A model-guided dissociation between subcortical and cortical contributions to word recognition

Molecular dynamics simulations and bioinformatics' analysis of deleterious missense single nucleotide polymorphisms in Glyoxalase-1 gene

Glyoxalase-1 (Glo-1) is a key member of the Glyoxalase system, the primary line of defense against dicarbonyl stress which, in tandem, with reduced levels of expression or activity of Glyoxalase-1 enzyme, has been implicated in various human diseases like type 2 diabetes mellitus (T2DM) and its vascular complications. The association of Glo-1 single nucleotide polymorphisms with genetic susceptibility to T2DM and its vascular complications is yet to be explored. Therefore, in this study, we have employed a computational approach to identify the most damaging missense or nonsynonymous SNPs (nsSNPs) in Glo-1 gene. Initially, we characterized missense SNPs that are damaging to the structural and functional integrity of Glo-1 using various bioinformatic tools. These tools included SIFT, PolyPhen-2, SNAP, PANTHER, PROVEAN, PhD-SNP, SNPs&GO, I-Mutant, MUpro and MutPred2. One of these missense SNPs (rs1038747749; corresponding to amino acid change Arginine to Glutamine at position 38) was found to be highly conserved in evolution and is an important part of the enzyme's active site, glutathione binding site, as well as the dimeric interface based on the results obtained from ConSurf and NCBI Conserved Domain Search tools. Project HOPE reported that this mutation replaces a positively charged polar amino acid (Arginine) with a small, neutrally charged amino acid (Glutamine). Comparative modelling of wildtype and mutant (R38Q) Glo-1 proteins was performed in the run up to molecular dynamics simulation analysis which showed that rs1038747749 adversely impacts Glo-1 protein's stability, rigidity, compactness, hydrogen bonds/interactions as demonstrated by the results of various parameters computed during the analysis.Communicated by Ramaswamy H. Sarma.

Structural gray matter features and behavioral preliterate skills predict future literacy - A machine learning approach

When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in a priori selected areas of the future reading network. With surprisingly high accuracy, future literacy was predicted, predominantly based on gray matter volume in the left occipito-temporal cortex and local gyrification in the left insular, inferior frontal, and supramarginal gyri. Furthermore, phonological awareness significantly predicted future literacy. In sum, the results indicate that the brain morphology of the large-scale reading network at a preliterate age can predict how well children learn to read.

Entropy Could Quantify Brain Activation Induced by Mechanical Impedance-Restrained Active Arm Motion: A Functional NIRS Study

Brain activation has been used to understand brain-level events associated with cognitive tasks or physical tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli through elbow extension-flexion motions by using our exoskeleton robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy with the other two. The results show that entropy is superior, in that its change appeared in limited, well established, motor areas, while signal amplitude and beta value changes appeared in a widespread fashion, contradicting the modularity theory. Entropy can predict increase in brain activation with task duration, while the other two cannot. When stimuli shifted from the rest state to the task state, entropy exhibited a similar increase as the other two did. Although entropy showed only a part of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation that the other two did not show. Moreover, entropy was capable of identifying the physiologically important location.

Disrupted Subcortical-Cortical Connections in a Phonological but Not Semantic Task in Chinese Children With Dyslexia

Reading disability has been considered as a disconnection syndrome. Recently, an increasing number of studies have emphasized the role of subcortical regions in reading. However, the majority of research on reading disability has focused on the connections amongst brain regions within the classic cortical reading network. Here, we used graph theoretical analysis to investigate whether subcortical regions serve as hubs (regions highly connected with other brain regions) during reading both in Chinese children with reading disability (N = 15, age ranging from 11.03 to 13.08 years) and in age-matched typically developing children (N = 16, age ranging from 11.17 to 12.75 years) using a visual rhyming judgment task and a visual meaning judgment task. We found that the bilateral thalami were the unique hubs for typically developing children across both tasks. Additionally, subcortical regions (right putamen, left pallidum) were also unique hubs for typically developing children but only in the rhyming task. Among these subcortical hub regions, the left pallidum showed reduced connectivity with inferior frontal regions in the rhyming judgment but not semantic task in reading disabled compared with typically developing children. These results suggest that subcortical-cortical disconnection, which may be particularly relevant to the phonological and phonology-related learning process, may be associated with Chinese reading disability.

Neural correlates of affective contributions to lexical decisions in children and adults

The goal of the present study was to investigate whether 6-9-year old children and adults show similar neural responses to affective words. An event-related neuroimaging paradigm was used in which both age cohorts performed the same auditory lexical decision task (LDT). The results show similarities in (auditory) lexico-semantic network activation as well as in areas associated with affective information. In both age cohorts' activations were stronger for positive than for negative words, thus exhibiting a positivity superiority effect. Children showed less activation in areas associated with affective information in response to all three valence categories than adults. Our results are discussed in the light of computational models of word recognition, and previous findings of affective contributions to LDT in adults.