Disease-modifying properties of long-term lithium treatment for amnestic mild cognitive impairment: randomised controlled trial

Background Two recent clinical studies support the feasibility of trials to evaluate the disease-modifying properties of lithium in Alzheimer`s disease, although no benefits were obtained from short-term treatment. Aims To evaluate the effect of long-term lithium treatment on cognitive and biological outcomes in people with amnestic mild cognitive impairment (aMCI). Method Forty-five participants with aMCI were randomised to receive lithium (0.25-0.5mmol/l) (n=24) or placebo (n = 21) in a 12-month, double-blind trial. Primary outcome measures were the modification of cognitive and functional test scores, and concentrations of cerebrospinal fluid (CSF) biomarkers (amyloid-beta peptide (A beta(42)), total tau (T-tau), phosphorylated-tau) (P-tau). Trial registration: NCT01055392. Results Lithium treatment was associated with a significant decrease in CSF concentrations of P-tau (P=0.03) and better perform-ance on the cognitive subscale of the Alzheimer`s Disease Assessment Scale and in attention tasks. Overall tolerability of lithium was good and the adherence rate was 91%. Conclusions The present data support the notion that lithium has disease-modifying properties with potential clinical implications in the prevention of Alzheimer`s disease.

Use of the frontal assessment battery in evaluating executive dysfunction in patients with Huntington`s disease

The frontal assessment battery (FAB) is a bedside cognitive scale designed to measure executive functions. Huntington`s disease (HD) is a neurodegenerative disorder characterized by motor, behavioral, and cognitive dysfunction. The aim of this study was to check the validity of the FAB for the evaluation of cognitive impairment in patients with HD. Forty-one patients diagnosed with HD and 53 healthy controls matched by education, sex and age were evaluated with a validated Brazilian version of the UHDRS, the VFT, the SDMT, the SIT, the MMSE, and the FAB. The diagnosis of HD was made by DNA analysis. FAB scores were lower in patients than in the controls (p < 0.001) and had significant correlations with the VFT (r = 0.79; p < 0.05), the SDMT (r = 0.80; p < 0.05), the SIT (r = 0.72; p < 0.05), the MMSE (r = 0.83; p < 0.05), the FCS (r = 0.79; p < 0.05) and the motor section of the UHDRS (r = -0.80; p < 0.05). The FAB differentiated between HD patients in the initial and later stages of the disease. The one-year longitudinal evaluation revealed a global trend toward a worsening in the second score of the FAB. The results demonstrate that the FAB presents good internal consistency and also convergent and discriminative validity; therefore it is a useful scale to assess executive functions and to evaluate cognitive impairment in patients with HD.

Semantic priming in Parkinson's disease: Evidence for delayed spreading activation

Nineteen persons with Parkinson's disease (PD) and 19 matched control participants completed a battery of online lexical decision tasks designed to isolate the automatic and attentional aspects of semantic activation within the semantic priming paradigm. Results highlighted key processing abnormalities in PD. Specifically, persons with PD exhibited a delayed time course of semantic activation. In addition, results suggest that experimental participants were unable to implicitly process prime information and, therefore, failed to engage strategic processing mechanisms in response to manipulations of the relatedness proportion. Results are discussed in terms of the 'Gain/Decay' hypothesis (Milberg, McGlinchey-Berroth, Duncan, & Higgins, 1999) and the dopaminergic modulation of signal to noise ratios in semantic networks.

Anti-apolipoprotein A-I antibodies in the pathogenesis of Alzheimer disease : project plan

RESUMO: A doença de Alzheimer (AD) é a forma mais comum de demência em todo o mundo e sua prevalência deverá duplicar até 2050. Os mecanismos precisos responsáveis pela AD são desconhecidas mas as características histopatológicas estão bem caracterizadas. A hipótese mais importante para a perda neuronal e declínio cognitivo na AD é a cascata amilóide que indica que AD é o resultado da sobreprodução de beta amilóide (Aβ) e / ou remoção ineficaz; a acumulação do BA no cérebro seria o passo crítico na patogénese da AD. Actualmente, a identificação de proteínas que se ligam ao Aβ e modulam a sua agregação e neurotoxicidade pode proporcionar a base para novas abordagens terapêuticas. A apolipoproteína AI (ApoA-I), o principal componente das HDL humanas, interage com o domínio extracelular da proteína precursora de amilóide (APP), bem como com o Aβ. Estudos epidemiológicos têm mostrado uma diminuição acentuada da ApoA-I plasmática em doentes com AD, com uma correlação inversa entre o nível de ApoA-I e o risco de AD. Este trabalho pretende apresentar um projecto que tem como objectivo investigar se os anticorpos anti-apo AI podem impedir a formação de complexos Aβ / ApoA-I, bloqueando o efeito protector da ApoA-I. A hipótese baseia-se na possibilidade dos doentes com AD terem anticorpos anti-ApoA-I plasmáticos e de estes poderem interferir com a formação do complexo no LCR.------- ABSTRACT:Alzheimer’s disease (AD) is the most common form of dementia world-wide and its prevalence is expected to double by the year 2050. The precise mechanisms responsible for AD are unknown but the histopathologic features are well-characterised. The most compelling hypothesis for neuronal loss and cognitive decline in AD is the amyloid cascade hypothesis which states that AD is the result of amyloid beta (Aβ) overproduction and/or ineffective clearance and its accumulation in the brain would be the critical step in AD pathogenesis. Currently, identification of proteins that bind Aβ and modulate its aggregation and neurotoxicity could provide the basis for novel treatment approaches. Apolipoprotein A-I (ApoA-I), the main constituent of human HDL, ApoA-I interacts with the extracellular domain of amyloid precursor protein (APP), as well as with Aβ itself. Epidemiological studies have shown a marked decrease of plasma ApoA-I levels in AD patients, with an inverse correlation between the ApoA-I level and the risk of AD. This work intends to present a project that aims to investigate if anti-ApoA-I antibodies may prevent the formation of the Aβ /ApoA-I complex and by doing so blocking the protective effect of ApoA-I in AD. We base the hypothesis on the possibility that patients with AD might have anti-ApoA-I antibodies in plasma and that these can interfere with the complex formation in the cerebrospinal fluid (CSF).

The choroid plexus transcriptome reveals changes in type I and II interferon responses in a mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a marked decline in cognition and memory function. Increasing evidence highlights the essential role of neuroinflammatory and immune-related molecules, including those produced at the brain barriers, on brain immune surveillance, cellular dysfunction and amyloid beta (Aß) pathology in AD. Therefore, understanding the response at the brain barriers may unravel novel pathways of relevance for the pathophysiology of AD. Herein, we focused on the study of the choroid plexus (CP), which constitutes the blood-cerebrospinal fluid barrier, in aging and in AD. Specifically, we used the PDGFB-APPSwInd (J20) transgenic mouse model of AD, which presents early memory decline and progressive Aß accumulation, and littermate age-matched wild-type (WT) mice, to characterize the CP transcriptome at 3, 5-6 and 11-12months of age. The most striking observation was that the CP of J20 mice displayed an overall overexpression of type I interferon (IFN) response genes at all ages. Moreover, J20 mice presented a high expression of type II IFN genes in the CP at 3months, which became lower than WT at 5-6 and 11-12months. Importantly, along with a marked memory impairment and increased glial activation, J20 mice also presented a similar overexpression of type I IFN genes in the dorsal hippocampus at 3months. Altogether, these findings provide new insights on a possible interplay between type I and II IFN responses in AD and point to IFNs as targets for modulation in cognitive decline.

Neurocognitive profile and cognitive intervention in Alzheimer's disease

Tese de Doutoramento em Psicologia (Especialidade de Psicologia Clínica)

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Summary Report for the Iowa Alzheimer's Task Force 2007 from the Iowa Alzheimer's Disease Demonstration Projects

The Iowa Department of Elder Affairs, in collaboration with the University of Iowa College of Nursing, has been engaged in developing and evaluating community based services for persons with dementia in the state of Iowa over the past 7 years under a grant form the Administration on Aging. This grant tested out several models of care (dementia nurse care manager, memory loss nurse specialist, “People Living Alone Need Support” (PLANS), varying models of respite care), surveyed agencies and service providers in regard to how they provide services for persons with dementia, and provided training to case management, community college instructors, adult day service providers and other related services providers including assisted living and nursing home facilities.

Final Report: 2007 Alzheimer's Disease Task Force, January 2008

Following an overview of the history of the task force and background information on Alzheimer’s disease, the report is divided into four sections. These sections correspond to the delineation of four subcommittees into which task force members were divided. It should be noted that the term “Alzheimer’s Disease” is used to encompass not only Alzheimer’s disease but also additional brain disorders such as vascular dementia, mixed dementia, mild cognitive impairment, dementia with Lewy bodies, and other types of dementia. Interspersed throughout the report are verbatim comments received from Iowans who responded to on-line surveys about how Alzheimer’s disease has affected their lives. Their words poignantly give voice to the emotions, frustrations, and hopes of Iowans who are personally experiencing the impact of Alzheimer’s disease. The Report includes 22 recommendations to the Iowa General Assembly designed to improve the availability and quality of services for people with dementia, their caregivers, and their families. The recommendations fall into four categories; a) Education and Training; b) Services and Housing; c) Wellness and Disease Management; and, d) Funding and Reimbursement.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Early Detection of Alzheimer's Disease Beta-amyloid Pathology -Applicability of Positron Emission Tomography with the Amyloid Radioligand 11C-PIB

Early Detection of Alzheimer's Disease Beta-amyloid Pathology -Applicability of Positron Emission Tomography with the Amyloid Radioligand ¹¹C-PIB Accumulation of beta amyloid (Abeta) in the brain is characteristic for Alzheimer’s disease (AD). Carbon-11 labeled 2-(4’-methylaminophenyl)-6-hydroxybenzothiazole (¹¹C-PIB) is a novel positron emission tomography (PET) amyloid imaging agent that appears to be applicable for in vivo Abeta plaque detection and quantitation. The biodistribution and radiation dosimetry of ¹¹C-PIB were investigated in 16 healthy subjects. The reproducibility of a simplified ¹¹C-PIB quantitation method was evaluated with a test-retest study on 6 AD patients and 4 healthy control subjects. Brain ¹¹C-PIB uptake and its possible association with brain atrophy rates were studied over a two-year follow-up in 14 AD patients and 13 healthy controls. Nine monozygotic and 8 dizygotic twin pairs discordant for cognitive impairment and 9 unrelated controls were examined to determine whether brain Abeta accumulation could be detected with ¹¹C-PIB PET in cognitively intact persons who are at increased genetic risk for AD. The highest absorbed radiation dose was received by the gallbladder wall (41.5 mjuGy/MBq). About 20 % of the injected radioactivity was excreted into urine, and the effective whole-body radiation dose was 4.7 mjuSv/MBq. Such a dose allows repeated scans of individual subjects. The reproducibility of the simplified ¹¹C-PIB quantitation was good or excellent both at the regional level (VAR 0.9-5.5 %) and at the voxel level (VAR 4.2-6.4 %). ¹¹C-PIB uptake did not increase during 24 months’ follow-up of subjects with mild or moderate AD, even though brain atrophy and cognitive decline progressed. Baseline neocortical ¹¹C-PIB uptake predicted subsequent volumetric brain changes in healthy control subjects (r = 0.725, p = 0.005). Cognitively intact monozygotic co-twins – but not dizygotic co-twins – of memory-impaired subjects exhibited increased ¹¹C-PIB uptake (117-121 % of control mean) in their temporal and parietal cortices and the posterior cingulate (p<0.05), when compared with unrelated controls. This increased uptake may be representative of an early AD process, and genetic factors seem to play an important role in the development of AD-like Abeta plaque pathology. ¹¹C-PIB PET may be a useful method for patient selection and follow-up for early-phase intervention trials of novel therapeutic agents. AD might be detectable in high-risk individuals in its presymptomatic stage with ¹¹C-PIB PET, which would have important consequences both for future diagnostics and for research on disease-modifying treatments.