Hu, X. P. | Search bioRxiv

bioRxiv2024

Locus Coeruleus Contrast and Diffusivity: Effects of Age and Relations to Memory

Neurocognitive aging researchers are increasingly focused on the locus coeruleus, a neuromodulatory brainstem structure that degrades with age. With this rapid growth, the field will benefit from consensus regarding which magnetic resonance imaging (MRI) metrics of locus coeruleus structure are most sensitive to age and cognition. To address this need, the current study acquired magnetization transfer- and diffusion-weighted MRI images in younger and older adults who also completed a free recall memory task. Results revealed significantly larger differences between younger and older adults for maximum than average magnetization transfer-weighted contrast (MTC), axial than mean or radial single-tensor diffusivity (DTI), and free than restricted multi-compartment diffusion (NODDI) metrics in the locus coeruleus; with maximum MTC being the best predictor of age group. Age effects for the MTC and NODDI metrics interacted with sex such that larger age group differences were seen in males than females. Age group differences were also larger for DTI metrics in the rostral, and NODDI metrics in the caudal, locus coeruleus subdivision. Within older adults, however, there were no significant effects of age on any measure of locus coeruleus structure. Finally, independent of age and sex, higher restricted diffusion in the locus coeruleus was significantly related to better (lower) recall variability, but not mean recall. Whereas MTC has been widely used in the literature, our comparison between the average and maximum MTC metrics, and inclusion of DTI and NODDI metrics, make important and novel contributions to our understanding of the aging of locus coeruleus structure.

neuroscience↗

bioRxiv2022

Modeling Transient Brain Coactivity Patterns in Latent Space with FMRI Data

The brain is a complex dynamic system that constantly evolves. Characterization of the spatiotemporal dynamics of brain activity is fundamental to understanding how brain works. Current studies with functional connectivity and linear models are limited by low temporal resolution and insufficient model capacity. With a generative variational auto encoder (VAE), the present study mapped the high-dimensional transient co-activity patterns (CAPs) of functional magnetic resonance imaging data to a low-dimensional latent representation that followed a multivariate gaussian distribution. We demonstrated with multiple datasets that the VAE model could effectively represent the transient CAPs in the latent space. Transient CAPs from high-intensity and low-intensity values reflected the same functional structure of brain and could be reconstructed from the same distribution in the latent space. With the reconstructed latent time courses, preceding CAPs successful predicted the following transient CAP with a long short-term memory recurrent neural network. Our methods provide a new avenue to characterize the brains transient co-activity maps and model the complex dynamics between them in a framewise manner.

bioengineering↗