Biology subjects

Myall, D.

Publications and source records attributed to Myall, D..

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↗

bioRxiv2020

Microstructural integrity of the major nuclei of the thalamus in Parkinson's disease

BackgroundPrevious research has shown an association between thalamus and cognition in Parkinsons disease (PD). ObjectivesTo investigate the microstructural integrity of the nuclei of the thalamus and relationship with cognition. MethodsLevel II Movement Disorder Society Task Force Criteria characterised patients with Parkinsons disease as cognitively normal (PDN, n=51); with mild cognitive impairment (PD-MCI, n=16) or with dementia (PDD, n=15). Twenty-three healthy control subjects were included for comparison. A k-means clustering approach segmented the thalamus into regions representing nine major nuclei. Volume, fractional anisotropy and mean diffusivity of nuclei were compared between cognitive groups and the relationship with cognitive domain z-scores investigated using hierarchical Bayesian regression models. ResultsThere was an overall progressive increase in mean diffusivity as cognition deteriorated (PDN: 1.4 {micro}m2/s (95% uncertainty interval [0.2, 2.7]), PDMCI: 2.4 {micro}m2/s [0.8,4.0], PDD: 4.5 {micro}m2/s [2.8, 6.3]). The largest increase was in the lateral dorsal nucleus (PDN: 0.3 {micro}m2/s [-6.7, 7.2], PDMCI: 5.4 {micro}m2/s [-4.7, 16.1], PDD: 14.8 {micro}m2/s [5.0, 25.0]). Fractional anisotropy showed minimal change between cognitive groups (PDN: 0.001 [-0.005, 0,007], PDMCI: -0.005 [-0.013, 0.003], PDD: -0.005 [-0.014, 0.003]). Increase in mean diffusivity of the thalamus is associated with a global decline in cognition, the magnitude of the effect was greatest in lateral dorsal nucleus. Fractional anisotropy only showed evidence of a relationship with cognitive domain scores in the lateral dorsal nucleus. ConclusionsThe relationship between lateral dorsal nucleus integrity and cognitive changes is likely due to its primary connectivity with frontal and temporal regions.

neuroscience↗