The Topology of Pediatric Structural Asymmetries in Language-Related Cortex

Deep Learning Classification of Reading Disability with Regional Brain Volume Features

Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contributes to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases.

Persistence Cycles for Visual Exploration of Persistent Homology

Persistent homology is a fundamental tool in topological data analysis used for the most diverse applications. Information captured by persistent homology is commonly visualized using scatter plots representations. Despite being widely adopted, such a visualization technique limits user understanding and is prone to misinterpretation. This article proposes a new approach for the efficient computation of persistence cycles, a geometric representation of the features captured by persistent homology. We illustrate the importance of rendering persistence cycles when analyzing scalar fields, and we discuss the advantages that our approach provides compared to other techniques in topology-based visualization. We provide an efficient implementation of our approach based on discrete Morse theory, as a new module for the Topology Toolkit. We show that our implementation has comparable performance with respect to state-of-the-art toolboxes while providing a better framework for visually analyzing persistent homology information.

Introducing Structural Symmetry and Asymmetry Implications in Development of Recent Pharmacy and Medicine

Structural symmetry, anti-symmetry, or asymmetry represent a foundational property that, for chemical compounds, often determines their chemical and biological activity [...]

Sex-Related Left-Lateralized Development of the Crus II Region of the Ansiform Lobule in Cynomolgus Monkeys

The asymmetric development of the cerebellum has been reported in several mammalian species. The current study quantitatively characterized cerebellar asymmetry and sexual dimorphism in cynomolgus macaques using magnetic resonance (MR) imaging-based volumetry. Three-dimensional T1W MR images at 7-tesla were acquired ex vivo from fixed adult male (n = 5) and female (n = 5) monkey brains. Five transverse domains of the cerebellar cortex, known as cerebellar compartmentation defined by the zebrin II/aldolase expression pattern, were segmented on MR images, and the left and right sides of their volumes were calculated. Asymmetry quotient (AQ) analysis revealed significant left-lateralization at the population level in the central zone posterior to the cerebellar transverse domains, which included lobule VII of the vermis with the crura I and II of ansiform lobules, in males but not females. Next, the volume of the cerebellar hemispherical lobules was calculated. Population-level leftward asymmetry was revealed in the crus II regions in males using AQ analysis. The AQ values of the other hemispherical lobules showed no left/right side differences at the population level in either sex. The present findings suggest a sexually dimorphic asymmetric aspect of the cerebellum in cynomolgus macaques, characterized by a leftward lateralization of the crus II region in males, but no left/right bias in females.

Cortical asymmetries at different spatial hierarchies relate to phonological processing ability

The ability to map speech sounds to corresponding letters is critical for establishing proficient reading. People vary in this phonological processing ability, which has been hypothesized to result from variation in hemispheric asymmetries within brain regions that support language. A cerebral lateralization hypothesis predicts that more asymmetric brain structures facilitate the development of foundational reading skills like phonological processing. That is, structural asymmetries are predicted to linearly increase with ability. In contrast, a canalization hypothesis predicts that asymmetries constrain behavioral performance within a normal range. That is, structural asymmetries are predicted to quadratically relate to phonological processing, with average phonological processing occurring in people with the most asymmetric structures. These predictions were examined in relatively large samples of children (N = 424) and adults (N = 300), using a topological asymmetry analysis of T1-weighted brain images and a decoding measure of phonological processing. There was limited evidence of structural asymmetry and phonological decoding associations in classic language-related brain regions. However, and in modest support of the cerebral lateralization hypothesis, small to medium effect sizes were observed where phonological decoding accuracy increased with the magnitude of the largest structural asymmetry across left hemisphere cortical regions, but not right hemisphere cortical regions, for both the adult and pediatric samples. In support of the canalization hypothesis, small to medium effect sizes were observed where phonological decoding in the normal range was associated with increased asymmetries in specific cortical regions for both the adult and pediatric samples, which included performance monitoring and motor planning brain regions that contribute to oral and written language functions. Thus, the relevance of each hypothesis to phonological decoding may depend on the scale of brain organization.