Browsing by Author "Santana, Isabel"
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- Cognitive deficits in middle-aged and older adults with bipolar disorder and cognitive complaints: Comparison with mild cognitive impairmentPublication . Silva, Dina Lúcia Gomes da; Santana, Isabel; Couto, Frederico Simões do; Maroco, João; Guerreiro, Manuela; Mendonça, Alexandre deObjective Cognitive impairment has been reported in elderly bipolar disorder (BD) patients, however, few studies have evaluated middle-aged and older BD patients using standardized cognitive assessments and none (to our knowledge) analysed middle-aged and older BD patients with recent cognitive complaints. The main objective of this study is to characterize the cognitive deficits of middle-aged and older patients with BD and compare them with the common agerelated cognitive deficits observed in Mild Cognitive Impairment (MCI). Methods For this retrospective study, a systematic search for all cases of BD patients submitted to a neuropsychological assessment from 1999–2007, at participant institutions, was performed, and cases were matched (1:2) by gender and age to a sample of MCI subjects. Results A total sample of 135 patients, 45 patients with the diagnosis of BD, clinically stable, mean age of 63.8 8.8 years, and 90 patients with the diagnosis of MCI, mean age of 64.2 8.4 years, was studied. Patients with MCI were more impaired in verbal memory, whereas BD patients showed more deficits in attention, motor initiative, calculation and verbal abstraction. Interestingly, discriminant analysis classified about half of the BD group as belonging to the MCI group. This BD subgroup showed deficits in episodic memory similar to MCI patients. Conclusions Patients with BD and patients with MCI have distinct profiles of cognitive impairment. A subgroup of BD patients with recent cognitive complaints may actually suffer from concomitant incipient MCI, and this finding may have diagnostic and therapeutical implications.
- Cognitive impairment in neurodegenerative diseases: A trans-diagnostic approach using a lesion-symptom mapping analysisPublication . Morais, Ricardo Félix; Pires, Ricardo; Jesus, Tiago; Baptista de Lemos Guerra de Oliveira, Raquel Maria; Duro, Diana; Lima, Marisa; Baldeiras, Inês; Oliveira, Tiago Gil; Santana, IsabelIntroduction: Neurodegenerative disorders, such as Alzheimer’s disease (AD) and frontotemporal dementia (bvFTD), reflect a spectrum of cognitive impairments unified by cognitive decline. Traditional diagnostic approaches often overlook shared landscapes of these disorders. A transdiagnostic approach, cutting across conventional boundaries, may improve understanding of shared mechanisms. This study uses lesion-symptom mapping (LSM) to identify critical brain structures responsible for cognitive impairments. Methods: Patients diagnosed with Mild Cognitive Impairment (MCI), probable AD, and probable bvFTD were recruited from our memory clinic. Diagnoses were made by a multidisciplinary team using established criteria. Participants underwent detailed medical and neurological examinations, neuroimaging, cerebrospinal fluid analysis, and neuropsychological assessment. MRI scans were processed using FreeSurfer. LSM was used to assess correlations between brain structures and cognitive performance. Results: Significant correlations were found between neuropsychological test scores and reduced volume in specific brain regions. The Free and Cued Selective Reminding Test was linked to the right hippocampus and left nucleus accumbens. The Brief Visuospatial Memory Test-Revised correlated with the right hippocampus, left nucleus accumbens, and right middle temporal gyrus. Verbal fluency was linked to the left superior temporal sulcus and left middle temporal gyrus. Digit Span forward correlated with left superior frontal gyrus and left inferior parietal region, while Digit Span backward was linked to the right precuneus. Digit-Symbol Coding was associated with the left inferior parietal region. Conclusions: This study highlights common neural targets in MCI, AD, and bvFTD and their link with cognitive impairment, emphasizing the value of LSM within a transdiagnostic approach to neurodegenerative diseases.
- Comparison of four verbal memory tests for the diagnosis and predictive value of mild cognitive impairmentPublication . Silva, Dina Lúcia Gomes da; Guerreiro, Manuela; Maroco, João; Santana, Isabel; Rodrigues, Ana; Marques, José Bravo; Mendonça, Alexandre deBackground: Mild cognitive impairment (MCI) is considered to be an early stage of a neurodegenerative disorder, particularly Alzheimer’s disease, and the clinical diagnosis requires the objective demonstration of cognitive deficits. The aim of the present study was to evaluate the predictive value of 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) in the MCI criteria. Methods: Participants were consecutive patients with subjective cognitive complaints who performed a comprehensive neuropsychological evaluation and were not demented, observed in a memory clinic setting. Results: At baseline, 272 non-demented patients reporting subjective cognitive complaints 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 four memory tests revealed that only the CVLT test remained significant as a predictor of conversion to dementia. Non-demented patients with cognitive complaints diagnosed as having MCI according to abnormal ( < 1.5 SD) learning in the CVLT test had a 3.61 higher risk of becoming demented during the follow-up. Conclusion: 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.
- Construct and diagnostic validities of the Free and Cued Selective Reminding Test in the alzheimer’s disease spectrumPublication . Lemos, Raquel; Maroco, João; Simões, Mário R.; Santana, IsabelThe Free and Cued Selective Reminding Test (FCSRT) is a memory test that controls attention and acquisition, by providing category cues in the learning process. Because it enables an assessment of memory not confounded by normal age-related changes in cognition and a high accuracy on Alzheimer's disease (AD) evaluation, it has been suggested by the International Working Group on AD. Our aim was to assess the construct related validity of the FCSRT in the AD spectrum disorders.
- Construct validity of the Montreal Cognitive Assessment (MoCA)Publication . Freitas, Sandra; Simões, Mário R.; Maroco, João; Alves, Lara; Santana, IsabelThe Montreal Cognitive Assessment (MoCA) is a brief instrument developed for the screening of milder forms of cognitive impairment. The present study aims to assess the construct related validity of the MoCA through the establishment of the factorial, convergent, and discriminant related validities, and the reliability of data. In a Portuguese sample of 830 participants, several models were tested using Confirmatory Factor Analysis. Although all tested models showed a good fit, the six-factor model based on the conceptual model proposed by the MoCA’s authors showed a significantly better fit. The results allowed us to establish the factorial, convergent, and discriminant validity of this six-dimensional structure. An overall psychometric adequacy of the items, and a good reliability were also found. This study contributes to overcome an important gap in the construct related validity of this instrument. The present findings corroborate the six-dimensional structure of the MoCA and provide good evidence of the construct related validity. The MoCA has proved to be an appropriate measure for cognitive screening taking into account different cognitive domains, which will enable clinicians and researchers to use this test and its six latent dimensions to achieve a better understanding of the individuals’ cognitive profile.
- Data mining methods in the prediction of dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forestsPublication . Maroco, João Lúcia Gomes da; Silva, Dina Lúcia Gomes da; Rodrigues, Ana; Guerreiro, Manuela; Santana, Isabel; Mendonça, Alexandre deBackground: Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. 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. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman’s nonparametric test. Results: Press’ Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest 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). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions: When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.
- Free and Cued Selective Reminding Test is superior to the Wechsler Memory Scale in discriminating mild cognitive impairment from alzheimer's diseasePublication . Lemos, Raquel; Cunha, Catarina; Marôco, João Paulo; Afonso, Ana; Simões, Mário R.; Santana, IsabelAim: The Logical Memory (LM) and the Verbal Paired Associative Learning (VPAL) are subtests from the Wechsler Memory Scale commonly used to characterize the memory deficit of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). The Free and Cued Selective Reminding Test (FCSRT) was suggested to assess the memory impairment of AD spectrum patients by the International Working Group on AD. In the present study, we compared the properties of the tests and their accuracy in classifying aMCI and AD. Methods: A group of aMCI patients (n=85) and AD patients (n=43) were included. The reliability and the validity of the three tests were analyzed. Results: AD patients showed a significant pattern of worse impairment on all tests than aMCI. The FCSRT was able to classify more patients as having memory impairment in the aMCI group rather than the WMS subtests. The FCSRT proved to be good in discriminating the two groups in both lower and higher educational levels, whereas the LM was more useful in higher educated patients. Conclusions: Although the instruments had good results, the FCSRT was more accurate in discriminating MCI from AD, and less influenced by the educational level.
- Neuropsychological contribution to predict conversion to dementia in patients withmild cognitive impairment due Alzheimer’s diseasePublication . Silva, Dina Lúcia Gomes da; Cardoso, Sandra; Guerreiro, Manuela; Marôco, J. P.; Mendes, Tiago; Alves, Luísa; Nogueira, Joana Maia; Baldeiras, Ines; Santana, Isabel; Mendonça, Alexandre deDiagnosis of Alzheimer's disease (AD) confirmed by biomarkers allows the patient to make important life decisions. However, doubt about the fleetness of symptoms progression and future cognitive decline remains. Neuropsychological measures were extensively studied in prediction of time to conversion to dementia for mild cognitive impairment (MCI) patients in the absence of biomarker information. Similar neuropsychological measures might also be useful to predict the progression to dementia in patients with MCI due to AD.
- Neuropsychological profile of amyloid-positive versus amyloid-negative amnestic Mild Cognitive ImpairmentPublication . Alves, Luísa; Cardoso, Sandra; Silva, Dina; Mendes, Tiago; Maroco, J. P.; Nogueira, Joana; Lima, Marisa; Pereira, Miguel Tábuas; Baldeiras, Inês; Santana, Isabel; De Mendonça, Alexandre; Guerreiro, ManuelaPatients diagnosed with amnestic mild cognitive impairment (aMCI) are at high risk of progressing to dementia. It became possible, through the use of biomarkers, to diagnose those patients with aMCI who have Alzheimer's disease. However, it is presently unfeasible that all patients undergo biomarker testing. Since neuropsychological testing is required to make a formal diagnosis of aMCI, it would be interesting if it could be used to predict the amyloid status of patients with aMCI.
- Prediction of dementia patients: A comparative approach using parametric vs. non parametric classifiersPublication . Maroco, João; Silva, Dina Lúcia Gomes da; Guerreiro, Manuela; Mendonça, Alexandre de; Santana, IsabelIn this paper, we report a comparison study of 7 non parametric classifiers (Multilayer perceptron Neural Networks, Radial Basis Function Neural Networks, SupportVectorMachines, CART, CHAID and QUEST Classification trees and Random Forests) as compared to Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression tested in a real data application of mild cognitive impaired elderly patients conversion to dementia. When classification results are compared both on overall accuracy, specificity and sensitivity, Linear Discriminant Analysis and Random Forests rank first among all the classifiers.