Fusion analysis of gray matter and white matter in bipolar disorder by multimodal CCA-joint ICA

Structure of brain grey and white matter in infants with spastic cerebral palsy and periventricular white matter injury

AIM

To investigate the possible covariation of grey matter volume (GMV) and white matter fractional anisotropy in infants with spastic cerebral palsy (CP) and periventricular white matter injury.

METHOD

Thirty-nine infants with spastic CP and 25 typically developing controls underwent structural magnetic resonance imaging and diffusion tensor imaging. Multimodal canonical correlation analysis with joint independent component analysis were used to capture differences in GMV and fractional anisotropy between groups. Correlation analysis was performed between imaging findings and clinical features.

RESULTS

Infants with spastic CP showed one joint group-discriminating component (i.e. GMV-fractional anisotropy) associated with regions in the cortico-basal ganglia-thalamo-cortical loop and in the corpus callosum compared to typically developing controls and one modality-specific group-discriminating component (i.e. GMV). Significant negative correlations were found between loadings in certain regions and the motor function score in spastic CP.

INTERPRETATION

In infants with spastic CP, covarying GMV-fractional anisotropy and altered GMV in specific regions were implicated in motor dysfunction, which confirmed that simultaneous GMV and fractional anisotropy changes underly motor deficits, but might also extend to sensory, cognitive, or visual dysfunction. These findings also suggest that multimodal fusion analysis allows for a more comprehensive understanding of the relevance between grey and white matter structures and its crucial role in the neuropathological mechanisms of spastic CP.

Data-driven multimodal fusion: approaches and applications in psychiatric research

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

Independent component analysis for internet gaming disorder

Introduction: There is a growing interest in the study of the neurobiological correlates of internet gaming disorder (IGD), and new techniques are beginning to be implemented for this purpose, such as independent component analysis (ICA). Aims: The present narrative review aimed to explore the studies that had used ICA for the study of the different brain networks possibly associated with IGD. Methods: We specifically focussed on three of the main networks: default-mode network, executive-control and salience networks. Results: Most studies have identified alterations in these three brain networks in individuals with IGD, which may be involved in the development and maintenance of this disorder. Conclusion: More studies are needed to deepen an understanding of the specific role of each in the symptomatology and treatment of IGD.

Shared and specific characteristics of regional cerebral blood flow and functional connectivity in unmedicated bipolar and major depressive disorders

BACKGROUND

Identifying brain similarities and differences between bipolar disorder (BD) and major depressive disorder (MDD) can help us better understand their pathophysiological mechanisms and develop more effective treatments. However, the features of whole-brain regional cerebral blood flow (CBF) and intrinsic functional connectivity (FC) underlying BD and MDD have not been directly compared.

METHODS

Eighty-eight unmedicated BD II depression patients, 95 unmedicated MDD patients, and 96 healthy controls (HCs) underwent three-dimensional arterial spin labeling (3D ASL) and resting-state functional MRI (rs-fMRI). The functional properties of whole brain CBF and seed-based resting-state FC further performed based on those regions with changed CBF were analyzed between the three groups.

RESULTS

The patients with BD and MDD showed commonly increased CBF in the left posterior lobe of the cerebellum and the left middle temporal gyrus (MTG) compared with HCs. The CBF of the left MTG was positively associated with 24-items Hamilton Depression Rating Scale scores in MDD patients. Decreased FC between the left posterior lobe of the cerebellum and the left inferior frontal gyrus (IFG) was observed only in patients with BD compared with HCs.

CONCLUSION

Patients with BD and those with MDD shared common features of CBF in the posterior lobe of the cerebellum and the MTG. The altered posterior lobe of the cerebellum-IFG FC can be considered as a potential biomarker for the differentiation of patients with BD from those with MDD.

Fusion analysis of gray matter and white matter in subjective cognitive decline and mild cognitive impairment by multimodal CCA-joint ICA

BACKGROUND

Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI.

MATERIALS AND METHODS

In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups.

RESULTS

A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups.

CONCLUSIONS

The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.

Reviewing applications of structural and functional MRI for bipolar disorder

Bipolar disorders (BDs) represent one of the leading causes of disability and morbidity globally. The use of functional magnetic resonance imaging (fMRI) is being increasingly studied as a tool to improve the diagnosis and treatment of BDs. While morphological biomarkers can be identified through the use of structural magnetic resonance imaging (sMRI), recent studies have demonstrated that varying degrees of both structural and functional impairments indicate differing bipolar subtypes. Within fMRI, resting-state fMRI has specifically drawn increased interest for its capability to detect different neuronal activation patterns compared to task-based fMRI. This study aims to review recently published literature regarding the use of fMRI to investigate structural-functional relationships in BD diagnosis and specifically resting-state fMRI to provide an opinion on fMRI's modern clinical application. All sources in this literature review were collected through searches on both PubMed and Google Scholar databases for terms such as 'resting-state fMRI' and 'functional neuroimaging biomarkers of bipolar disorder'. While there are promising results supporting the use of fMRI for improving differential accuracy and establishing clinically relevant biomarkers, additional evidence will be required before fMRI is considered a dependable component of the overall BD diagnostic process.