Multimodal Fusion of Brain Imaging Data: Methods and Applications

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

Single-cell RNA Sequencing Reveals Sexually Dimorphic Transcriptome and Type 2 Diabetes Genes in Mouse Islet β Cells

Type 2 diabetes(T2D)is characterized by the malfunction of pancreaticβcells.Susceptibility and pathogenesis of T2D can be affected by multiple factors,including sex differences.However,the mechanisms underlying sex differences in T2D susceptibility and pathogenesis remain unclear.Using single-cell RNA sequencing(scRNA-seq),we demonstrate the presence of sexually dimorphic transcriptomes in mouseβcells.Using a high-fat diet-induced T2D mouse model,we identified sex-dependent T2D altered genes,suggesting sex-based differences in the pathological mechanisms of T2D.Furthermore,based on islet transplantation experiments,we found that compared to mice with sexmatched islet transplants,sex-mismatched islet transplants in healthy mice showed down-regulation of genes involved in the longevity regulating pathway ofβcells.Moreover,the diabetic mice with sex-mismatched islet transplants showed impaired glucose tolerance.These data suggest sexual dimorphism in T2D pathogenicity,indicating that sex should be considered when treating T2D.We hope that our findings could provide new insights for the development of precision medicine in T2D.

Developing Neuroimaging Biomarker for Brain Diseases with a Machine Learning Framework and the Brainnetome Atlas

Neuroimaging made it possible to quantify brain structure and function.However,there are few neuroimaging biomarkers for the early diagnosis,prognosis,and evaluation of therapy for brain diseases.The development of neuroimaging biomarkers for brain diseases faces two major bottleneck problems.First,the neuroimaging datasets of brain diseases are always characterized by small sample size,high dimension,and large heterogeneity.Second,a fine-grained individualized human brain atlas for effective dimensionality reduction has always been lacking.