Longitudinal change in neuropsychological performance using latent growth models: A study of mild cognitive impairment
Department of Social and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA, . Brain Imaging and Behavior
(Impact Factor: 4.6).
05/2012; 6(4). DOI: 10.1007/s11682-012-9161-8
The goal of the current study was to examine cognitive change in both healthy controls (n = 229) and individuals with mild cognitive impairment (MCI) (n = 397) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied latent growth modeling to examine baseline and longitudinal change over 36 months in five cognitive factors derived from the ADNI neuropsychological test battery (memory, executive function/processing speed, language, attention and visuospatial). At baseline, MCI patients demonstrated lower performance on all of the five cognitive factors when compared to controls. Both controls and MCI patients declined on memory over 36 months; however, the MCI patients declined at a significantly faster rate than controls. The MCI patients also declined over 36 months on the remaining four cognitive factors. In contrast, the controls did not exhibit significant change over 36 months on the non-memory cognitive factors. Within the MCI group, executive function declined faster than memory, while the other factor scores changed slower than memory over time. These findings suggest different patterns of cognitive change in healthy older adults and MCI patients. The findings also suggest that, when compared with memory, executive function declines faster than other cognitive factors in patients with MCI. Thus, decline in non-memory domains may be an important feature for distinguishing healthy older adults and persons with MCI.
Available from: Shelli R Kesler
- "Executive functions (EFs), specifically the working memory components, have been shown to exacerbate memory deficits and could represent a critical factor in AD progression (Ranganath et al., 2005; Nagata et al., 2011; Parks et al., 2011; Clément et al., 2013). It has been shown that EF in MCI patients, and not in normal aging, declines faster than memory (Johnson et al., 2012). Functional neuroimaging studies on MCI showed hyper-activation and hypo-activation in prefrontal regions in MCI patients with high and low cognitive functions, respectively (Dannhauser et al., 2005; Yetkin et al., 2006), an observation that suggest a breakdown of executive function network with progression of AD. "
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ABSTRACT: Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer's disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.
Available from: João Maroco
- "Nevertheless there is still no consensus about which neuropsychological tests should be selected to assess these domains, as well as the most appropriate cut-off to use    to pinpoint the initial process of decline. Second, most studies published so far were cross-sectional or longitudinal with relatively short follow-up periods             . The importance of conducting longitudinal studies with longer follow-up periods should be emphasized. "
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ABSTRACT: The use of neuropsychological tests to detect cognitive decline in the initial phases of Alzheimer's disease (AD) has faced significant limitations, namely the fact that most cohort studies of conversion to dementia had relatively short follow-up periods. The aim of the present study is to assess the predictive value for future conversion to dementia of a comprehensive neuropsychological battery applied to a cohort of non-demented patients followed-up for 5 years. Participants (n = 250) were selected from the Cognitive Complaints Cohort (CCC) having cognitive complaints, assessment with a comprehensive neuropsychological battery, and a follow-up period of 5 years (unless patients have converted to dementia earlier). During the follow-up period (2.6 ± 1.8 years for converters and 6.1 ± 2.1 years for non-converters), 162 patients (64.8%) progressed to dementia (mostly AD), 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. The present results show that it is possible to predict, on the basis of the initial clinical and neuropsychological evaluation, whether non-demented patients with cognitive complaints will probably convert to dementia, or remain stable, at a reasonably long and clinically relevant term.
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ABSTRACT: Recent changes in diagnostic criteria for Alzheimer's disease (AD) state that biomarkers can enhance certainty in a diagnosis of AD. In the present study, we combined cognitive function and brain morphology, a potential imaging biomarker, to predict conversion from mild cognitive impairment to AD. We identified four biomarkers, or cortical signatures of cognition (CSC), from regressions of cortical thickness on neuropsychological factors representing memory, executive function/processing speed, language, and visuospatial function among participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Neuropsychological factor scores were created from a previously validated multidimensional factor structure of the neuropsychological battery in ADNI. Mean thickness of each CSC at the baseline study visit was used to evaluate risk of conversion to clinical AD among participants with mild cognitive impairment (MCI) and rate of decline on the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score. Of 307 MCI participants, 119 converted to AD. For all domain-specific CSC, a one standard deviation thinner cortical thickness was associated with an approximately 50 % higher hazard of conversion and an increase of approximately 0.30 points annually on the CDR-SB. In combined models with a domain-specific CSC and neuropsychological factor score, both CSC and factor scores predicted conversion to AD and increasing clinical severity. The present study indicated that factor scores and CSCs for memory and language both significantly predicted risk of conversion to AD and accelerated deterioration in dementia severity. We conclude that predictive models are best when they utilize both neuropsychological measures and imaging biomarkers.
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