Biology subjects

Pitcher, T.

Publications and source records attributed to Pitcher, T..

3 recordsLinked to original sources

bioRxiv2026

Thalamic nuclei insights into Alzheimer's disease

INTRODUCTIONThalamic nuclei support multiple cognitive processes, yet their integrity in biologically-defined Alzheimers disease (AD) remains unknown. METHODAmyloid status was determined using PET Centiloids >24 in 1,327 participants from ADNI. Combined with clinical diagnosis, this yielded six groups: amyloid-negative or positive CN-MCI-dementia/AD. Thalamic nuclei volumes were extracted from T1-weighted MRI using the HIPS-THOMAS algorithm. RESULTSLarge volume reductions in the anteroventral, mediodorsal, and pulvinar nuclei were observed in amyloid-positive MCI and AD. Reduced volumes were also evident in amyloid-positive CN, supporting preclinical AD. Adding the anteroventral nucleus improved cognitive status classification in Random Forest analyses. A phenotypic model integrating thalamic nuclei clearly distinguished amyloid-positive groups from amyloid-negative CN and reclassified non-AD patients with 68% of amyloid-negative MCI subjects as CN-like, and 27% of amyloid-positive CN as MCI-like. DISCUSSIONThalamic volumetry from conventional T1-weighted MRI enhances clinical insight into AD and provides a practical biomarker for disease intervention.

neuroscience↗

bioRxiv2026

Source-space EEG functional connectivity and prediction of cognition in Parkinsons disease: No added benefit of individualized head models over standard templates

IntroductionCognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. MethodsTo compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source space resting-state EEG, parcellated with the HCP-MMP1 atlas, was used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The resulting twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. ResultsTheta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition. The strongest performance was observed for theta- and alpha-band AEC and dwPLI features (max R{superscript 2} = 0.170, 95% CI = 0.067-0.262; max r = 0.439, 95% CI = 0.328-0.537). Standard and individualized head models showed comparable predictive performance across nearly all modalities. The feature-importance neuroanatomical patterns for Cole-Anticevic networks were also similar between the two head-model options. ConclusionsWe found that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource-intensive standard template head model is sufficient for this purpose. This supports feasible, scalable, and clinically accessible EEG-based FC biomarkers of cognition in PD.

neuroscience↗

bioRxiv2018

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvaths across-tissue age predictor.

bioinformatics↗