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

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

Utilisation du paillage léger et de la haie vive dans la lutte contre l'érosion en zone semi-aride de montagne (Cap-Vert)

Face à l'érosion hydrique, la préservation des sols et leur capacité à produire constituent une des préoccupations majeures des îles du Cap-Vert. Parmi les différentes techniques pouvant être mises en oeuvre, deux sont expérimentées : le paillage léger et la haie vive. Les résultats obtenus en matière de lutte contre l'érosion et de production de cultures annuelles par ces techniques prises individuellement ou en association sont présentées. Le travail a été effectué en zone aride de montagne. (Résumé d'auteur

Erosion en montagnes semi-arides et méditerranéennes

Dans la vallée de Godim située en zone semi aride de l'île de Santiago, le PRODAP (projet de développement de l'agriculture et de l'élevage à base communautaire) poursuit, depuis 1994, un programme d'action sur la gestion conservatoire de l'eau et des sols. Les techniques du paillage léger constitué de résidus de maïs, à raison de 1500 kmMS/ha, et de la haie mixte composée de #Leuceana leucocephala, #Pennisetum purpureum et #Vetiver$ disposée en courbe de niveau sont testées en milieu contrôlé sur sols à forte pente (50%). Dans cet article, les auteurs s'intéressent à l'influence de ces techniques sur l'érosion et la productivité des sols. Concernant la technique du paillage, l'étude est menée à différentes échelles sous pluies naturelles (100 m2, 4 m2) et artificielles (1 m2). Les premiers résultats cumulés obtenus (campagne 1995 et 1996) montrent que le paillis léger a réduit considérablement le ruissellement et l'érosion : les charges solides de la parcelle préparée conventionnellement sont 478 fois supérieurs à celles de la parcelle paillée. Les différences entre les coefficients de ruissellement sont moins importants, mais la parcelle conventionnelle présente néanmoins un ruissellement 9 fois supérieur à la parcelle paillée. Au bout de 3 années d'implantation, l'action antiérosive de la haie mixte ne donne pas d'effet aussi spectaculaire que le paillage léger : l'érosion a été 4 fois moins importante que sur la parcelle témoin et le taux de ruissellement réduit de moitié. L'effet combiné du paillis et de la haie mixte a pratiquement annulé l'érosion. La présence de la haie permet une production de biomasse totale, pour les 2 années consécutives, plus importante avec un gain moyen de biomasse de 28% en 1995 et de 38% en 1996 par rapport à la parcelle témoin... (D'après résumé d'auteur

Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans

Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at-risk for Alzheimer’s disease (AD), so that early treatment can be administered. In this paper, we propose a pMCI/sMCI classification framework that harnesses information available in longitudinal magnetic resonance imaging (MRI) data, which could be incomplete, to improve diagnostic accuracy. Volumetric features were first extracted from the baseline MRI scan and subsequent scans acquired after 6, 12, and 18 months. Dynamic features were then obtained by using the 18th-month scan as the reference and computing the ratios of feature differences for the earlier scans. Features that are linearly or non-linearly correlated with diagnostic labels are then selected using two elastic net sparse learning algorithms. Missing feature values due to the incomplete longitudinal data are imputed using a low-rank matrix completion method. Finally, based on the completed feature matrix, we build a multi-kernel support vector machine (mkSVM) to predict the diagnostic label of samples with unknown diagnostic statuses. Our evaluation indicates that a diagnosis accuracy as high as 78.2% can be achieved when information from the longitudinal scans is used – 6.6% higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy