The relationship between apathy and nonparametric variables of rest activity rhythm in older adults with cerebral small vessel disease

The goal of the current study was to demonstrate if the rest-activity rhythm (RAR) was altered in apathetic older adults with cerebral small vessel disease (CSVD) and find out the relationship between apathy/depression severity and RAR features in CSVD patients. This is a cross-sectional observational investigation including 53 CSVD cases (54.74% men), aged 70.70 ± 6.18 years old. The participants were assessed by neuropsychiatric inventory (NPI) subscale of apathy (NPI-apathy) and depression (NPI-depression) in succession, according to updated diagnostic criteria for apathy (DCA). Each subject wore an actigraph device (ActiGraph GT3X) in their nondominant hand for 7 days to collect raw data. Using a non-parametric methodological analysis, this study determined RAR variables such as interdaily stability (IS), intraday variability (IV) and relative amplitude (RA). Patients in the apathy-positive group had a higher Fazekas score than those in the apathy-negative group. IS, but not IV, RA, or objective sleep variables, differed between elderly patients with varying degrees of CSVD burden. Furthermore, apathy severity was statistically correlated with RA after adjusting for age, gender and education level, whereas depression severity was not associated with RAR variables. Finally, we discovered that the severity of apathy had no significant relationship with the severity of depression. All these findings indicated that the RAR altered in apathetic older adults with CSVD, and apathy was associated with decreased RAR amplitude.</p

MOT16 ablation experiment (confidence 0.3).

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.</div

Feature extraction model diagram.

Images republished from Vidsplay.com [21] under a CC BY license, with permission, original copyright [2023].</p

The result of the target complete occlusion experiment.

Images republished from Vidsplay.com [21] under a CC BY license, with permission, original copyright [2023].</p

MOT16 ablation experiment DPM.

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.</div

Data_Sheet_2_Detecting apathy in patients with cerebral small vessel disease.docx

BackgroundApathy is attracting more and more attention in clinical practice. As one of the most common features of cerebral small vessel disease (CSVD), the assessment of apathy still mainly relies on observers. With the development of Information and Communication Technologies (ICTs), new objective tools take part in the early detection of apathy.ObjectivesTo detect apathy in patients with CSVD and find out the relationship between apathy and actigraphic data sampled from the diurnal and nocturnal periods.MethodsA total of 56 patients with CSVD were recruited for a cross-sectional observational study. Apathy was diagnosed by the diagnostic criteria for apathy in neurocognitive disorders. The presence of lacunes, white matter hyperintensities, cerebral microbleeds (CMBs), and perivascular spaces (PVS) in magnetic resonance imaging (MRI) images were rated independently. Actigraph devices were worn in the non-dominant hands of each subject for 7 consecutive days to collect samples of raw data, and diurnal vector magnitude (VM) and a series of sleep quality variables were obtained.ResultsWe found that the frequency of apathy in Chinese patients with CSVD reached 37.50%. Patients in the Apathy+ group showed more lacunes and CMBs, and higher Fazekas scores in comparison to apathy-group individuals. Diurnal VM, instead of other sleep quality variables, was lower in CSVD patients with apathy relative to those without apathy. Lastly, we discovered that diurnal VM and total time in bed (TTB) correlated negatively with apathy severity in patients with CSVD.ConclusionActigraphy is a promising choice to evaluate apathy in patients with CSVD.</p

Least squares handling occlusion.

Images republished from Vidsplay.com [21] under a CC BY license, with permission, original copyright [2023].</p

MOT16 ablation experiment (confidence 0.5).

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.</div

Comparison results of HOTA indicators.

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.</div

MOT16 ablation experiment CenterNet.

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.</div