Commonality of neuronal coherence for motor skill acquisition and interlimb transfer.

This study explores the neural underpinnings of motor skill learning and its transfer across limbs. In a randomized controlled trial, 48 right-handed individuals underwent training on either simple or complex motor tasks using their left or right hand. After completing 10 blocks of training, we assessed skill acquisition and transfer, with a 64-channel EEG capturing brain activity.

Photoinitiated CVD antifouling coatings enable long-term stability of flexible multifunctional neural probes for chronic neural recording.

Flexible neural probes with integrated recording, optical stimulation, and drug delivery capabilities offer unprecedented access to neural circuit dynamics. However, their long-term utility is compromised by foreign body responses that isolate recording sites from target neurons. This study introduces photoinitiated chemical vapor deposition (piCVD) as a transformative approach to neural interface stability through ultrathin (<100 nm) anti-fouling coatings.

Silicon nanocolumn-based disposable and flexible ultrasound patches.

Traditional wearable ultrasound devices pose challenges concerning the rigidity and environmental impact of lead-based piezoelectric materials. This study proposes a silicon nanocolumn capacitive micromachined ultrasonic transducer (snCMUT) array for real-time wearable ultrasound imaging in disposable patches. Using a lead-free design, snCMUT incorporates silicon nanocolumns to address existing issues and achieves high transmission efficiency (220 kPa/V), flexibility, and low power consumption.

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine . However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide.

Inhibition of nonsense-mediated decay in TDP-43 deficient neurons reveals novel cryptic exons.

TAR DNA-binding protein 43 kDa (TDP-43) is an essential splicing repressor whose loss of function underlies the pathophysiology of amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD). Nuclear clearance of TDP-43 disrupts its function and leads to the inclusion of aberrant cryptic exons. These cryptic exons frequently introduce premature termination codons resulting in the degradation of affected transcripts through nonsense-mediated mRNA decay (NMD).

Human development, inequality, and their associations with brain structure across 29 countries.

Background: The macro-social and environmental conditions in which people live, such as the level of a country's development or inequality, are associated with brain-related disorders. However, the relationship between these systemic environmental factors and the brain remains unclear. We aimed to determine the association between the level of development and inequality of a country and the brain structure of healthy adults.

APOE ε4 carriers share immune-related proteomic changes across neurodegenerative diseases.

The APOE ε4 genetic variant is the strongest genetic risk factor for late-onset Alzheimer's disease (AD) and is increasingly being implicated in other neurodegenerative diseases. Using the Global Neurodegeneration Proteomics Consortium SomaScan dataset covering 1,346 cerebrospinal fluid (CSF) and 9,924 plasma samples, we used machine learning-based proteome profiling to identify an APOE ε4 proteomic signature shared across individuals with AD, frontotemporal dementia (FTD), Parkinson's disease dementia (PDD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and nonimpaired controls. This signature was enriched in pro-inflammatory immune and infection pathways as well as immune cells, including monocytes, T cells and natural killer cells.

Microencapsulation system for scalable differentiation of peripheral motor neurons from human induced pluripotent stem cells.

Stem cell-derived neural cells hold great potential for treating neurological disorders, but clinical translation is limited by the need for scalable, consistent, and functionally robust production systems. To address these challenges, we developed a microfluidic alginate encapsulation chip (MAEC) system for the high-throughput production of mature peripheral motor neurons from human induced pluripotent stem cells. Alginate was selected for its biocompatibility, low immunogenicity, and calcium-triggered gelation, enabling precise size control.

BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.

Background: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety, representing a promising class of functional peptides for diabetic management. However, conventional computational approaches for ADPs prediction mainly rely on manually extracted sequence features.

NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.

Background: Accurate identification of anti-inflammatory peptides (AIPs) is crucial for drug development and inflammatory disease treatment. However, the short length and limited informational content of peptide sequences make precise computational recognition particularly challenging. While various machine learning and deep learning approaches have been explored, their limitations in feature representation and model integration hinder the effective discovery of novel AIPs.

Deficiency of calretinin in prefrontal cortex causes behavioral deficits relevant to autism spectrum disorder in mice.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms including deficits in social interaction, repetitive and stereotyped behaviors, along with higher levels of anxiety and cognitive impairments. Previous studies demonstrate pronounced reduced density of calretinin (CR)-expressing GABAergic interneurons in both ASD patients and animal models. The object of the current study was to determine the role of CR in ASD-relevant behavioral aberrations.

Oxytocin Intervention Mitigates Pathological and Behavioral Impairments in APP/PS1 Mice Subjected to Early Social Isolation.

Background: Neuropsychiatric symptoms, such as anxiety and depression, are prevalent during the prodromal phase of Alzheimer's disease (AD). Social isolation (SI) has been implicated as a potential exacerbating factor for emotional disturbances in AD pathogenesis. Despite the well-established role of oxytocin (OXT) in regulating social behavior and mental health, its function and mechanisms in alleviating AD-related psychiatric symptoms remain poorly understood.

Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling.

Depression is not a unitary disorder; it is heterogeneous in nature. Likewise, no 2 individuals with depression are entirely alike, and therefore their associated symptoms are highly personalized. Over the past decade, numerous approaches have been developed to identify neuroimaging-derived biomarkers to advance our understanding of the neurobiology of patients with depression at the group level.

An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is a biomolecular process that underpins the formation of membrane-less organelles within living cells. This phenomenon, along with the resulting condensate bodies, is increasingly recognized for its critical roles in various biological processes, such as ribonucleic acid (RNA) metabolism, chromatin rearrangement, and signal transduction. Notably, regulator proteins play a central role in the process of LLPS.

The effects of sex and age on movie-watching functional connectivity and movie clip classification.

Functional connectivity (FC) is a key tool for understanding the complex interactions within the human brain, highlighting connections between various regions. This study delves into the multifaceted influences shaping functional magnetic resonance imaging FC patterns during movie watching, focusing on the effects of sex, age, and movie clip. Leveraging the Human Connectome Project dataset, we systematically examine FC patterns elicited during movie watching.

Augmented 3D Printing for Multiscale Microphysiological Systems.

Microphysiological systems (MPS) can recapitulate physiological features of human organs; however, existing engineering techniques are limited to fabricating sophisticated and functional MPS. Although 3D printing offers the potential to enhance the complexity of MPS and simplify the fabrication process, existing 3D-printed MPS suffer from challenges in fabricating multiscale structures spanning nanometers to centimeters. Recent studies suggested that these issues can be addressed by integrating multiscale ancillary techniques into 3D printing.

Multi-task machine learning reveals the functional neuroanatomy fingerprint of mental processing.

Mental processing delineates the functions of human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of functional neuroanatomy substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a 'functional neuroanatomy fingerprint', i.

Investigating the neural correlates of the left thalamus in women with fibromyalgia: A Granger causality and voxel-based morphometry approach.

Background: Fibromyalgia is a chronic pain disorder characterized by widespread musculoskeletal pain, fatigue, and psychological distress. Despite its high prevalence, the neurobiological mechanisms underlying fibromyalgia remain poorly understood. This study aims to investigate the structural and functional correlates of the left thalamus in fibromyalgia patients using a combined Granger causality and voxel-based morphometry approach.

Advances in Miniaturized Bioreactors: Bridging Biotechnology and Tissue Engineering for Enhanced Drug Development.

We review the recent advances in micro-scale bioreactors, analyzing their structural designs, limitations, and benefits for drug development, advanced biotechnology, and tissue engineering.

Nucleocytoplasmic HDAC Inhibition Drives Acetylation-dependent TDP-43 Mislocalization and Disulfide-linked Oligomerization.

TDP-43 proteinopathies, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), are characterized by aberrant cytoplasmic mislocalization and aggregation of TDP-43. Here, we established a live-cell TDP43-BiFC model to visualize TDP-43 oligomerization in real time and screened diverse cellular stressors. Histone deacetylase (HDAC) inhibition emerged as the most potent trigger of TDP-43 oligomerization.

Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicentre validation study.

Background: Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third of patients fail to achieve seizure freedom after surgery. This study aimed to evaluate preoperative individual morphometric network characteristics and develop a machine learning model to predict surgical outcome in mTLE.

Methods: This multicentre, retrospective study included 189 mTLE patients who underwent unilateral temporal lobectomy and 78 normal controls between February 2018 and June 2023.

The power spectrum map of gyro-sulcal functional activity dissociation in macaque brains.

Nonhuman primates, particularly rhesus macaques, have served as crucial animal models for investigating complex brain functions. While previous studies have explored neural activity features in macaques, the gyro-sulcal functional dissociation characteristics are largely unknown. In this study, we employ a deep learning model named one-dimensional convolutional neural network to differentiate resting state functional magnetic resonance imaging signals between gyri and sulci in macaque brains, and further investigate the frequency-specific dissociations between gyri and sulci inferred from the power spectral density of resting state functional magnetic resonance imaging.

A Cerebellar Partitioning Method Using Spectral Clustering With Optimized Nonlinear Functional Connectivity.

Cerebellum has a stronger individual specificity of functional signals than the brain and is associated with a variety of neuropsychiatric disorders, and increasing attention is being paid to neuropsychiatric symptoms caused by cerebellar dysfunction. However, there is a lack of a suitable cerebellar partition utilizing researchers to fully understand the functional and structural organization of the cerebellum, reduce data dimensionality, and improve the applicability of various types of models to cerebellar functional imaging data, impeding progress in cerebellum-related research. In this study, we use order-preserving variations with spatial constraints to optimize functional connectivity matrices and employ a spectral clustering algorithm combined with a clustering ensemble technique to construct a cerebellar partitioning algorithm with a variable number of partitions.

Brain structural features with functional priori to classify Parkinson's disease and multiple system atrophy using diagnostic MRI.

Clinical two-dimensional (2D) MRI data has seen limited application in the early diagnosis of Parkinson's disease (PD) and multiple system atrophy (MSA) due to quality limitations, yet its diagnostic and therapeutic potential remains underexplored. This study presents a novel machine learning framework using reconstructed clinical images to accurately distinguish PD from MSA and identify disease-specific neuroimaging biomarkers. The structure constrained super-resolution network (SCSRN) algorithm was employed to reconstruct clinical 2D MRI data for 56 PD and 58 MSA patients.