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Ubeda Matzilevich E, Daniel PL, Little S. Towards therapeutic electrophysiological neurofeedback in Parkinson's disease. Parkinsonism Relat Disord 2024; 121:106010. [PMID: 38245382 DOI: 10.1016/j.parkreldis.2024.106010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/22/2024]
Abstract
Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.
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Affiliation(s)
- Elena Ubeda Matzilevich
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
| | - Pria Lauren Daniel
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA; Department of Psychology, University of California San Diego, CA, USA.
| | - Simon Little
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
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Ricciardi L, Apps M, Little S. Uncovering the neurophysiology of mood, motivation and behavioral symptoms in Parkinson's disease through intracranial recordings. NPJ Parkinsons Dis 2023; 9:136. [PMID: 37735477 PMCID: PMC10514046 DOI: 10.1038/s41531-023-00567-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 08/07/2023] [Indexed: 09/23/2023] Open
Abstract
Neuropsychiatric mood and motivation symptoms (depression, anxiety, apathy, impulse control disorders) in Parkinson's disease (PD) are highly disabling, difficult to treat and exacerbated by current medications and deep brain stimulation therapies. High-resolution intracranial recording techniques have the potential to undercover the network dysfunction and cognitive processes that drive these symptoms, towards a principled re-tuning of circuits. We highlight intracranial recording as a valuable tool for mapping and desegregating neural networks and their contribution to mood, motivation and behavioral symptoms, via the ability to dissect multiplexed overlapping spatial and temporal neural components. This technique can be powerfully combined with behavioral paradigms and emerging computational techniques to model underlying latent behavioral states. We review the literature of intracranial recording studies investigating mood, motivation and behavioral symptomatology with reference to 1) emotional processing, 2) executive control 3) subjective valuation (reward & cost evaluation) 4) motor control and 5) learning and updating. This reveals associations between different frequency specific network activities and underlying cognitive processes of reward decision making and action control. If validated, these signals represent potential computational biomarkers of motivational and behavioural states and could lead to principled therapy development for mood, motivation and behavioral symptoms in PD.
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Affiliation(s)
- Lucia Ricciardi
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK.
| | - Matthew Apps
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Simon Little
- Movement Disorders and Neuromodulation Centre, University of California San Francisco, San Francisco, CA, USA
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Chandrabhatla AS, Pomeraniec IJ, Horgan TM, Wat EK, Ksendzovsky A. Landscape and future directions of machine learning applications in closed-loop brain stimulation. NPJ Digit Med 2023; 6:79. [PMID: 37106034 PMCID: PMC10140375 DOI: 10.1038/s41746-023-00779-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/17/2023] [Indexed: 04/29/2023] Open
Abstract
Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
- Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Taylor M Horgan
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Elizabeth K Wat
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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de Hemptinne C, Chen W, Racine CA, Seritan AL, Miller AM, Yaroshinsky MS, Wang SS, Gilron R, Little S, Bledsoe I, San Luciano M, Katz M, Chang EF, Dawes HE, Ostrem JL, Starr PA. Prefrontal Physiomarkers of Anxiety and Depression in Parkinson's Disease. Front Neurosci 2021; 15:748165. [PMID: 34744613 PMCID: PMC8568318 DOI: 10.3389/fnins.2021.748165] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/17/2021] [Indexed: 11/19/2022] Open
Abstract
Objective: Anxiety and depression are prominent non-motor symptoms of Parkinson’s disease (PD), but their pathophysiology remains unclear. We sought to understand their neurophysiological correlates from chronic invasive recordings of the prefrontal cortex (PFC). Methods: We studied four patients undergoing deep brain stimulation (DBS) for their motor signs, who had comorbid mild to moderate anxiety and/or depressive symptoms. In addition to their basal ganglia leads, we placed a permanent prefrontal subdural 4-contact lead. These electrodes were attached to an investigational pulse generator with the capability to sense and store field potential signals, as well as deliver therapeutic neurostimulation. At regular intervals over 3–5 months, participants paired brief invasive neural recordings with self-ratings of symptoms related to depression and anxiety. Results: Mean age was 61 ± 7 years, mean disease duration was 11 ± 8 years and a mean Unified Parkinson’s Disease Rating Scale, with part III (UPDRS-III) off medication score of 37 ± 13. Mean Beck Depression Inventory (BDI) score was 14 ± 5 and Beck Anxiety Index was 16.5 ± 5. Prefrontal cortex spectral power in the beta band correlated with patient self-ratings of symptoms of depression and anxiety, with r-values between 0.31 and 0.48. Mood scores showed negative correlation with beta spectral power in lateral locations, and positive correlation with beta spectral power in a mesial recording location, consistent with the dichotomous organization of reward networks in PFC. Interpretation: These findings suggest a physiological basis for anxiety and depression in PD, which may be useful in the development of neurostimulation paradigms for these non-motor disease features.
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Affiliation(s)
- Coralie de Hemptinne
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Witney Chen
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Caroline A Racine
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andreea L Seritan
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew M Miller
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Maria S Yaroshinsky
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Sarah S Wang
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Roee Gilron
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ian Bledsoe
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Marta San Luciano
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Maya Katz
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Heather E Dawes
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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