Biology subjects

Busch, E. L.

Publications and source records attributed to Busch, E. L..

3 recordsLinked to original sources

bioRxiv2022

The LEGO theory of the developing functional connectome

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine-scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9 - 10 years; n = 1,115 individuals, both sexes, 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition. Significance statementYears of human magnetic resonance imaging (MRI) research demonstrate that individual variability in resting-state functional connectivity relates to genetics and cognition. However, the various spatial scales where individual differences in connectivity could occur have yet to be considered in childhood brain- behavior association studies. Here, we use novel machine learning approaches to examine the reliability, heritability, and behavioral relevance of different spatial scales of the resting-state functional connectome during childhood. We show that broad features of the connectome are strongly related to heritability, whereas fine details are more reliable and strongly associated with neurocognitive performance. These data indicate that reliable, heritable, and behaviorally-relevant individual differences exist at dissociable scales of the functional connectome.

neuroscience↗

bioRxiv2022

Temporal PHATE: A multi-view manifold learningmethod for brain state trajectories

1The complexity and intelligence of the brain give the illusion that measurements of brain activity will have intractably high dimensionality, rife with collection and biological noise. Nonlinear dimensionality reduction methods like UMAP and t-SNE have proven useful for high-throughput biomedical data. However, they have not been used extensively for brain imaging data such as from functional magnetic resonance imaging (fMRI), a noninvasive, secondary measure of neural activity over time containing redundancy and co-modulation from neural population activity. Here we introduce a nonlinear manifold learning algorithm for timeseries data like fMRI, called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a lower intrinsic dimensionality from timeseries data, T-PHATE exploits autocorrelative structure within the data to faithfully denoise dynamic signals and learn activation manifolds. We empirically validate T-PHATE on three human fMRI datasets, showing that T-PHATE significantly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality reduction benchmarks.These notable improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally-diffuse processes.

neuroscience↗

bioRxiv2020

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

neuroscience↗