Development and preliminary validation of a virtual reality memory test for assessing visuospatial memory.

Background: Visuospatial memory impairment is a common symptom of Alzheimer's disease; however, conventional visuospatial memory tests are insufficient to fully reflect visuospatial memory impairment in daily life. Methods: To address patients' difficulties in locating and recalling misplaced objects, we introduced a novel visuospatial memory test, the Hidden Objects Test (HOT), conducted in a virtual environment. We categorized HOT scores into prospective memory, item free-recall, place free-recall, item recognition, and place-item matching scores. To validate the VR memory test, we compared HOT scores among individuals with Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI), and normal controls (NC), and also compared these scores with those of conventional neuropsychological tests. We tracked the participants' movement paths in the virtual environment and assessed basic features, such as total distance, duration, and speed. Additionally, we performed walking trajectory pattern mining such as outlier and stay-point detection. Results: We designed and implemented the HOT to simulate a house's living room and assess participants' ability to locate hidden objects. Our preliminary results showed that the total HOT score differed among 17 patients with AD, 14 with aMCI, and 15 NC (p < 0.001). The total HOT score correlated positively with conventional memory test scores (p < 0.001). Walking trajectories showed that patients with AD and aMCI wandered rather than going straight to the hidden objects. In terms of basic features, the total duration was significantly greater in AD than in NC (p = 0.008). In terms of trajectory pattern mining, the number of outliers, which were over 95% of the estimated trajectory, was significantly higher in AD than in NC (p = 0.002). The number of stay points, an index in which participants stayed in the same position for more than 2 s, was significantly higher in patients with AD and aMCI compared with NC (AD vs. NC: p = 0.003, aMCI vs. NC: p = 0.019). Conclusion: The HOT simulating real life showed potential as an ecologically valid test for assessing visuospatial memory function in daily life. Walking trajectory analysis suggested that patients with AD and aMCI wandered rather than going straight toward the hidden objects.

Development of Machine Learning Model for VO2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO2max) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland-Altman plot of measured and estimated VO2max, the VO2max values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: -0.33 mL·kg-1·min-1, bias: 0.30 mL·kg-1·min-1, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO2max and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.

Social Event Memory Test (SEMT): A Video-based Memory Test for Predicting Amyloid Positivity for Alzheimer's Disease.

Recent improvements in neuroimaging and molecular markers of Alzheimer's disease (AD) have aided diagnosis in the early stage of the disease, which greatly increases the chance for successful prevention and treatment. However, the expanding resources for AD diagnosis are unlikely to benefit all elderly due to economic burden. Here, we aimed to develop an inexpensive and sensitive method to detect early-stage AD. A scenario for real-world social event memory test (SEMT) was created and filmed in 360° video. Participants watched the 7-min video through head-mounted display (HMD) and then answered questionnaire about the video. We categorized the SEMT score into recall, recognition, and place-matching scores and compared them to scores on the Mini-Mental State Examination and Seoul Verbal Learning Test. Using the SEMT scores, we built a logistic regression model that discriminated between amyloid positivity and negativity of the participants, with a cross-validation AUC. Furthermore, a classifier was created using support vector machine, which produced 93.8-95.1% sensitivity in classifying individuals into four groups of normal, mild cognitive impairment with or without amyloid, and AD elderly. The high correlation between the SEMT score and amyloid positivity in individuals who experienced virtual social gathering through an HMD opens a new possibility for early diagnosis of AD.

Difficulty-related changes in inter-regional neural synchrony are dissociated between target and non-target processing.

The major purpose of this study was to explore the changes in the local/global gamma-band neural synchronies during target/non-target processing due to task difficulty under an auditory three-stimulus oddball paradigm. Multichannel event-related potentials (ERPs) were recorded from fifteen healthy participants during the oddball task. In addition to the conventional ERP analysis, we investigated the modulations in gamma-band activity (GBA) and inter-regional gamma-band phase synchrony (GBPS) for infrequent target and non-target processing due to task difficulty. The most notable finding was that the difficulty-related changes in inter-regional GBPS (33-35 Hz) at P300 epoch (350-600 ms) completely differed for target and non-target processing. As task difficulty increased, the GBPS significantly reduced for target processing but increased for non-target processing. This result contrasts with the local neural synchrony in gamma-bands, which was not affected by task difficulty. Another major finding was that the spatial patterns of functional connectivity were dissociated for target and non-target processing with regard to the difficult task. The spatial pattern for target processing was compatible with the top-down attention network, whereas that for the non-target corresponded to the bottom-up attention network. Overall, we found that the inter-regional gamma-band neural synchronies during target/non-target processing change significantly with task difficulty and that this change is dissociated between target and non-target processing. Our results indicate that large-scale neural synchrony is more relevant for the difference in information processing between target and non-target stimuli.