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About Me.

My research focus has always been on deciphering the dynamical principles governing the brain and, as such, I have been captivated by the constraining role brain rhythms play in shaping our cognition. My work has already unearthed the significance of simple diffusion dynamics in determining fMRI functional connectivity, especially its perturbation following a stroke. Furthermore, through multiple modalities - MEG, EEG, and LFP - I've delved into the dual nature of oscillatory dynamics, which can both aid and disrupt beneficial synchronization across neural populations in the cortex and subcortex.

I now believe that sleep presents the most pristine lens through which we can understand these rhythms. Sleep is a behavioral state that naturally strips away many confounding factors, making it uniquely positioned to reveal how brain rhythms orchestrate human cognition. Detailed mechanistic work from Mircea Steriade and others has developed the field of rhythms in sleep such that, in combination with the unique data and statistical skills I can access, I can make significant progress as an independent investigator specializing in electrical rhythms during sleep.

My academic journey began under the aegis of Prof. Ramesh Srinivasan at the Human Neuroscience lab in 2019, a hub renowned for its rigorous methodologies, mechanistic cognitive neuroscience, and pioneering EEG theoretical endeavors. Concurrently, to build my intuitions about the inherent assumptions of neuroscientific data analysis, I undertook a Masters in Statistics. This enabled me to craft a generative model for M/EEG functional connectivity anchored by the brain’s anatomical connectivity. This model, which I had the privilege of presenting at UCI’s Workshop on Big Data in Brain Science in 2017, has since been showcased at multiple conferences and is now published at Network Neuroscience.

My postdoctoral journey was enriched by a generative model of rhythms, forming the bedrock for a real-time phase estimation framework, which has now been published at eLife and presented in talks at the New England Statistical Symposium and the 13th Annual Brain Informatics Conference. Alongside this, my collaboration with Dr. Catherine Chu has allowed me a deeper plunge into sleep neuroscience, specifically through thalamocortical recordings during sleep in epileptic patients. This venture has unraveled the intricate ties between cognitive dysfunction in epilepsy and thalamic sleep spindle disruptions: work that I presented at the 1st Annual MGH-MIT iBrain Seminars.

Now, firmly anchored in a postdoctoral role with Dr. Chu, I am focusing on joint analysis of thalamic, cortical, and hippocampal recordings. Marrying my statistical prowess, neural data analytics, and cognitive neuroscience expertise with the unparalleled knowledge of my mentors in epileptology (Dr. Chu) and computational neuroscience (Prof. Kramer), I seek to embark on investigating the triple-coupling of thalamic sleep spindles, hippocampal ripples, and pre-frontal cortex slow oscillations. This research is paramount for not only understanding the basic science of electrophysiological intricacies during sleep but also illuminating sleep-dependent memory consolidation pathways, paving the way for interventions to augment healthy cross-rhythm coupling.

With aspirations of joining a research institution as a faculty member, I want to advance our understanding of the dynamical constraints shaping brain function. My mission extends beyond basic understanding: I endeavor to unravel the network complexities of neurological disorders and develop interventions using real-time stimulation that ensures healthy brain dynamics.

Journal Papers and Preprints

  1. A. Wodeyar, D. Chinappen, D. Mylonas, B. Baxter, D. S. Manoach, U. T. Eden, ... & C. J. Chu (2023), "Human thalamic recordings reveal that epileptic spikes block sleep spindle production during non-rapid eye movement sleep", bioRxiv, (2023).
     

  2. A. Wodeyar, F. A. Marshall, C. J. Chu, U. T. Eden and M. A. Kramer, “Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty”, bioArxiv (2023).
     

  3. A. Wodeyar, and R. Srinivasan, “Structural Connectome constrained Graphical Lasso for MEG Partial Coherence”, Network Neuroscience. (2022)
     

  4. W. Rasheed, A. Wodeyar, R. Srinivasan, & R. D. Frostig, Sensory stimulation-based protection from impending stroke following MCA occlusion is correlated with desynchronization of widespread spontaneous local field potentials. Scientific reports, 12(1), 1-11. (2022)
     

  5. J.M. Cassidy, A. Wodeyar, S. C. Cramer and R. Srinivasan, “Coherent neural oscillations inform early stroke motor recovery”, Human Brain Mapping, 1-12 (2021).
     

  6. A. Wodeyar, M. Schatza, A. S. Widge, U. T. Eden, M. A. Kramer, “A State Space Modeling Approach to Real-Time Phase Estimation”,  eLife (2021)
     

  7. E. G. Wann, A. Wodeyar, R. Srinivasan and R. D. Frostig, “Rapid development of strong, persistent, spatiotemporally extensive cortical synchrony and underlying oscillations following acute MCA focal ischemia”, Scientific reports 10, 1-14 (2020)
     

  8. A. Wodeyar, J.M. Cassidy, S. C. Cramer and R. Srinivasan, “Damage to the structural connectome reflected in resting-state fMRI functional connectivity”, Network Neuroscience, 4,1197–1218 (2020)
     

  9. J.M. Cassidy, A. Wodeyar, W. Jennifer, K. Kaur, A. K. Masuda, R. Srinivasan and S. C.Cramer , “Low-frequency oscillations are a biomarker of injury and recovery after stroke”, Stroke, 51, 1442–1450 (2020)
     

  10. A. Wodeyar and R. Srinivasan, “Network Structure During Encoding Predicts Working Memory Performance”, bioArxiv (2018).

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