Cognitive aging creates major individual and societal burden, motivating search for treatment and preventive care strategies. Behavioural interventions can improve cognitive performance in older... Show moreCognitive aging creates major individual and societal burden, motivating search for treatment and preventive care strategies. Behavioural interventions can improve cognitive performance in older age, but effects are small. Basic research has implicated dopaminergic signalling in plasticity. We investigated whether supplementation with the dopamine-precursor L-dopa improves effects of cognitive training on performance. Sixty-three participants for this randomised, parallel-group, double-blind, placebo-controlled trial were recruited via newspaper advertisements. Inclusion criteria were: age of 65-75 years, Mini-Mental State Examination score >25, absence of serious medical conditions. Eligible subjects were randomly allocated to either receive 100/25 mg L-dopa/benserazide (n = 32) or placebo (n = 31) prior to each of twenty cognitive training sessions administered during a four-week period. Participants and staff were blinded to group assignment. Primary outcomes were latent variables of spatial and verbal fluid intelligence. Compared to the placebo group, subjects receiving L-dopa improved less in spatial intelligence (-0.267 SDs; 95%CI [-0.498, -0.036]; p = 0.024). Change in verbal intelligence did not significantly differ between the groups (-0.081 SDs, 95%CI [-0.242, 0.080]; p = 0.323). Subjects receiving L-dopa also progressed slower through the training and the groups displayed differential volumetric changes in the midbrain. No statistically significant differences were found for the secondary cognitive outcomes. Adverse events occurred for 10 (31%) and 7 (23%) participants in the active and control groups, correspondingly. The results speak against early pharmacological interventions in older healthy adults to improve broader cognitive functions by targeting the dopaminergic system and provide no support for learning-enhancing properties of L-dopa supplements in the healthy elderly. The findings warrant closer investigation about the cognitive effects of early dopamine-replacement therapy in neurological disorders. This trial was preregistered at the European Clinical Trial Registry, EudraCT#2016-000891-54 (2016-10-05). Show less
Lebedev, A.V.; Westman, E.; Westen, G.J.P.; Van Kramberger, M.G.; Lundervold, A.; Aarsland, D.; ... ; Simmons, A . 2014
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to... Show moreComputer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% — test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%–83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation. Show less
