Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression

Diagnostic value of structural, functional and effective connectivity in bipolar disorder

INTRODUCTION

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

RESULTS

A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

DISCUSSION

Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

The causal relationship between human brain morphometry and knee osteoarthritis: a two-sample Mendelian randomization study

BACKGROUND

Knee Osteoarthritis (KOA) is a prevalent and debilitating condition affecting millions worldwide, yet its underlying etiology remains poorly understood. Recent advances in neuroimaging and genetic methodologies offer new avenues to explore the potential neuropsychological contributions to KOA. This study aims to investigate the causal relationships between brain-wide morphometric variations and KOA using a genetic epidemiology approach.

METHOD

Leveraging data from 36,778 UK Biobank participants for human brain morphometry and 487,411 UK Biobank participants for KOA, this research employed a two-sample Mendelian Randomization (TSMR) approach to explore the causal effects of 83 brain-wide volumes on KOA. The primary method of analysis was the Inverse Variance Weighted (IVW) and Wald Ratio (WR) method, complemented by MR Egger and IVW methods for heterogeneity and pleiotropy assessments. A significance threshold of p < 0.05 was set to determine causality. The analysis results were assessed for heterogeneity using the MR Egger and IVW methods. Brain-wide volumes with Q_pval < 0.05 were considered indicative of heterogeneity. The MR Egger method was employed to evaluate the pleiotropy of the analysis results, with brain-wide volumes having a p-value < 0.05 considered suggestive of pleiotropy.

RESULTS

Our findings revealed significant causal associations between KOA and eight brain-wide volumes: Left parahippocampal volume, Right posterior cingulate volume, Left transverse temporal volume, Left caudal anterior cingulate volume, Right paracentral volume, Left paracentral volume, Right lateral orbitofrontal volume, and Left superior temporal volume. These associations remained robust after tests for heterogeneity and pleiotropy, underscoring their potential role in the pathogenesis of KOA.

CONCLUSION

This study provides novel evidence of the causal relationships between specific brain morphometries and KOA, suggesting that neuroanatomical variations might contribute to the risk and development of KOA. These findings pave the way for further research into the neurobiological mechanisms underlying KOA and may eventually lead to the development of new intervention strategies targeting these neuropsychological pathways.

Advancing translational research in neuroscience through multi-task learning

Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.

Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

AIM

This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression.

METHOD AND SUBJECTS

We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups.

RESULTS AND DISCUSSION

Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects.

CONCLUSION

The study provides supportive evidence for impaired functional connectivity networks in MDE patients.