Dr. Arian Ashourvan, Postdoctoral fellow, University of Pennsylvania,


Dr. Arian Ashourvan, Postdoctoral fellow, University of Pennsylvania,

SAGE 4101

September 18, 2019 12:00 PM - 1:30 PM



“A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. The development of functional neuroimaging techniques and tools from computational neuroscience has markedly expanded opportunities to study the intrinsic organization of brain activity. However, current computational models are fundamentally constrained by assumptions about external stimuli and how they drive brain activity. To address this limitation, we propose a scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model under unknown exogenous inputs, and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we use functional magnetic resonance imaging data acquired from 99 subjects (Human Connectome Project) during task and at rest. We find significant task-related changes in both the identified system and the estimated external inputs, demonstrating high similarity to known task regressors. Finally, through examination of fluctuations in the spatial distribution of the oscillatory modes of estimated systems during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that either these brain regions display a time-varying relationship with one another, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. This embodied model of brain activity enables deeper insight into the relationship between cortical functional dynamics and their drivers.”


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