Piiwaa, K. | Search bioRxiv

bioRxiv2021

Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity

Mapping individual differences in behavior is fundamental to personalized neuroscience. Here, we establish that statistical patterns of smartphone-based mobility features represent unique "footprints" that allow individual identification. Critically, mobility footprints exhibit varying levels of person-specific distinctiveness and are associated with individual differences in affective instability, circadian irregularity, and brain functional connectivity. Together, this work suggests that real-world mobility patterns may provide an individual-specific signature linking brain, behavior, and mood.

neuroscience↗

bioRxiv2019

Advantages of Multi-shell Diffusion Models for Studies of Brain Development in Youth

Diffusion tensor imaging (DTI) has advanced our understanding of how brain microstructure evolves over development. However, the proliferation of multi-shell diffusion imaging sequences has coincided with notable advances in the modeling of neuronal diffusion patterns, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). The relative utility of these newer diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the relative vulnerability of these models to in-scanner motion has not been described. Accordingly, in a sample of 123 youth (ages 12-30) we evaluated DTI, NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales, including mean white matter values, voxelwise analyses, and tractography-based structural brain networks. Our results reveal that multi-shell diffusion imaging sequences can be leveraged to robustly characterize neurodevelopment, even within the framework of DTI. However, these metrics of diffusion are variably impacted by motion, highlighting the importance of modeling choices for studies of movement-prone populations. Our findings suggest that while traditional DTI is sensitive to neurodevelopmental trends, contemporary modeling techniques confer key advantages for neurodevelopmental inquiries.

neuroscience↗