Biology subjects

Manning, J. R.

Publications and source records attributed to Manning, J. R..

23 records · Page 2Linked to original sources

bioRxiv2019

Cardiomyocyte-Specific Deletion of GCN5L1 Limits Cardiac Contractile Decline in Obese Pre-Diabetic Mice

Reduced substrate flexibility, and a shift towards increased cardiac fatty acid utilization, is a key feature of obesity and diabetes. We previously reported that increased acetylation of several mitochondrial FAO enzymes, regulated in part via increased abundance of the mitochondrial acetyltransferase GCN5L1, correlated with increased FAO enzyme activity in the heart. The focus of the current study was to investigate whether decreased acetylation, via cardiomyocyte-specific deletion of GCN5L1 (GCN5L1 cKO), regulates cardiac energy metabolism following exposure to a high fat diet (HFD). Excess dietary fat led to similar cardiac hypertrophy in wildtype and GCN5L1 cKO mice. We show that acetylation of pyruvate dehydrogenase (PDH) was significantly reduced in HFD GCN5L1 cKO hearts, which correlated with its increased enzymatic activity relative to HFD wildtype controls. The acetylation of both electron transport chain Complex I protein NDUFB8, and manganese superoxide dismutase 2 (SOD2), was significantly reduced by GCN5L1 deletion in HFD animals, resulting in decreased lipid peroxidation. Finally, we show that in contrast to wildtype mice, GCN5L1 cKO hearts maintain ex vivo contractility and workload in response to a HFD. In summary, we show that GCN5L1 deletion limits cardiac functional decline observed in HFD mice, by increasing fuel substrate flexibility and limiting reactive oxygen species damage.

physiology↗

bioRxiv2019

High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brains functional connectome that display homologous lower-level dynamic correlations. We tested the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We developed an approach to estimating high-order dynamic correlations in timeseries data, and we applied the approach to neuroimaging data collected as human participants either listened to a ten-minute story or listened to a temporally scrambled version of the story. We trained across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We found that classifiers trained to decode from high-order dynamic correlations yielded the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

neuroscience↗

bioRxiv2018

Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience

How we process ongoing experiences is shaped by our personal history, current needs, and future goals. Consequently, brain regions involved in generating these subjective appraisals, such as the vmPFC, often appear to be heterogeneous across individuals even in response to the same external information. To elucidate the role of the vmPFC in processing our ongoing experiences, we developed a computational framework and analysis pipeline to characterize the spatiotemporal dynamics of individual vmPFC responses as participants viewed a 45-minute television drama. Through a combination of functional magnetic resonance imaging, facial expression tracking, and self-reported emotional experiences across four studies, our data suggest that the vmPFC slowly transitions through a series of discretized states that broadly map onto affective experiences. Although these transitions typically occur at idiosyncratic times across people, participants exhibited a marked increase in state alignment during high affectively valenced events in the show. Our work suggests that the vmPFC ascribes affective meaning to our ongoing experiences.

neuroscience↗

bioRxiv2018

How is experience transformed into memory?

The mental contexts in which we interpret experiences are often person-specific, even when the experiences themselves are shared. We developed a geometric framework for mathematically characterizing the subjective conceptual content of dynamic naturalistic experiences. We model experiences and memories as trajectories through word embedding spaces whose coordinates reflect the universe of thoughts under consideration. Memory encoding can then be modeled as geometrically preserving or distorting the shape of the original experience. We applied our approach to data collected as participants watched and verbally recounted a television episode while undergoing functional neuroimaging. Participants recountings all preserved coarse spatial properties (essential narrative elements), but not fine spatial scale (low-level) details, of the episodes trajectory. We also identified networks of brain structures sensitive to these trajectory shapes. Our work provides insights into how we preserve and distort our ongoing experiences when we encode them into episodic memories.

neuroscience↗

bioRxiv2017

Towards Human Super EEG

We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different peoples brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individuals brain: given recordings from a limited set of locations in that individuals brain, along with the observed spatial correlations learned from other peoples recordings, how much can be inferred about ongoing activity at other locations throughout that individuals brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

neuroscience↗