in light emission process [J. Appl. Phys. 110, 114509 (2011)]. We further demonstrated that increasing quantum confinement of charge carriers (quantum wells to quantum dots) affects the overall light emission process and identified electrical signature of excitonic Mott transition [Appl. Phys. Lett. 102, 053508 (2013); J. Appl. Phys. 120, 144304 (2016)]. We established that to construct an efficient device, one cannot completely rely on characterizing individual constituent material layers but the dynamics of the entire combined system needs to be understood [Appl. Phys. Lett. 105, 123503 (2014)].