Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

Neurocognitive change over the course of a multiday external lumbar drain trial in patients with suspected normal pressure hydrocephalus

Objective: To characterize neurocognitive response to cerebrospinal fluid (CSF) diversion during a multiday external lumbar drainage (ELD) trial in patients with suspected normal pressure hydrocephalus (NPH). Methods: Inpatients (N = 70) undergoing an ELD trial as part of NPH evaluation participated. Cognition and balance were assessed using standardized measures before and after a three-day ELD trial. Cognitive change pre- to post-ELD trial was assessed in relation to change in balance, baseline neuroimaging findings, NPH symptoms, demographics, and other disease-relevant clinical parameters. Results: Multiday ELD resulted in significant cognitive improvement (particularly on measures of memory and language). This improvement was independent of demographics, test-retest interval, number of medical and psychiatric comorbidities, NPH symptom duration, estimated premorbid intelligence, baseline level of cognitive impairment, cerebrovascular disease burden, degree of ventriculomegaly, or other NPH-related morphological brain alterations. Balance scores evidenced a greater magnitude of improvement than cognitive scores and were weakly, but positively correlated with cognitive change scores. Conclusions: Findings suggest that cognitive improvement associated with a multiday ELD trial can be sufficiently captured with bedside neurocognitive testing. These findings support the utility of neuropsychological consultation, along with balance assessment, in informing clinical decision-making regarding responsiveness to temporary CSF diversion for patients undergoing elective NPH evaluation. Implications for the understanding of neuroanatomical and cognitive underpinnings of NPH are discussed.

Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network

Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly's health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F1-scores of 0.914 and more.Clinical relevance- The proposed method showed excellent agreements with the GAITRite system in analyzing spatiotemporal gait parameters. Our approach can be applied to monitor the elderly's health conditions based on their gait parameters for early diagnosis of diseases, proper treatment, and timely intervention.

TRPV4 mRNA is elevated in the caudate nucleus with NPH but not in Alzheimer's disease

Symptoms of normal pressure hydrocephalus (NPH) and Alzheimer's disease (AD) are somewhat similar, and it is common to misdiagnose these two conditions. Although there are fluid markers detectable in humans with NPH and AD, determining which biomarker is optimal in representing genetic characteristics consistent throughout species is poorly understood. Here, we hypothesize that NPH can be differentiated from AD with mRNA biomarkers of unvaried proximity to telomeres. We examined human caudate nucleus tissue samples for the expression of transient receptor potential cation channel subfamily V member 4 (TRPV4) and amyloid precursor protein (APP). Using the genome data viewer, we analyzed the mutability of TRPV4 and other genes in mice, rats, and humans through matching nucleotides of six genes of interest and one house keeping gene with two factors associated with high mutation rate: 1) proximity to telomeres or 2) high adenine and thymine (A + T) content. We found that TRPV4 and microtubule associated protein tau (MAPT) mRNA were elevated in NPH. In AD, mRNA expression of TRPV4 was unaltered unlike APP and other genes. In mice, rats, and humans, the nucleotide size of TRPV4 did not vary, while in other genes, the sizes were inconsistent. Proximity to telomeres in TRPV4 was <50 Mb across species. Our analyses reveal that TRPV4 gene size and mutability are conserved across three species, suggesting that TRPV4 can be a potential link in the pathophysiology of chronic hydrocephalus in aged humans (>65 years) and laboratory rodents at comparable ages.

Plantar pressure-based temporal analysis of gait disturbance in idiopathic normal pressure hydrocephalus: Indications from a pilot longitudinal study

BACKGROUND AND OBJECTIVE

Idiopathic normal pressure hydrocephalus (iNPH) is a common yet potentially reversible neurodegenerative disease, and gait disturbance is a major symptom. Lots of methodological and clinical work has been conducted on gait disturbance analysis for differential diagnosis, presurgical test, and postsurgery assessment of iNPH. Nevertheless, the verification analysis was mostly lacking for surgery response, and the temporal characteristics of ground reaction force has been rarely investigated.

METHODS

In this work, we propose that plantar pressure features fundamentally signifies iNPH gait disturbance and improvement after cerebrospinal fluid (CSF) drainage by lumbar puncture tap test as well as surgical shunt implantation. The plantar pressure signals of six iNPH patients and eight healthy controls were collected, and an online database of sixteen healthy controls were used. For patients, data were collected in five periods, which are the baseline before the tap test, 8, 24, and 72 hours after the tap test, and one month after the shunt implantation surgery, respectively. Fast dynamic time warping (DTW) with an improved DTW barycenter averaging (DBA) method was proposed for temporal analysis with the measured and online plantar pressure data. An plantar-pressure variation index (PPVI) was formulated to characterize the temporal dynamic stability of walking.

RESULTS

The PPVI based on temporal analysis of plantar pressure well discriminated the impaired gait (baseline, 24 and 72 hours after tap test) with the improved gait (8 hours after tap test and follow up after surgery) of the patients. Further, the PPVI was close for the improved gait of the patients and the healthy gait measured in our study as well as in the online database.

CONCLUSIONS

Plantar pressure-based temporal features are promisingly effective for clinical examination and treatment of iNPH.

Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach

OBJECTIVE

Phase-contrast MRI allows detailed measurements of various parameters of CSF motion. This examination is technically demanding and machine dependent. The literature on this topic is ambiguous. Machine learning (ML) approaches have already been successfully utilized in medical research, but none have yet been applied to enhance the results of CSF flowmetry. The aim of this study was to evaluate the possible contribution of ML algorithms in enhancing the utilization and results of MRI flowmetry in idiopathic normal pressure hydrocephalus (iNPH) diagnostics.

METHODS

The study cohort consisted of 30 iNPH patients and 15 healthy controls examined on one MRI machine. All major phase-contrast parameters were inspected: peak positive, peak negative, and average velocities; peak amplitude; positive, negative, and average flow rates; and aqueductal area. The authors applied ML algorithms to 85 complex features calculated from a phase-contrast study.

RESULTS

The most distinctive parameters with p < 0.005 were the peak negative velocity, peak amplitude, and negative flow. From the ML algorithms, the Adaptive Boosting classifier showed the highest specificity and best discrimination potential overall, with 80.4% ± 2.9% accuracy, 72.0% ± 5.6% sensitivity, 84.7% ± 3.8% specificity, and 0.812 ± 0.047 area under the receiver operating characteristic curve (AUC). The highest sensitivity was 85.7% ± 5.6%, reached by the Gaussian Naive Bayes model, and the best AUC was 0.854 ± 0.028 by the Extra Trees classifier.

CONCLUSIONS

Feature extraction algorithms combined with ML approaches simplify the utilization of phase-contrast MRI. The highest-performing ML algorithm was Adaptive Boosting, which showed good calibration and discrimination on the testing data, with 80.4% accuracy, 72.0% sensitivity, 84.7% specificity, and 0.812 AUC. Phase-contrast MRI boosted by the ML approach can help to determine shunt-responsive iNPH patients.