Structural Cerebral Network Differences in Prodromal Alzheimer's Disease and Prodromal Dementia with Lewy Bodies.

INTRODUCTION: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have long prodromal phases without dementia. However, the patterns of cerebral network alteration in this early stage of the disease remain to be clarified. METHOD: Participants were 48 patients with mild cognitive impairment (MCI) due to AD (MCI-AD), 18 patients with MCI with DLB (MCI with Lewy bodies: MCI-LB), and 23 healthy controls who underwent a 1.5-Tesla magnetic resonance imaging scan. Cerebral networks were extracted from individual T1-weighted images based on the intracortical similarity, and we estimated the differences of network metrics among the three diagnostic groups. RESULTS: Whole-brain analyses for degree, betweenness centrality, and clustering coefficient images were performed using SPM8 software. The patients with MCI-LB showed significant reduction of degree in right putamen, compared with healthy subjects. The MCI-AD patients showed significant lower degree in left insula and bilateral posterior cingulate cortices compared with healthy subjects. There were no significant differences in small-world properties and in regional gray matter volume among the three groups. CONCLUSIONS: We found the change of degree in the patients with MCI-AD and with MCI-LB, compared with healthy controls. These findings were consistent with the past single-photon emission computed tomography studies focusing on AD and DLB. The disease-related difference in the cerebral neural network might provide an adjunct biomarker for the early detection of AD and DLB.

Effects of a multicomponent day-care program on cerebral blood flow in patients with mild cognitive impairment.

BACKGROUND: Mild cognitive impairment (MCI) is a prodromal phase of dementia and is considered an important period for intervention to prevent conversion to dementia. It has been well established that multicomponent day-care programs including exercise training, cognitive intervention and music therapy have beneficial effects on cognition, but the effects on cerebral blood flow (CBF) in MCI remain unknown. This study examined whether a multicomponent day-care program would have beneficial effects on the longitudinal changes of CBF in MCI patients. METHODS: Participants were 24 patients with MCI attending a day-care program; they underwent two 99 mTc-ethyl cysteinate dimer single photon emission computed tomography scans during the study period. We evaluated the association between the changes of regional cerebral blood flow and the attendance rate. RESULTS: There was a significant negative correlation between the reduction of regional CBF in the right parietal region and the attendance rate. We found no significant relation between the baseline CBF images and the attendance rate. CONCLUSIONS: Our results suggest that continuous participation in a multicomponent day-care program might prevent reduction in brain activity in patients with MCI.

The Multicomponent Day-Care Program Prevents Volume Reduction in a Memory-Related Brain Area in Patients with Mild Cognitive Impairment.

INTRODUCTION: Mild cognitive impairment (MCI) is considered an important period for interventions to prevent progression to dementia. Nonpharmacological interventions for MCI include exercise training, cognitive intervention, and music therapy. These play an important role in improving cognitive function, but their effects on brain plasticity in individuals with MCI are largely unknown. We investigated the effects of a multicomponent day-care program provided by the University of Tsukuba Hospital on the longitudinal brain volume changes in MCI patients. METHODS: MCI patients who participated in the multicomponent day-care program and underwent whole-brain magnetic resonance imaging (MRI) twice during their participation (n = 14), were included. We divided them into two groups according to their attendance rate and conducted a between-group analysis of longitudinal volume changes in the whole cerebral cortex. Regional brain volumes derived from the patients' MRI were calculated with Freesurfer 6.0.0. RESULTS: The neuroimaging analysis demonstrated that the left rostral anterior cingulate cortex volume was significantly preserved in the high-attendance group compared to that of the low-attendance group. CONCLUSION: Our results suggest that continuous participation in a multicomponent day-care program could help prevent a volume reduction in memory-related brain areas in patients with MCI.

Relationships between the Fear of COVID-19 Scale and regional brain atrophy in mild cognitive impairment.

BACKGROUND: Several studies have reported that the pandemic of coronavirus disease 2019 (COVID-19) influenced cognitive function in the elderly. However, the effect of COVID-19-related fear on brain atrophy has not been evaluated. In this study, we evaluated the relation between brain atrophy and the effect of COVID-19-related fear by analysing changes in brain volume over time using magnetic resonance imaging (MRI). METHODS: Participants were 25 Japanese patients with mild cognitive impairment (MCI) or subjective cognitive decline (SCD), who underwent 1.5-tesla MRI scan twice, once before and once after the pandemic outbreak of COVID-19, and the Fear of Coronavirus Disease 2019 Scale (FCV-19S) assessment during that period. We computed regional brain atrophy per day between the 1st and 2nd scan, and evaluated the relation between the FCV-19S scores and regional shrinkage. RESULTS: There was significant positive correlation between the total FCV-19S score and volume reduction per day in the right posterior cingulate cortex. Regarding the subscales of FCV-19S, we found significant positive correlation between factor 2 of the FCV-19S and shrinkage of the right posterior cingulate cortex. CONCLUSIONS: There was positive correlation between the FCV-19S score and regional brain atrophy per day. Although it is already known that the psychological effects surrounding the COVID-19 pandemic cause cognitive function decline, our results further suggest that anxiety and fear related to COVID-19 cause regional brain atrophy.

Structural brain network correlations with amyloid burden in elderly individuals at risk of Alzheimer's disease.

Alzheimer's disease (AD) has a long preclinical phase during which beta-amyloid accumulates in the brain without cognitive impairment. However, the pattern of brain network alterations in this early stage of the disease remains to be clarified. In this study we examined the relationships between regional brain network indices and beta-amyloid deposits. Twenty-four elderly subjects with the APOE4 allele underwent both a 1.5-Tesla magnetic resonance imaging (MRI) scan and a positron emission tomography (PET) scan using [18F]Florbetapir. We computed network metrics such as the degree, betweenness centrality, and clustering coefficient, and examined the relationships between the beta-amyloid accumulation and these regional brain network connectivity metrics. We found a significant positive correlation between the global standardized uptake value ratio (SUVR) of [18F]Florbetapir and the betweenness centrality in the left parietal region. However, there were no significant correlations between the SUVR score and other network indices or the regional gray matter volume. Our data suggest a relationship between the beta-amyloid accumulation and the regional brain network connectivity in subjects at risk of AD. The brain connectome may provide an adjunct biomarker for the early detection of AD.

Handwriting Features of Multiple Drawing Tests for Early Detection of Alzheimer's Disease: A Preliminary Result.

Early detection of Alzheimer's disease (AD) has become increasingly important. Healthy monitoring technology focusing on behavioral changes is a promising approach in this vein. Among such technologies, handwriting features measured by digital tablet devices have attracted attention as potential indicators for detecting AD and mild cognitive impairment (MCI). However, previous studies have mainly investigated features in single tasks, and it remains unclear whether combining the features of multiple tasks could improve the performance of detecting AD and MCI. In this study, we investigated features in five representative tasks used in neuropsychological tests collected from 71 seniors including some diagnosed with MCI and AD. We found that our three-class classification model improved diagnosis accuracy by up to 11.3% by combining features of multiple tasks, for a final accuracy of 74.6%. We also suggested that drawing behaviors during multiple tasks might be useful for estimating disease progression simply by utilizing the labels of disease groups.

Multimodal Behavior Analysis Towards Detecting Mild Cognitive Impairment: Preliminary Results on Gait and Speech.

Behavioral analysis for identifying changes in cognitive and physical functioning is expected to help detect dementia such as mild cognitive impairment (MCI) at an early stage. Speech and gait features have been especially recognized as behavioral biomarkers for dementia that possibly occur early in its course, including MCI. However, there are no studies investigating whether exploiting the combination of multimodal behavioral data could improve detection accuracy. In this study, we collected speech and gait behavioral data from Japanese seniors consisting of cognitively healthy adults and patients with MCI. Comparing the models using single modality behavioral data, we showed that the model using multimodal behavioral data could improve detection by up to 5.9%, achieving 82.4% accuracy (chance 55.9%). Our results suggest that the combination of multimodal behavioral features capturing different functional changes resulting from dementia might improve accuracy and help timely diagnosis at an early stage.