Butyrate attenuates high-fat diet-induced glomerulopathy through GPR43-Sirt3 pathway.

The incidence of obesity-related glomerulopathy (ORG) is rising worldwide with very limited treatment methods. Paralleled with the gut–kidney axis theory, the beneficial effects of butyrate, one of the short-chain fatty acids (SCFA) produced by gut microbiota, on metabolism and certain kidney diseases have gained growing attention. However, the effects of butyrate on ORG and its underlying mechanism are largely unexplored.

Toddler disorganized attachment in relation to cortical thickness and socioemotional problems in late childhood.

Disorganized attachment is a risk for mental health problems, with increasing work focused on understanding biological mechanisms. Examining late childhood brain morphology may be informative - this stage coincides with the onset of many mental health problems. Past late childhood research reveals promising candidates, including frontal lobe cortical thickness and hippocampal volume.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories.

Ensemble Vision Transformer for Dementia Diagnosis.

In recent years, deep learning has gained momentum in computer-aided Alzheimer's Disease (AD) diagnosis. This study introduces a novel approach, Monte Carlo Ensemble Vision Transformer (MC-ViT), which develops an ensemble approach with Vision transformer (ViT). Instead of using traditional ensemble methods that deploy multiple learners, our approach employs a single vision transformer learner.

Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data.

This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the - Hodge-Laplacian (HL) operator. In particular, we propose Laguerre polynomial approximations of HL spectral filters and prove that their spatial localization on graphs is related to the polynomial order.

Examining the interaction between prenatal stress and polygenic risk for attention-deficit/hyperactivity disorder on brain growth in childhood: Findings from the DREAM BIG consortium.

This study explored the interactions among prenatal stress, child sex, and polygenic risk scores (PGS) for attention-deficit/hyperactivity disorder (ADHD) on structural developmental changes of brain regions implicated in ADHD. We used data from two population-based birth cohorts: Growing Up in Singapore Towards healthy Outcomes (GUSTO) from Singapore (n = 113) and Generation R from Rotterdam, the Netherlands (n = 433). Prenatal stress was assessed using questionnaires.

Chronic jet lag alters gut microbiome and mycobiome and promotes the progression of MAFLD in HFHFD-fed mice.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide. Circadian disruptors, such as chronic jet lag (CJ), may be new risk factors for MAFLD development. However, the roles of CJ on MAFLD are insufficiently understood, with mechanisms remaining elusive.

Metformin alleviates hepatic iron overload and ferroptosis through AMPK-ferroportin pathway in HFD-induced NAFLD.

Metformin prevents progression of non-alcoholic fatty liver disease (NAFLD). However, the potential mechanism is not entirely understood. Ferroptosis, a recently recognized nonapoptotic form of regulated cell death, has been reported to be involved in the pathogenesis of NAFLD.

A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.

The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition.

White matter functional gradients and their formation in adolescence.

It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction.

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks.

Metabolism-related brain morphology accelerates aging and predicts neurodegenerative diseases and stroke: a UK Biobank study.

Metabolic syndrome (MetS) is characterized by a constellation of metabolic risk factors, including obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) levels, hypertension, and hyperglycemia, and is associated with stroke and neurodegenerative diseases. This study capitalized on brain structural images and clinical data from the UK Biobank and explored the associations of brain morphology with MetS and brain aging due to MetS. Cortical surface area, thickness, and subcortical volumes were assessed using FreeSurfer.

Amyloid-β accumulation in relation to functional connectivity in aging: A longitudinal study.

The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n = 300) and explored possible relationships between amyloid beta (Aβ) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aβ accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions.

Pathways link environmental and genetic factors with structural brain networks and psychopathology in youth.

Adolescence is a period of significant brain development and maturation, and it is a time when many mental health problems first emerge. This study aimed to explore a comprehensive map that describes possible pathways from genetic and environmental risks to structural brain organization and psychopathology in adolescents. We included 32 environmental items on developmental adversity, maternal substance use, parental psychopathology, socioeconomic status (SES), school and family environment; 10 child psychopathological scales; polygenic risk scores (PRS) for 10 psychiatric disorders, total problems, and cognitive ability; and structural brain networks in the Adolescent Brain Cognitive Development study (ABCD, n = 9168).

Maternal preconception circulating blood biomarker mixtures, child behavioural symptom scores and the potential mediating role of neonatal brain microstructure: the S-PRESTO cohort.

Human brain development starts in the embryonic period. Maternal preconception nutrition and nutrient availability to the embryo may influence brain development at this critical period following conception and early cellular differentiation, thereby affecting offspring neurodevelopmental and behavioural disorder risk. However, studying this is challenging due to difficulties in characterizing preconception nutritional status and few studies have objective neurodevelopmental imaging measures in children.

Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer's disease.

Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD). This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by introducing Monte Carlo sampling and an ensemble neural network in the integration with ResNet50. Our goals are to improve the 2D-slice CNN performance and to design the MCENN model insensitive to image resolution.

Interindividual variability in functional connectivity discovers differential development of cognition and transdiagnostic dimensions of psychopathology in youth.

Cognitive and psychological development during adolescence is different from one another, which is rooted in individual differences in maturational changes in the adolescent brain. This study employed multi-modal MRI data and characterized interindividual variability in functional connectivity (IVFC) and its associations with cognition and psychopathology using the Philadelphia Neurodevelopmental Cohort (PNC) of 755 youth. We employed resting state functional MRI (rs-fMRI) and diffusion weighted images (DWIs) to estimate brain structural and functional networks.

Chinese adult brain atlas with functional and white matter parcellation.

Brain atlases play important roles in studying anatomy and function of the brain. As increasing interests in multi-modal magnetic resonance imaging (MRI) approaches, such as combining structural MRI, diffusion weighted imaging (DWI), and resting-state functional MRI (rs-fMRI), there is a need to construct integrated brain atlases based on these three imaging modalities. This study constructed a multi-modal brain atlas for a Chinese aging population (n = 180, age: 22-79 years), which consists of a T1 atlas showing the brain morphology, a high angular resolution diffusion imaging (HARDI) atlas delineating the complex fiber architecture, and a rs-fMRI atlas reflecting brain intrinsic functional organization in one stereotaxic coordinate.

Predicting diagnosis 4 years prior to Alzheimer's disease incident.

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930).

Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity.

We develop a deep learning framework, spatio-temporal directed acyclic graph with attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional magnetic resonance imaging (fMRI). This ST-DAG-Att framework comprises of two neural networks, (1) spatio-temporal graph convolutional network (ST-graph-conv) to learn the spatial and temporal information of functional time series at multiple temporal and spatial graph scales, where the graph is represented by the brain functional network, the spatial convolution is over the space of this graph, and the temporal convolution is over the time dimension; (2) functional connectivity convolutional network (FC-conv) to learn functional connectivity features, where the functional connectivity is derived from embedded multi-scale fMRI time series and the convolutional operation is applied along both edge and node dimensions of the brain functional network. This framework also consists of an attention component, i.

Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering.

This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator. We define spectral filters via the LB operator on a graph and explore the feasibility of Chebyshev, Laguerre, and Hermite polynomials to approximate LB-based spectral filters. We then update the LB operator for pooling in the LB-CNN.

Common functional brain networks between attention deficit and disruptive behaviors in youth.

Attention deficits (AD) and disruptive behavior (DB) are highly comorbid youth externalizing behaviors. This study aimed to study reliable functional brain networks shared by AD and DB in youth aged from 8 to 21 years from the Philadelphia Neurodevelopmental Cohort (PNC). The PNC study assessed AD and DB behaviors via Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS).