Neuromechanical control in Parkinson’s Disease: definition of new biomarkers of motor and non-motor symptoms

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder. Despite the extensive knowledge that has been acquired about it, some characteristics of the disorder remain unclear. Pharmacological and advanced therapies have progressed in recent years, however, optimal solutions are not always available for patients. Moreover, diagnosis of PD may be challenging since it relies on the clinical assessment of already developed motor symptoms. Early diagnosis is the objective of current research but practical solutions are not available yet. The translation from clinical management of the disorder to a biomarker-supported entity is foreseen. In this doctoral thesis, three aspects of PD were explored. Potential biomarkers were proposed for their management that aspired to solve the previously presented limitations. Firstly, an index for the quantification of face mobility degree was developed with the aim to be clinically applicable in hypomimia, a disabling impairment that causes face expression. Face mobility index (FMI) was derived from video acquisitions of facial expressions. The proposed approach was applied in two cohorts of healthy and PD subjects. FMI gives an overall score of face mobility that can be easily interpreted by physicians. Moreover, validation of the index against clinical scales proved its ability to recognize different levels of impairment in a pathological cohort with similar characteristics. In the second part, a neurophysiological assessment was employed to address cognitive impairments and advanced therapies. To this objective, data from a cohort of consecutive patients undergoing deep brain stimulation (DBS) surgery were collected. Externalized local field potentials (LFP) and electrocardiogram were acquired synchronously. Low-frequency bands (theta and alpha) and heart rate variability (HRV) parameters were retrieved, respectively. These features were finally correlated with clinical scales of depression, anxiety, and apathy. The obtained results were promising demonstrating the relationship between low frequencies and HRV, with deficits of the parasympathetic system linked to hyperarousal symptoms. The outcome of this study could be pivotal to better understanding the neurophysiological causes of cognitive impairments. Moreover, future developments could include applications of these biomarkers on adaptive DBS for controlling non-motor symptoms. Lastly, the interaction between motor and non-motor symptoms was investigated with two sub-studies. In the first one, a gait (speed) and cognition (verbal fluency) dual-task paradigm was implemented with the aim to assess DBS effects on these cognitive-demanding situations. Two cohorts of PD subjects were assessed, one with DBS and one without. The comparison highlighted the opposite effects of DBS in improving gait while worsening verbal fluency. Limitations included the reduced accuracy of the acquired data, that was the basis for the second study. This work represented a proof of concept that integrated acquisition of electroencephalogram and electromyography. The protocol was tested on a healthy participant and corticomuscular coherence was retrieved. This represents a sensitive biomarker of neurophysiology and could be employed as a measure of the interplay between central and peripheral nervous systems in complex dual-task paradigms, as the one presented in the first study. Future developments will include the testing of this protocol on PD individuals. Overall, the obtained results support the use of quantitative and repeatable biomarkers for PD management both for motor and non-motor symptoms. Translation to clinical practice has proven to be feasible with the development of easy-to-interpret metrics and portable systems. These tools could be included in the clinical routine with multiple objectives such as early diagnosis, tracking of disease progression, application on advanced therapies, and assessment of treatments.