Machine Learning for Brain Imaging Genomics Methods:A Review

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly developed.As an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain imaging.This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans.It is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association modelling.In addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is ex-plored.In this paper,the related background and fundamental work in imaging genomics are first reviewed.Then,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome prediction.Finally,this paper discusses some prospects for future work.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiatric disorders.IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities.In this review,we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics.We first described IDPs through their phenotypic classification and neuroimaging genomics.Secondly,we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials.Thirdly,considering the genetic evidence of IDPs,we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization.Finally,we discussed machine learning as an optimum approach for validating biomarkers.Together,future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

A review of brain imaging biomarker genomics in Alzheimer’s disease:implementation and perspectives

Alzheimer’s disease(AD)is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology.Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses.This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner.In this review,we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies:(1)exploring novel biomarkers and seeking mutual interpretability and(2)providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics.Importantly,we highlight the necessity of brain imaging biomarker genomics,discuss the strengths and limitations of current methods,and propose directions for development of this research field.

Multiple functional linear model for association analysis of RNA-seq with imaging

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions （2DFPCA） for representing imaging data and develop a multiple functional linear model （MFLM） in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA- seq data of ovarian cancer and kidney renal clear cell carcinoma （KIRC） studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.

＂RADIOTRANSCRIPTOMICS＂： A synergy of imaging and transcriptomics in clinical assess- ment

Recent advances in quantitative imaging and ＂omics＂ technology have generated a wealth of mineable biological ＂big data＂. With the push towards a P4 ＂predictive, preventive, personalized, and participatory＂ approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose ＂radiotranscriptomics＂ as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.