Evaluation of exercise on individuals with dementia and their carers: a randomised controlled trial

Background Almost all of the 820,000 people in the UK with dementia will experience Behavioural and Psychological Symptoms of Dementia (BPSD). However, research has traditionally focused on treating cognitive symptoms, thus neglecting core clinical symptoms that often have a more profound impact on living with dementia. Recent evidence (Kales et al, 2007; Ballard et al, 2009) indicates that the popular approach to managing BPSD - prescription of anti-psychotic medication - can increase mortality and the risk of stroke in people with dementia as well as impair quality of life and accelerate cognitive decline. Consequently, there is a need to evaluate the impact that non-pharmacological interventions have on BPSD; we believe physical exercise is a particularly promising approach. Methods/Design We will carry out a pragmatic, randomised, single-blind controlled trial to evaluate the effectiveness of exercise (planned walking) on the behavioural and psychological symptoms of individuals with dementia. We aim to recruit 146 people with dementia and their carers to be randomized into two groups; one will be trained in a structured, tailored walking programme, while the other will continue with treatment as usual. The primary outcome (BPSD) will be assessed with the Neuropsychiatric Inventory (NPI) along with relevant secondary outcomes at baseline, 6 and 12 weeks. Discussion Designing this study has been challenging both ethically and methodologically. In particular to design an intervention that is simple, measurable, safe, non-invasive and enjoyable has been testing and has required a lot of thought. Throughout the design, we have attempted to balance methodological rigour with study feasibility. We will discuss the challenges that were faced and overcome in this paper

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

Maximum (prior) brain size, not atrophy, correlates with cognition in community-dwelling older people: a cross-sectional neuroimaging study

<p>Abstract</p> <p>Background</p> <p>Brain size is associated with cognitive ability in adulthood (correlation ~ .3), but few studies have investigated the relationship in normal ageing, particularly beyond age 75 years. With age both brain size and fluid-type intelligence decline, and regional atrophy is often suggested as causing decline in specific cognitive abilities. However, an association between brain size and intelligence may be due to the persistence of this relationship from earlier life.</p> <p>Methods</p> <p>We recruited 107 community-dwelling volunteers (29% male) aged 75–81 years for cognitive testing and neuroimaging. We used principal components analysis to derived a 'general cognitive factor' (g) from tests of fluid-type ability. Using semi-automated analysis, we measured whole brain volume, intracranial area (ICA) (an estimate of maximal brain volume), and volume of frontal and temporal lobes, amygdalo-hippocampal complex, and ventricles. Brain atrophy was estimated by correcting WBV for ICA.</p> <p>Results</p> <p>Whole brain volume (WBV) correlated with general cognitive ability (g) (r = .21, P < .05). Statistically significant associations between brain areas and specific cognitive abilities became non-significant when corrected for maximal brain volume (estimated using ICA), i.e. there were no statistically significant associations between atrophy and cognitive ability. The association between WBV and g was largely attenuated (from .21 to .03: i.e. attenuating the variance by 98%) by correcting for ICA. ICA accounted for 6.2% of the variance in g in old age, whereas atrophy accounted for < 1%.</p> <p>Conclusion</p> <p>The association between brain regions and specific cognitive abilities in community dwelling people of older age is due to the life-long association between whole brain size and general cognitive ability, rather than atrophy of specific regions. Researchers and clinicians should therefore be cautious of interpreting global or regional brain atrophy on neuroimaging as contributing to cognitive status in older age without taking into account prior mental ability and brain size.</p

Association between IgM Anti-Herpes Simplex Virus and Plasma Amyloid-Beta Levels

OBJECTIVE: Herpes simplex virus (HSV) reactivation has been identified as a possible risk factor for Alzheimer's disease (AD) and plasma amyloid-beta (Aβ) levels might be considered as possible biomarkers of the risk of AD. The aim of our study was to investigate the association between anti-HSV antibodies and plasma Aβ levels. METHODS: The study sample consisted of 1222 subjects (73.9 y in mean) from the Three-City cohort. IgM and IgG anti-HSV antibodies were quantified using an ELISA kit, and plasma levels of Aβ(1-40) and Aβ(1-42) were measured using an xMAP-based assay technology. Cross-sectional analyses of the associations between anti-HSV antibodies and plasma Aβ levels were performed by multi-linear regression. RESULTS: After adjustment for study center, age, sex, education, and apolipoprotein E-e4 polymorphism, plasma Aβ(1-42) and Aβ(1-40) levels were specifically inversely associated with anti-HSV IgM levels (β = -20.7, P=0.001 and β = -92.4, P=0.007, respectively). In a sub-sample with information on CLU- and CR1-linked SNPs genotyping (n=754), additional adjustment for CR1 or CLU markers did not modify these associations (adjustment for CR1 rs6656401, β = -25.6, P=0.002 for Aβ(1-42) and β = -132.7, P=0.002 for Aβ(1-40;) adjustment for CLU rs2279590, β = -25.6, P=0.002 for Aβ(1-42) and β = -134.8, P=0.002 for Aβ(1-40)). No association between the plasma Aβ(1-42)-to-Aβ(1-40) ratio and anti-HSV IgM or IgG were evidenced. CONCLUSION: High anti-HSV IgM levels, markers of HSV reactivation, are associated with lower plasma Aβ(1-40) and Aβ(1-42) levels, which suggest a possible involvement of the virus in the alterations of the APP processing and potentially in the pathogenesis of AD in human

Decreased olfactory discrimination is associated with impulsivity in healthy volunteers

In clinical populations, olfactory abilities parallel executive function, implicating shared neuroanatomical substrates within the ventral prefrontal cortex. In healthy individuals, the relationship between olfaction and personality traits or certain cognitive and behavioural characteristics remains unexplored. We therefore tested if olfactory function is associated with trait and behavioural impulsivity in nonclinical individuals. Eighty-three healthy volunteers (50 females) underwent quantitative assessment of olfactory function (odour detection threshold, discrimination, and identifcation). Each participant was rated for trait impulsivity index using the Barratt Impulsiveness Scale and performed a battery of tasks to assess behavioural impulsivity (Stop Signal Task, SST; Information Sampling Task, IST; Delay Discounting). Lower odour discrimination predicted high ratings in non-planning impulsivity (Barratt Non-Planning impulsivity subscale); both, lower odour discrimination and detection threshold predicted low inhibitory control (SST; increased motor impulsivity). These fndings extend clinical observations to support the hypothesis that defcits in olfactory ability are linked to impulsive tendencies within the healthy population. In particular, the relationship between olfactory abilities and behavioural inhibitory control (in the SST) reinforces evidence for functional overlap between neural networks involved in both processes. These fndings may usefully inform the stratifcation of people at risk of impulse-control-related problems and support planning early clinical interventions

Staged decline of neuronal function in vivo in an animal model of Alzheimer's disease

The accumulation of amyloid-β in the brain is an essential feature of Alzheimer's disease. However, the impact of amyloid-β-accumulation on neuronal dysfunction on the single cell level in vivo is poorly understood. Here we investigate the progression of amyloid-β load in relation to neuronal dysfunction in the visual system of the APP23×PS45 mouse model of Alzheimer's disease. Using in vivo two-photon calcium imaging in the visual cortex, we demonstrate that a progressive deterioration of neuronal tuning for the orientation of visual stimuli occurs in parallel with the age-dependent increase of the amyloid-β load. Importantly, we find this deterioration only in neurons that are hyperactive during spontaneous activity. This impairment of visual cortical circuit function also correlates with pronounced deficits in visual-pattern discrimination. Together, our results identify distinct stages of decline in sensory cortical performance in vivo as a function of the increased amyloid-β-load

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features