Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today’s technological potential. Adaptive DBS, where stimulation parameters depend on the patient’s physiological state, emerges as an important step towards “smart” DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Bringing astrocytes into the spotlight of electrical brain stimulation

“…However, these therapeutic approaches are constrained by side effects whose source is not understood [27,28] and their efficacy cannot be uniformly applied to the full spectrum of patients [28]. In the last years, major improvements have been made in the surgical technique [29] while, in comparison, there has been virtually no advance in the stimulation protocols since its implementation [28,30]. To increase the efficacy of the treatments (e.g.…”

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

“…Using power densities within the beta [69] and gamma [73] bands as features, Hidden Markov Models [70][74][73], SVM [71], convolutional neural networks (CNN) [70][75][72], linear discriminant analysis (LDA) [70], and logistic regression [76] were performed. It was also recommended in a couple studies that deep learning methods such as CNNs are worth investigating as they capture nonlinear temporal dynamics and waveform shape [70][72]. For example, Haddock et al (2019) [27] developed a deep learning method that classified the behaviors of ET patients based on the PSD of ECoG and used the classification results to turn DBS ON/OFF.…”

“…In recent closed-loop DBS control systems, machine learning methods have been developed to map biomarker features (input) to patients' observed states (output) and could further deliver an appropriate DBS setting [25][69] [70]. Therefore, it is imperative to identify key biomarkers that need to be extracted from the LFP and EMG as they will serve as input features for a judiciously Hjorth parameters [72], beta band power [25], and burst duration [70]. Using power densities within the beta [69] and gamma [73] bands as features, Hidden Markov Models A key improvement to existing machine learning methods is to incorporate physiological characterizations of the input-output mapping.…”

“…[70][74][73], SVM[71], convolutional neural networks (CNN)[70][75][72], linear discriminant analysis (LDA)[70], and logistic regression[76] were performed. It was also recommended in a couple studies that deep learning methods such as CNNs are worth investigating as they capture nonlinear temporal dynamics and waveform shape[70][72].…”

A Paradigm Shift in the Management of Patients with Parkinson’s Disease

“…Achieving high accuracy in detecting all motor symptoms is a fundamental requirement for the successful integration of wearable technologies into routine medical practice. Precision, consistency, and reliability are indispensable factors in the ongoing advancement of closed-loop systems that utilize real-time symptom data to automate treatment adjustments [71]. Adopting a user-centered design approach that involves patients, caregivers, and physicians in the development of wearable systems' features is expected to enhance user engagement and compliance [64].…”