General Psychopathology, Cognition, and the Cerebral Cortex in 10-Year-Old Children: Insights From the Adolescent Brain Cognitive Development Study

General psychopathology and cognition are likely to have a bidirectional influence on each other. Yet, the relationship between brain structure, psychopathology, and cognition remains unclear. This brief report investigates the association between structural properties of the cerebral cortex [surface area, cortical thickness, intracortical myelination indexed by the T1w/T2w ratio, and neurite density assessed by restriction spectrum imaging (RSI)] with general psychopathology and cognition in a sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Higher levels of psychopathology and lower levels of cognitive ability were associated with a smaller cortical surface area. Inter-regionally—across the cerebral cortex—the strength of association between an area and psychopathology is strongly correlated with the strength of association between an area and cognition. Taken together, structural deviations particularly observed in the cortical surface area influence both psychopathology and cognition.

Mid-level feature differences underlie early animacy and object size distinctions: Evidence from EEG decoding

Responses to visually-presented objects along the cortical surface of the human brain have a large-scale organization reflecting the broad categorical divisions of animacy and object size. Mounting evidence indicates that this topographical organization is driven by differences between objects in mid-level perceptual features. With regard to the timing of neural responses, images of objects quickly evoke neural responses with decodable information about animacy and object size, but are mid-level features sufficient to evoke these rapid neural responses? Or is slower iterative neural processing required to untangle information about animacy and object size from mid-level features? To answer this question, we used electroencephalography(EEG) to measure human neural responses to images of objects and their texform counterparts - unrecognizable images which preserve some mid-level feature information about texture and coarse form. We found that texform images evoked neural responses with early decodable information about both animacy and real-world size, as early as responses evoked by original images. Further, successful cross-decoding indicates that both texform and original images evoke information about animacy and size through a common underlying neural basis. Broadly, these results indicate that the visual system contains a mid-level feature bank carrying linearly decodable information on animacy and size, which can be rapidly activated without requiring explicit recognition or protracted temporal processing.

Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network

Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.

The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain-Computer Interface Platform

Progress toward the development of brain-computer interfaces has signaled the potential to restore, replace, or augment lost or impaired neurological function in a variety of disease states. Existing brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes, which limit addressable applications of the technology and the number of eligible patients. Here we describe a novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate communication with large portions of the cortical surface in bidirectional fashion (enabling both recording and stimulation). We demonstrate the safety and feasibility of rapidly delivering reversible implants containing over 2,000 microelectrodes to multiple functional regions in both hemispheres of the Gottingen minipig brain simultaneously, without requiring a craniotomy, at an effective insertion rate faster than 40 ms per channel, without damaging the cortical surface. We further demonstrate the performance of this system for high-density neural recording, focal cortical stimulation, and accurate neural decoding. Such a system promises to accelerate efforts to better decode and encode neural signals, and to expand the patient population that could benefit from neural interface technology.

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no comprehensive comparison of these approaches to one another, nor to existing Euclidean methods, to date. This paper benchmarks a collection of geometric and traditional deep learning models on phenotype prediction and segmentation of sphericalised neonatal cortical surface data, from the publicly available Developing Human Connectome Project (dHCP). Tasks include prediction of postmenstrual age at scan, gestational age at birth and segmentation of the cortical surface into anatomical regions defined by the M-CRIB-S atlas. Performance was assessed not only in terms of model precision, but also in terms of network dependence on image registration, and model interpretation via occlusion. Networks were trained both on sphericalised and anatomical cortical meshes. Findings suggest that the utility of geometric deep learning over traditional deep learning is highly task-specific, which has implications for the design of future deep learning models on the cortical surface. The code, and instructions for data access, are available from https://github.com/Abdulah-Fawaz/Benchmarking-Surface-DL.

Surface area in the insula was associated with 28-month functional outcome in first-episode psychosis

Many studies have tested the relationship between demographic, clinical, and psychobiological measurements and clinical outcomes in ultra-high risk for psychosis (UHR) and first-episode psychosis (FEP). However, no study has investigated the relationship between multi-modal measurements and long-term outcomes for >2 years. Thirty-eight individuals with UHR and 29 patients with FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, and functional near-infrared spectroscopy). We explored the characteristics associated with 13- and 28-month clinical outcomes. In UHR, the cortical surface area in the left orbital part of the inferior frontal gyrus was negatively associated with 13-month disorganized symptoms. In FEP, the cortical surface area in the left insula was positively associated with 28-month global social function. The left inferior frontal gyrus and insula are well-known structural brain characteristics in schizophrenia, and future studies on the pathological mechanism of structural alteration would provide a clearer understanding of the disease.