Adaptive Parameter Selection for Deep Brain Stimulation in Parkinson’s Disease

Each year, around 60,000 people are diagnosed with Parkinson’s disease (PD) and the economic burden of PD is at least $14.4 billion a year in the United States. Pharmaceutical costs for a Parkinson’s patient can be reduced from$12,000 to $6,000 per year with the addition of neuromodulation therapies such as Deep Brain Stimulation (DBS), transcranial Direct Current Stimulation (tDCS), Transcranial Magnetic Stimulation (TMS), etc. In neurodegenerative disorders such as PD, deep brain stimulation (DBS) is a desirable approach when the medication is less effective for treating the symptoms. DBS incorporates transferring electrical pulses to a specific tissue of the central nervous system and obtaining therapeutic results by modulating the neuronal activity of that region. The hyperkinetic symptoms of PD are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this work, we developed several computational models of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. These models are able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Traditional High Frequency Stimulations (HFS) has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we investigated the effects of the stimulation waveform and interphase delays on the overall efficiency and efficacy of DBS. We also propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitude needed. The efficiency and efficacy of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behaviors observed in neurological disorders. Adaptive stimulation can open doors towards simultaneous stimulation with MRI recordings. We additionally propose a new pipeline to investigate the effect of Transcranial Magnetic Stimulation (TMS) on patient specific models. The pipeline allows us to generate a full head segmentation based on each individual MRI data. In the next step, the neurosurgeon can adaptively choose the proper location of stimulation and transmit accurate magnetic field with this pipeline

Quantitative EEG and neuromodulation for the treatment of central neuropathic pain in paraplegic patients

Approximately, 2/3 of patients with a spinal cord injury (SCI) suffer from chronic pain, leading to a reduction in quality of life. The prevalence of chronic central neuropathic pain (CNP) in the SCI population is 40%. Recent neuroimaging studies provided evidence that CNP is accompanied by modified brain activity at surface and deep cortical levels and that CNP is resistant to different pharmacological and non-pharmacological treatments. Our current knowledge on how CNP affects the brain activity of SCI patients is mainly based on fMRI studies. Although these studies provide precise spatial localisation of brain regions most affected by CNP, they indirectly measure brain activity through measuring blood oxygenation. Therefore they lack information specific to neuronal activity such as dynamic, time and frequency dependant oscillatory activity of cortical structures. Therefore, in Phase 1 of this study, electroencephalogram (EEG) activity of paraplegic patients with CNP (PWP) is compared with the EEG activity of able-bodied (AB) participants and paraplegic patients without CNP (PNP). It was found that CNP leads to frequency dependant EEG signatures both in the relaxed state and during motor tasks that are not restricted to the cortical representation of the body part perceived as being painful. The pharmacological treatment of CNP has a number of side effects and does not provide significant pain relief. The effect of non-pharmacological treatments is inconsistent. Neurofedback (NF) is a non-pharmacological treatment, based on the voluntarily modulation of brain activity to control pain intensity. Using NF training the patient can learn and apply a mental strategy to control pain, without the need for an external device. However, NF requires a large number of training sessions to learn the necessary mental strategy. Therefore, in Phase 2 of this study, the effect on pain intensity of a large number of NF sessions, using different NF training protocols, was assessed. The clinically and statistically significant reduction of pain observed in this study demonstrates that NF training has the potential to manage chronic CNP in paraplegic patients. This study also provides evidence that the reduction of pain achieved using NF training may not be due to a placebo effect. Furthermore, the study demonstrates the immediate global effect of NF training on power and coherence. To date, no neuroimaging studies that have applied NF training with patients with CNP have shown changes in brain activity before the first and after the last training session. Therefore, in phase 3 of this study, the long-term neurological effect of NF training was assessed using EEG. This study provides evidence that NF training does not only induce an effect on spontaneous EEG activity, but also induces changes on evoked EEG activity. In conclusion, this study compared the EEG activity of three groups (AB, PWP, and PNP) and found that CNP (PWP group) leads to frequency dependant dynamic oscillatory signatures. The study also reported that NF training has a potential to reduce pain and this reduction of pain might not be an effect of placebo. Furthermore, it was found that NF training induce long-term changes in the EEG activity recorded in relaxed state and during motor tasks. This long-term change in EEG activity was noticed at the surface and deep cortical structures

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202