Biology subjects

Bandt, S. K.

Publications and source records attributed to Bandt, S. K..

3 recordsLinked to original sources

bioRxiv2021

Analyzing Brain Morphology in Alzheimer's Disease Using Discriminative and Generative Spiral Networks

Several patterns of atrophy have been identified and strongly related to Alzheimers disease (AD) pathology and its progression. Morphological changes in brain shape have been identified up to ten years before clinical diagnoses of AD, making its early detection more relevant. We propose novel geometric deep learning frameworks for the analysis of brain shape in the context of neurodegeneration caused by AD. Our deep neural networks learn low-dimensional shape descriptors of multiple neuroanatomical structures, instead of handcrafted features for each structure. A discriminative network using spiral convolution on 3D meshes is constructed for the in-vivo binary classification of AD from healthy controls (HCs) using a fast and efficient "spiral" convolution operator on 3D triangular mesh surfaces of human brain subcortical structures extracted from T1-weighted magnetic resonance imaging (MRI). Our network architecture consists of modular learning blocks using residual connections to improve overall classifier performance. In this work: (1) a discriminative network is used to analyze the efficacy of disease classification using input data from multiple brain structures and compared to using a single hemisphere or a single structure. It also outperforms prior work using spectral graph convolution on the same the same tasks, as well as alternative methods that operate on intermediate point cloud representations of 3D shapes. (2) Additionally, visual interpretations for regions on the surface of brain structures that are associated to true positive AD predictions are generated and fall in accordance with the current reports on the structural localization of pathological changes associated to AD. (3) A conditional generative network is also implemented to analyze the effects of phenotypic priors given to the model (i.e. AD diagnosis) in generating subcortical structures. The generated surface meshes by our model indicate learned morphological differences in the presence of AD that agrees with the current literature on patterns of atrophy associated to the disease. In particular, our inference results demonstrate an overall reduction in subcortical mesh volume and surface area in the presence of AD, especially in the hippocampus. The low-dimensional shape descriptors obtained by our generative model are also evaluated in our discriminative baseline comparisons versus our discriminative network and the alternative shape-based approaches.

neuroscience↗

bioRxiv2020

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study.

Brain structure is tightly coupled with brain functions, but it remains unclear how cognition is related to brain morphology, and what is consistent across neurodevelopment. In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures. T1-weighted MRIs from two independent cohorts, the Human Connectome Project (HCP; age: 28.81{+/-}3.70) and the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93{+/-}0.62) were independently analyzed. Cortical and subcortical surfaces were extracted and modeled as surface meshes. Three gCNNs were trained and evaluated using six-fold nested cross-validation. Overall, combining cortical and subcortical surfaces yielded the best predictions on both HCP (R=0.454) and ABCD datasets (R=0.314), and outperformed the current literature. Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry underlying Gf.

neuroscience↗

bioRxiv2020

Geometric deep learning on brain shape predicts sex and age

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Surface-based approaches have been shown to be particularly efficient in their ability to accurately describe the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features in order to capture all relevant geometric properties of cortical folding. In this paper, we propose a deep-learning based method to analyze cortical folding patterns in a data-driven way that alleviates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses convolutional neural network architecture adapted to the surface representation of the cortical ribbon. MRI data from 6,410 healthy subjects obtained from 11 publicly available data repositories were used to predict age and sex via brain shape analysis. Ages ranged from 6-89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. Two gCNNs were trained, the first of which to predict subjects self-identified sex, the second of which to predict subjects age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subjects sex with an average accuracy of 87.99% and achieved a Persons coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subjects age.

neuroscience↗