Background: Physical activity has been suggested to prevent the conversion of mild cognitive impairment (MCI) to dementia in patients. We investigated the association between the continuance and regularity of physical activity and the risk of developing dementia in patients with MCI. Methods: We analyzed 6-year followed up data for 247,149 individuals in the National Health Insurance Service (NHIS) cohort of Korea who were enrolled between January 1, 2009, and December 31, 2015. The patients were divided into four groups: those who did not engage in physical activity consistently (Never-PA group), those who initiated physical activity (Initiation-PA group), those who ceased physical activity (Withdrawal-PA group), and those who performed physical activity consistently (Maintenance-PA group). We also divided the patients into two groups: those who engaged in physical activity irregularly (Irregular-PA) and those who undertook physical activity regularly (Regular-PA). Results: When the risk for the Never-PA group was set as the benchmark (ref = 1), the Maintenance-PA group had the lowest incidence of dementia of the Alzheimer type (DAT) compared to the other groups (HR = 0.82, 95% CI 0.79–0.86). The DAT risk of the Initiation-PA group (HR = 0.89, 95% CI 0.85–0.93) was lower than the Never-PA group. In addition, compared to the Irregular-PA group, the Regular-PA group had a 15% reduced risk for developing DAT. Conclusions: Although no causal inference could be made, continued regular physical activity in patients with MCI is associated with a protective effect against developing DAT. Moreover, ceasing physical activity could halt this protective effect.

Background: Studies assessing the prognostic effect of inflammatory markers of blood cells on the outcomes of patients with acute ischemic stroke treated with endovascular treatment (EVT) are sparse. We evaluated whether the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) affect reperfusion status in patients receiving EVT. Methods: Using a multicenter registry database, 282 patients treated with EVT were enrolled in this study. The primary outcome measure was unsuccessful reperfusion rate after EVT defined by thrombolysis in cerebral infarction grades 0–2a. Logistic regression analysis was performed to analyze the association between NLR/PLR and unsuccessful reperfusion rate after EVT. Results: Both NLR and PLR were higher in the unsuccessful reperfusion group than in the successful reperfusion group (p < 0.001). Multivariate analysis showed that both NLR and PLR were significantly associated with unsuccessful reperfusion (adjusted odds ratio (95% confidence interval): 1.11 (1.04–1.19), PLR: 1.004 (1.001–1.01)). The receiver operating characteristic curve showed that the predictive ability of both NLR and PLR was close to good (area under the curve (AUC) of NLR: 0.63, 95% CI (0.54–0.72), p < 0.001; AUC of PLR: 0.65, 95% CI (0.57–0.73), p < 0.001). The cutoff values of NLR and PLR were 6.2 and 103.6 for unsuccessful reperfusion, respectively. Conclusion: Higher NLR and PLR were associated with unsuccessful reperfusion after EVT. The combined application of both biomarkers could be useful for predicting outcomes after EVT.

Background: Several studies have reported changes in the corpus callosum (CC) in Alzheimer’s disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer’s disease. Methods: We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made. Results: The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65–0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649). Conclusions: Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.

The protective effect of education has been well established in Alzheimer’s disease, whereas its role in patients with isolated cerebrovascular diseases remains unclear. We examined the correlation of education with cortical thickness and cerebral small vessel disease markers in patients with pure subcortical vascular mild cognitive impairment (svMCI) and patients with pure subcortical vascular dementia (SVaD). We analyzed 45 patients with svMCI and 47 patients with SVaD with negative results on Pittsburgh compound B positron emission tomographic imaging who underwent structural brain magnetic resonance imaging. The main outcome was cortical thickness measured using surface-based morphometric analysis. We also assessed the volumes of white matter hyperintensities (WMH) and numbers of lacunes as other outcomes. To investigate the correlation of education with cortical thickness, WMH volume, and number of lacunes, multiple linear regression analyses were performed after controlling for covariates, including Mini Mental State Examination, in the svMCI and SVaD groups. In the SVaD group, higher education was correlated with more severe cortical thinning in the bilateral dorsolateral frontal, left medial frontal, and parahippocampal areas, whereas there was no correlation of education with cortical thickness in the svMCI group. There was no correlation between education and cerebral small vessel disease, including WMH and lacunes, in both patients with svMCI and patients with SVaD. Our findings suggest that the compensatory effects of education on cortical thinning apply to patients with SVaD, which might be explained by the cognitive reserve hypothesis.

Although amnestic mild cognitive impairment (aMCI) is generally considered to be a prodromal stage of Alzheimer’s disease, patients with aMCI show heterogeneous patterns of progression. Moreover, there are few studies investigating data-driven cognitive trajectory in aMCI. We therefore classified patients with aMCI based on their cognitive trajectory, measured by clinical dementia rating sum of boxes (CDR-SOB). Then, we compared the clinical and neuroimaging features among groups classified by cognitive trajectory. We retrospectively recruited 278 patients with aMCI who underwent three or more timepoints of neuropsychological testing. They also had magnetic resonance imaging (MRI) including structured three-dimensional volume images. Cortical thickness was measured using surface-based methods. We performed trajectory analyses to classify our aMCI patients according to their progression and investigate their cognitive trajectory using CDR-SOB. Trajectory analyses showed that patients with aMCI were divided into three groups: stable (61.8%), slow decliner (31.7%), and fast decliner (6.5%). Changes throughout a mean follow-up duration of 3.7 years in the CDR-SOB for the subgroups of stable/slow/fast decliners were 1.3-, 6.4-, and 12-point increases, respectively. Decliners were older and carried apolipoprotein E4 (APOE4) genotypes more frequently than stable patients. Compared with the stable group, decliners showed a higher frequency of aMCI patients with both visual and verbal memory dysfunction, late stage aMCI, and multiple domain dysfunction. In addition, compared with the stable group, the slow decliners showed cortical thinning predominantly in bilateral parietotemporal areas, while the fast decliners showed cortical thinning predominantly in bilateral frontotemporal areas. Both decliner groups showed worse cognitive function in attention, language, visuospatial, memory, and frontal/executive domains than the stable group. Our data-driven trajectory analysis provides new insights into heterogeneous cognitive trajectories of aMCI and further suggests that baseline clinical and neuroimaging profiles might predict aMCI patients with poor prognosis.

We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the mPDT. For the scoring protocol, the U-Net was trained with 199 PDT hand-drawn images of 512 × 512 resolution obtained via the mPDT in order to generate a trained model, Deep5, for segmenting a drawn right or left pentagon. The U-Net was also trained with 199 images of 512 × 512 resolution to attain the trained model, DeepLock, for segmenting an interlocking figure. Here, the epochs were iterated until the accuracy was greater than 98% and saturated. The mobile senor data primarily consisted of x and y coordinates, timestamps, and touch-events of all the samples with a 20 ms sampling period. The velocities were then calculated using the primary sensor data. With Deep5, DeepLock, and the sensor data, four parameters were extracted. These included the number of angles (0–4 points), distance/intersection between the two drawn figures (0–4 points), closure/opening of the drawn figure contours (0–2 points), and tremors detected (0–1 points). The parameters gave a scaling of 11 points in total. The performance evaluation for the mPDT included 230 images from subjects and their associated sensor data. The results of the performance test indicated, respectively, a sensitivity, specificity, accuracy, and precision of 97.53%, 92.62%, 94.35%, and 87.78% for the number of angles parameter; 93.10%, 97.90%, 96.09%, and 96.43% for the distance/intersection parameter; 94.03%, 90.63%, 92.61%, and 93.33% for the closure/opening parameter; and 100.00%, 100.00%, 100.00%, and 100.00% for the detected tremor parameter. These results suggest that the mPDT is very robust in differentiating dementia disease subtypes and is able to contribute to clinical practice and field studies.