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Halliday, G.

Publications and source records attributed to Halliday, G..

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Dopamine Depletion Alters Macroscopic Network Dynamics in Parkinsons Disease

AbstractParkinsons disease is primarily characterised by diminished dopaminergic function, however the impact of these impairments on large-scale brain dynamics remains unclear. It has been difficult to disentangle the direct effects of Parkinsons disease from compensatory changes that reconfigure the functional signature of the whole brain network. To examine the causal role of dopamine depletion in network-level topology, we investigated time-varying network structure in 37 individuals with idiopathic Parkinsons disease, both On and Off dopamine replacement therapy, along with 50 age-matched, healthy control subjects using resting-state functional MRI. By tracking dynamic network-level topology, we found that the Parkinsons disease Off state was associated with greater network-level integration than in the On state. The extent of integration in the Off state inversely correlated with motor symptom severity, suggesting that a shift toward a more integrated network topology may be a compensatory mechanism associated with preserved motor function in the dopamine depleted Off state. Furthermore, we were able to demonstrate that measures of both cognitive and brain reserve (i.e., premorbid intelligence and whole brain grey matter volume) had a positive relationship with the relative increase in network integration observed in the dopaminergic Off state. This suggests that each of these factors plays an important role in promoting network integration in the dopaminergic Off state. Our findings provide a mechanistic basis for understanding the PD Off state and provide a further conceptual link with network-level reconfiguration. Together, our results highlight the mechanisms responsible for pathological and compensatory change in Parkinsons disease.

neuroscience

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