Parkinson's Disease (PD) is a progressive disorder of the nervous system characterized by muscle tremors, muscle rigidity, decreased mobility (bradykinesia), stooped posture, slow voluntary movements, and a mask-like facial expression. It may take time to diagnose because some of its symptoms are associated with the natural process of aging (Tolosa et al., 2006). Globally, disability and death in PD are increasing faster than any other neurological disorder. The World Health Organization (WHO) reports that the prevalence of PD has doubled in the past 25 years and world estimates count over 8.5 million individuals with PD in 2019. In people with early-onset PD, the initial symptoms can arise between the ages of 21 and 40 years, while the first symptoms in juvenile-onset disease occur before the age of 20 years. Nowadays, a standard criterion in the evaluation of PD is still one of the main goals for clinicians. Finding the right category for the progression of the disease is necessary to prescribe the best treatment. Specific signs, symptoms, or test results can help in the classification of the disease. Over the years, accuracy has been improved by new diagnostic protocols that consider qualitative and quantitative aspects (Maffoni et al., 2017; María et al., 2020). Defining early-stage Parkinson's subjects when the symptoms are silent or weak remains a challenge.

PD stages are identified based on clinical observations: according to the Hoehn-Yahr staging system, stages are based primarily on motor symptoms (Goetz et al., 2007). Pre-clinical is characterized by the absence of signs or symptoms - genetic testing and counseling are available to identify risk factors. Prodromal corresponds to a stage of neurodegenerative changes. Symptoms are unspecific but the identification of early changes allows to intervene with initial therapies. Early-stage symptoms include mild tremors and some walking difficulty. It can affect only one side of the body and produce a decrease of facial expressions. These symptoms do not interfere with daily life much and are not always obvious (Goetz et al., 2007). Middle-stage balance and coordination are affected: a moderate-to-severe disability that affects daily life (Goetz et al., 2007). Later-stage subjects have difficulty standing and walking even with aids. Patients in this stage have severe disability (Goetz et al., 2007). Figure 1 reports the general classification of PD stages according to its main symptoms.

A preliminary analysis can be carried out by clinicians taking into account qualitative and quantitative aspects of PD, as shown in Figure 2.

Gait mobility and gait impairment is also seen to evolve with the progression of the disease are included as parameters of investigation. Issues with gait initiation, freezing of gait, reduced balance, and difficulties in postural control (Martignon et al., 2021) are some of common symptoms. Table 1 summarizes the main tests used in mobility and gait analyses.

Among the innovative techniques, blood tests show potential to be used for the detection of early-stage PD. Researchers identified a molecular profile that defines the disease but it is still under investigation and not yet available clinically (Agliardi et al., 2021).

In recent years, non-invasive brain imaging techniques have become more accurate for the detection of differences in brain morphology and functional activities in Parkinson's subjects (Politis, 2014). Brain positron emission tomography (PET) can estimate the disease progression and can be used to confirm the clinical diagnosis of PD. With specific radioactive drugs (18F-DOPA and 18-FDG) absorbed into the bloodstream, PET can provide very precise brain region and activation in PD subjects (Lu and Yuan, 2015). Single positron emission tomography (SPECT) is also used as method to confirm a Parkinson's diagnosis by highlighting cerebral blood flow and dopamine transporters in the brain (Lu and Yuan, 2015). In patients with Parkinson's disease, a distinct intensity pattern can be noted in the brain region that is deeply affected by degeneration, namely the basal ganglia that controls movement. Magnetic resonance imaging (MRI) is also used to diagnose PD in early onset subjects. MRIs can show small changes and damage in the brain tissue that can indicate PD. Often, these markers are present even before symptoms of PD begin. Transcranial sonography (TCS) has been established as a valuable supplementary tool in the diagnosis of PD. Alterations in the area of the hypoechogenic mesencepahlic brainstem can be visualized in about 90% of PD patients, which is measured planimetrically to determine the magnitude of the change (Berg et al., 2008). Increased iron levels contribute to this sonographic abnormality and indicate that iron can be responsible for the change in the echo signal. Iron accumulation can be a very early indicator in the pathogenesis of PD. Electroencephalography (EEG) can detect damage in the central nervous system and alterations in neurophysiological activity associated with PD. In recent studies, quantitative analysis of EEG data identified significant differences in PD patients versus healthy subjects. In particular, the anterior cingulate and temporal lobe are areas with an established pathology in PD. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in PD patients (Waninger et al., 2020).

Map structure and functions of the brain are obtained measuring the signals produced by neural activity. Each region can have a particular influence according to the disease and the activation of an area can be considered important in the understanding of the progression of the disease. Although cortical EEG coherence can serve as a reliable measure of disease severity, the use of EEG to study PD has not been fully investigated. Neurophysiological signals provide instantaneous information and can aid in improving the accuracy of the diagnosis.

EEG signals have different specific frequency bands. Features in sub-bands are particularly important to characterize different brain states. The standard frequency bands of interest are δ-band (0–4 Hz), θ-band (4–8 Hz), α-band (8–13 Hz), and β-band (13–30 Hz). Moreover, the quantification of EEG rhythms could provide an important biomarker for different neuropsychiatric and neurological disorders, such as schizophrenia, Alzheimer's disease, epilepsy, and Parkinson's disease (Hampel et al., 2010; Gandal et al., 2012; Kheiri et al., 2012). The combination of new analysis methods and EEG signal processing can contribute to the detection of early-stage PD. EEG reveals more important information underlying brain dysfunctions, which would be lost if analysis were restricted to traditional methods. Nowadays, many novel methods are suggested for EEG signal processing.

A recent study analyzed the EEG signals from 15 early-stage PD patients and 15 age-matched healthy controls during eyes-closed resting state (Han et al., 2013). Most EEG electrodes showed an increase in θ-band relative power for PD patients, while several other electrodes decreased, such as in the frontal and occipital cortex (Fp1, Fp2, F7, F3, Fz, Oz). Moreover, an increase in δ-band relative powers were reported, and a decrease in α-band and β-band relative powers for PD patients compared with healthy patients. Other studies present higher spectral power in the low frequency domain of EEG, compared with controls. Also in these cases, subjects were in the resting awake condition with the eyes closed (Moazami-Goudarzi et al., 2008; Serizawa et al., 2008).

Postural control (PC) and adaptation are part of a complex system to maintain or restore balance from any position or during motor activity. The central nervous system is fundamental in PC strategies and electroencephalography can underline the different cortical brain activities under different postural perturbations (Mochizuki et al., 2010; Barollo et al., 2020). PD usually interferes in this regulatory system, as can be clearly demonstrated by most motor symptoms, but to date, no study has yet been conducted on the analysis of postural kinematics in movement disorders. Our aim is to (i) investigate the postural strategy adopted in PD individuals and in healthy subjects; (ii) describe adaptation and how the brain adapts to the induced movement of a platform and visual stimuli using virtual reality (VR). However, postural control and adaptation have been extensively studied in healthy and blind subjects. In a recent study (Barollo et al., 2020), postural kinematics from HD-EEG have been measured during a postural perturbation applied to calf muscles. The main changes in cortical activity were found in Absolute Spectral Power (ASP) over four frequency bands. For postural adaptation, increases in the θ band in the frontal-central region for closed-eyes trials, and in the θ and β bands in the parietal region for open-eyes trials were reported. In habituation of the stance, no significant variations in ASP were observed during closed-eyes trials, whereas an increase in the θ, α, and β bands were observed with open eyes (Curtis et al., 2001). Furthermore, open-eyed trials generally yielded a greater number of significant differences across all bands during both adaptation and habituation, suggesting that cortical activity during postural perturbation may be regulated with visual feedback. This clearly shows a correspondence in cortical activity and postural kinematics during postural perturbation, and could also be developed for pathological postural control.

Other studies show similar results in healthy subjects, suggesting cortex activity as the main change in the frontal-central and frontal-parietal cortical regions during balance perturbation, specifically within α and θ frequencies (Sipp et al., 2013; Hülsdünker et al., 2015). Moreover, the increase of the ASP in the central region is demonstrated during high-demand postural correction, such as balance maintenance without allowing corrective foot placement (Barollo et al., 2020). In accordance, the increase in θ activity in the frontal-central regions implies the processing of postural stability during balance control. Thus, ASP differences in the θ band signify the planning of corrective steps and the analysis of the consequences of the subject falling. Instead, the significant differences in the α band reflect an inhibition of error detection within the cingulate cortex due to habituation. Other studies have been carried out with the blind (including both congenital blindness and acquired blindness). Congenitally blind subjects had poorer postural control (anterior-posterior and medio-lateral body swing) compared to sighted subjects. They use a more efficient mechanism for maintaining balance control through joint stiffness. These findings demonstrate that motor coordination, localization, or perception of body segments and movements in visually impaired individuals can be compensated by enhancing the proprioceptive and vestibular systems. Blindness leads to impaired postural balance and imbalance in static and dynamic tasks (Parreira et al., 2017). An overview of EEG studies in postural control is provided in Figure 3.

Our novel BioVRSea setup introduces a unique multi-biometric system that combines virtual reality and a moving platform to evaluate the postural control response. The system is designed to imitate the sensation of being at sea on a small boat, a situation which involves different balancing strategies. During the experiment, there are six phases (see Table 2, Figure 4) in which different biosignals are measured such as electromyography (EMG), center of pressure (CoP), and electroencephalography (EEG). Some of our recent studies emphasize the importance of BioVRSea setup allowing cohort differentiation and pathology assessment (Recenti et al., 2021; Aubonnet et al., 2022). The advantage of using BioVRSea is that we are measuring quantitative signals associated with postural control in a challenging environment. The experiment is a prototype and the purpose of the current research with BioVRSea is to gather as much data as possible with many simultaneous measurements in order to extrapolate the most relevant features which could then be used in a clinical setting, with a lower-profile machine that could be accessed easily by those with mobility problems. Current diagnosis of PD relies primarily on the presence of motor symptoms in the patient (such as MDS diagnosis criteria (Postuma et al., 2015) and generally lacks any quantitative measurement such as we perform in the BioVRSea experiment.

In this paper, candidates with early-stage Parkinson's Disease undergo a postural control task in the BioVRSea environment. We focus on the differences between the two cohorts in the PRE and POST phases. Our focus on PRE and POST will allow the study of early stage PD while they are adapting to a motor stimulus.

The following results are reported for the analysis of the PRE and POST phases of the experiment with the aim of distinguishing between the PD and healthy groups based on their biosignal responses. Our experiment was able to identify changes in many of the analyzed domains.

The protocol is a visual-motor simulation of being on a boat and part of the subjects experienced the feeling of seasickness. Just under half of PD subjects experienced actual discomfort with various symptoms (reported on questionnaires), and a smaller percentage of them reported a self-assessment of motion sickness in daily life.

In previous work, we compared the identical PRE and POST phases in order to characterize different cohorts while they performed unassisted standing under conditions of sensory conflict after exposure to the complex postural control task of the moving platform and VR scene. EEG delta and theta power spectrum analysis and EMG activity in the soleus muscle proved to be strong discriminators between groups (Jacob et al., 2022).

In this paper, we use a similar multi-factorial approach to characterize PD and healthy participants on the basis of their postural control response during the BioVRSea experiment. In particular, we compared the balance response after a visual stimulus only–PRE phase: VR visual sea motion simulation; to the balance response obtained after a complete immersive sensory experience–POST phase: VR visual and correspondent motion stimulation.

This study is part of an extensive work in which a larger population is monitored. We have collected data from 324 volunteers (females 183, males 141, general age 33 ± 14). The overall population is between the ages of 18 and 29. Recruiting a larger group of older adults may open a new way to highlight age-related postural control strategies using the same protocol.

Our paper confirms that a comprehensive multi-factorial approach (our unique BioVRSea paradigm) is useful in discriminating early PD subjects. In addition, the ability to look at multiple parameters at once introduces the ability to further analyse the correlation and timing of our set of specific features changes. In fact, there are no previous multi-metric experiments, such as BioVRSea, and there are no experiments with larger patient cohorts, therefore a larger study is foreseen to provide more definitive conclusion. Several neural and motor strategies difference have been highlighted, and are in line with known literature.

It is also worth providing some elements for consideration, specifically in the PD diagnosis. Firstly, the 'early stage classification' has no absolute value, it is a subjective evaluation and may suffers from a degree of variability. This influences the time of the diagnosis, which, in turn, may also present early stage cohorts with non-homogeneous symptoms and impairment. This BioVRsea paradigm seems very promising for comprehensive quantitative assessments and may pave the way for highlighting the most relevant features in the specific motor diseases analyzed. This may favor the introduction of less complex quantitative analyses, specifically for clinical operators, to replicate only the most poignant aspects of our paradigm. Our results suggest that a simpler experimental design including concurrent EEG, bilateral lower limbs EMG and CoP analyses with a balance challenge (not necessarily VR led) can discriminate early stage PD and has the potential to stratify further stages of the pathology.

Postural control alteration is one of the major risk factors to facilitate the occurrence of falls in the elderly. The frailty index, which is the expression of the health status of older individuals, is related to falls (Tornero-Quinones et al., 2020; Taguchi et al., 2022). Higher levels of frailty indicate the presence of chronic diseases (Vinik et al., 2017), such as diabetes, chronic pain, and polypharmacy. Taken separately, those clinical conditions have been established to change the postural control and increase falls (Efstathiou et al., 2022; Rasmussen et al., 2023). We speculate that BioVRSea protocol may be added to the routine assessment of individuals with a high frailty index to identify early postural control deficit and address early intervention to prevent falls. Furthermore, individuals with other neurodegenerative disorders (i.e., multiple sclerosis - Comber et al., 2018; Molhemi et al., 2021 - or dementia - Nyman et al., 2019; Chepisheva, 2023), or neurological conditions (i.e., stroke - Chen et al., 2016 - or brain injury - Perez et al., 2020) may find advantage in the use BioVRSea protocol to explore the postural control alterations.

BioVRSea is an innovative model to analyze postural control response that can be used for different clinical applications. It may also prove useful to classify other related diseases and conditions of movement disorders, such as Progressive Supranuclear Palsy (PSP) or Huntington's disease (Porciuncula et al., 2020) and many other disorders that involve changes in postural control such as loss of balance and slow movement, where a similar performance can be expected. Having new collaborations with different disease categories will add more value and amplify the research.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Icelandic National Bioethics Committee (Application no: 17-183-S1). The patients/participants provided their written informed consent to participate in this study.

The idea for the paper was conceived by PG, HP, and VM. FP, LG, DJ, and SP processed the raw data from EEG, EMG, CoP, and HR and made the figures and tables. CG performed statistical analysis on the processed data. FP, LG, DJ, CG, PG, GD, and AF contributed to the text for the manuscript. All authors contributed to the article and approved the submitted version.