Biology subjects

Mayer, A. R.

Publications and source records attributed to Mayer, A. R..

4 recordsLinked to original sources

bioRxiv2026

Oscillating Hypercapnia Induces Neural Abundant Protein Efflux and Potential Depletion in Health and Chronic Traumatic Brain Injury

Emerging preclinical and clinical evidence suggests that low frequency hemodynamic oscillations drive CSF flow, which in turn mediates glymphatic clearance. The current study investigated whether CO2-induced low frequency hemodynamic oscillations during magnetic resonance imaging would increase clearance of proteins (glial fibrillary acidic protein, neurofilament light chain, ptau217 and brain-derived tau) from brain to blood, and temporarily improve cognitive performance in individuals with chronic traumatic brain injury (TBI) and age/sex-matched healthy controls. Results indicated that cerebrovascular reactivity, normalized CSF volume, and predicted brain age significantly differed between chronic TBI and controls, while bulk CSF flow differed only at trend levels. Multiple protein concentrations were significantly increased at [~]45 minutes post-hypercapnia, decreased at [~]90 minutes, and returned to pre-hypercapnia levels by [~]150 minutes. Protein efflux was more strongly associated with total CSF volume and total white matter volume rather than cerebrovascular reactivity or bulk CSF flow. Both groups exhibited reduced cognitive interference post-hypercapnia, and hypercapnia associated symptoms quickly returned to baseline levels. In conclusion, hypercapnia temporarily increases clearance of multiple neural abundant proteins into blood, and this effect is moderated by atrophy. Current results suggest that hypercapnia may therapeutically combat pathological protein aggregation post-trauma, and prophylactically during normal aging.

neuroscience↗

bioRxiv2021

The neurocomputational architecture of explore-exploit decision making

Humans and other animals often make the difficult decision to try new options (exploration) and forego immediate rewards (exploitation). Novelty-seeking is an adaptive solution to this explore-exploit dilemma, but our understanding of the neural computations supporting novelty-seeking in humans is limited. Here, we presented the same explore-exploit decision making task to monkeys and humans and found evidence that the computational basis for novelty-seeking is conserved across primate species. Critically, through computational model-based decomposition of event-related functional magnetic resonance imaging (fMRI) in humans, these findings reveal a previously unidentified cortico-subcortical architecture mediating explore-exploit behavior in humans.

neuroscience↗

bioRxiv2018

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45{+/-}8.9%, sensitivity 0.74{+/-}0.11, specificity 0.79{+/-}0.11) than STR (accuracy 67.45{+/-}8.13%, sensitivity 0.65{+/-}0.11, specificity 0.71{+/-}0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

neuroscience↗

bioRxiv2017

Replicability of time-varying connectivity patterns in large resting state fMRI samples

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured surrogate datasets establishes that the correlation structures in the data do not arise by chance. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance.

neuroscience↗