Alpha-band hypersynchronization in progressive mild cognitive impairment. A magnetoencephalography study

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer?s disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchro- nization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD

Magnetic resonance imaging In Alzheimer’s disease, mild cognitive impairment and normal aging : Multi-template tensor-based morphometry and visual rating

Alzheimer's disease (AD) is the most common neurodegenerative disease preceded by a stage of mild cognitive impairment (MCI). The structural brain changes in AD can be detected more than 20 years before symptoms appear. If we are to reveal early brain changes in AD process, it is important to develop new diagnostic methods. Magnetic resonance imaging (MRI) is an imaging technique used in the diagnosis and monitoring of neurodegenerative diseases. Magnetic resonance imaging can detect the typical signs of brain atrophy of degenerative diseases, but similar changes can also be seen in normal aging. Visual rating methods (VRM) have been developed for visual evaluation of atrophy in dementia. A computer-based tensor-based morphometry (TBM) analysis is capable of assessing the brain volume changes typically encountered in AD. This study compared the VRM and TBM analysis in MCI and AD subjects by cross-sectional and longitudinal examination. The working hypothesis was that TBM analysis would be better than the visual methods in detecting atrophy in the brain. TBM was also used to analyze volume changes in the deep gray matter (DGM). Possible associations between TBM changes and neuropsychological tests performances were examined. This working hypothesis was that the structural DGM changes would be associated with impairments in cognitive functions. In the cross-sectional study, TBM distinguished the MCI from controls more sensitively than VRM, but the methods were equally effective in differentiating AD from MCI and controls. In the longitudinal study, both methods were equally good in the evaluation of atrophy in MCI, if the groups were sufficiently large and the disease progressed to AD. Volume changes were found in DGM structures, and the atrophy of DGM structures was related to cognitive impairment in AD. Based on these results, a TBM analysis is more sensitive in detecting brain changes in early AD as compared to VRM. In addition, the study produced information about the involvement of the deep gray matter in cognitive impairment in AD.Magneettikuvaus Alzheimerin taudissa, lievässä muistihäiriössä ja normaalissa ikääntymisessä: Tensoripohjainen muotoanalyysi ja visuaalinen arviointimenetelmä Alzheimerin tauti (AT) on yleisin dementoiva sairaus, jota edeltää yleensä lievä muistitoimintojen heikentyminen. AT:n aivomuutoksia voidaan todeta yli 20 vuotta ennen sairastumista. Jotta vielä varhaisempia AT:n aivomuutoksia voidaan todeta, on tärkeää kehittää uusia diagnostisia menetelmiä. Magneettikuvausta (MK) käytetään rappeuttavien aivosairauksien diagnostiikassa ja seurannassa. MK:lla voidaan havaita aivorappeumasairauksille tyypillistä kutistumista, mutta samanlaisia muutoksia voi esiintyä myös normaalissa ikääntymisessä. Aivorappeuman arviointiin on kehitetty silmämääräisiä arviointimenetelmiä. Tietokoneperusteinen tensoripohjainen muotoanalyysi (TPM) laskee esimerkiksi AT:lle tyypillisiä aivojen tilavuusmuutoksia. Tämä tutkimus vertaili silmämääräisiä arvioitimenetelmiä ja TPM:ä lievässä muistitoimintojen heikentymisessä ja AT:ssa poikittais- ja pitkittäistutkimuksella. TPM:n oletettiin olevan silmämääräisiä menetelmiä parempi tunnistamaan aivojen kutistumismuutoksia. Lisäksi TPM:llä tutkittiin AT:iin liittyviä aivojen syvän harmaan aiheen muutoksia, joita verrattiin neuropsykologisten testien tuloksiin. Syvän harmaan aineen kutistumisen oletettiin olevan yhteydessä tietojenkäsittelyn heikentymiseen. Tulosten perustella TPM tunnisti AT:iin liittyviä aivomuutoksia silmämääräistä menetelmää paremmin jo lievän muistitoimintojen heikentymisen vaiheessa. AT:iin liittyviä aivomuutoksia löytyi myös aivojen syvästä harmaasta aineesta ja ne olivat osittain yhteydessä neuropsykologisten testien tuloksiin. Tutkimuksen perusteella TPM voi parantaa AT:n varhaisdiagnostiikkaa verrattuna silmämääräisiin arviointimenetelmiin. Tutkimus antoi myös tietoa aivojen syvän harmaan aineen osallisuudesta ihmisen tietojenkäsittelyyn

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints.

BACKGROUND: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). OBJECTIVE: This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. METHODS: Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. RESULTS: The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%. CONCLUSION: This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD

A longitudinal observational study of home-based conversations for detecting early dementia:protocol for the CUBOId TV task

INTRODUCTION: Limitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the ‘TV task’, designed to track changes in ecologically valid conversations with disease progression. METHODS AND ANALYSIS: CUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8–25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone. ETHICS AND DISSEMINATION: CUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals

Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD

Detection of Mild Cognitive Impairment using Diffusion Compartment Imaging

The result of applying the Neurite Orientation Density and Dispersion Index (NODDI) algorithm to improve the prediction accuracy for patients diagnosed with MCI is reported. Calculations were carried out using a collection of 68 patients (34 control and 34 with MCI) gathered from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Patient data includes the use of high-resolution Magnetic Resonance Images as with as Diffusion Tensor Imaging. A Linear Regression accuracy of 83% was observed using the added NODDI summary statistic: Orientation Dispersion Index (ODI). A statistically significant difference in groups was found between control patients and patients with MCI with a power 0.96. In order to confirm performance, comparison of accuracy of prediction without the use and with the use of the ODI values is also presented. The impact of this increase in accuracy on the early detection of MCI is also presented. Results show a 4.68% increase in prediction accuracy through the inclusion of the ODI values. Future work includes the use of tractography to better locate the specific area of interest. Increasing the cohort would also add validity to the results in this paper. Expanding the number of tracts utilized in this study would also validate the use of the NODDI algorithm to detect neurological deterioration in tracts associated with memory. The inclusion of more complex prediction models would also add possible increases in performance in modeling patients with MCI

Neuropsychological predictors of the outcome in non-demented subjects with cognitive complaints

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2012Nowadays, life expectancy has increased and gradually the prevalence of neurodegenerative disorders in the aging population began to represent a major public health problem. Alzheimer’s disease (AD) is the most common dementia and affects millions of older adults. Despite recent advances in the knowledge of AD biomarkers of pathophysiological processes, clearly the phenotype remains aetiologically heterogeneous. Understanding the clinical phenotype variation contingent to the neuropathological progression is crucial to provide intervention in the earliest phases of neurodegeneration. Newly research biomarkers have been proposed for early diagnosis of AD, however cognitive impairment remains a prominent and early feature of AD. Neuropsychological markers could offer a relatively inexpensive and noninvasive indicator of future progression to dementia because biological markers are expensive, some of them only available at few specialized centers, and, in the case of lumbar puncture, invasive. Therefore, it would not be reasonable to offer the newer and expensive biomarker techniques to all patients with cognitive complaints. Importantly, new treatments of disease modification approach require the selection of those patients with higher risk of conversion to dementia. Thus, the main goal of the present thesis was to improve the predictive value of neuropsychological measures to future conversion to dementia of patients presenting with cognitive complaints who do not fulfil the dementia criteria. Four steps were conducted in order to reach that main goal: 1. º Original published articles reporting values of sensitivity, specificity and effect sizes for neuropsychological tests to predict conversion to dementia in patients at risk of future cognitive decline were analysed in a systematic review of literature. Twenty-four studies published in the last 20 years were selected. Neuropsychological tests administered vary considerably among studies, yet the battery of tests applied generally assessed verbal memory performances, and many included also cognitive areas such as executive functions, attention and language. Methodological constrains limited the ability to provide reasonable predictive values; some studies have reported rather disparate global sensitivity and specificity rates for the neuropsychological tests to predict conversion to dementia. Conversely, other studies reported high and balanced sensitivity/specificity ratios (≥80%), mainly for verbal episodic memory tests, however the follow-up period of those studies was generally short (≈2 years). Certainly, it would be important to achieve a consensus according to the more feasible and accurate neuropsychological tests to administer for the assessment of patients at risk of conversion to dementia. On the other hand, cohort studies with longer follow-up periods would be important to propose neuropsychological tests with higher predictive accuracy and clinical relevance regarding conversion to dementia. 2. º Newer statistical classification methods derived from data mining and machine learning methods were applied to improve accuracy, sensitivity and specificity of predictors obtained from neuropsychological testing. Data used to perform the comparison of classification methods was extracted from a cohort study (CCC – Cognitive Complaints Cohort) with 775 elderly non-demented patients with cognitive complaints referred for neuropsychological evaluation. Seven non-parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press’Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Comparison of classifiers highlighted three methods more adequate to study the predictive value of neuropsychological tests in longitudinal clinical cohort studies. Support Vector Machines demonstrated the larger overall classification accuracy (Median (Me) = 0.76) and area under the ROC (Me =0.90). However, this method showed high specificity (Me = 1.0) but very low sensitivity (Me = 0.3). Random Forests ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73), specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72), specificity (Me = 0.66) and sensitivity (Me = 0.64). Results indicated the innovative data mining method of Random Forests, along with more traditional methods, namely the Linear Discriminant Analysis, should be the option in cohort studies of neuropsychological predictors of future dementia. 3. º Verbal memory is one of the first cognitive areas to decline, therefore, the predictive value of Mild Cognitive Impairment (MCI) for the conversion to dementia when using four different verbal memory tests (Logical Memory, LM; California Verbal Learning Test, CVLT; Verbal Paired-Associate Learning, VPAL; and Digit Span, DS) was analysed. Participants were consecutive patients with subjective cognitive complaints who performed a comprehensive neuropsychological evaluation and were not demented, observed in a memory clinic setting. At baseline, 272 patients from CCC reporting subjective cognitive complaints and not demented were included. During the follow-up time (3.0±1.9 years), 58 patients converted to dementia, and 214 did not. Statistically significant differences between the converters and non-converters were present in LM, VPAL and CVLT. A multivariate Cox regression analysis combining the 4 memory tests revealed that only the CVLT test remained significant as predictor of conversion to dementia. Non-demented patients with cognitive complaints diagnosed as MCI according to abnormal (< 1.5 SD) learning in the CVLT test had 3.6 higher risk of becoming demented in the follow-up. As so, the verbal memory assessment using the CVLT should be preferred in the diagnostic criteria of MCI for a more accurate prediction of conversion to dementia. 4. º The predictive value for future conversion to dementia of a comprehensive neuropsychological battery applied to a cohort of nondemented patients followed-up for 5 years was presented. Two hundred and fifty subjects were selected from CCC having cognitive complaints, assessment with a comprehensive neuropsychological battery, and follow-up of at least 5 years (if patients have not converted to dementia earlier). During the follow-up period (2.6±1.8 years for converters and 6.1±2.1 for non converters), 162 patients (64.8%) progressed to dementia (mostly Alzheimer’s disease), and 88 (35.2%) did not. A Linear Discriminant Analysis (LDA) model constituted by Digit Span backward, Semantic Fluency, Logical Memory (immediate recall) and Forgetting Index significantly discriminated converters from non-converters (λ Wilks=0.64; χ2(4)=81.95; p<0.001; RCanonical=0.60). Logical Memory (immediate recall) was the strongest predictor with a standardized canonical discriminant function coefficient of 0.70. The LDA classificatory model showed good sensitivity, specificity and accuracy values (78.8%, 79.9% and 78.6%, respectively) of the neuropsychological tests to predict long-term conversion to dementia. Results showed that it is possible to predict, on the basis of the initial clinical and neuropsychological evaluation, namely with routine tests from a comprehensive neuropsychological battery, whether non-demented patients with cognitive complaints will probably convert to dementia, or remain stable. This prediction is obtained with very good accuracy values (≈80%), similar to those reported for the newly research biomarkers, and at a reasonably long and clinically relevant term (5 years).A esperança média de vida tem vindo a aumentar e consequentemente, de modo gradual, também a prevalência de doenças neurodegenerativas, representando actualmente na população mais envelhecida um alarmante problema de saúde pública. A doença de Alzheimer é a forma mais comum de demência e afecta milhões de indivíduos adultos. Recentemente tem sido possível alcançar avanços significativos na compreensão e no conhecimento sobre os biomarcadores que traduzem os processos patofisiológicos associados à doença de Alzheimer, no entanto, é importante salientar que o fenótipo manifestado pode ainda ser de etiologia heterogénea. Compreender melhor a variação das expressões de fenótipo contigentes ao processo neuropatológico é essencial para uma identificação e intervenção mais precoce no processo neurodegenerativo. Recentemente foram propostos novos biomarcadores, ainda limitados ao âmbito da investigação, com o propósito de realizar mais cedo o diagnóstico de doença de Alzheimer. Não obstante o seu potencial, será de referir que a presença de significativas alterações cognitivas continua a ser um elemento de diagnóstico incontornável e um indicador precoce da doença de Alzheimer. Os marcadores neuropsicológicos poderão oferecer indicadores de uma futura progressão para demência que serão economicamente mais acessíveis e clinicamente menos invasivos do que a realização dos métodos necessários aos marcadores biológicos, que além de serem mais dispendiosos, apenas se encontram disponíveis em alguns centros médicos especializados e serão em alguns casos métodos invasivos (e.g., recolha de líquido cefalorraquidiano através de punção lombar). Por conseguinte, não será razoável assumir que se irá disponibilizar a todos os indivíduos com manifestas queixas subjectivas de alterações cognitivas os recentes biomarcadores, por requerem técnicas dispendiosas e/ou invasivas. Por outro lado, é importante referir que a abordagem em presente desenvolvimento para tratar a doença incidindo na modificação dos seus factores causais requer uma selecção inicial do maior número possível de indivíduos para os quais o risco de progressão para demência seja significativo. Assim sendo, o objectivo central da presente tese foi o de melhorar o valor preditivo das medidas neuropsicológicas para a determinação de uma futura progressão para demência de indivíduos com queixa de alterações cognitivas que contudo não preenchem ainda os critérios para o diagnóstico de demência. De modo a concretizar o objectivo central, quatro estudos foram desenvolvidos: 1.º - Uma revisão sistemática da literatura foi realizada com base em estudos originais publicados sobre o valor preditivo da avaliação neuropsicológica de uma futura progressão para demência, apresentando para tal os valores de sensibilidade, especificidade e magnitude do efeito para cada uma das provas neuropsicológicas. A selecção dos artigos permitiu a identificação de 24 artigos publicados nos últimos 20 anos. Os testes neuropsicológicos aplicados mudavam consideravelmente consoante o estudo em questão, contudo verificava-se que no conjunto de estudos era consistente a aplicação de provas de avaliação da memória verbal, mas também de avaliação de funções executivas, capacidade de atenção e linguagem. A presença de limitações metodológicas condicionou a potencialidade de apresentar valores preditivos razoáveis em alguns estudos, além disso, noutros estudos os valores de sensibilidade e especificidade apresentados para as provas neuropsicológicas enquanto preditoras de futura progressão para demência eram consideravelmente díspares. No entanto será importante salientar que também foi possível identificar em parte dos estudos descritos a presença de valores muito positivos e de razões equilibradas entre sensibilidade e especificidade (≥80%), principalmente para provas de avaliação da memória verbal episódica, contudo os tempos de seguimento eram na sua maioria curtos (aproximadamente 2 anos). Com certeza que seria relevante encontrar um consenso que pudesse futuramente guiar uma escolha viável e precisa das provas neuropsicológicas a aplicar para melhor predizer uma futura progressão para demência. Por outro lado, a existência de estudos de coorte longitudinais com períodos de seguimento mais alargados seria essencial para melhorar a precisão dos valores preditivos da avaliação neuropsicológica, tornando-se estes clinicamente mais relevantes no que respeita a uma futura progressão para demência. 2.º Os novos métodos de classificação estatística associados a técnicas de Prospecção de dados (em inglês data mining) e Sistemas de Aprendizagem (em inglês machine learning) foram aplicados com o intuito de melhorar a precisão, sensibilidade e especificidade dos preditores obtidos pela avaliação neuropsicológica. Para a comparação dos métodos classificatórios recorreu-se à base de dados CCC (CCC – Cognitive Complaints Cohort) que era constituída na altura por 775 casos de pacientes idosos não-dementes com queixas de alterações cognitivas e que foram referenciados para realizarem uma avaliação neuropsicológica. A comparação dos métodos estatísticos realizou-se entre 7 classificadores não-paramétricos provenientes de métodos de Prospecção de dados (Redes Neuronais com Perceptrões Multicamada; Redes Neuronais com Funções de Base Radial; Máquinas de Vectores de Suporte; CART; CHAID; Árvores de Classificação QUEST e Árvores de Classificação Aleatória) que foram comparados com três classificadores tradicionais (Análise Discriminante Linear; Análise Discriminante Quadrática, e Regressão Logística) em termos de precisão classificatória, especificidade, sensibilidade, área abaixo da curva ROC e Press’Q. O modelo para a predição consistia em 10 testes neuropsicológicos utilizados recorrentemente para o diagnóstico de demência. A comparação de classificadores identificou três métodos como os mais adequados para testar o valor preditivo dos testes neuropsicológicos em estudos longitudinais de coortes clínicas. As Máquinas de Vectores de Suporte demonstraram valores mais elevados de precisão classificatória (Mediana (Me)= 0,76) e de área abaixo da curva ROC (Me= 0,90). De salientar que, no que respeita à especificidade, este método revelou um valor elevado (Me= 1,0), contudo o valor de sensibilidade era consideravelmente baixo (Me= 0,30). As Florestas Aleatórias foram o segundo método com melhores resultados em termos de precisão (Me= 0,73), área abaixo da curva ROC (Me= 0,73), especificidade (Me= 0,73) e sensibilidade (Me= 0,64). A Análise Discriminante Linear demonstrou igualmente valores razoáveis de precisão (Me= 0,66), área abaixo da curva ROC (Me= 0,72), especificidade (Me= 0,66) e sensibilidade (Me= 0,64). Os resultados apresentados indicam que os melhores métodos classificatórios para analisar os preditores neuropsicológicos de futura progressão para demência correspondem às Florestas Aleatórias no âmbito dos mais inovadores métodos de Prospecção de dados e à Análise Discriminante Linear, enquanto método de eleição de entre os mais tradicionais para classificação de dados. 3.º A memória verbal é considerada uma das primeiras áreas cognitivas a manifestar declínio nos casos de Doença de Alzheimer. Por conseguinte, o valor preditivo de progressão para demência (Doença de Alzheimer) associado ao Defeito Cognitivo Ligeiro (DCL) foi analisado contemplando para o diagnóstico de DCL quatro testes diferentes de avaliação da memória verbal (Memória Lógica (LM); Teste de Aprendizagem Verbal de Califórnia (CVLT); Aprendizagem Verbal Associativa com Pares de Palavras (VPAL); e, Memória de Dígitos (DS)). Para o estudo foi seleccionada uma amostra consecutiva de pacientes com queixas de alterações cognitivas que em consequência das mesmas foram referenciados para realizar uma avaliação neuropsicológica pormenorizada numa clínica de memória, mas que não preenchiam ainda os critérios para o diagnóstico de demência. Uma amostra inicial de 272 pacientes com queixas cognitivas e não-dementes foram seleccionados da coorte CCC para o presente estudo. No decurso do período de seguimento (3,0±1,9 anos) ocorreu a conversão para demência em 58 pacientes, enquanto 214 permaneceram cognitivamente estáveis. Nas provas de LM, VPAL e CVLT verificaram-se diferenças estatisticamente significativas entre o grupo que converteu e o que não converteu. Através de uma análise de Regressão Multivariada de COX com um modelo constituído pelas quatro provas de memória verbal demonstrou-se que apenas a prova CVLT mantém a significância enquanto preditor de futura conversão para demência. Assim sendo, pacientes que não se encontram dementes mas que manifestam queixas de alterações cognitivas, com o diagnóstico de DCL recorrendo à pontuação na prova CVLT, se apresentarem defeito nesta prova (< 1,5 desvios-padrão abaixo da média de referência) têm um risco acrescido de evoluir para demência dentro do período de seguimento. Consequentemente, uma avaliação neuropsicológica incluindo a prova CVLT deve ser contemplada para os critérios de diagnóstico de DCL de modo a predizer com maior precisão uma futura conversão para demência. 4.º Uma coorte constituída por 250 indivíduos (seleccionados da base de dados CCC) com queixas cognitivas mas sem critérios de demência e com seguimento clínico superior a 5 anos (com excepção para os casos que evoluíram para demência antes dos 5 anos) foi analisada com vista à determinação do valor preditivo dos testes neuropsicológicos a longo prazo. Durante o período de seguimento (2,6±1,8 anos para os indivíduos que evoluíram para demência e 6,1±2,1 anos para os que permaneceram estáveis a nível cognitivo) 162 indivíduos (64,8%) apresentaram os critérios para o diagnóstico de demência (principalmente para Doença de Alzheimer), enquanto que 88 (35,2%) permaneceram estáveis. Foi possível discriminar entre os indivíduos que progrediram para demência e os que permaneceram estáveis através de um modelo de Análise Discriminante Linear (ADL) com os resultados iniciais da avaliação nas provas: Memória de Dígitos inversa, Fluência Semântica, Memória Lógica (evocação imediata), e o Índice de Esquecimento da Memória Lógica (λ Wilks= 0,64; χ2 (4)= 81,95; p< 0,001; RCanonical= 0,60). O preditor neuropsicológico mais robusto, com coeficiente estandardizado da função discriminante (canónica) de 0,70, foi a prova de Memória Lógica (evocação imediata). O modelo classificatório da ADL demonstrou valores muito positivos para a sensibilidade, especificidade e precisão classificatória (78,8%, 79,9% e 78,6%, respectivamente), dos testes neuropsicológicos para predizer uma futura progressão para demência a longo prazo. Os resultados apresentados evidenciam a possibilidade de predizer, com base numa avaliação inicial, clínica e neuropsicológica, com uma bateria de provas cognitivas aplicada na rotina clínica, se o indivíduo que apresenta queixas cognitivas irá evoluir para demência ou permanecer estável nos próximos anos. Será de salientar que o valor preditivo foi obtido com uma precisão bastante aceitável (≈ 80%), na ordem dos valores obtidos para os biomarcadores mais recentes, e no âmbito de um período de seguimento consideravelmente longo e portanto clinicamente relevante (5 anos)