New Method Enhances Brain Imaging Analysis
MSIVA simplifies neuroimaging data, revealing links to behavior and health conditions.
Xinhui Li, P. Kochunov, T. Adali, R. F. Silva, V. Calhoun
― 4 min read
Table of Contents
Neuroimaging is a way to look at the brain's structure and how it works. This helps scientists see how the brain relates to behavior. Techniques like magnetic resonance imaging (MRI) are used, but handling the data can be tough. MRI data can be very complex since it includes thousands of tiny units called voxels. However, researchers have found that even though the data looks complicated, its true complexity can be much simpler.
Recent research has shown that in some cases, groups of neurons form patterns that influence how we move or think. These patterns help with actions such as reaching for something or making decisions. Therefore, it is crucial to create models that can learn the simpler patterns within this complex data. Different types of imaging techniques provide various kinds of information about the brain. For example, structural MRI gives detailed pictures of brain anatomy but doesn’t capture changes over time. On the other hand, Functional MRI tracks blood flow in the brain, showing activity but with less detail.
Combining information from structural and functional MRI provides a more complete look at the brain. As researchers have access to more data from various imaging techniques, it becomes important to develop methods that can accurately analyze this information from all sources.
Methods for Analyzing Neuroimaging Data
Scientists have come up with various methods to analyze data from multiple imaging techniques. Some popular methods include joint independent component analysis, independent vector analysis, and canonical correlation analysis. One newer method, called Multidataset Independent Subspace Analysis (MISA), combines different models to discover hidden patterns in brain data.
MISA can reveal important data from both structural and functional MRI. A recent improvement on MISA, called Multimodal Independent Vector Analysis (MMIVA), aims to find connections between different Brain Patterns across various types of data. However, MMIVA has some problems. It assumes that brain patterns are simple and independent, which may not always be the case.
To address these limitations, a new method called Multimodal Subspace Independent Vector Analysis (MSIVA) has been introduced. MSIVA builds on MMIVA by allowing for more complex relationships between brain patterns and identifying unique sources of information from different types of data. MSIVA can capture the connections between patterns across different imaging techniques and also looks for distinct patterns unique to each technique.
Evaluating MSIVA
To see how well MSIVA works, comparisons were made against other approaches using synthetic data that simulates real brain data. The goal was to check if MSIVA could accurately identify the hidden patterns in this synthetic data. Following this, MSIVA was tested on two large sets of actual brain imaging data. The first data set came from the UK Biobank, which includes health information and brain scans of many participants. The second data set included patients with schizophrenia.
Results showed that MSIVA could find hidden relationships better than the other methods tested. In both the synthetic and real data, MSIVA provided clearer insights into the brain's functions and how they relate to behavior.
Linking Brain Patterns to Behavior
After using MSIVA to identify brain patterns, the next step was to see how these patterns linked to different characteristics of individuals, such as age and diagnosis of health conditions. For the UK Biobank data, results indicated that certain brain areas were closely associated with aging, and other areas related to gender differences.
In the patient dataset, similar analyses found connections between brain patterns and the presence of schizophrenia. This demonstrates how MSIVA can uncover important links between brain function and key health-related traits.
Exploring Brain-Age Differences
One of the interesting analyses conducted involved looking at the "brain-age gap." This concept refers to how an individual's brain age, as indicated by imaging data, compares to their actual chronological age. Understanding this gap helps researchers identify factors that may influence brain health.
In the analysis, it was found that lifestyle choices like how much time one spends watching television or engaging in physical activity could affect the brain-age gap. People who exercised more tended to have a brain that appeared younger, while more time spent watching TV was associated with an older-looking brain.
Conclusion
In summary, the new method MSIVA provides a powerful tool for researchers studying the brain. By revealing complex patterns in brain imaging data, MSIVA helps link these patterns to various traits and health conditions. This knowledge can lead to better insights into brain health, aging, and psychological conditions. As more neuroimaging data becomes available, methods like MSIVA will be essential in helping researchers understand the intricate workings of the human brain.
Title: Multimodal subspace independent vector analysis effectively captures the latent relationships between brain structure and function
Abstract: A key challenge in neuroscience is to understand the structural and functional relationships of the brain from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the relationships between true latent sources are likely more complex - statistical dependence may exist within and between modalities, and span one or more dimensions. Here we present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources from multiple data modalities by defining both cross-modal and unimodal subspaces with variable dimensions. In particular, MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding sub-spaces across modalities. As we demonstrate, a main benefit of MSIVA is the ability to capture subject-level variability at the voxel level within independent subspaces, contrasting with the rigidity of traditional methods that share the same independent components across subjects. We compared MSIVA to a unimodal initialization baseline and a multimodal initialization baseline, and evaluated all three approaches with five candidate subspace structures on both synthetic and neuroimaging datasets. We show that MSIVA successfully identified the ground-truth subspace structures in multiple synthetic datasets, while the multimodal baseline failed to detect high-dimensional subspaces. We then demonstrate that MSIVA better detected the latent subspace structure in two large multimodal neuroimaging datasets including structural MRI (sMRI) and functional MRI (fMRI), compared with the unimodal baseline. From subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis, our findings suggest that the estimated sources from MSIVA with optimal subspace structure are strongly associated with various phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to multiple phenotype measures such as age (e.g., cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (e.g., cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), schizophrenia (e.g., cerebellum, temporal pole, and frontal operculum cortex in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on phenotypic and neuropsychiatric biomarkers of linked brain structure and function.
Authors: Xinhui Li, P. Kochunov, T. Adali, R. F. Silva, V. Calhoun
Last Update: 2024-10-22 00:00:00
Language: English
Source URL: https://www.biorxiv.org/content/10.1101/2023.09.17.558092
Source PDF: https://www.biorxiv.org/content/10.1101/2023.09.17.558092.full.pdf
Licence: https://creativecommons.org/licenses/by/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
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