Psychosocial factors underlying ethnic disparities in diabetes outcome

Honors (Bachelor's)Honors Individual Major Program (HIMP)University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/112086/1/ejcorn.pd

Network Spreading Dynamics In Cognition And Neurodegeneration

The macroscale network structure of the brain is fundamental to the pathophysiology and treatment of several neuropsychiatric diseases, including epilepsy, neurodegenerative disease, depression, and psychosis. Functional interactions at this scale index disease symptoms and guide exogenous interventions, such as brain stimulation or pharmacology. However, a lack of tools for measuring the underlying neurobiological drivers of these functional interactions, as well as phenotypic heterogeneity within disorders, hinders the ability to expand upon existing treatments and target them to the appropriate populations. Dynamical systems models have the potential to move beyond a statistical description of neural systems by positing mechanisms that link the physical form of a system with its emergent function. A subset of dynamical systems models, linear network spreading models, have proved especially useful for capturing activity fluctuations in neural systems. Existing tools allow these models to be studied through the lens of network control theory, which captures the system’s response to external inputs. Here, we use network spreading models and other computational tools to study structure-function relationships in the human brain and the mechanisms of Parkinson’s disease pathophysiology. First, we employed a network spreading model to characterize the neural substrates of individual differences in impulse control throughout development. Second, we incorporated external inputs into those models in order to explain the temporal progression of large- scale cortical activity patterns. Third, we used a network spreading model to confirm that endogenous levels of α-synuclein, along with both anterograde and retrograde transsynaptic diffusion drive Parkinson’s disease progression. Finally, we identify latent patterns of co- occurring pathologies in neuropathological autopsy data that can be predicted from in vivo biomarkers using statistical models. This collection of studies expands our understanding of how brain activity and misfolded proteins spread throughout the brain’s white matter connections and provides a computational framework for addressing heterogeneity in neurologic diseases. These findings are complementary to the aim of developing network-oriented therapies and lay a general framework for parsing disease heterogeneity across multiple fields of medicine

Non-equilibrium dynamics and entropy production in the human brain

Living systems operate out of thermodynamic equilibrium at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear whether non-equilibrium dynamics manifest at macroscopic scales, and if so, how such dynamics support higher-order biological functions. Here we present a framework to probe for non-equilibrium dynamics by quantifying entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain fundamentally operates out of equilibrium at large scales. Moreover, we find that the brain produces more entropy -- operating further from equilibrium -- when performing physically and cognitively demanding tasks. By simulating an Ising model, we show that macroscopic non-equilibrium dynamics can arise from asymmetries in the interactions at the microscale. Together, these results suggest that non-equilibrium dynamics are vital for cognition, and provide a general tool for quantifying the non-equilibrium nature of macroscopic systems.Comment: 18 pages, 14 figure

Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

OBJECTIVE: Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS: We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue

Network Spreading Dynamics in Cognition and Neurodegeneration

The macroscale network structure of the brain is fundamental to the pathophysiology and treatment of several neuropsychiatric diseases, including epilepsy, neurodegenerative disease, depression, and psychosis. Functional interactions at this scale index disease symptoms and guide exogenous interventions, such as brain stimulation or pharmacology. However, a lack of tools for measuring the underlying neurobiological drivers of these functional interactions, as well as phenotypic heterogeneity within disorders, hinders the ability to expand upon existing treatments and target them to the appropriate populations. Dynamical systems models have the potential to move beyond a statistical description of neural systems by positing mechanisms that link the physical form of a system with its emergent function. A subset of dynamical systems models, linear network spreading models, have proved especially useful for capturing activity fluctuations in neural systems. Existing tools allow these models to be studied through the lens of network control theory, which captures the system’s response to external inputs. Here, we use network spreading models and other computational tools to study structure-function relationships in the human brain and the mechanisms of Parkinson’s disease pathophysiology. First, we employed a network spreading model to characterize the neural substrates of individual differences in impulse control throughout development. Second, we incorporated external inputs into those models in order to explain the temporal progression of large- scale cortical activity patterns. Third, we used a network spreading model to confirm that endogenous levels of α-synuclein, along with both anterograde and retrograde transsynaptic diffusion drive Parkinson’s disease progression. Finally, we identify latent patterns of co- occurring pathologies in neuropathological autopsy data that can be predicted from in vivo biomarkers using statistical models. This collection of studies expands our understanding of how brain activity and misfolded proteins spread throughout the brain’s white matter connections and provides a computational framework for addressing heterogeneity in neurologic diseases. These findings are complementary to the aim of developing network-oriented therapies and lay a general framework for parsing disease heterogeneity across multiple fields of medicine

Network Spreading Dynamics in Cognition and Neurodegeneration

The macroscale network structure of the brain is fundamental to the pathophysiology and treatment of several neuropsychiatric diseases, including epilepsy, neurodegenerative disease, depression, and psychosis. Functional interactions at this scale index disease symptoms and guide exogenous interventions, such as brain stimulation or pharmacology. However, a lack of tools for measuring the underlying neurobiological drivers of these functional interactions, as well as phenotypic heterogeneity within disorders, hinders the ability to expand upon existing treatments and target them to the appropriate populations. Dynamical systems models have the potential to move beyond a statistical description of neural systems by positing mechanisms that link the physical form of a system with its emergent function. A subset of dynamical systems models, linear network spreading models, have proved especially useful for capturing activity fluctuations in neural systems. Existing tools allow these models to be studied through the lens of network control theory, which captures the system’s response to external inputs. Here, we use network spreading models and other computational tools to study structure-function relationships in the human brain and the mechanisms of Parkinson’s disease pathophysiology. First, we employed a network spreading model to characterize the neural substrates of individual differences in impulse control throughout development. Second, we incorporated external inputs into those models in order to explain the temporal progression of large- scale cortical activity patterns. Third, we used a network spreading model to confirm that endogenous levels of α-synuclein, along with both anterograde and retrograde transsynaptic diffusion drive Parkinson’s disease progression. Finally, we identify latent patterns of co- occurring pathologies in neuropathological autopsy data that can be predicted from in vivo biomarkers using statistical models. This collection of studies expands our understanding of how brain activity and misfolded proteins spread throughout the brain’s white matter connections and provides a computational framework for addressing heterogeneity in neurologic diseases. These findings are complementary to the aim of developing network-oriented therapies and lay a general framework for parsing disease heterogeneity across multiple fields of medicine

Information content of brain states is explained by structural constraints on state energetics

Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. The structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state-transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in fMRI data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions - especially those to high information content states - are less costly than expected from random network null models, thereby indicating the brain's marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations.Comment: 16 pages, 4 figures + supplement (5 pages, 5 figures