Brain health measurement: a scoping review

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.HighlightsMultifaceted perspective for studying longitudinal brain health changes.Showcases the versatility of methodologies through assessment-only, neuroimaging-only, and image-assisted predictive approaches.Provides predictive insights by revealing the neural patterns corresponding to alterations in behavior.Graphical Abstract

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A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data

Ajith,

M. Aycock,

B. Tone

et al. 2024

Aperture Neuro

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The investigation of brain health development is paramount, as a healthy brain underpins cognitive and physical well-being, and mitigates cognitive decline, neurodegenerative diseases, and mental health disorders. This study leverages the UK Biobank dataset containing static functional network connectivity (sFNC) data derived from resting-state functional magnetic resonance imaging (rs-fMRI) and assessment data. We introduce a novel approach to forecasting a brain health index (BHI) by deploying three distinct models, each capitalizing on different modalities for training and testing. The first model exclusively employs psychological assessment measures, while the second model harnesses both neuroimaging and assessment data for training but relies solely on assessment data during testing. The third model encompasses a holistic strategy, utilizing neuroimaging and assessment data for the training and testing phases. The proposed models employ a two-step approach for calculating the BHI. In the first step, the input data is subjected to dimensionality reduction using principal component analysis (PCA) to identify critical patterns and extract relevant features. The resultant concatenated feature vector is then utilized as input to variational autoencoders (VAE). This network generates a low-dimensional representation of the input data used for calculating BHI in new subjects without requiring imaging data. The results suggest that incorporating neuroimaging data into the BHI model, even when predicting from assessments alone, enhances its ability to accurately evaluate brain health. The VAE model exemplifies this improvement by reconstructing the sFNC matrix more accurately than the assessment data. Moreover, these BHI models also enable us to identify distinct behavioral and neural patterns. Hence, this approach lays the foundation for larger-scale efforts to monitor and enhance brain health, aiming to build resilient brain systems.

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Brain health measurement: a scoping review

Cited by 3 publications

References 39 publications

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health

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

A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data

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