The memory binding test in a longitudinal study of cognitive aging and preclinical disease.

OBJECTIVE: The Memory Binding Test (MBT) shows promise in detecting early cognitive changes associated with Alzheimer's disease (AD). This study assesses the psychometric properties (i.e., construct and criterion validity, test-retest reliability) of the MBT and its sensitivity to incipient disease and incident cognitive impairment. METHOD: One hundred forty-nine cognitively unimpaired adults ages 45-85 completed the MBT and neuropsychological tests at baseline; 132 returned for 2-year follow-up. Based on neuroradiological ratings of amyloid positron emission tomography and MRI markers at baseline, they were categorized as healthy (n = 94) or having preclinical disease (n = 55, either on the AD continuum or having non-AD pathologic change). Construct validity was assessed by the associations between MBT scores, demographics, and neuropsychological scores within the healthy group. Criterion validity was assessed by testing how MBT scores correlate with AD biomarkers, differ and discriminate between groups at baseline, and predict incident cognitive impairment. RESULTS: MBT scores decreased with age and were strongly associated with memory and global cognition. MBT scores were largely not associated with amyloid, hippocampal volume, or AD signature cortical volume but related to white matter lesion volume in those with preclinical disease. The preclinical groups performed worse on MBT immediate free recall at baseline than the healthy group, but no scores predicted incident cognitive impairment at follow-up. Most scores demonstrated modest test-retest reliability. CONCLUSIONS: This study demonstrates that the MBT has adequate construct validity in cognitively unimpaired adults, moderate sensitivity to preclinical disease cross-sectionally, and limited prognostic utility. Careful consideration of demographic influences on score interpretation remains necessary. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

Fiber Ball White Matter Modeling Reveals Microstructural Alterations in Healthy Brain Aging.

Age-related white matter degeneration is characterized by myelin breakdown and neuronal fiber loss that preferentially occur in regions that myelinate later in development. Conventional diffusion MRI (dMRI) has demonstrated age-related increases in diffusivity but provide limited information regarding the tissue-specific changes driving these effects. A recently developed dMRI biophysical modeling technique, Fiber Ball White Matter (FBWM) modeling, offers enhanced biological interpretability by estimating microstructural properties specific to the intra-axonal and extra-axonal spaces. We used FBWM to illustrate the biological mechanisms underlying changes throughout white matter in healthy aging using data from 63 cognitively unimpaired adults ages 45-85 with no radiological evidence of neurodegeneration or incipient Alzheimer's disease. Conventional dMRI and FBWM metrics were computed for two late-myelinating (genu of the corpus callosum and association tracts) and two early-myelinating regions (splenium of the corpus callosum and projection tracts). We examined the associations between age and these metrics in each region and tested whether age was differentially associated with these metrics in late- vs. early-myelinating regions. We found that conventional metrics replicated patterns of age-related increases in diffusivity in late-myelinating regions. FBWM additionally revealed specific intra- and extra-axonal changes suggestive of myelin breakdown and preferential loss of smaller-diameter axons, yielding in vivo corroboration of findings from histopathological studies of aged brains. These results demonstrate that advanced biophysical modeling approaches, such as FBWM, offer novel information about the microstructure-specific alterations contributing to white matter changes in healthy aging. These tools hold promise as sensitive indicators of early pathological changes related to neurodegenerative disease.