The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits

Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.

A new discovery on visual information dynamic changes from V1 to V2: corner encoding

The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, and only a small fraction of visual information reaches the primary visual cortex (V1). This, nevertheless, does not affect our visual perception. Furthermore, how neurons in the secondary visual cortex (V2) encode such a small amount of visual information has yet to be addressed. To this end, the current paper established a visual network model for retina-lateral geniculate nucleus (LGN)-V1–V2 and quantitatively accounted for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrated that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode “corner” information, due to the effects of synaptic plasticity, but the similar function does not exist in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the “contour” feature (edge and corner) is achieved in the pathway of retina-LGN-V1–V2.

Linguistic modulation of the neural encoding of phonemes

Speech comprehension entails the neural mapping of the acoustic speech signal onto learned linguistic units. This acousto-linguistic transformation is bi-directional, whereby higher-level linguistic processes (e.g., semantics) modulate the acoustic analysis of individual linguistic units. Here, we investigated the cortical topography and linguistic modulation of the most fundamental linguistic unit, the phoneme. We presented natural speech and 'phoneme quilts' (pseudo-randomly shuffled phonemes) in either a familiar (English) or unfamiliar (Korean) language to native English speakers while recording fMRI. This design dissociates the contribution of acoustic and linguistic processes towards phoneme analysis. We show that (1) the four main phoneme classes (vowels, nasals, plosives, fricatives) are differentially and topographically encoded in human auditory cortex, and that (2) their acoustic analysis is modulated by linguistic analysis. These results suggest that the linguistic modulation of cortical sensitivity to phoneme classes minimizes prediction error during natural speech perception, thereby aiding speech comprehension in challenging listening situations.

A New Discovery on Visual Information Dynamic Changes from Retina to V2

The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, only a small fraction of visual information reaches the V1 area. This, nevertheless, does not affect our visual perception. Furthermore, how neurons in V2 encode such a small amount of visual information has yet to be addressed. To this end, the current paper establishes a visual network model for retina-LGN-V1-V2 and quantitatively accounts for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrate that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode “corner” information, due to the effects of synaptic plasticity, of which function is not existed in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the “contour” feature (edge and corner) is achieved in the pathway of retina-LGN-V1-V2.

Individual Differences in the Neural Localization of Relational Networks of Semantic Concepts

Semantic concepts relate to each other to varying degrees to form a network of zero-order relations, and these zero-order relations serve as input into networks of general relation types as well as higher order relations. Previous work has studied the neural mapping of semantic concepts across domains, although much work remains to be done to understand how the localization and structure of those architectures differ depending on various individual differences in attentional bias toward different content presentation formats. Using an item-wise model of semantic distance of zero-order relations (Word2vec) between stimuli (presented both in word and picture forms), we used representational similarity analysis to identify individual differences in the neural localization of semantic concepts and how those localization differences can be predicted by individual variance in the degree to which individuals attend to word information instead of pictures. Importantly, there were no reliable representations of this zero-order semantic relational network when looking at the full group, and it was only through considering individual differences that a stable localization difference became evident. These results indicate that individual differences in the degree to which a person habitually attends to word information instead of picture information substantially affects the neural localization of zero-order semantic representations.