Ajith, M. | Search bioRxiv

bioRxiv2024

Multimodal Predictive Modeling: Scalable Imaging Informed Approaches to Predict Future Brain Health

BackgroundPredicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions iden-tified through neuroimaging serve as the fundamental basis and early indica-tors that precede the manifestation of observable behaviors or psychological states. New MethodIn this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into fu-ture behavioral outcomes. We employed three methodologies for evalua-tion: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially con-ditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone. ResultsOur performance evaluation indicates that the image-assisted method ex-cels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant in-formation from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data. Comparison with Existing MethodsThe proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimag-ing data with assessment factors, ConclusionThis study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity. HighlightsO_LIMultifaceted perspective for studying longitudinal brain health changes. C_LIO_LIShowcases the versatility of methodologies through assessment-only, neuroimaging-only, and image-assisted predictive approaches. C_LIO_LIProvides predictive insights by revealing the neural patterns corresponding to alterations in behavior. C_LI Graphical Abstract

neuroscience↗

bioRxiv2023

A Deep Learning Approach for Mental Health Quality Prediction Using Functional Network Connectivity and Assessment Data

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed models performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

neuroscience↗