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Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

ABSTRACT: Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

SUBMITTER: Maglanoc LA

PROVIDER: S-EPMC7267936 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Maglanoc Luigi A LA Kaufmann Tobias T Jonassen Rune R Hilland Eva E Beck Dani D Landrø Nils Inge NI Westlye Lars T LT

Human brain mapping 20191001 1

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, ...[more]

PMID: 31571370

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Project description:Background:HIV RNA is identified in cerebrospinal fluid (CSF) within eight days of estimated viral exposure. Neurological findings and impaired neuropsychological testing performance are documented in a subset of individuals with acute HIV infection (AHI). The purpose of this study was to determine whether microstructural white matter and resting-state functional connectivity (rsFC) are disrupted in AHI. Methods:We examined 49 AHI (100% male; mean age?=?30?±?SD 9.9) and 23 HIV-uninfected Thai participants (78% male; age?=?30?±?5.5) with diffusion tensor imaging (DTI) and rsFC acquired at 3 Tesla, and four neuropsychological tests (summarized as NPZ-4). MRI for the AHI group was performed prior to combination antiretroviral treatment (ART) in 26 participants and on average two days (range:1-5) after ART in 23 participants. Fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) were quantified for DTI. Seed-based voxelwise rsFC analyses were completed for the default mode (DMN), fronto-parietal, and salience and 6 subcortical networks. rsFC and DTI analyses were corrected for family-wise error, with voxelwise comparisons completed using t-tests. Group-specific voxelwise regressions were conducted to examine relationships between imaging indices, HIV disease variables, and treatment status. Results:The AHI group had a mean (SD) CD4 count of 421(234)?cells/mm3 plasma HIV RNA of 6.07(1.1)?log10?copies/mL and estimated duration of infection of 20(5.5) days. Differences between AHI and CO groups did not meet statistical significance for DTI metrics. Within the AHI group, voxelwise analyses revealed associations between brief exposure to ART and higher FA and lower RD and MD bilaterally in the corpus callosum, corona radiata, and superior longitudinal fasciculus (p?<?0.05). Diffusion indices were unrelated to clinical variables or NPZ-4. The AHI group had reduced rsFC between left parahippocampal cortex (PHC) of the DMN and left middle frontal gyrus compared to CO (p?<?0.002). Within AHI, ART status was unrelated to rsFC. However, higher CD4 cell count associated with increased rsFC for the right lateral parietal and PHC seeds in the DMN. Direct associations were noted between NPZ-4 correspond to higher rsFC of the bilateral caudate seed (p?<?0.002). Conclusions:Study findings reveal minimal disruption to structural and functional brain integrity in the earliest stages of HIV. Longitudinal studies are needed to determine if treatment with ART initiated in AHI is sufficient to prevent the evolution of brain dysfunction identified in chronically infected individuals.

Dataset Information

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Publications

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

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