Phillips V Z, et al. J Clin Neurol. 2023 Mar;19(2):115-124. https://doi.org/10.3988/jcn.2022.0406

The Valsalva maneuver (VM) with beat-to-beat blood pressure and heart rate monitoring are used to evaluate orthostatic intolerance (OI). However, they lack the ability to detect cerebral hemodynamic changes, which may be a cause of OI symptoms. Therefore, we utilized near-infrared spectroscopy during VM. Patients with OI symptoms and normal healthy subjects were recruited. Patients were subgrouped according to VM results: patients with normal VM (NVM) and abnormal VM (AbVM). Oxyhemoglobin (HbO), deoxyhemoglobin, and total hemoglobin changes were measured at four different source–detector distances (SD) (15, 30, 36, and 45 mm), and latency, amplitude, duration, and integrated total signal were calculated. Those parameters were compared between a normal healthy control (HC) group and the two OI patient subgroups. We found that HbO increment latency at 30-mm SD in the HC, NVM, and AbVM groups was as follows: 0.39 ± 0.23 s , 2.79 ± 0.36 s , and 8.14 ± 0.55 s , respectively ( p < 0.05 ). Among the four parameters we evaluated, latency of HbO increment was the best marker for differentiating OI.

Orthostatic intolerance (OI) is a form of autonomic dysfunction in which symptoms manifest due to postural changes. OI can have many symptoms, ranging from orthostatic hypotension (OH) [i.e., a sustained drop in blood pressure (BP) due to orthostatic stress] to syncope.¹ OH is the most common OI symptom for patients with Parkinson’s disease (PD), even in the early stages of the disease.^{2 – 5} In PD patients, OH is related to increased postural sway,⁶ risk of falling injury,^7,8 cardiovascular events,⁹ and cognitive impairment.¹⁰ Therefore, OH can impair the quality of life and increase mortality rates among patients with PD. Considering the high prevalence and detrimental effects of OH in PD patients, timely detection and management of OH is critical to promote favorable functional outcomes and to decrease mortality rates.

The head-up tilt test (HUTT) with simultaneous blood pressure (BP) monitoring has been widely used to diagnose orthostatic hypotension (OH) and it comprises three phases: dynamic tilt (supine to 70° tilt), static tilt (remain tilted at 70°), and post-tilt (return to supine).^{1 – 5} OH is one etiology of orthostatic intolerance (OI) diagnosed based on a reduction in BP within 3 min of the static tilt phase. However, the HUTT has poor reproducibility and low sensitivity.^6,7 Given that OH is common among patients with Parkinson’s disease (PD), early and accurate diagnosis is crucial for providing optimal intensive care to them.^{8 – 11} Several studies have explored alternative biomarkers to diagnose OH and overcome the limitations of HUTT.^{12 – 14}

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near‐infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting‐state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN‐based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting‐state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head‐up tilt test.

Small animal models have made significant contributions to enhancing our understanding of many different types of neurological disorders, including Alzheimer’s disease, stroke, and Parkinson’s disease.^{1 – 4} Such models allow for experiments in which various parameters can be tightly controlled, from the type of disease to the stimulus provided to the animal. Brain activity in small animals can be measured both non-invasively, such as with positron emission tomography (PET)⁵ or functional magnetic resonance imaging (fMRI),^6,7 and invasively, such as with optical microscopy imaging.^8,9 However, the use of these modalities for small animal models is limited because they typically require immobilization during imaging.