Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium.

Objective: Obsessive-compulsive disorder (OCD) is associated with altered brain function related to processing of negative emotions. To investigate neural correlates of negative valence in OCD, we pooled fMRI data of 633 individuals with OCD and 453 healthy controls from 16 studies using different negatively-valenced tasks across the ENIGMA-OCD Working-Group.

Methods: Participant data were processed uniformly using HALFpipe, to extract voxelwise participant-level statistical images of one common first-level contrast: negative vs.

Obstructive sleep apnea subtyping based on apnea and hypopnea specific hypoxic burden is associated with brain aging and cardiometabolic syndrome.

Background: Conventional metrics such as the apnea-hypopnea index (AHI) may not fully capture the diverse clinical manifestations of obstructive sleep apnea (OSA). This study aims to establish a novel OSA subtype classification based on the patterns of apneic and hypopneic hypoxic burden (HB), a potential biomarker that more accurately reflects the severity and duration of respiratory events. We further examined the associations of these HB-based subtypes with cardiometabolic risk and brain health outcomes.

Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.

Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored. We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK BioBank and BD using genome-wide association study summary statistics.

Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation.

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques.

Alzheimer's Disease Sequencing Project Release 4 Whole Genome Sequencing Dataset.

The Alzheimer's Disease Sequencing Project (ADSP) is a national initiative to understand the genetic architecture of Alzheimer's Disease and Related Dementias (AD/ADRD) by sequencing whole genomes of affected participants and age-matched cognitive controls from diverse populations. The Genome Center for Alzheimer's Disease (GCAD) processed whole-genome sequencing data from 36,361 ADSP participants, including 35,014 genetically unique participants of which 45% are from non-European ancestry, across 17 cohorts in 14 countries in this fourth release (R4). This sequencing effort identified 387 million bi-allelic variants, 42 million short insertions/deletions, and 2.

Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes.

Aims/hypothesis: Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

Methods: We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294).

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts.

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo.

Adaptive Subtype and Stage Inference for Alzheimer's Disease.

Subtype and Stage Inference (SuStaIn) is a useful Event-based Model for capturing both the temporal and the phenotypical patterns for any progressive disorders, which is essential for understanding the heterogeneous nature of such diseases. However, this model cannot capture subtypes with different progression rates with respect to predefined biomarkers with fixed events prior to inference. Therefore, we propose an adaptive algorithm for learning subtype-specific events while making subtype and stage inference.

Locus coeruleus MRI contrast, cerebral perfusion, and plasma Alzheimer's disease biomarkers in older adults.

The locus coeruleus (LC) is among the first brain structures impacted by Alzheimer's disease (AD), and noradrenergic denervation may contribute to early neurovascular dysfunction in AD. Mechanistic links between the LC and cerebral perfusion have been demonstrated in rodents, but there have been no similar studies in aging humans. Community-dwelling older adults with no history of stroke or dementia (N=66) underwent structural (T1-MPRAGE; T1-FSE) and perfusion (resting pCASL) MRI.

An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study.

Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments.

Stress-Resilience Impacts Psychological Wellbeing: Evidence from Brain-Gut Microbiome Interactions.

The brain-gut-microbiome (BGM) system plays an influential role on mental health. We characterized BGM patterns related to resilience using fecal samples and multimodal MRI. Data integration analysis using latent components showed the high resilience phenotype was associated with lower depression and anxiety symptoms, higher frequency of bacterial transcriptomes (related to environmental adaptation, genetic propagation, energy metabolism, anti-inflammation), increased metabolites (N-acetylglutamate; dimethylglycine), and cortical signatures (increased resting state functional connectivity between reward circuits and sensorimotor networks; decreased grey matter volume and white matter tracts within the emotion regulation network).

TCMChat: A generative large language model for traditional Chinese medicine.

The utilization of ground-breaking large language models (LLMs) accompanied with dialogue system has been progressively prevalent in the medical domain. Nevertheless, the expertise of LLMs in Traditional Chinese Medicine (TCM) remains restricted despite several TCM LLMs proposed recently. Herein, we introduced TCMChat (https://xomics.

Neuroinflammation modifies the relationship between stress and perivascular spaces in an elderly population with different levels of cognitive impairment.

Background: Perivascular spaces (PVS) are fluid-filled spaces surrounding the brain parenchymal vasculature. Literature suggests that PVS may play a significant role in aging and neurological disorders, including Alzheimer's disease (AD). The aim of this study is to investigate whether the relationship between MRI-visible PVS and stress is influenced by neuroinflammation in an elderly population with different levels of cognitive impairment.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.

Chemogenomic Screening in a Patient-Derived 3D Fatty Liver Disease Model Reveals the CHRM1-TRPM8 Axis as a Novel Module for Targeted Intervention.

Metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of chronic liver disease with few therapeutic options. To narrow the translational gap in the development of pharmacological MASH treatments, a 3D liver model from primary human hepatocytes and non-parenchymal cells derived from patients with histologically confirmed MASH was established. The model closely mirrors disease-relevant endpoints, such as steatosis, inflammation and fibrosis, and multi-omics analyses show excellent alignment with biopsy data from 306 MASH patients and 77 controls.

The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities.

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on = 34 interviews with diverse stakeholders.

A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning.

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective.

The privacy-sensitive nature of medical image data is often bounded by strict data sharing regulations that necessitate the need for novel modeling and analysis techniques. Federated learning (FL) enables multiple medical institutions to collectively train a deep neural network without sharing sensitive patient information. In addition, FL uses its collaborative approach to address challenges related to the scarcity and non-uniform distribution of heterogeneous medical domain data.

Machine learning-assisted SERS sensor for fast and ultrasensitive analysis of multiplex hazardous dyes in natural products.

The adulteration of natural products with multiple azo dyes has become a serious public health concern. Thus, on-site trace additive detection is demanded. Herein, we developed a gold-nanorod-based surface-enhanced Raman scattering (SERS) sensor to detect trace amounts of azo dyes, including lemon yellow, sunset yellow, golden orange II, acid red 73, coccine, and azorubine.

P-Wave Duration Is Associated With Aging Patterns in Structural Brain Networks.

Background: Impaired cardiac function is associated with cognitive impairment and brain imaging features of aging. Cardiac arrhythmias, including atrial fibrillation, are implicated in clinical and subclinical brain injuries. Even in the absence of a clinical diagnosis, subclinical or prodromal substrates of arrhythmias, including an abnormally long or short P-wave duration (PWD), a measure associated with atrial abnormalities, have been associated with stroke and cognitive decline.

Biological validation of peak-width of skeletonized mean diffusivity as a VCID biomarker: The MarkVCID Consortium.

Background: Peak-width of skeletonized mean diffusivity (PSMD), a neuroimaging marker of cerebral small vessel disease (SVD), has shown excellent instrumental properties. Here, we extend our work to perform a biological validation of PSMD.

Methods: We included 396 participants from the Biomarkers for Vascular Contributions to Cognitive Impairment and Dementia (MarkVCID-1) Consortium and three replication samples (Cohorts for Heart and Aging Research in Genomic Epidemiology = 6172, Rush University Medical Center = 287, University of California Davis Alzheimer's Disease Research Center = 567).