Biology subjects

Pechman, K.

Publications and source records attributed to Pechman, K..

3 recordsLinked to original sources

bioRxiv2023

Learning site-invariant features of connectomes to harmonize complex network measures

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

neuroscience↗

bioRxiv2023

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging

AbstractO_ST_ABSINTRODUCTIONC_ST_ABSIt is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODSDiffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimers Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8{+/-}8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97{+/-}2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42{+/-}1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTSWhile we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONSThere is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTSO_LILongitudinal data was free-water corrected and harmonized C_LIO_LIGlobal effects of white matter decline were seen in normal and abnormal aging C_LIO_LIThe free-water metric was most vulnerable to abnormal aging C_LIO_LICingulum free-water was the most vulnerable to abnormal aging C_LI

neuroscience↗

bioRxiv2020

Free-water metrics in medial temporal lobe white matter tract projections relate to longitudinal cognitive decline

ObjectiveHippocampal volume is a sensitive marker of neurodegeneration and a well-established predictor of age-related cognitive impairment. Recently, free-water (FW) magnetic resonance imaging (MRI) has shown associations with pathology in Alzheimers disease (AD), but it is still unclear whether these metrics are associated with measures of cognitive impairment. Here, we investigate whether FW and FW-corrected fractional anisotropy (FAT) within medial temporal lobe white matter tracts (cingulum, fornix, uncinate fasciculus, inferior longitudinal fasciculus, and tapetum) provides meaningful contribution to cognition and cognitive decline beyond hippocampal volume. Participants and MethodsVanderbilt Memory & Aging Project participants (n=319, 73{+/-}7 years, 59% male) with normal cognition and mild cognitive impairment (40% of cohort) underwent baseline brain MRI, including structural MRI to quantify hippocampal volume, diffusion MRI to quantify medial temporal lobe white matter tract FW and FAT, and longitudinal neuropsychological assessment with a mean follow-up of 3.5 years. Linear regressions were conducted to determine how hippocampal volume and white matter tract FW and FAT interact with baseline memory and executive function performances. Competitive model analyses determined the unique variance provided by white matter tract FW and FAT beyond that of hippocampal volume and other comorbidities. Linear mixed-effects models were conducted to determine how baseline hippocampal volume and white matter tract FW and FAT interact to explain longitudinal change in memory and executive function performances. ResultsFW in the inferior longitudinal fasciculus, tapetum, uncinate fasciculus, and cingulum were robustly associated with baseline memory and executive function. Further, competitive model analysis showed that tract FW contributed unique variance beyond other comorbidities and hippocampal volume for memory ({Delta}Radj2 range: 0.82-2.00%) and executive function ({Delta}Radj2 range: 0.88-1.87%). Longitudinal analyses demonstrated significant interactions of hippocampal volume and FAT in the inferior longitudinal fasciculus (p=0.02), tapetum (p=0.02), uncinate fasciculus (p=0.02), and cingulum (p=0.002) with decline in memory. For decline in executive function, we found significant interactions of hippocampal volume and FAT in inferior longitudinal fasciculus (p=0.03), tapetum (p=0.02), uncinate fasciculus (p=0.02), and fornix (p=0.02), as well as cingulum (p=0.02) and fornix (p=0.02) FW. ConclusionsOur results highlight novel associations between FW and FAT measures of medial temporal lobe tract microstructure and cognitive performance such that individuals with smaller hippocampal volumes and lower tract microstructure experience greater cognitive decline. These results suggest that white matter has a unique role in cognitive decline and, therefore, could be used to provide better disease staging, allowing for more precise disease monitoring in AD.

neuroscience↗