Frontiers in Neuroscience | New and Recent Articles

BackgroundTimely identification of individuals at risk for Alzheimer’s disease (AD) progression remains a major clinical challenge. Traditional cognitive assessments provide limited prognostic insight, while many machine learning (ML) models rely on costly biomarkers or poorly interpretable algorithms that limit clinical scalability. This study evaluated whether widely available baseline demograp…

BackgroundMost studies on social exclusion adopt virtual paradigms focusing on unilateral responses, while neglecting real-world face-to-face interaction and its neural basis. Functional near-infrared spectroscopy (fNIRS) hyperscanning allows recording of interpersonal neural synchronization (INS) during dyadic interaction, providing a novel approach for investigating interpersonal cooperation.Me…

ObjectiveTo compare left vs. right steep turns in terms of workload-related neurophysiological signatures using electroencephalogram (EEG) and machine learning.MethodsThirty-seven flight cadets performed one left and one right steep turn in an SR20 desktop flight simulator while a 32-channel EEG (Emotiv EPOC Flex 32) was recorded. From 2-s sliding windows (50% overlap), 800 features per window we…

BackgroundWolfram syndrome is a rare autosomal recessive disorder characterized by antibody-negative early-onset diabetes mellitus, optic atrophy, sensorineural hearing loss, arginine-vasopressin deficiency, and progressive neurodegeneration of the brainstem and cerebellum. It is caused primarily by pathogenic variants in the WFS1 gene, which encodes a transmembrane endoplasmic reticulum–resident…

IntroductionSince 2001, approximately 17.3% of enlisted personnel have experienced a traumatic brain injury (TBI) according to the United States military. Visual deficits (e.g., convergence insufficiency or pursuit abnormalities) are reported as chronic, persistent symptoms of TBI, which can impact daily activities such as reading, computer work, and driving.MethodsIn the present study, diffusion…

Studies of Alzheimer’s disease (AD) have long been dominated by the amyloid cascade hypothesis, although mounting evidence suggests that amyloid-β (Aβ) deposition might be a late downstream event, rather than the initiating trigger of AD. Here, I propose a unifying Triple-Hit Hypothesis in which AD develops through a sequential interaction among three causative processes that have been individual…

This paper presents a neuromorphic processing system integrating a compressed sensing spiking neural network (CSSNN) designed for sparse signal classification. The proposed CSSNN combines data coding, data compression, and SNN classification, enabling end-to-end optimization of network performance and model compression. Evaluated on the MNIST, N-MNIST, and DVS Gesture datasets, under uniform comp…

BackgroundChronic pain (CP) is a public health challenge recognized as involving large-scale functional brain dysregulation. Acupuncture is widely used as a non-pharmacological intervention for CP, yet its central mechanisms remain incompletely understood. fMRI provides an approach for investigating acupuncture-related brain alterations in CP.MethodsEight databases were searched from inception to…

The caudate nucleus, a key component of the dorsal striatum, has traditionally been recognized for its roles in motor control and cognitive functions. However, emerging neuroimaging and neurophysiological findings show its crucial involvement in gustatory function as well. The papers analyzed in this comprehensive overview indicate that the caudate nucleus and dorsal striatum exhibit consistent a…

IntroductionThe quality of dyadic cooperation or conversation can be predicted from the interacting individuals’ physiological responses, and several studies have combined physiological responses with machine learning algorithms to classify conversation states. However, most of these studies have focused on either a single physiological response or two-class classification. In this study, we used…

Spiking neural networks (SNNs) are hybrid dynamical systems that operate on spatiotemporal data, yet their learnable parameters are often limited to synaptic weights, contributing little to temporal pattern recognition. Learnable parameters that delay spike times can improve classification performance in temporal tasks, but existing methods rely on large networks and offline learning, making them…

IntroductionWe present novel evidence that humans are capable of producing, perceiving, and cortically processing ultrasound (US), extending the recognized limits of human auditory function. This previously unacknowledged sensory ability was identified in an expert practitioner of the traditional Chinese health exercise “The Six Healing Sounds.Methods and resultsHigh-resolution recordings demonst…

Perineuronal nets (PNNs) in the primary visual cortex (V1) are specialized extracellular matrix structures that form predominantly on parvalbumin+ GABAergic neurons, marking the closure of visual critical period plasticity. More recently, PNNs are used to characterize deficits in critical period plasticity in mouse models for neurodevelopmental disorders such as Rett syndrome, Fragile X syndrome,…

Traumatic brain injury (TBI) in young children can rarely exhibit a biphasic clinical course with delayed neurological deterioration. We report a 2-year-old boy who fell from 50 cm and briefly lost consciousness with vomiting, initially found to have a right frontotemporoparietal acute subdural hematoma (SDH) with midline shift but no brain contusions. After transient stabilization, he developed …

Background and hypothesisAssessing schizophrenia risk factors is crucial for developing early preventive interventions. We hypothesized that unaffected siblings, who share high genetic risk, exhibit neuroanatomical signatures similar to affected patients, potentially reflecting early pathogenic processes.Study designTo overcome single-center limitations, we analyzed 1,018 participants from five i…

We address the prediction of non-imaging variables based on structural brain connectivity derived from diffusion magnetic resonance images, using graph-based machine learning. We predict age and the mini-mental state examination (MMSE) score as examples of a demographic and a clinical variable. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brai…

BackgroundAneurysmal subarachnoid hemorrhage (aSAH) is a devastating cerebrovascular disease associated with high rates of mortality and long-term disability. Early risk stratification is essential to guide personalized management. Systemic inflammation plays a key role in secondary brain injury after aSAH. The systemic inflammation response index (SIRI), a novel inflammatory marker combining neu…

Event Vision Sensors, or neuromorphic cameras, report sparse, and asynchronous image change-related data and enable microsecond-scale sensing and high dynamic range, but challenge physics-based sensor design approaches. In response to log-intensity threshold-crossing instances, this event representation does not readily integrate with forward operators used to describe most computational imaging …

BackgroundThe glymphatic system has been proposed as a key pathway for interstitial fluid clearance in the brain, but its role in chronic neuropathic pain conditions such as postherpetic neuralgia (PHN) remains unclear. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index has been suggested as a noninvasive proxy of perivascular-aligned diffusivity related to glymphat…