Rosa, M. | bioRxiv Science

bioRxiv2018

Continuity between koniocellular layers of dorsal lateral geniculate and inferior pulvinar nuclei in common marmosets

In primates, the koniocellular (K) layers of the dorsal lateral geniculate nucleus (LGN) and the calbindin-rich subdivisions of the inferior pulvinar (IPul) nucleus are considered part of a thalamic matrix system which projects diffusely to superficial cortical layers. Activity in the matrix system is proposed to coordinate oscillatory activity in thalamocortical loops. Further, since both K cells and IPul are involved in visual processing pathways, especially in alternative pathways to visual cortex after V1 lesion in early life (\"blindsight\"), their functional similarities have been strongly implicated. Here we tested the hypothesis that calbindin-positive K cells and IPul cells constitute a continuous group of cells. By combining immunohistochemistry and a high-throughput neuronal tracing method, we found that both K cells and IPul form reciprocal connections with striate and extrastriate cortices; whereas principal laminae of LGN do not receive inputs from extrastriate cortex and only project sparsely to these areas. Retrograde labelled cells in lateral division of IPul merged seamlessly into the retrograde labelled cells in K layers. These results supported the continuity between LGN K layers and IPul, providing the anatomical basis for functional congruity of this part of dorsal thalamic matrix.

neuroscience↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗