Insights from a model based study on optimizing non invasive brain electrical stimulation for Parkinson's disease.

Parkinson's Disease (PD) is a disorder in the central nervous system which includes symptoms such as tremor, rigidity, and Bradykinesia. Deep brain stimulation (DBS) is the most effective method to treat PD motor symptoms especially when the patient is not responsive to other treatments. However, its invasiveness and high risk, involving electrode implantation in the Basal Ganglia (BG), prompt recent research to emphasize non-invasive Transcranial Electrical Stimulation (TES). TES proves to be effective in treating some PD symptoms with inherent safety and no associated risks. This study explores the potential of using TES, to modify the firing pattern of cells in BG that are responsible for motor symptoms in PD. The research employs a mathematical model of the BG to examine the impact of applying TES to the brain. This is conducted using a realistic head model incorporating the Finite Element Method (FEM). According to our findings, the firing pattern associated with Parkinson's disease shifted towards a healthier firing pattern through the use of tACS. Employing an adaptive algorithm that continually monitored the behavior of BG cells (specifically, Globus Pallidus Pars externa (GPe)), we determined the optimal electrode number and placement to concentrate the current within the intended region. This resulted in a peak induced electric field of 1.9 v/m at the BG area. Our mathematical modeling together with precise finite element simulation of the brain and BG suggests that proposed method effectively mitigates Parkinsonian behavior in the BG cells. Furthermore, this approach ensures an improvement in the condition while adhering to all safety constraints associated with the current injection into the brain.

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain's active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Toward a Multi-Modal Brain-Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS.

Parkinson's disease (PD) causes impairments in cortical structures leading to motor and cognitive symptoms. While common disease management and treatment strategies mainly depend on the subjective assessment of clinical scales and patients' diaries, research in recent years has focused on advances in automatic and objective tools to help with diagnosing PD and determining its severity. Due to the link between brain structure deficits and physical symptoms in PD, objective brain activity and body motion assessment of patients have been studied in the literature. This study aimed to explore the relationship between brain activity and body motion measures of people with PD to look at the feasibility of diagnosis or assessment of PD using these measures. In this study, we summarised the findings of 24 selected papers from the complete literature review using the Scopus database. Selected studies used both brain activity recording using functional near-infrared spectroscopy (fNIRS) and motion assessment using sensors for people with PD in their experiments. Results include 1) the most common study protocol is a combination of single tasks. 2) Prefrontal cortex is mostly studied region of interest in the literature. 3) Oxygenated haemoglobin (HbO 2) concentration is the predominant metric utilised in fNIRS, compared to deoxygenated haemoglobin (HHb). 4) Motion assessment in people with PD is mostly done with inertial measurement units (IMUs) and electronic walkway. 5) The relationship between brain activity and body motion measures is an important factor that has been neglected in the literature.