A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson's Disease.

BACKGROUND: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson's disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. METHODS: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. RESULTS: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2-3%. CONCLUSIONS: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

A review of clinical studies of electrodermal activity and transcranial magnetic stimulation.

There is a growing body of evidence indicative of changes in autonomic nervous system (ANS) activity in patients with disorders of the central nervous system (CNS). Non-invasive measures of the ANS, including heart rate variability (HRV), electrodermal activity (EDA), and pupillary light reflex (PLR) may have value as markers of symptom severity, subtype, risk profile, and/or treatment response. In this paper we provide an introduction into the anatomy and physiology of EDA and review the literature published after 2007 in which EDA was an outcome measure of cortical stimulation with transcranial magnetic stimulation (TMS). Eleven studies were included and considered regarding the potential of EDA as an outcome measure reflecting ANS activity in TMS research and treatment. These studies are summarized according to study population, experimental methodology, cortical region targeted, and correlation with other measures of ANS activity. Results indicate that EDA changes vary with the frequency and target of TMS. Inhibitory TMS to the dorsolateral prefrontal cortex (dlPFC) was the most common paradigm in these studies, consistently resulting in decreased EDA.

Autonomic measures identify stress, pain, and instability associated with retinopathy of prematurity ophthalmologic examinations.

Background: Retinopathy of prematurity (ROP) ophthalmologic examinations cause stress and pain. Infants' stress and pain can be measured non-invasively using skin conductance (SC) and high frequency heart rate variability (HF-HRV), reflecting sympathetic-mediated sweating and parasympathetic activity, respectively. Objectives: To test the utility of SC to detect sympathetic activation during ROP examination, and the contribution of HF-HRV to predict stability post-examination. Methods: In this prospective, single center study, we measured SC continuously pre-, during, and post-examination, and HRV at 24 h pre-ROP examination. Clinical data included stability [apneas, bradycardias, and desaturations (A/B/Ds)], and interventions post-examination. Results: SC increased 56% above baseline during ROP examination (p = 0.001) and remained elevated post-examination (p = 0.02). Post-hoc analysis showed higher illness acuity, represented by need for respiratory support, was associated with lower HF-HRV at 24 h pre-ROP examination (p = 0.001). Linear regression indicated lower HF-HRV at 24 h pre-examination contributed to the need for higher intervention (i.e., stimulation to breathe, oxygen support) particularly among infants with higher illness acuity [F(1, 15) = 5.05, p = 0.04; ß = -1.33, p = 0.04]. Conclusion: ROP examination induced a 2-fold increase in sympathetic activation which remained above baseline in recovery. Also, we propose that the low parasympathetic tone associated with autonomic imbalance contributes to instability and need for higher intervention to assure stabilization with A/B/D events. Our findings provide insight into the underestimation of adverse events associated with ROP examination and identification of infants who may be more vulnerable to potential sequelae following ROP examinations.

Skin Conductance as a Viable Alternative for Closing the Deep Brain Stimulation Loop in Neuropsychiatric Disorders.

Markers from local field potentials, neurochemicals, skin conductance, and hormone concentrations have been proposed as a means of closing the loop in Deep Brain Stimulation (DBS) therapy for treating neuropsychiatric and movement disorders. Developing a closed-loop DBS controller based on peripheral signals would require: (i) the recovery of a biomarker from the source neural stimuli underlying the peripheral signal variations; (ii) the estimation of an unobserved brain or central nervous system related state variable from the biomarker. The state variable is application-specific. It is emotion-related in the case of depression or post-traumatic stress disorder, and movement-related for Parkinson's or essential tremor. We present a method for closing the DBS loop in neuropsychiatric disorders based on the estimation of sympathetic arousal from skin conductance measurements. We deconvolve skin conductance via an optimization formulation utilizing sparse recovery and obtain neural impulses from sympathetic nerve fibers stimulating the sweat glands. We perform this deconvolution via a two-step coordinate descent procedure that recovers the sparse neural stimuli and estimates physiological system parameters simultaneously. We next relate an unobserved sympathetic arousal state to the probability that these neural impulses occur and use Bayesian filtering within an Expectation-Maximization framework for estimation. We evaluate our method on a publicly available data-set examining the effect of different types of stress on peripheral signal changes including body temperature, skin conductance and heart rate. A high degree of arousal is estimated during cognitive tasks, as are much lower levels during relaxation. The results demonstrate the ability to decode psychological arousal from neural activity underlying skin conductance signal variations. The complete pipeline from recovering neural stimuli to decoding an emotion-related brain state using skin conductance presents a promising methodology for the ultimate realization of a closed-loop DBS controller. Closed-loop DBS treatment would additionally help reduce unnecessary power consumption and improve therapeutic gains.