Biology subjects

Fulcher, B.

Publications and source records attributed to Fulcher, B..

3 recordsLinked to original sources

bioRxiv2020

Topological Augmentation of Latent Information Streams in Feed-Forward Neural Networks

Network neuroscience has yielded crucial insights into the systems-level organisation of the brain, however the indirect nature of neuroimaging recordings has rendered the discovery of generative mechanisms for a given function inherently challenging. In parallel, neural network machine-learning models have exhibited breakthrough performance in tackling a range of complex problems, however the principles that govern learning-induced modifications to network structure remain poorly understood, in part due to a lack of analytic tools to quantify the dynamics of network structure. While the question of how network reconfiguration supports learning is mirrored in machine learning and network neuroscience, the different contexts of these fields provides a timely opportunity to bring them together synergistically to investigate the problem. Here we combine these two approaches to reveal connections between the brains network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify handwritten digits and then used a combination of systems neuroscience and information theoretic tools to perform virtual brain analytics on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterised by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function - in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training - while simultaneously enriching our understanding of the methods used by systems neuroscience.

neuroscience↗

bioRxiv2020

Core and Matrix Thalamic Sub-Populations Relate to Spatio-Temporal Cortical Connectivity Gradients

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting Core cells and supragranular-projecting Matrix cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core-Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.

neuroscience↗

bioRxiv2016

Transcriptional signatures of connectomic subregions of the human striatum

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint - a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide cortical and subcortical areas using their patterns of connectivity. However, it remains unclear whether these connectomically-defined subregions of the brain can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with comprehensive transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum, in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal, and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level. Genes contributing strongly to the classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine, and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism, and Parkinsons disease. Our findings highlight a close link between regional variations in transcriptional activity and interregional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

neuroscience↗