Periodic Reporting for period 1 - gaitDCODE (Decoding impairments of gait and balance from local field potentials in patients with Parkinson's disease.)
Berichtszeitraum: 2018-04-01 bis 2020-03-31
Impairments of gait and balance are among the most incapacitating and least well-understood symptoms of Parkinson's disease (PD). In individuals suffering from such deficits, ambulation and everyday independence get severely affected due to the high risk of fall-related injuries. Well-established neuromodulation therapies addressing Basal Ganglia dysfunction in PD, which for decades have been optimized to address upper-limb motor symptoms, are highly effective for the symptomatic treatment of tremor, bradykinesia and rigidity. However they often fail to alleviate, or can even aggravate gait deficits. For instance, aspects such as gait initiation, postural instability and freezing of gait have been reported to worsen with continuous high-frequency (130Hz) deep brain stimulation (DBS) presumably due to the divergence in the nature and dynamics of the circuits that control leg function during gait. This has compelled the search for alternative ad-hoc non- continuous stimulation paradigms. Yet to date, the relevant approaches for therapeutic intervention remain unclear, and the underlying mechanisms largely unknown.
A key limitation holding back the design of evidence-based therapies is the lack of mechanistic readouts that correlate pathological neural activity patterns and leg dysfunction during gait. However this identification is contingent on technologies, concepts and methodologies allowing to simultaneously record and link brain states to whole-body biomechanical features representative of gait deficits.
To address this knowledge gap, project gaitDCODE established a high-resolution tracking platform at the Lausanne University Hospital, endowed with unique equipment for recording and modulating gait wirelessly and in real-time, in order to map the activity of subthalamic nucleus onto kinematic, kinetic, and muscle activity patterns while patients execute a range of activities of daily living. We further established the analytical concepts and methodologies necessary to identify and extract robust features representative of gait deficits from the high-dimensional, time-varying biomechanics that govern locomotion. Jointly, this approach allowed to record and link brain and locomotor states with a level of detail not yet shown in Parkinson's disease.
gaitDCODE followed an incremental roadmap, and identified neural correlates underlying leg motor function (and in particular of leg force modulation). It then leveraged this understanding to develop decoding algorithms that accurately predicted leg force modulations in real-time for different conditions, hence opening the possibility to design closed-loop neuromodulation therapies that may be automatically address such motor requirements based on online feedback of leg motor performance.
A key limitation holding back the design of evidence-based therapies is the lack of mechanistic readouts that correlate pathological neural activity patterns and leg dysfunction during gait. However this identification is contingent on technologies, concepts and methodologies allowing to simultaneously record and link brain states to whole-body biomechanical features representative of gait deficits.
To address this knowledge gap, project gaitDCODE established a high-resolution tracking platform at the Lausanne University Hospital, endowed with unique equipment for recording and modulating gait wirelessly and in real-time, in order to map the activity of subthalamic nucleus onto kinematic, kinetic, and muscle activity patterns while patients execute a range of activities of daily living. We further established the analytical concepts and methodologies necessary to identify and extract robust features representative of gait deficits from the high-dimensional, time-varying biomechanics that govern locomotion. Jointly, this approach allowed to record and link brain and locomotor states with a level of detail not yet shown in Parkinson's disease.
gaitDCODE followed an incremental roadmap, and identified neural correlates underlying leg motor function (and in particular of leg force modulation). It then leveraged this understanding to develop decoding algorithms that accurately predicted leg force modulations in real-time for different conditions, hence opening the possibility to design closed-loop neuromodulation therapies that may be automatically address such motor requirements based on online feedback of leg motor performance.
We performed recordings in patients who underwent deep brain stimulation surgery, and we recorded their brain signals as they performed a set of locomotor tasks (either joint-specific leg movements, or unconstrained walking) with different levels of effort.
We found distinct modulations in brain signals that strongly correlated with leg motor states, predominantly in specific frequency bands. The amount and temporal evolution of oscillatory power traces in these bands accurately predicted the force exerted by the patients over the course of the performed task. Importantly, modulations emerged regardless of the mobilized joint of the leg or direction of leg movement, although power differences across force levels varied for each condition. These observations motivated the design of decoding algorithms that accurately predicted leg force modulations in real-time.
These results open up new avenues for the development of adaptive neuromodulation therapies that employ predictions of leg motor states in real time to target and prevent gait and balance deficits in people with PD.
Furthermore, these results were shared among colleagues from the movement disorders and neuro-rehabilitaiton disciplines, both in the framework of conferences and visits to specific laboratories. They were additionally shared with the public through social media, university presentations at the master's level, dedicated international outreach programs, and via collaborations with industrial partners working in complementary fields, who gladly contributed to prepare short "user stories" the interest of biomedical applications
We found distinct modulations in brain signals that strongly correlated with leg motor states, predominantly in specific frequency bands. The amount and temporal evolution of oscillatory power traces in these bands accurately predicted the force exerted by the patients over the course of the performed task. Importantly, modulations emerged regardless of the mobilized joint of the leg or direction of leg movement, although power differences across force levels varied for each condition. These observations motivated the design of decoding algorithms that accurately predicted leg force modulations in real-time.
These results open up new avenues for the development of adaptive neuromodulation therapies that employ predictions of leg motor states in real time to target and prevent gait and balance deficits in people with PD.
Furthermore, these results were shared among colleagues from the movement disorders and neuro-rehabilitaiton disciplines, both in the framework of conferences and visits to specific laboratories. They were additionally shared with the public through social media, university presentations at the master's level, dedicated international outreach programs, and via collaborations with industrial partners working in complementary fields, who gladly contributed to prepare short "user stories" the interest of biomedical applications
Project gaitDCODE brings important clarifications about the role of specific circuits in the brain for the control of leg movements, and how these may underlie leg motor deficits in Parkinson's disease. Additionally the validation that these biomarkers may be decoded in real-time is an important proof of concept for envisioning the deployment of closed-loop therapies, automatically personalized for each patient based on their ongoing requirements.
The concepts, analytical methods and technologies employed in this project hold promises to help establish a bridge between the "movement disorders" and the "neuro-rehabilitation" disciplines in order to help further advance the understanding and implementation of solutions for locomotor problems in Parkinson's disease.
The concepts, analytical methods and technologies employed in this project hold promises to help establish a bridge between the "movement disorders" and the "neuro-rehabilitation" disciplines in order to help further advance the understanding and implementation of solutions for locomotor problems in Parkinson's disease.