Biology subjects

Stark, C. E. L.

Publications and source records attributed to Stark, C. E. L..

5 recordsLinked to original sources

bioRxiv2025

Advanced diffusion imaging in grey matter reflects individual differences in cognitive ability in older adults.

Diffusion Weighted Imaging is a tool that can non-invasively provide insights into the microstructure of a given brain region. Various advanced techniques exist within the diffusion weighted imaging space that each provide valuable insights into different aspects of microstructure. In the following study, we sought to examine whether the combination of derived diffusion metrics (tensors, neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) MRI) in grey-matter regions could reliably predict cognitive performance in older adults, and whether these findings were replicable across datasets. First, we demonstrated that all combinations of diffusion metrics could reliably determine participant characteristics and were significant predictors of age. Second, we found that a combination of Tensor, NODDI, and MAP-MRI metrics within the hippocampus could predict RAVLT performance in older adults above and beyond any combination of two metrics alone. We also found these diffusion metrics were able to reliably predict RAVLT performance, but not Trails B or Digit Symbol Substitution Task performance. We also found that these same combinations of metrics could predict working memory performance, but not memory performance within a region associated with working memory (Brodmann Areas 9 and 46). Taken together, these findings indicate that these diffusion metrics provide valuable information on grey-matter microstructure independent of one another, and that the ability to obtain both NODDI and MAP-MRI based information from multi-shell diffusion scans more than justifies the added length.

neuroscience↗

bioRxiv2024

APOE4 Increases Susceptibility to Amyloid, Accelerating Episodic Memory Decline

Apolipoprotein E4 (APOE4) is the strongest genetic risk factor for sporadic Alzheimers disease (AD). Individuals with one copy of APOE4 exhibit greater amyloid-beta (A{beta}) deposition compared to noncarriers, an effect that is even more pronounced in APOE4 homozygotes. Interestingly, APOE4 carriers not only show more AD pathology but also experience more rapid cognitive decline, particularly in episodic memory. The underlying mechanisms driving this domain-specific vulnerability, however, remain unclear. In this study, we examined whether the accelerated decline in episodic memory among APOE4 carriers is due to increased A{beta} deposition or heightened susceptibility to A{beta}-related effects. Using data from the Alzheimers Disease Research Initiative, we modeled amyloid duration, the estimated number of years an individual has been amyloid-positive, and its impact on cognitive trajectories. Our findings reveal that APOE4 is associated with more rapid episodic memory decline as a function of amyloid duration. This decline was dose-dependent, with APOE4 homozygotes declining more rapidly than heterozygotes, and it was consistently observed across multiple episodic memory tasks and measures. Importantly, this pattern was not observed in other cognitive domains, such as processing speed, executive function, visuospatial skills, language, or crystallized intelligence. These results suggest that cognitive trajectories in AD differ by APOE genotype, with APOE4 conferring increased vulnerability to hippocampal dysfunction early in the disease course. Future research should investigate whether these cognitive differences stem from distinct pathological cascades in APOE4 carriers.

neuroscience↗

bioRxiv2024

Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum

Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimers or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and A{beta}/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical A{beta} and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimers detection and monitoring.

neuroscience↗

bioRxiv2023

A (Sub)field Guide to Quality Control in Hippocampal Subfield Segmentation on High-resolution T2-weighted MRI

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.

neuroscience↗

bioRxiv2023

The AGNOSTIC MRS Benchmark Dataset: Deep Learning for Out-of-voxel Artifacts

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

neuroscience↗