Gee, J. C. | Search bioRxiv

bioRxiv2020

The influence of axial length upon the retinal ganglion cell layer of the human eye

PurposeWe examined the relationship between axial length and the thickness and volume of the ganglion cell layer (GCL) of the retina, and related these measures to the size of the optic chiasm. MethodsWe used optical coherence tomography to measure the thickness of the GCL over a 50{degrees} extent of the horizontal meridian in 50 normally-sighted participants with a wide range of axial lengths. Using a model eye informed by individual biometry, we converted GCL thickness to tissue volume per square degree. The volume of the optic chiasm was measured for 40 participants using magnetic resonance imaging. ResultsWhile GCL thickness decreases with increasing axial length, there is a positive relationship between GCL tissue volume and axial length, leading us to conclude that increasing axial length is associated with decreased retinal ganglion cell packing, increased cell size, or both. We characterize how retinal ganglion cell tissue varies systematically in volume and spatial distribution as a function of axial length. This model allows us to remove the effect of axial length from individual difference measures of GCL volume. We find that variation in GCL volume correlates well with the size of the optic chiasm as measured using magnetic resonance imaging. ConclusionsOur results provide the volume of ganglion cell tissue in the retina, adjusted for the effects of axial length upon ganglion cell size and/or packing. The resulting volume measure accounts for individual differences in the size of the optic chiasm, supporting its use to characterize the post-retinal visual pathway.

neuroscience↗

bioRxiv2019

Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder

Bipolar Disorder (BP) is a mental disorder that affects 1 [~] 2% of the population. Early diagnosis and targeted treatment can benefit from associated biological markers. The existing methods typically utilize biomarkers from anatomical MRI or functional BOLD imaging, but lack the ability of revealing the relationship between integrated modalities and disease. In this paper, we developed an Edge-weighted Graph Attention Network (EGAT) with Dense Hierarchical Pooling (DHP), to better understand the underlying roots of the disorder from the view of structure-function integration. For the input, the underlying graphs are constructed from functional connectivity matrices and the nodal features consist of both the anatomical features and the statistics of the connectivity. We investigated the potential benefits of using EGAT to classify BP vs. Healthy Control (HC). Compared with traditional machine learning classifiers, our proposed EGAT embedding increased improved 10 [~] 20% in the accuracy and F1-score, compared with alternative classifiers. More specifically, by examining the attention map and gradient sensitivity of nodal features, we indicated that associated with the abnormality of anatomical geometric properties, multiple interactive patterns among Default Mode, Fronto-parietal and Cingulo-opercular networks contribute to identifying BP.

neuroscience↗