Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Disturbance of deep-sea environments induced by the M9.0 Tohoku Earthquake

The impacts of the M9.0 Tohoku Earthquake on deep-sea environment were investigated 36 and 98 days after the event. The light transmission anomaly in the deep-sea water after 36 days became atypically greater (∼35%) and more extensive (thickness ∼1500 m) near the trench axis owing to the turbulent diffusion of fresh seafloor sediment, coordinated with potential seafloor displacement. In addition to the chemical influx associated with sediment diffusion, an influx of 13C-enriched methane from the deep sub-seafloor reservoirs was estimated. This isotopically unusual methane influx was possibly triggered by the earthquake and its aftershocks that subsequently induced changes in the sub-seafloor hydrogeologic structures. The whole prokaryotic biomass and the development of specific phylotypes in the deep-sea microbial communities could rise and fall at 36 and 98 days, respectively, after the event. We may capture the snap shots of post-earthquake disturbance in deep-sea chemistry and microbial community responses

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction

Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods

Fructan and its relationship to abiotic stress tolerance in plants

Numerous studies have been published that attempted to correlate fructan concentrations with freezing and drought tolerance. Studies investigating the effect of fructan on liposomes indicated that a direct interaction between membranes and fructan was possible. This new area of research began to move fructan and its association with stress beyond mere correlation by confirming that fructan has the capacity to stabilize membranes during drying by inserting at least part of the polysaccharide into the lipid headgroup region of the membrane. This helps prevent leakage when water is removed from the system either during freezing or drought. When plants were transformed with the ability to synthesize fructan, a concomitant increase in drought and/or freezing tolerance was confirmed. These experiments indicate that besides an indirect effect of supplying tissues with hexose sugars, fructan has a direct protective effect that can be demonstrated by both model systems and genetic transformation

In vivo expression of the HBZ gene of HTLV-1 correlates with proviral load, inflammatory markers and disease severity in HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP)

<p>Abstract</p> <p>Background</p> <p>Recently, human T-cell leukemia virus type 1 (HTLV-1) basic leucine zipper factor (HBZ), encoded from a minus strand mRNA was discovered and was suggested to play an important role in adult T cell leukemia (ATL) development. However, there have been no reports on the role of HBZ in patients with HTLV-1 associated inflammatory diseases.</p> <p>Results</p> <p>We quantified the HBZ and tax mRNA expression levels in peripheral blood from 56 HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients, 10 ATL patients, 38 healthy asymptomatic carriers (HCs) and 20 normal uninfected controls, as well as human leukemic T-cell lines and HTLV-1-infected T-cell lines, and the data were correlated with clinical parameters. The spliced HBZ gene was transcribed in all HTLV-1-infected individuals examined, whereas tax mRNA was not transcribed in significant numbers of subjects in the same groups. Although the amount of HBZ mRNA expression was highest in ATL, medium in HAM/TSP, and lowest in HCs, with statistical significance, neither tax nor the HBZ mRNA expression per HTLV-1-infected cell differed significantly between each clinical group. The HTLV-1 HBZ, but not tax mRNA load, positively correlated with disease severity and with neopterin concentration in the cerebrospinal fluid of HAM/TSP patients. Furthermore, HBZ mRNA expression per HTLV-1-infected cell was decreased after successful immunomodulatory treatment for HAM/TSP.</p> <p>Conclusion</p> <p>These findings suggest that <it>in vivo </it>expression of HBZ plays a role in HAM/TSP pathogenesis.</p

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

Feasibility of a multidimensional home-based exercise programme for the elderly with structured support given by the general practitioner's surgery: Study protocol of a single arm trial preparing an RCT [ISRCTN58562962]

<p>Abstract</p> <p>Background</p> <p>Physical activity programmes can help to prevent functional decline in the elderly. Until now, such programmes use to target either on healthy community-dwelling seniors or on elderly living in special residences or care institutions. Sedentary or frail people, however, are difficult to reach when they live in their own homes. The general practitioner's (GP) practice offers a unique opportunity to acquire these people for participation in activity programmes. We conceptualised a multidimensional home-based exercise programme that shall be delivered to the target group through cooperation between GPs and exercise therapists. In order to prepare a randomised controlled trial (RCT), a feasibility study is being conducted.</p> <p>Methods</p> <p>The study is designed as a single arm interventional trial. We plan to recruit 90 patients aged 70 years and above through their GPs. The intervention lasts 12 weeks and consists of physical activity counselling, a home-exercise programme, and exercise consultations provided by an exercise therapist in the GP's practice and via telephone. The exercise programme consists of two main components: 1. a combination of home-exercises to improve strength, flexibility and balance, 2. walking for exercise to improve aerobic capacity. Primary outcome measures are: appraisal by GP, undesirable events, drop-outs, adherence. Secondary outcome measures are: effects (a. motor tests: timed-up-and-go, chair rising, grip strength, tandem stand, tandem walk, sit-and-reach; b. telephone interview: PRISCUS-Physical Activity Questionnaire, Short Form-8 Health Survey, three month recall of frequency of falls, Falls Efficacy Scale), appraisal by participant, exercise performance, focus group discussion. Data analyses will focus on: 1. decision-making concerning the conduction of a RCT, 2. estimation of the effects of the programme, detection of shortcomings and identification of subgroups with contrary results, 3. feedback to participants and to GPs.</p> <p>Conclusion</p> <p>A new cooperation between GPs and exercise therapists to approach community-dwelling seniors and to deliver a home-exercise programme is object of research with regard to feasibility and acceptance. In case of success, an RCT should examine the effects of the programme. A future implementation within primary medical care may take advantage from the flexibility of the programme.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN58562962.</p

Accurate Distinction of Pathogenic from Benign CNVs in Mental Retardation

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics