Open Access

Background: The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. Methods: We used 115 complete datasets from MCI patients of the "Dementia Competence Network", a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. Results: Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. Conclusion: A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.

Background: Autobiographical memory (AM) changes are the hallmark of Alzheimer's disease (AD) and mild cognitive impairment (MCI). In recent neuroimaging studies, AM changes have been associated with numerous cerebral sites, such as the frontal cortices, the mesial temporal lobe, or the posterior cingulum. Regional glucose uptake in these sites was investigated for underlying subdimensions using factor analysis. Subsequently, the factors were examined with respect to AM performance in a subgroup of patients. Methods: Data from 109 memory clinic referrals, who presented with MCI (n = 60), mild AD (n = 49), or were cognitively intact, were analyzed. The glucose metabolic rates determined by positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) in 34 cerebral sites important for AM were investigated for underlying subdimensions by calculating factor analysis with varimax rotation. Subsequently, the respective factor scores were correlated with the episodic and semantic AM performance of 22 patients, which was measured with a semi-structured interview assessing episodic memories (characterized by event-related emotional, sensory, contextual, and spatial–temporal details) and personal semantic knowledge from three periods of life (primary school, early adulthood, and recent years). Results: Factor analysis identified seven factors explaining 69% of the variance. While patients with MCI and AD showed lower values than controls on the factors frontal cortex, mesial temporal substructures, and occipital cortex, patients with MCI presented with increased values on the factors posterior cingulum and left temporo-prefrontal areas. The factors anterior cingulum and right temporal cortex showed only minor, non-significant group differences. Solely, the factor mesial temporal substructures was significantly correlated with both episodic memories (r = 0.424, p < 0.05) and personal semantic knowledge (r = 0.547, p < 0.01) in patients with MCI/AD. Conclusions: The factor structure identified corresponds by large to the morphological and functional interrelations of the respective sites. While reduced glucose uptake on the factors frontal cortex, mesial temporal substructures, and occipital cortex in the patient group may correspond to neurodegenerative changes, increased values on the factors posterior cingulum and left temporo-prefrontal areas in MCI may result from compensatory efforts. Interestingly, changes of the mesial temporal substructures were correlated with both semantic and episodic AM. Our findings suggest that AM deficits do not only reflect neurodegenerative changes but also refer to compensatory mechanisms as they involve both quantitative losses of specific memories and qualitative changes with a semantization of memories.

There is mounting evidence that aerobic exercise has a positive effect on cognitive functions in older adults. To date, little is known about the neurometabolic and molecular mechanisms underlying this positive effect. The present study used magnetic resonance spectroscopy and quantitative MRI to systematically explore the effects of physical activity on human brain metabolism and grey matter (GM) volume in healthy aging. This is a randomised controlled assessor-blinded two-armed trial (n=53) to explore exercise-induced neuroprotective and metabolic effects on the brain in cognitively healthy older adults. Participants (age >65) were allocated to a 12-week individualised aerobic exercise programme intervention (n=29) or a 12-week waiting control group (n=24). The main outcomes were the change in cerebral metabolism and its association to brain-derived neurotrophic factor (BDNF) levels as well as changes in GM volume. We found that cerebral choline concentrations remained stable after 12 weeks of aerobic exercise in the intervention group, whereas they increased in the waiting control group. No effect of training was seen on cerebral N-acetyl-aspartate concentrations, nor on markers of neuronal energy reserve or BDNF levels. Further, we observed no change in cortical GM volume in response to aerobic exercise. The finding of stable choline concentrations in the intervention group over the 3 month period might indicate a neuroprotective effect of aerobic exercise. Choline might constitute a valid marker for an effect of aerobic exercise on cerebral metabolism in healthy aging.

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer’s disease (AD) and may reflect primary or secondary circuitry degeneration (i.e., due to cortical atrophy). The interpretation of diffusion tensor imaging (DTI) eigenvectors, known as multiple indices, may provide new insights into the main pathological models supporting primary or secondary patterns of WM disruption in AD, the retrogenesis, and Wallerian degeneration models, respectively. The aim of this review is to analyze the current literature on the contribution of DTI multiple indices to the understanding of AD neuropathology, taking the retrogenesis model as a reference for discussion. A systematic review using MEDLINE, EMBASE, and PUBMED was performed. Evidence suggests that AD evolves through distinct patterns of WM disruption, in which retrogenesis or, alternatively, the Wallerian degeneration may prevail. Distinct patterns of WM atrophy may be influenced by complex interactions which comprise disease status and progression, fiber localization, concurrent risk factors (i.e., vascular disease, gender), and cognitive reserve. The use of DTI multiple indices in addition to other standard multimodal methods in dementia research may help to determine the contribution of retrogenesis hypothesis to the understanding of neuropathological hallmarks that lead to AD.

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD) and may reflect primary or secondary circuitry degeneration (i.e., due to cortical atrophy). The interpretation of diffusion tensor imaging (DTI) eigenvectors, known as multiple indices, may provide new insights into the main pathological models supporting primary or secondary patterns of WM disruption in AD, the retrogenesis, and Wallerian degeneration models, respectively. The aim of this review is to analyze the current literature on the contribution of DTI multiple indices to the understanding of AD neuropathology, taking the retrogenesis model as a reference for discussion. A systematic review using MEDLINE, EMBASE, and PUBMED was performed. Evidence suggests that AD evolves through distinct patterns of WM disruption, in which retrogenesis or, alternatively, the Wallerian degeneration may prevail. Distinct patterns of WM atrophy may be influenced by complex interactions which comprise disease status and progression, fiber localization, concurrent risk factors (i.e., vascular disease, gender), and cognitive reserve. The use of DTI multiple indices in addition to other standard multimodal methods in dementia research may help to determine the contribution of retrogenesis hypothesis to the understanding of neuropathological hallmarks that lead to AD.