Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors.

BACKGROUND AND OBJECTIVES: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. METHODS: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden. RESULTS: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden. CONCLUSIONS: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Cross-sectional associations between cortical thickness and independent gait domains in older adults.

BACKGROUND: Although the prevalence of gait disturbance is increasing with population aging, our understanding of its underlying neural basis is still limited. The precise brain regions linked to specific gait domains have not been well defined. In this study, we aim to investigate the associations of cortical thickness and different gait domains, and to explore whether these associations could be explained by cerebral small vessel disease. METHODS: A total of 707 community-dwelling participants from the Taizhou Imaging Study (mean age: 60.2 ± 3.0 years, 57.4% female) were involved. All participants underwent brain MRI and gait assessment. We obtained quantitative gait parameters using wearable devices and then summarized them into three independent gait domains through factor analysis. Cortical thickness was analyzed and visualized using FreeSurfer and Surfstat. RESULTS: Three independent domains (pace, rhythm, and variability) were summarized from 12 gait parameters. Among gait domains, poorer pace was associated with the thinner cortical thickness of multiple regions, which included areas related with motor function (e.g., the primary motor cortex, premotor cortex, and supplementary motor area), sensory function (e.g., the postcentral gyrus and paracentral lobule), visuospatial attention (e.g., the lateral occipital cortex and lingual gyrus), and identification and cognition (e.g., the fusiform gyrus and entorhinal cortex). Such a relationship was only slightly attenuated after adjustment for cerebrovascular risk factors and cerebral small vessel disease. No statistically significant association was found between cortical thickness and the rhythm or variability domains. CONCLUSIONS: Poorer pace is independently associated with thinner cortical thickness in areas important for motor, sensory, cognitive function, and visuospatial attention. Our study emphasizes the importance of cortical thickness in gait control and adds value in investigating neural mechanisms of gait.

Very Early-Onset Alzheimer's Disease in the Third Decade of Life with de novo PSEN1 Mutations.

Mutations in Presenilin-1 (PSEN1) have been found to be associated with very early onset Alzheimer's disease (VEOAD). Here, we reported two patients with VEOAD caused by de novo PSEN1 mutations. A 33-year-old man with a de novo p.F177S mutation in PSEN1 presented with progressive decline in memory and daily function. A 37-year-old woman with a de novo PSEN1 p.L381V mutation presented with onset memory impairment, developed cerebellar syndrome, rigidity, and spastic paraparesis. The Amyloid/Tau/Neurodegeneration (ATN) biomarker profiles of both patients were A + T + (N)+. Our finding increases the genetic knowledge of VEOAD and extends the ethnic distribution of PSEN1 mutations.

Carotid Atherosclerotic Calcification Characteristics Relate to Post-stroke Cognitive Impairment.

Background: Together with cerebral small vessel disease (CSVD), large vessel atherosclerosis is considered to be an equally important risk factor in the progression of vascular cognitive impairment. This article aims to investigate whether carotid atherosclerotic calcification is associated with the increased risk of post-stroke cognitive impairment (PSCI). Methods: A total of 128 patients (mean age: 62.1 ± 12.2 years, 37 women) suffering from ischemic stroke underwent brain/neck computer tomography angiography examination. The presence and characteristic of carotid calcification (size, number and location) were analyzed on computer tomography angiography. White matter hyperintensity (WMH) was assessed using Fazekas scales. PSCI was diagnosed based on a battery of neuropsychological assessments implemented 6-12 months after stroke. Results: Among 128 patients, 26 developed post-stroke dementia and 96 had carotid calcification. Logistic regression found carotid calcification (odds ratio [OR] = 7.15, 95% confidence interval [CI]: 1.07-47.69) and carotid artery stenosis (OR = 6.42, 95% CI: 1.03-40.15) both significantly increased the risk for post-stroke dementia. Moreover, multiple, thick/mixed, and surface calcifications exhibited an increasing trend in PSCI (P trend = 0.004, 0.016, 0.045, respectively). The prediction model for post-stroke dementia including carotid calcification (area under curve = 0.67), WMH (area under curve = 0.67) and other covariates yielded an area under curve (AUC) of 0.90 (95% CI: 0.82-0.99). Conclusion: Our findings demonstrated that the quantity and location of carotid calcifications were independent indicators for PSCI. The significant role of large vessel atherosclerosis in PSCI should be concerned in future study.

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

Cerebral small vessel disease is associated with gait disturbance among community-dwelling elderly individuals: the Taizhou imaging study.

Gait disturbance is considered to be a significant clinical manifestation of cerebral small vessel disease (CSVD). We aimed to investigate the association between different imaging markers of CSVD or total CSVD burden and gait disturbance in a community-dwelling population. In the cross-sectional Taizhou Imaging Study (TIS), 314 participants free of neurological disorders underwent MRI scanning and gait assessment with quantitative wearable devices as well as clinical rating scales. In linear regression, after adjustment for demographics and vascular risks, total CSVD burden was associated with prolonged 3-m walking (ß=0.118, P=0.035), shorter stride length (ß=-0.106, P=0.042), and poorer Timed-Up-and-Go (TUG) performance (ß=0.146, P=0.009). Lacunes were positively associated with 3-m walking (ß=0.118, P=0.037) and duration of TUG test (ß=0.112, P=0.047). White matter hyperintensities and cerebral microbleeds were associated with prolonged stride time (ß=0.134, P=0.024) and increased stance phase time percentage (ß=0.115, P=0.038), respectively. Logistic regression revealed that participants with high CSVD burden or more lacunes were more likely to have an impaired gait velocity and an impaired TUG test. These results suggest that total CSVD burden and CSVD imaging markers are associated with gait disturbance among community-dwelling elderly people. Different CSVD imaging markers may cause gait disturbance through different pathways.

Predictors of Cognitive Impairment After Stroke: A Prospective Stroke Cohort Study.

BACKGROUND: Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-stroke patients. OBJECTIVE: To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI. METHODS: We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency. RESULTS: One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age (ß= 0.065, OR = 1.067, 95% CI = 1.016-1.120), years of education (ß= -0.346, OR = 0.707, 95% CI = 0.607-0.824), periventricular hyperintensity grading (ß= 1.253, OR = 3.501, 95% CI = 1.652-7.417), diabetes mellitus (ß= 1.762, OR = 5.825, 95% CI = 2.068-16.412), and the number of acute nonlacunar infarcts (ß= 0.569, OR = 1.766, 95% CI = 1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUC = 0.884, 95% CI = 0.832-0.935). CONCLUSIONS: The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.

Total Cerebral Small Vessel Disease Burden Is Related to Worse Performance on the Mini-Mental State Examination and Incident Dementia: A Prospective 5-Year Follow-Up.

Individual cerebral small vessel disease (CSVD) may cause cognitive decline. However, the association between total burden of CSVD and cognitive deterioration in the general population remains unclear. We aimed to determine whether total CSVD score is associated with cognitive performance change and incident dementia in the general population. In the longitudinal population-based Taizhou Imaging Study, 556 participants free of neurological disorders underwent brain MRI and neuropsychological testing at baseline. A total of 456 participants were followed up for cognitive performance for a mean (standard deviation) of 4.6 (0.6) years. Total CSVD score (range 0-4) was calculated by assigning 1 point for the presence of each of the following markers: lacune, white matter hyperintensity, cerebral microbleed, and perivascular space. Beta regression was used to evaluate the association between total CSVD burden and MMSE score change. The association of prevalent CSVD with incident dementia was studied using Fisher's exact test. CSVDs were present in 262 individuals (47.1%). The total CSVD score was significantly associated with MMSE score decline (p = 0.001). Compared to those with no CSVD, participants with 4 CSVD markers had a steeper decline in MMSE score (ß: -0.53, 95% CI: -0.86 to -0.21; p = 0.001). A total of 15 participants developed dementia during follow-up. The presence of more than three CSVD markers at baseline was associated with a significantly higher risk of dementia (p = 0.020). Total CSVD burden appears to be associated with MMSE score decline and incident dementia in a general population in China.