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Sloane KL, Hamilton RH. Transcranial Direct Current Stimulation to Ameliorate Post-Stroke Cognitive Impairment. Brain Sci 2024; 14:614. [PMID: 38928614 PMCID: PMC11202055 DOI: 10.3390/brainsci14060614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/04/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Post-stroke cognitive impairment is a common and disabling condition with few effective therapeutic options. After stroke, neural reorganization and other neuroplastic processes occur in response to ischemic injury, which can result in clinical improvement through spontaneous recovery. Neuromodulation through transcranial direct current stimulation (tDCS) is a promising intervention to augment underlying neuroplasticity in order to improve cognitive function. This form of neuromodulation leverages mechanisms of neuroplasticity post-stroke to optimize neural reorganization and improve function. In this review, we summarize the current state of cognitive neurorehabilitation post-stroke, the practical features of tDCS, its uses in stroke-related cognitive impairment across cognitive domains, and special considerations for the use of tDCS in the post-stroke patient population.
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Affiliation(s)
- Kelly L. Sloane
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physical Medicine and Rehabilitation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Roy H. Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physical Medicine and Rehabilitation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Hou JF, Nayeem MOG, Caplan KA, Ruesch EA, Caban-Murillo A, Criado-Hidalgo E, Ornellas SB, Williams B, Pearce AA, Dagdeviren HE, Surets M, White JA, Shapiro MG, Wang F, Ramirez S, Dagdeviren C. An implantable piezoelectric ultrasound stimulator (ImPULS) for deep brain activation. Nat Commun 2024; 15:4601. [PMID: 38834558 PMCID: PMC11150473 DOI: 10.1038/s41467-024-48748-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 05/13/2024] [Indexed: 06/06/2024] Open
Abstract
Precise neurostimulation can revolutionize therapies for neurological disorders. Electrode-based stimulation devices face challenges in achieving precise and consistent targeting due to the immune response and the limited penetration of electrical fields. Ultrasound can aid in energy propagation, but transcranial ultrasound stimulation in the deep brain has limited spatial resolution caused by bone and tissue scattering. Here, we report an implantable piezoelectric ultrasound stimulator (ImPULS) that generates an ultrasonic focal pressure of 100 kPa to modulate the activity of neurons. ImPULS is a fully-encapsulated, flexible piezoelectric micromachined ultrasound transducer that incorporates a biocompatible piezoceramic, potassium sodium niobate [(K,Na)NbO3]. The absence of electrochemically active elements poses a new strategy for achieving long-term stability. We demonstrated that ImPULS can i) excite neurons in a mouse hippocampal slice ex vivo, ii) activate cells in the hippocampus of an anesthetized mouse to induce expression of activity-dependent gene c-Fos, and iii) stimulate dopaminergic neurons in the substantia nigra pars compacta to elicit time-locked modulation of nigrostriatal dopamine release. This work introduces a non-genetic ultrasound platform for spatially-localized neural stimulation and exploration of basic functions in the deep brain.
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Affiliation(s)
- Jason F Hou
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | | | - Kian A Caplan
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Evan A Ruesch
- Department of Psychological and Brain Sciences, The Center for Systems Neuroscience, Boston University, Boston, 02215, MA, USA
| | - Albit Caban-Murillo
- Department of Psychological and Brain Sciences, The Center for Systems Neuroscience, Boston University, Boston, 02215, MA, USA
| | - Ernesto Criado-Hidalgo
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Sarah B Ornellas
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Brandon Williams
- Center for Systems Neuroscience, Neurophotonics Center, Department of Biomedical Engineering, Boston University, 610 Commonwealth Ave., Boston, MA, 02215, USA
| | - Ayeilla A Pearce
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Huseyin E Dagdeviren
- Department of Neurosurgery, Faculty of Medicine, Istanbul University, Istanbul, 34093, Turkey
| | - Michelle Surets
- Department of Psychological and Brain Sciences, The Center for Systems Neuroscience, Boston University, Boston, 02215, MA, USA
| | - John A White
- Center for Systems Neuroscience, Neurophotonics Center, Department of Biomedical Engineering, Boston University, 610 Commonwealth Ave., Boston, MA, 02215, USA
| | - Mikhail G Shapiro
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Fan Wang
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Steve Ramirez
- Department of Psychological and Brain Sciences, The Center for Systems Neuroscience, Boston University, Boston, 02215, MA, USA
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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Eleni Karakatsani M, Estrada H, Chen Z, Shoham S, Deán-Ben XL, Razansky D. Shedding light on ultrasound in action: Optical and optoacoustic monitoring of ultrasound brain interventions. Adv Drug Deliv Rev 2024; 205:115177. [PMID: 38184194 DOI: 10.1016/j.addr.2023.115177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/27/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
Monitoring brain responses to ultrasonic interventions is becoming an important pillar of a growing number of applications employing acoustic waves to actuate and cure the brain. Optical interrogation of living tissues provides a unique means for retrieving functional and molecular information related to brain activity and disease-specific biomarkers. The hybrid optoacoustic imaging methods have further enabled deep-tissue imaging with optical contrast at high spatial and temporal resolution. The marriage between light and sound thus brings together the highly complementary advantages of both modalities toward high precision interrogation, stimulation, and therapy of the brain with strong impact in the fields of ultrasound neuromodulation, gene and drug delivery, or noninvasive treatments of neurological and neurodegenerative disorders. In this review, we elaborate on current advances in optical and optoacoustic monitoring of ultrasound interventions. We describe the main principles and mechanisms underlying each method before diving into the corresponding biomedical applications. We identify areas of improvement as well as promising approaches with clinical translation potential.
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Affiliation(s)
- Maria Eleni Karakatsani
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Switzerland; Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland
| | - Héctor Estrada
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Switzerland; Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland
| | - Zhenyue Chen
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Switzerland; Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland
| | - Shy Shoham
- Department of Ophthalmology and Tech4Health and Neuroscience Institutes, NYU Langone Health, NY, USA
| | - Xosé Luís Deán-Ben
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Switzerland; Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland.
| | - Daniel Razansky
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Switzerland; Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland.
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Sharma V, Páscoa dos Santos F, Verschure PFMJ. Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case. Front Neurol 2023; 14:1279875. [PMID: 38099071 PMCID: PMC10719856 DOI: 10.3389/fneur.2023.1279875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023] Open
Abstract
BrainX3 is an interactive neuroinformatics platform that has been thoughtfully designed to support neuroscientists and clinicians with the visualization, analysis, and simulation of human neuroimaging, electrophysiological data, and brain models. The platform is intended to facilitate research and clinical use cases, with a focus on personalized medicine diagnostics, prognostics, and intervention decisions. BrainX3 is designed to provide an intuitive user experience and is equipped to handle different data types and 3D visualizations. To enhance patient-based analysis, and in keeping with the principles of personalized medicine, we propose a framework that can assist clinicians in identifying lesions and making patient-specific intervention decisions. To this end, we are developing an AI-based model for lesion identification, along with a mapping of tract information. By leveraging the patient's lesion information, we can gain valuable insights into the structural damage caused by the lesion. Furthermore, constraining whole-brain models with patient-specific disconnection masks can allow for the detection of mesoscale excitatory-inhibitory imbalances that cause disruptions in macroscale network properties. Finally, such information has the potential to guide neuromodulation approaches, assisting in the choice of candidate targets for stimulation techniques such as Transcranial Ultrasound Stimulation (TUS), which modulate E-I balance, potentiating cortical reorganization and the restoration of the dynamics and functionality disrupted due to the lesion.
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Affiliation(s)
- Vivek Sharma
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
| | - Francisco Páscoa dos Santos
- Eodyne Systems S.L., Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Paul F. M. J. Verschure
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
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