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Bastola S, Jahromi S, Chikara R, Stufflebeam SM, Ottensmeyer MP, De Novi G, Papadelis C, Alexandrakis G. Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study. Bioengineering (Basel) 2024; 11:897. [PMID: 39329639 PMCID: PMC11428344 DOI: 10.3390/bioengineering11090897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/28/2024] Open
Abstract
Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.
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Affiliation(s)
- Subrat Bastola
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
| | - Saeed Jahromi
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Rupesh Chikara
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA;
| | - Mark P. Ottensmeyer
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Gianluca De Novi
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Christos Papadelis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - George Alexandrakis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
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Skandalakis GP, Neudorfer C, Payne CA, Bond E, Tavakkoli AD, Barrios-Martinez J, Trutti AC, Koutsarnakis C, Coenen VA, Komaitis S, Hadjipanayis CG, Stranjalis G, Yeh FC, Banihashemi L, Hong J, Lozano AM, Kogan M, Horn A, Evans LT, Kalyvas A. Establishing connectivity through microdissections of midbrain stimulation-related neural circuits. Brain 2024; 147:3083-3098. [PMID: 38808482 PMCID: PMC11370807 DOI: 10.1093/brain/awae173] [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: 12/14/2023] [Revised: 03/15/2024] [Accepted: 04/21/2024] [Indexed: 05/30/2024] Open
Abstract
Comprehensive understanding of the neural circuits involving the ventral tegmental area is essential for elucidating the anatomofunctional mechanisms governing human behaviour, in addition to the therapeutic and adverse effects of deep brain stimulation for neuropsychiatric diseases. Although the ventral tegmental area has been targeted successfully with deep brain stimulation for different neuropsychiatric diseases, the axonal connectivity of the region is not fully understood. Here, using fibre microdissections in human cadaveric hemispheres, population-based high-definition fibre tractography and previously reported deep brain stimulation hotspots, we find that the ventral tegmental area participates in an intricate network involving the serotonergic pontine nuclei, basal ganglia, limbic system, basal forebrain and prefrontal cortex, which is implicated in the treatment of obsessive-compulsive disorder, major depressive disorder, Alzheimer's disease, cluster headaches and aggressive behaviours.
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Affiliation(s)
- Georgios P Skandalakis
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens 10676, Greece
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Caitlin A Payne
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Evalina Bond
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Armin D Tavakkoli
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
| | | | - Anne C Trutti
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam 15926, The Netherlands
| | - Christos Koutsarnakis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens 10676, Greece
| | - Volker A Coenen
- Department of Stereotactic and Functional Neurosurgery, Medical Center of the University of Freiburg, Freiburg 79106, Germany
- Medical Faculty of the University of Freiburg, Freiburg 79110, Germany
- Center for Deep Brain Stimulation, Medical Center of the University of Freiburg, Freiburg 79106, Germany
| | - Spyridon Komaitis
- Queens Medical Center, Nottingham University Hospitals NHS Foundation Trust, Nottingham NG7 2UH, UK
| | | | - George Stranjalis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens 10676, Greece
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Layla Banihashemi
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jennifer Hong
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Andres M Lozano
- Division of Neurosurgery, University Health Network, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Michael Kogan
- Department of Neurosurgery, University of New Mexico School of Medicine, Albuquerque, NM 87106, USA
| | - Andreas Horn
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Linton T Evans
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Aristotelis Kalyvas
- Division of Neurosurgery, University Health Network, University of Toronto, Toronto, ON M5T 1P5, Canada
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3
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Qin Y, Wu J, Bulger E, Cao J, Dehghani H, Shinn-Cunningham B, Kainerstorfer JM. Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex. BIOMEDICAL OPTICS EXPRESS 2024; 15:4859-4876. [PMID: 39347003 PMCID: PMC11427190 DOI: 10.1364/boe.531576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/10/2024] [Accepted: 07/19/2024] [Indexed: 10/01/2024]
Abstract
Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.
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Affiliation(s)
- Yutian Qin
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Jingyi Wu
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Eli Bulger
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Jiaming Cao
- School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK
| | - Hamid Dehghani
- School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK
| | - Barbara Shinn-Cunningham
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Jana M. Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
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4
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Borhanazad M, van Wijk BC, Buizer AI, Kerkman JN, Bekius A, Dominici N, Daffertshofer A. Lateralized modulation of cortical beta power during human gait is related to arm swing. iScience 2024; 27:110301. [PMID: 39055930 PMCID: PMC11269954 DOI: 10.1016/j.isci.2024.110301] [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: 11/20/2023] [Revised: 05/15/2024] [Accepted: 06/14/2024] [Indexed: 07/28/2024] Open
Abstract
Human gait is a complex behavior requiring dynamic control of upper and lower extremities that is accompanied by cortical activity in multiple brain areas. We investigated the contribution of beta (15-30 Hz) and gamma (30-50 Hz) band electroencephalography (EEG) activity during specific phases of the gait cycle, comparing treadmill walking with and without arm swing. Modulations of spectral power in the beta band during early double support and swing phases source-localized to the sensorimotor cortex ipsilateral, but not contralateral, to the leading leg. The lateralization disappeared in the condition with constrained arms, together with an increase of activity in bilateral supplementary motor areas. By contrast, gamma band modulations that localized to the presumed leg area of sensorimotor cortex around the heel-strike events were unaffected by arm movement. Our findings demonstrate that arm swing is accompanied by considerable cortical activation that should not be neglected in gait-related neuroimaging studies.
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Affiliation(s)
- Marzieh Borhanazad
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Bernadette C.M. van Wijk
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Annemieke I. Buizer
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Department of Rehabilitation Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam 1081 HZ, the Netherlands
| | - Jennifer N. Kerkman
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Annike Bekius
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Centre, Utrecht University, Utrecht 3584 CG, the Netherlands
| | - Nadia Dominici
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Andreas Daffertshofer
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, the Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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5
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Meyer GM, Hollunder B, Li N, Butenko K, Dembek TA, Hart L, Nombela C, Mosley P, Akram H, Acevedo N, Borron BM, Chou T, Castaño Montoya JP, Strange B, Barcia JA, Tyagi H, Castle DJ, Smith AH, Choi KS, Kopell BH, Mayberg HS, Sheth SA, Goodman WK, Leentjens AFG, Richardson RM, Rossell SL, Bosanac P, Cosgrove GR, Kuhn J, Visser-Vandewalle V, Figee M, Dougherty DD, Siddiqi SH, Zrinzo L, Joyce E, Baldermann JC, Fox MD, Neudorfer C, Horn A. Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites. Biol Psychiatry 2024; 96:101-113. [PMID: 38141909 PMCID: PMC11190041 DOI: 10.1016/j.biopsych.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) is a promising treatment option for treatment-refractory obsessive-compulsive disorder (OCD). Several stimulation targets have been used, mostly in and around the anterior limb of the internal capsule and ventral striatum. However, the precise target within this region remains a matter of debate. METHODS Here, we retrospectively studied a multicenter cohort of 82 patients with OCD who underwent DBS of the ventral capsule/ventral striatum and mapped optimal stimulation sites in this region. RESULTS DBS sweet-spot mapping performed on a discovery set of 58 patients revealed 2 optimal stimulation sites associated with improvements on the Yale-Brown Obsessive Compulsive Scale, one in the anterior limb of the internal capsule that overlapped with a previously identified OCD-DBS response tract and one in the region of the inferior thalamic peduncle and bed nucleus of the stria terminalis. Critically, the nucleus accumbens proper and anterior commissure were associated with beneficial but suboptimal clinical improvements. Moreover, overlap with the resulting sweet- and sour-spots significantly estimated variance in outcomes in an independent cohort of 22 patients from 2 additional DBS centers. Finally, beyond obsessive-compulsive symptoms, stimulation of the anterior site was associated with optimal outcomes for both depression and anxiety, while the posterior site was only associated with improvements in depression. CONCLUSIONS Our results suggest how to refine targeting of DBS in OCD and may be helpful in guiding DBS programming in existing patients.
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Affiliation(s)
- Garance M Meyer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
| | - Barbara Hollunder
- Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ningfei Li
- Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Lauren Hart
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Cristina Nombela
- Biological and Health Psychology, School of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
| | - Philip Mosley
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia; Neurosciences Queensland, St. Andrew's War Memorial Hospital, Spring Hill, Queensland, Australia; Queensland Brain Institute, University of Queensland, St. Lucia, Brisbane, Queensland, Australia; Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health and Biosecurity, Herston, Queensland, Australia
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, United Kingdom
| | - Nicola Acevedo
- Centre for Mental Health, Swinburne University, Melbourne, Victoria, Australia; St. Vincent's Hospital, Melbourne, Victoria, Australia
| | - Benjamin M Borron
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Tina Chou
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Juan Pablo Castaño Montoya
- Department of Neurosurgery, Hospital Clínico San Carlos, Instituto de Investigacion Sanitaria San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| | - Juan A Barcia
- Department of Neurosurgery, Hospital Clínico San Carlos, Instituto de Investigacion Sanitaria San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Himanshu Tyagi
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, United Kingdom
| | - David J Castle
- University of Tasmania and Centre for Mental Health Service Innovation, Tasmania, Australia; State-wide Mental Health Service, Tasmania, Australia
| | - Andrew H Smith
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Brian H Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sameer A Sheth
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Department of Psychiatry and Behavioral Science, Baylor College of Medicine, Houston, Texas
| | - Wayne K Goodman
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Department of Psychiatry and Behavioral Science, Baylor College of Medicine, Houston, Texas
| | - Albert F G Leentjens
- Department of Psychiatry, Maastricht University Medical Center, Maastricht, the Netherlands
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Susan L Rossell
- Centre for Mental Health, Swinburne University, Melbourne, Victoria, Australia; St. Vincent's Hospital, Melbourne, Victoria, Australia
| | - Peter Bosanac
- St. Vincent's Hospital, Melbourne, Victoria, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - G Rees Cosgrove
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Johanniter Hospital Oberhausen, EVKLN, Oberhausen, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, United Kingdom
| | - Eileen Joyce
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, United Kingdom
| | - Juan Carlos Baldermann
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Andreas Horn
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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6
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Wang Y, Yu L, Mao H, Chen X, Hu P, Ge Y, Liu Y, Zhang J, Cheng H. Deep Brain Stimulation Modulates the Visual Pathway to Improve Freezing of Gait in Parkinson's Disease Patients. World Neurosurg 2024; 187:e148-e155. [PMID: 38636635 DOI: 10.1016/j.wneu.2024.04.055] [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: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVE To investigate the involvement of the visual cortex in improving freezing of gait (FoG) after subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) patients using whole-brain seed-based functional connectivity. METHODS A total of 66 PD patients with FoG who underwent bilateral STN-DBS were included in our study. Patients were divided into a FoG responder group and an FoG nonresponder group according to whether FoG improved 1 year after DBS. We compared the differences in clinical characteristics, brain structural imaging, and seed-based functional connectivity between the 2 groups. The locations of active contacts were further analyzed. RESULTS All PD patients benefited from STN-DBS. No significant differences in the baseline characteristics or brain structures were found between the 2 groups. Seed-based functional connectivity analysis revealed that better connectivity in bilateral primary visual areas was associated with better clinical improvement in FoG (P < 0.05 familywise error corrected). Further analysis revealed that this disparity was associated with the location of the active contacts within the rostral region of the sensorimotor subregion in the FoG responder group, in contrast to the findings in the FoG nonresponder group. CONCLUSIONS This study suggested that DBS in the rostral region of the STN sensorimotor subregion may alleviate FoG by strengthening functional connectivity in primary visual areas, which has significant implications for guiding surgical strategies for FoG in the future.
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Affiliation(s)
- Yi Wang
- Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Liangchen Yu
- Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Hongliang Mao
- Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Xianwen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Yue Ge
- Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China
| | - Yue Liu
- First Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Jiarui Zhang
- First Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Hongwei Cheng
- Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, P.R. China.
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7
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Jacobsen NA, Ferris DP. Exploring Electrocortical Signatures of Gait Adaptation: Differential Neural Dynamics in Slow and Fast Gait Adapters. eNeuro 2024; 11:ENEURO.0515-23.2024. [PMID: 38871456 PMCID: PMC11242882 DOI: 10.1523/eneuro.0515-23.2024] [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: 12/06/2023] [Revised: 05/13/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024] Open
Abstract
Individuals exhibit significant variability in their ability to adapt locomotor skills, with some adapting quickly and others more slowly. Differences in brain activity likely contribute to this variability, but direct neural evidence is lacking. We investigated individual differences in electrocortical activity that led to faster locomotor adaptation rates. We recorded high-density electroencephalography while young, neurotypical adults adapted their walking on a split-belt treadmill and grouped them based on how quickly they restored their gait symmetry. Results revealed unique spectral signatures within the posterior parietal, bilateral sensorimotor, and right visual cortices that differ between fast and slow adapters. Specifically, fast adapters exhibited lower alpha power in the posterior parietal and right visual cortices during early adaptation, associated with quicker attainment of steady-state step length symmetry. Decreased posterior parietal alpha may reflect enhanced spatial attention, sensory integration, and movement planning to facilitate faster locomotor adaptation. Conversely, slow adapters displayed greater alpha and beta power in the right visual cortex during late adaptation, suggesting potential differences in visuospatial processing. Additionally, fast adapters demonstrated reduced spectral power in the bilateral sensorimotor cortices compared with slow adapters, particularly in the theta band, which may suggest variations in perception of the split-belt perturbation. These findings suggest that alpha and beta oscillations in the posterior parietal and visual cortices and theta oscillations in the sensorimotor cortex are related to the rate of gait adaptation.
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Affiliation(s)
- Noelle A Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611-6131
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611-6131
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8
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Jacobsen NA, Ferris DP. Electrocortical theta activity may reflect sensory prediction errors during adaptation to a gradual gait perturbation. PeerJ 2024; 12:e17451. [PMID: 38854799 PMCID: PMC11162180 DOI: 10.7717/peerj.17451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/03/2024] [Indexed: 06/11/2024] Open
Abstract
Locomotor adaptation to abrupt and gradual perturbations are likely driven by fundamentally different neural processes. The aim of this study was to quantify brain dynamics associated with gait adaptation to a gradually introduced gait perturbation, which typically results in smaller behavioral errors relative to an abrupt perturbation. Loss of balance during standing and walking elicits transient increases in midfrontal theta oscillations that have been shown to scale with perturbation intensity. We hypothesized there would be no significant change in anterior cingulate theta power (4-7 Hz) with respect to pre-adaptation when a gait perturbation is introduced gradually because the gradual perturbation acceleration and stepping kinematic errors are small relative to an abrupt perturbation. Using mobile electroencephalography (EEG), we measured gait-related spectral changes near the anterior cingulate, posterior cingulate, sensorimotor, and posterior parietal cortices as young, neurotypical adults (n = 30) adapted their gait to an incremental split-belt treadmill perturbation. Most cortical clusters we examined (>70%) did not exhibit changes in electrocortical activity between 2-50 Hz. However, we did observe gait-related theta synchronization near the left anterior cingulate cortex during strides with the largest errors, as measured by step length asymmetry. These results suggest gradual adaptation with small gait asymmetry and perturbation magnitude may not require significant cortical resources beyond normal treadmill walking. Nevertheless, the anterior cingulate may remain actively engaged in error monitoring, transmitting sensory prediction error information via theta oscillations.
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Affiliation(s)
- Noelle A. Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Daniel Perry Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
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9
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Rajamani N, Friedrich H, Butenko K, Dembek T, Lange F, Navrátil P, Zvarova P, Hollunder B, de Bie RMA, Odekerken VJJ, Volkmann J, Xu X, Ling Z, Yao C, Ritter P, Neumann WJ, Skandalakis GP, Komaitis S, Kalyvas A, Koutsarnakis C, Stranjalis G, Barbe M, Milanese V, Fox MD, Kühn AA, Middlebrooks E, Li N, Reich M, Neudorfer C, Horn A. Deep brain stimulation of symptom-specific networks in Parkinson's disease. Nat Commun 2024; 15:4662. [PMID: 38821913 PMCID: PMC11143329 DOI: 10.1038/s41467-024-48731-1] [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: 03/14/2023] [Accepted: 05/13/2024] [Indexed: 06/02/2024] Open
Abstract
Deep Brain Stimulation can improve tremor, bradykinesia, rigidity, and axial symptoms in patients with Parkinson's disease. Potentially, improving each symptom may require stimulation of different white matter tracts. Here, we study a large cohort of patients (N = 237 from five centers) to identify tracts associated with improvements in each of the four symptom domains. Tremor improvements were associated with stimulation of tracts connected to primary motor cortex and cerebellum. In contrast, axial symptoms are associated with stimulation of tracts connected to the supplementary motor cortex and brainstem. Bradykinesia and rigidity improvements are associated with the stimulation of tracts connected to the supplementary motor and premotor cortices, respectively. We introduce an algorithm that uses these symptom-response tracts to suggest optimal stimulation parameters for DBS based on individual patient's symptom profiles. Application of the algorithm illustrates that our symptom-tract library may bear potential in personalizing stimulation treatment based on the symptoms that are most burdensome in an individual patient.
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Affiliation(s)
- Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Helen Friedrich
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- University of Würzburg, Faculty of Medicine, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Till Dembek
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Florian Lange
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Pavel Navrátil
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Patricia Zvarova
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
| | - Barbara Hollunder
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
| | - Rob M A de Bie
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Vincent J J Odekerken
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jens Volkmann
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhipei Ling
- Department of Neurosurgery, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, 572000, China
| | - Chen Yao
- Department of Neurosurgery, The National Key Clinic Specialty, Shenzhen Key Laboratory of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, 518035, China
| | - Petra Ritter
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
- Bernstein center for Computational Neuroscience Berlin, Berlin, 10117, Germany
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Georgios P Skandalakis
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - Spyridon Komaitis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
- Centre for Spinal Studies and Surgery, Queen's Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Aristotelis Kalyvas
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Christos Koutsarnakis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - George Stranjalis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - Michael Barbe
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Vanessa Milanese
- Neurosurgical Division, Hospital Beneficência Portuguesa de São Paulo, São Paulo, Brazil
- Department of Neurosurgery, Mayo Clinic, Florida, USA
- Movement Disorders and Neuromodulation Unit, DOMMO Clinic, São Paulo, Brazil
| | - Michael D Fox
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
| | | | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Reich
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
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10
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Butenko K, Neudorfer C, Dembek TA, Hollunder B, Meyer GM, Li N, Oxenford S, Bahners BH, Al-Fatly B, Lofredi R, Gordon EM, Dosenbach NUF, Ganos C, Hallett M, Starr PA, Ostrem JL, Wu Y, Zhang C, Fox MD, Horn A. Engaging dystonia networks with subthalamic stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307896. [PMID: 38903109 PMCID: PMC11188120 DOI: 10.1101/2024.05.24.24307896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Deep brain stimulation is a viable and efficacious treatment option for dystonia. While the internal pallidum serves as the primary target, more recently, stimulation of the subthalamic nucleus (STN) has been investigated. However, optimal targeting within this structure and its complex surroundings have not been studied in depth. Indeed, multiple historical targets that have been used for surgical treatment of dystonia are directly adjacent to the STN. Further, multiple types of dystonia exist, and outcomes are variable, suggesting that not all types would profit maximally from the exact same target. Therefore, a thorough investigation of the neural substrates underlying effects on dystonia symptoms is warranted. Here, we analyze a multi-center cohort of isolated dystonia patients with subthalamic implantations (N = 58) and relate their stimulation sites to improvement of appendicular and cervical symptoms as well as blepharospasm. Stimulation of the ventral oral posterior nucleus of thalamus and surrounding regions was associated with improvement in cervical dystonia, while stimulation of the dorsolateral STN was associated with improvement in limb dystonia and blepharospasm. This dissociation was also evident for structural connectivity, where the cerebellothalamic, corticospinal and pallidosubthalamic tracts were associated with improvement of cervical dystonia, while hyperdirect and subthalamopallidal pathways were associated with alleviation of limb dystonia and blepharospasm. Importantly, a single well-placed electrode may reach the three optimal target sites. On the level of functional networks, improvement of limb dystonia was correlated with connectivity to the corresponding somatotopic regions in primary motor cortex, while alleviation of cervical dystonia was correlated with connectivity to the recently described 'action-mode' network that involves supplementary motor and premotor cortex. Our findings suggest that different types of dystonia symptoms are modulated via distinct networks. Namely, appendicular dystonia and blepharospasm are improved with modulation of the basal ganglia, and, in particular, the subthalamic circuitry, including projections from the primary motor cortex. In contrast, cervical dystonia was more responsive when engaging the cerebello-thalamo-cortical circuit, including direct stimulation of ventral thalamic nuclei. These findings may inform DBS targeting and image-based programming strategies for patient-specific treatment of dystonia.
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Affiliation(s)
- Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Barbara Hollunder
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Garance M Meyer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bahne H Bahners
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Bassam Al-Fatly
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Roxanne Lofredi
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Nico U F Dosenbach
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
| | - Christos Ganos
- Movement Disorder Clinic, Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, University of Toronto, Toronto Western Hospital, Toronto, ON, Canada
| | - Mark Hallett
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Jill L Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yiwen Wu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - ChenCheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiaotong University Schools of Medicine, Shanghai, China
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andreas Horn
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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11
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Desowska A, Coffman S, Kim I, Underwood E, Tao A, Lopez KL, Nelson CA, Hensch TK, Gabard-Durnam L, Cornelissen L, Berde CB. Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old. Neuroimage Clin 2024; 42:103614. [PMID: 38754325 PMCID: PMC11126539 DOI: 10.1016/j.nicl.2024.103614] [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: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. METHODS EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. RESULTS Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures. CONCLUSIONS We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Siobhan Coffman
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Isabelle Kim
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Ellen Underwood
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Alice Tao
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kelsie L Lopez
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Charles A Nelson
- Laboratories of Cognitive Neurosciences, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Takao K Hensch
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | | | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA.
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12
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Bonassi G, Zhao M, Samogin J, Mantini D, Marchese R, Contrino L, Tognetti P, Putzolu M, Botta A, Pelosin E, Avanzino L. Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2875. [PMID: 38732980 PMCID: PMC11086305 DOI: 10.3390/s24092875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.
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Affiliation(s)
- Gaia Bonassi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genoa, Italy;
| | - Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
| | - Roberta Marchese
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Luciano Contrino
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy; (L.C.); (P.T.)
| | - Paola Tognetti
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy; (L.C.); (P.T.)
| | - Martina Putzolu
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Viale Benedetto XV 3, 16132 Genoa, Italy;
| | - Alessandro Botta
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genoa, Italy;
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Viale Benedetto XV 3, 16132 Genoa, Italy;
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13
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Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [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: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
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Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [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: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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15
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Vorwerk J, Wolters CH, Baumgarten D. Global sensitivity of EEG source analysis to tissue conductivity uncertainties. Front Hum Neurosci 2024; 18:1335212. [PMID: 38532791 PMCID: PMC10963400 DOI: 10.3389/fnhum.2024.1335212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 03/28/2024] Open
Abstract
Introduction To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface. Methods We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions. Results For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity. Discussion Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.
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Affiliation(s)
- Johannes Vorwerk
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Carsten H. Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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16
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Hollunder B, Ostrem JL, Sahin IA, Rajamani N, Oxenford S, Butenko K, Neudorfer C, Reinhardt P, Zvarova P, Polosan M, Akram H, Vissani M, Zhang C, Sun B, Navratil P, Reich MM, Volkmann J, Yeh FC, Baldermann JC, Dembek TA, Visser-Vandewalle V, Alho EJL, Franceschini PR, Nanda P, Finke C, Kühn AA, Dougherty DD, Richardson RM, Bergman H, DeLong MR, Mazzoni A, Romito LM, Tyagi H, Zrinzo L, Joyce EM, Chabardes S, Starr PA, Li N, Horn A. Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation. Nat Neurosci 2024; 27:573-586. [PMID: 38388734 PMCID: PMC10917675 DOI: 10.1038/s41593-024-01570-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 01/05/2024] [Indexed: 02/24/2024]
Abstract
Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.
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Affiliation(s)
- Barbara Hollunder
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jill L Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ilkem Aysu Sahin
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pablo Reinhardt
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Zvarova
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mircea Polosan
- Université Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Psychiatry, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Harith Akram
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Chencheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pavel Navratil
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Martin M Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Till A Dembek
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hagai Bergman
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University, Hadassah Medical School, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Mahlon R DeLong
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Luigi M Romito
- Parkinson and Movement Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Himanshu Tyagi
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Department of Neuropsychiatry, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Eileen M Joyce
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Department of Neuropsychiatry, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Stephan Chabardes
- Université Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Neurosurgery, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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17
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Özden MÖ, Barbieri G, Gerken M. A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:1186. [PMID: 38400344 PMCID: PMC10892416 DOI: 10.3390/s24041186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently.
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Affiliation(s)
- Mesut-Ömür Özden
- Integrated Systems and Photonics, Department of Electrical and Information Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany;
| | | | - Martina Gerken
- Integrated Systems and Photonics, Department of Electrical and Information Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany;
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18
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Studnicki A, Ferris DP. Dual-layer electroencephalography data during real-world table tennis. Data Brief 2024; 52:110024. [PMID: 38287945 PMCID: PMC10823104 DOI: 10.1016/j.dib.2023.110024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/21/2023] [Accepted: 12/28/2023] [Indexed: 01/31/2024] Open
Abstract
Real-world settings are necessary to improve the ecological validity of neuroscience research, and electroencephalography (EEG) facilitates mobile electrocortical recordings because of its easy portability and high temporal resolution. Table tennis is a whole-body, goal-directed sport that requires constant visuomotor feedback, anticipation, strategic decision-making, object interception, and performance monitoring - making it an interesting testbed for a variety of neuroscience studies. Although traditionally plagued by artifact contamination, recent advances in signal processing and hardware approaches, such as the dual-layer approach, have allowed high fidelity EEG recordings during whole-body maneuvers. Here, we present a dual-layer EEG dataset from 25 healthy human participants playing table tennis with a human opponent and a ball machine. Our dataset includes synchronized, multivariate time series recordings from 120 scalp electrodes, 120 noise electrodes, 8 neck electromyography electrodes, and inertial measurement units on the participant, paddles, and ball machine to record hit events. We also include de-identified T1 anatomical MR images and digitized electrode locations to create subject-specific head models for source localization. In addition, we provide anonymized video recordings and Adobe Premiere project files with hit events labeled (originally used to mark successful/missed hits). Researchers could use the videos to mark their own events of interest. We formatted our dataset in the Brain Imaging Data Structure (BIDS) format to facilitate data reuse and to adhere to the scientific community's new organization standard.
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Affiliation(s)
- Amanda Studnicki
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Daniel P. Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, United States
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19
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Wyczesany M, Adamczyk AK, Leśniewska A, Hobot J, Barbalinardo G, Górski T, Adamczyk P, Ligeza TS. Inhibition of the dorsolateral cortex reveals specific mechanisms behind emotional control. Biol Psychol 2024; 186:108743. [PMID: 38195048 DOI: 10.1016/j.biopsycho.2024.108743] [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: 06/26/2023] [Revised: 11/21/2023] [Accepted: 01/04/2024] [Indexed: 01/11/2024]
Abstract
Reappraisal is a complex emotional control strategy based on cognitive change. To complete the reappraisal task, one is required to deeply elaborate on the affective stimulus to create its new interpretation. The involvement of the prefrontal cortex in this process was examined in the study, where inhibition of the left or right dorsolateral area was carried out using transcranial magnetic stimulation. In a between-subject design, we used an alternative control condition for the reappraisal task. It was intended to better account for overall task activity compared to typical passive conditions. Late positive potential was affected after inhibition of the prefrontal area, suggesting hindered emotional control. This effect was specific to the reappraisal task, which possibly reflects the disturbance of attention allocation to emotional stimuli. We could also observe an increased transfer of information from the visual area during the control task that was based on the elaboration of emotional stimuli but did not involve cognitive change. Our results support the additive impact of several factors on the overall efficiency of emotional control.
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Affiliation(s)
| | - Agnieszka K Adamczyk
- Institute of Psychology, Jagiellonian University, Kraków, Poland; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Anna Leśniewska
- Institute of Psychology, Jagiellonian University, Kraków, Poland
| | - Justyna Hobot
- Institute of Psychology, Jagiellonian University, Kraków, Poland; Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | | | - Tomasz Górski
- Institute of Psychology, Jagiellonian University, Kraków, Poland
| | | | - Tomasz S Ligeza
- Institute of Psychology, Jagiellonian University, Kraków, Poland
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20
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Studnicki A, Seidler RD, Ferris DP. A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations. J Neurophysiol 2023; 130:1444-1456. [PMID: 37964746 PMCID: PMC10994643 DOI: 10.1152/jn.00091.2023] [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: 03/01/2023] [Revised: 10/10/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.
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Affiliation(s)
- Amanda Studnicki
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States
| | - Rachael D Seidler
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, Florida, United States
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States
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21
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Fan H, Guo Z, Jiang Y, Xue T, Yin Z, Xie H, Diao Y, Hu T, Zhao B, Wu D, An Q, Xu Y, Gao Y, Bai Y, Zhang J. Optimal subthalamic stimulation sites and related networks for freezing of gait in Parkinson's disease. Brain Commun 2023; 5:fcad238. [PMID: 37701817 PMCID: PMC10493641 DOI: 10.1093/braincomms/fcad238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 07/10/2023] [Accepted: 09/02/2023] [Indexed: 09/14/2023] Open
Abstract
Freezing of gait is a common and debilitating symptom in Parkinson's disease. Although high-frequency subthalamic deep brain stimulation is an effective treatment for Parkinson's disease, post-operative freezing of gait severity has been reported to alleviate, deteriorate or remain constant. We conducted this study to explore the optimal stimulation sites and related connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait in Parkinson's disease. A total of 76 Parkinson's disease patients with freezing of gait who underwent bilateral high-frequency subthalamic stimulation were retrospectively included. The volumes of tissue activated were estimated based on individual electrode reconstruction. The optimal and sour stimulation sites were calculated at coordinate/voxel/mapping level and mapped to anatomical space based on patient-specific images and stimulation settings. The structural and functional predictive connectivity networks for the change of the post-operative Freezing of Gait-Questionnaire were also identified based on normative connectomes derived from the Parkinson's Progression Marker Initiative database. Leave-one-out cross-validation model validated the above results, and the model remained significant after including covariates. The dorsolateral two-thirds of the subthalamic nucleus was identified as the optimal stimulation site, while the ventrocentral portion of the right subthalamic nucleus and internal capsule surrounding the left central subthalamic nucleus were considered as the sour stimulation sites. Modulation of the fibre tracts connecting to the supplementary motor area, pre-supplementary motor area and pedunculopontine nucleus accounted for the alleviation of freezing of gait, whereas tracts connecting to medial and ventrolateral prefrontal cortices contributed to the deterioration of freezing of gait. The optimal/sour stimulation sites and structural/functional predictive connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait are identified and validated through sizable Parkinson's disease patients in this study. With the growing understanding of stimulation sites and related networks, individualized deep brain stimulation treatment with directional leads will become an optimal choice for Parkinson's disease patients with freezing of gait in the future.
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Affiliation(s)
- Houyou Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Zijian Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- School of Biomedical Engineering, Capital Medical University, 100069 Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
- Beijing Key Laboratory of Neurostimulation, 100070 Beijing, China
| | - Tao Xue
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Zixiao Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Hutao Xie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Yu Diao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Tianqi Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
- Beijing Key Laboratory of Neurostimulation, 100070 Beijing, China
| | - Delong Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Qi An
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Yichen Xu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Yuan Gao
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
- Beijing Key Laboratory of Neurostimulation, 100070 Beijing, China
| | - Yutong Bai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070 Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, 100070 Beijing, China
- Beijing Key Laboratory of Neurostimulation, 100070 Beijing, China
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22
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Jacobsen NA, Ferris DP. Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking. J Physiol 2023; 601:3921-3944. [PMID: 37522890 PMCID: PMC10528133 DOI: 10.1113/jp284505] [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: 02/07/2023] [Accepted: 07/11/2023] [Indexed: 08/01/2023] Open
Abstract
Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13 Hz) and beta band (13-30 Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7 Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.
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Affiliation(s)
- Noelle A Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
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23
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Hollunder B, Ostrem JL, Sahin IA, Rajamani N, Oxenford S, Butenko K, Neudorfer C, Reinhardt P, Zvarova P, Polosan M, Akram H, Vissani M, Zhang C, Sun B, Navratil P, Reich MM, Volkmann J, Yeh FC, Baldermann JC, Dembek TA, Visser-Vandewalle V, Alho EJL, Franceschini PR, Nanda P, Finke C, Kühn AA, Dougherty DD, Richardson RM, Bergman H, DeLong MR, Mazzoni A, Romito LM, Tyagi H, Zrinzo L, Joyce EM, Chabardes S, Starr PA, Li N, Horn A. Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.07.23286766. [PMID: 36945497 PMCID: PMC10029043 DOI: 10.1101/2023.03.07.23286766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.
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Affiliation(s)
- Barbara Hollunder
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jill L. Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Ilkem Aysu Sahin
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pablo Reinhardt
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Zvarova
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Mircea Polosan
- Univ. Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Psychiatry Department, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Chencheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pavel Navratil
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Martin M. Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Till A. Dembek
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andrea A. Kühn
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Darin D. Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hagai Bergman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University, Hassadah Medical School, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Mahlon R. DeLong
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Luigi M. Romito
- Parkinson and Movement Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Himanshu Tyagi
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Eileen M. Joyce
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Stephan Chabardes
- Univ. Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Neurosurgery, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Philip A. Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Ningfei Li
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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24
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Nielsen JD, Puonti O, Xue R, Thielscher A, Madsen KH. Evaluating the Influence of Anatomical Accuracy and Electrode Positions on EEG Forward Solutions. Neuroimage 2023:120259. [PMID: 37392808 DOI: 10.1016/j.neuroimage.2023.120259] [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: 09/04/2022] [Revised: 06/01/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout. Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors. We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.
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Affiliation(s)
- Jesper Duemose Nielsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark; Sino-Danish Centre for Education and Research, Aarhus, Denmark.
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Rong Xue
- University of Chinese Academic of Sciences, Beijing, China; State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; Beijing Institute for Brain Disorders, Beijing, China
| | - Axel Thielscher
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
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25
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Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [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] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
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26
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Herz DM, Bange M, Gonzalez-Escamilla G, Auer M, Muthuraman M, Glaser M, Bogacz R, Pogosyan A, Tan H, Groppa S, Brown P. Dynamic modulation of subthalamic nucleus activity facilitates adaptive behavior. PLoS Biol 2023; 21:e3002140. [PMID: 37262014 PMCID: PMC10234560 DOI: 10.1371/journal.pbio.3002140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/26/2023] [Indexed: 06/03/2023] Open
Abstract
Adapting actions to changing goals and environments is central to intelligent behavior. There is evidence that the basal ganglia play a crucial role in reinforcing or adapting actions depending on their outcome. However, the corresponding electrophysiological correlates in the basal ganglia and the extent to which these causally contribute to action adaptation in humans is unclear. Here, we recorded electrophysiological activity and applied bursts of electrical stimulation to the subthalamic nucleus, a core area of the basal ganglia, in 16 patients with Parkinson's disease (PD) on medication using temporarily externalized deep brain stimulation (DBS) electrodes. Patients as well as 16 age- and gender-matched healthy participants attempted to produce forces as close as possible to a target force to collect a maximum number of points. The target force changed over trials without being explicitly shown on the screen so that participants had to infer target force based on the feedback they received after each movement. Patients and healthy participants were able to adapt their force according to the feedback they received (P < 0.001). At the neural level, decreases in subthalamic beta (13 to 30 Hz) activity reflected poorer outcomes and stronger action adaptation in 2 distinct time windows (Pcluster-corrected < 0.05). Stimulation of the subthalamic nucleus reduced beta activity and led to stronger action adaptation if applied within the time windows when subthalamic activity reflected action outcomes and adaptation (Pcluster-corrected < 0.05). The more the stimulation volume was connected to motor cortex, the stronger was this behavioral effect (Pcorrected = 0.037). These results suggest that dynamic modulation of the subthalamic nucleus and interconnected cortical areas facilitates adaptive behavior.
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Affiliation(s)
- Damian M. Herz
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Manuel Bange
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Miriam Auer
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital of Wuerzburg, Wuerzburg, Germany
| | - Martin Glaser
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Rafal Bogacz
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Huiling Tan
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Peter Brown
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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27
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Riaz U, Razzaq FA, Areces-Gonzalez A, Piastra MC, Vega MLB, Paz-Linares D, Valdés-Sosa PA. Automatic Quality Control of the numerical accuracy of EEG Lead fields. Neuroimage 2023; 273:120091. [PMID: 37060935 DOI: 10.1016/j.neuroimage.2023.120091] [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: 09/22/2021] [Revised: 03/22/2023] [Accepted: 04/04/2023] [Indexed: 04/17/2023] Open
Abstract
Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρmin(ref,test)and source τmin(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards-leading us to explore further use of the channel ρmin(HHM-LF,test)and source τmin(HHM-LF,test)SI as a basis for our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρmin(HHM-LF,test)and source τmin(HHM-LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.
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Affiliation(s)
- Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fuleah A Razzaq
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Department of Informatics, University of Pinar del Rio Hermanos Saiz Montes de Oca, Cuba
| | | | - Maria L Bringas Vega
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A Valdés-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba.
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28
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Kumari R, Gibson H, Jarjees M, Turner C, Purcell M, Vučković A. The predictive value of cortical activity during motor imagery for subacute spinal cord injury-induced neuropathic pain. Clin Neurophysiol 2023; 148:32-43. [PMID: 36796284 DOI: 10.1016/j.clinph.2023.01.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/06/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023]
Abstract
OBJECTIVE The aim of this study is to explore whether cortical activation and its lateralization during motor imagery (MI) in subacute spinal cord injury (SCI) are indicative of existing or upcoming central neuropathic pain (CNP). METHODS Multichannel electroencephalogram was recorded during MI of both hands in four groups of participants: able-bodied (N = 10), SCI and CNP (N = 11), SCI who developed CNP within 6 months of EEG recording (N = 10), and SCI who remained CNP-free (N = 10). Source activations and its lateralization were derived in four frequency bands in 20 regions spanning sensorimotor cortex and pain matrix. RESULTS Statistically significant differences in lateralization were found in the theta band in premotor cortex (upcoming vs existing CNP, p = 0.036), in the alpha band at the insula (healthy vs upcoming CNP, p = 0.012), and in the higher beta band at the somatosensory association cortex (no CNP vs upcoming CNP, p = 0.042). People with upcoming CNP had stronger activation compared to those with no CNP in the higher beta band for MI of both hands. CONCLUSIONS Activation intensity and lateralization during MI in pain-related areas might hold a predictive value for CNP. SIGNIFICANCE The study increases understanding of the mechanisms underlying transition from asymptomatic to symptomatic early CNP in SCI.
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Affiliation(s)
- Radha Kumari
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Hannah Gibson
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mohammed Jarjees
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK; Medical Instrumentation Techniques Engineering Department, Northern Technical University, Mosul 41002, Iraq
| | - Christopher Turner
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Aleksandra Vučković
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK.
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Studnicki A, Ferris DP. Parieto-Occipital Electrocortical Dynamics during Real-World Table Tennis. eNeuro 2023; 10:ENEURO.0463-22.2023. [PMID: 37037603 PMCID: PMC10158585 DOI: 10.1523/eneuro.0463-22.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/22/2023] [Accepted: 03/01/2023] [Indexed: 04/12/2023] Open
Abstract
Traditional human electroencephalography (EEG) experiments that study visuomotor processing use controlled laboratory conditions with limited ecological validity. In the real world, the brain integrates complex, dynamic, multimodal visuomotor cues to guide the execution of movement. The parietal and occipital cortices are especially important in the online control of goal-directed actions. Table tennis is a whole-body, responsive activity requiring rapid visuomotor integration that presents a myriad of unanswered neurocognitive questions about brain function during real-world movement. The aim of this study was to quantify the electrocortical dynamics of the parieto-occipital cortices while playing a sport with high-density electroencephalography. We included analysis of power spectral densities (PSDs), event-related spectral perturbations, intertrial phase coherences (ITPCs), event-related potentials (ERPs), and event-related phase coherences of parieto-occipital source-localized clusters while participants played table tennis with a ball machine and a human. We found significant spectral power fluctuations in the parieto-occipital cortices tied to hit events. Ball machine trials exhibited more fluctuations in θ power around hit events, an increase in intertrial phase coherence and deflection in the event-related potential, and higher event-related phase coherence between parieto-occipital clusters as compared with trials with a human. Our results suggest that sport training with a machine elicits fundamentally different brain dynamics than training with a human.
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Affiliation(s)
- Amanda Studnicki
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611
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Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks. Neuroimage 2023; 268:119862. [PMID: 36610682 PMCID: PMC10144063 DOI: 10.1016/j.neuroimage.2023.119862] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.
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Medani T, Garcia-Prieto J, Tadel F, Antonakakis M, Erdbrügger T, Höltershinken M, Mead W, Schrader S, Joshi A, Engwer C, Wolters CH, Mosher JC, Leahy RM. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage 2023; 267:119851. [PMID: 36599389 PMCID: PMC9904282 DOI: 10.1016/j.neuroimage.2022.119851] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.
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Affiliation(s)
- Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States.
| | - Juan Garcia-Prieto
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States.
| | - Francois Tadel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Marios Antonakakis
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Wayne Mead
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Department of Applied Mathematics, University of Münster, Germany
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Christian Engwer
- Department of Applied Mathematics, University of Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - John C Mosher
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
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32
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Ríos AS, Oxenford S, Neudorfer C, Butenko K, Li N, Rajamani N, Boutet A, Elias GJB, Germann J, Loh A, Deeb W, Wang F, Setsompop K, Salvato B, Almeida LBD, Foote KD, Amaral R, Rosenberg PB, Tang-Wai DF, Wolk DA, Burke AD, Salloway S, Sabbagh MN, Chakravarty MM, Smith GS, Lyketsos CG, Okun MS, Anderson WS, Mari Z, Ponce FA, Lozano AM, Horn A. Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease. Nat Commun 2022; 13:7707. [PMID: 36517479 PMCID: PMC9751139 DOI: 10.1038/s41467-022-34510-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.
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Grants
- P30 AG066507 NIA NIH HHS
- R01 NS127892 NINDS NIH HHS
- R01 MH113929 NIMH NIH HHS
- R01 MH130666 NIMH NIH HHS
- P30 AG072979 NIA NIH HHS
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- Received grants and personal fees from Medtronic and Boston Scientific, grants from Abbott/St. Jude, and Functional Neuromodulation outside the submitted work.
- Received grants from Functional Neuromodulation during conduct of this study, grants and personal fees from Avid/Lily, and Merck, personal fees from Jannsen, GE Healthcare, Biogen and Neuronix outside the submitted work.
- Receives personal fees from Elsai, Lilly, Roche Novartis and Biogen outside the submitted work.
- Received personal fees from Allergan, Biogen, Roche-Genentech, Cortexyme, Bracket, Sanofi, and other type of support from Brain Health Inc and uMethod Health outside of the submitted work.
- Received grants from Functional Neuromodulation Inc. during conduct of this study, from Avanir and Eli Lily and NFL Benefits Office outside of the submitted work.
- Received grants from NIH, Tourette Association of America Grant, Parkinson’s Alliance, Smallwood Foundation, and personal fees from Parkinson’s Foundation Medical Director, Books4Patients, American Academy of Neurology, Peerview, WebMD/Medscape, Mededicus, Movement Disorders Society, Taylor and Francis, Demos, Robert Rose and non-financial support from Medtronic outside of the submitted work.
- Received grants from Medtronic and Functional Neuromodulation during conduct of this study, personal fees from Medtronic, St. Jude, Boston Scientific, and Functional Neuromodulation outside of submitted work
- Deutsches Zentrum für Luft- und Raumfahrt (German Centre for Air and Space Travel)
- National Institutes of Health (R01 13478451, 1R01NS127892-01 & 2R01 MH113929) New Venture Fund (FFOR Seed Grant).
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Affiliation(s)
- Ana Sofía Ríos
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Konstantin Butenko
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, M5T1W7, Canada
| | - Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Jurgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Wissam Deeb
- UMass Chan Medical School, Department of Neurology, Worcester, MA, 01655, USA
- UMass Memorial Health, Department of Neurology, Worcester, MA, 01655, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Bryan Salvato
- University of Florida Health Jacksonville, Jacksonville, FL, USA
| | - Leonardo Brito de Almeida
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Robert Amaral
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | - Paul B Rosenberg
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David F Tang-Wai
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Department of Medicine, Division of Neurology, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Stephen Salloway
- Department of Psychiatry and Human Behavior and Neurology, Alpert Medical School of Brown University, Providence, RI, USA
- Memory & Aging Program, Butler Hospital, Providence, USA
| | | | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gwenn S Smith
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Constantine G Lyketsos
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | | | - Zoltan Mari
- Johns Hopkins School of Medicine, Baltimore, MD, USA
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | | | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Departments of Neurology and Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.
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Snipes S, Krugliakova E, Meier E, Huber R. The Theta Paradox: 4-8 Hz EEG Oscillations Reflect Both Sleep Pressure and Cognitive Control. J Neurosci 2022; 42:8569-8586. [PMID: 36202618 PMCID: PMC9665934 DOI: 10.1523/jneurosci.1063-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/16/2022] [Accepted: 09/24/2022] [Indexed: 11/25/2022] Open
Abstract
Human electroencephalographic (EEG) oscillations characterize specific behavioral and vigilance states. The frequency of these oscillations is typically sufficient to distinguish a given state; however, theta oscillations (4-8 Hz) have instead been found in near-opposite conditions of drowsiness during sleep deprivation and alert cognitive control. While the latter has been extensively studied and is often referred to as "frontal midline theta," (fmTheta) the former has been investigated far less but is considered a marker for sleep pressure during wake. In this study we investigated to what extent theta oscillations differed during cognitive tasks and sleep deprivation. We measured high-density EEG in 18 young healthy adults (nine female) performing six tasks under three levels of sleep deprivation. We found both cognitive load and sleep deprivation increased theta power in medial prefrontal cortical areas; however, sleep deprivation caused additional increases in theta in many other, predominantly frontal, areas. The sources of sleep deprivation theta (sdTheta) were task dependent, with a visual-spatial task and short-term memory (STM) task showing the most widespread effects. Notably, theta was highest in supplementary motor areas during passive music listening, and highest in the inferior temporal cortex (responsible for object recognition) during a spatial game. Furthermore, while changes in task performance were correlated with increases in theta during sleep deprivation, this relationship was not specific to the EEG of the same task and did not survive correction for multiple comparisons. Altogether, these results suggest that both during sleep deprivation and cognition theta oscillations may preferentially occur in cortical areas not involved in ongoing behavior.SIGNIFICANCE STATEMENT Electroencephalographic (EEG) research in sleep has often remained separate from research in cognition. This has led to two incompatible interpretations of the function of theta brain oscillations (4-8 Hz): that they reflect local sleep events during sleep deprivation, or that they reflect cognitive processing during tasks. With this study, we found no fundamental differences between theta oscillations during cognition and theta during sleep deprivation that would suggest different functions. Instead, our results indicate that in both cases, theta oscillations are generated by cortical areas not required for ongoing behavior. Therefore, at least in humans, theta may reflect either cortical disengagement or inhibition.
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Affiliation(s)
- Sophia Snipes
- Child Development Center, University Children's Hospital Zürich, University of Zürich, 8032 Zürich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, 8092 Zürich, Switzerland
| | - Elena Krugliakova
- Child Development Center, University Children's Hospital Zürich, University of Zürich, 8032 Zürich, Switzerland
| | - Elias Meier
- Child Development Center, University Children's Hospital Zürich, University of Zürich, 8032 Zürich, Switzerland
| | - Reto Huber
- Child Development Center, University Children's Hospital Zürich, University of Zürich, 8032 Zürich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zürich, 8008 Zürich, Switzerland
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Patient-specific solution of the electrocorticography forward problem in deforming brain. Neuroimage 2022; 263:119649. [PMID: 36167268 DOI: 10.1016/j.neuroimage.2022.119649] [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: 09/30/2021] [Revised: 08/25/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.
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Kumari R, Jarjees M, Susnoschi-Luca I, Purcell M, Vučković A. Effective Connectivity in Spinal Cord Injury-Induced Neuropathic Pain. SENSORS (BASEL, SWITZERLAND) 2022; 22:6337. [PMID: 36080805 PMCID: PMC9460641 DOI: 10.3390/s22176337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
AIM The aim of this study was to differentiate the effects of spinal cord injury (SCI) and central neuropathic pain (CNP) on effective connectivity during motor imagery of legs, where CNP is typically experienced. METHODS Multichannel EEG was recorded during motor imagery of the legs in 3 groups of people: able-bodied (N = 10), SCI with existing CNP (N = 10), and SCI with no CNP (N = 20). The last group was followed up for 6 months to check for the onset of CNP. Source reconstruction was performed to obtain cortical activity in 17 areas spanning sensorimotor regions and pain matrix. Effective connectivity was calculated using the directed transfer function in 4 frequency bands and compared between groups. RESULTS A total of 50% of the SCI group with no CNP developed CNP later. Statistically significant differences in effective connectivity were found between all groups. The differences between groups were not dependent on the frequency band. Outflows from the supplementary motor area were greater for the able-bodied group while the outflows from the secondary somatosensory cortex were greater for the SCI groups. The group with existing CNP showed the least differences from the able-bodied group, appearing to reverse the effects of SCI. The connectivities involving the pain matrix were different between able-bodied and SCI groups irrespective of CNP status, indicating their involvement in motor networks generally. SIGNIFICANCE The study findings might help guide therapeutic interventions targeted at the brain for CNP alleviation as well as motor recovery post SCI.
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Affiliation(s)
- Radha Kumari
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mohammed Jarjees
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
- Medical Instrumentation Techniques Engineering Department, Northern Technical University, Mosul 41002, Iraq
| | - Ioana Susnoschi-Luca
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Aleksandra Vučković
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
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36
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Ladwig Z, Yu Y, Gratton C. Combined methods reveal task activation dynamics in human brain networks. PLoS Biol 2022; 20:e3001749. [PMID: 35984785 PMCID: PMC9390891 DOI: 10.1371/journal.pbio.3001749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
A clear understanding of how human brain networks reflect task performance has been lacking, in part due to methodological difficulties. A new study combines the temporal resolution of EEG, MRI source localization, and multivariate modeling to address this need.
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Affiliation(s)
- Zach Ladwig
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
| | - Yuhua Yu
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois, United States of America
- * E-mail:
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Gadot R, Vanegas Arroyave N, Dang H, Anand A, Najera RA, Taneff LY, Bellows S, Tarakad A, Jankovic J, Horn A, Shofty B, Viswanathan A, Sheth SA. Association of clinical outcomes and connectivity in awake versus asleep deep brain stimulation for Parkinson disease. J Neurosurg 2022; 138:1016-1027. [PMID: 35932263 DOI: 10.3171/2022.6.jns212904] [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: 12/21/2021] [Accepted: 06/09/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) for Parkinson disease (PD) is traditionally performed with awake intraoperative testing and/or microelectrode recording. Recently, however, the procedure has been increasingly performed under general anesthesia with image-based verification. The authors sought to compare structural and functional networks engaged by awake and asleep PD-DBS of the subthalamic nucleus (STN) and correlate them with clinical outcomes. METHODS Levodopa equivalent daily dose (LEDD), pre- and postoperative motor scores on the Movement Disorders Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III), and total electrical energy delivered (TEED) at 6 months were retroactively assessed in patients with PD who received implants of bilateral DBS leads. In subset analysis, implanted electrodes were reconstructed using the Lead-DBS toolbox. Volumes of tissue activated (VTAs) were used as seed points in group volumetric and connectivity analysis. RESULTS The clinical courses of 122 patients (52 asleep, 70 awake) were reviewed. Operating room and procedure times were significantly shorter in asleep cases. LEDD reduction, MDS-UPDRS III score improvement, and TEED at the 6-month follow-up did not differ between groups. In subset analysis (n = 40), proximity of active contact, VTA overlap, and desired network fiber counts with motor STN correlated with lower DBS energy requirement and improved motor scores. Discriminative structural fiber tracts involving supplementary motor area, thalamus, and brainstem were associated with optimal clinical improvement. Areas of highest structural and functional connectivity with VTAs did not significantly differ between the two groups. CONCLUSIONS Compared to awake STN DBS, asleep procedures can achieve similarly optimal targeting-based on clinical outcomes, electrode placement, and connectivity estimates-in more efficient procedures and shorter operating room times.
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Affiliation(s)
- Ron Gadot
- 1Department of Neurosurgery, Baylor College of Medicine
| | - Nora Vanegas Arroyave
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Huy Dang
- 1Department of Neurosurgery, Baylor College of Medicine
| | - Adrish Anand
- 1Department of Neurosurgery, Baylor College of Medicine
| | | | - Lisa Yutong Taneff
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Steven Bellows
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Arjun Tarakad
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Joseph Jankovic
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Andreas Horn
- 3Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany
| | - Ben Shofty
- 1Department of Neurosurgery, Baylor College of Medicine
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Mill RD, Hamilton JL, Winfield EC, Lalta N, Chen RH, Cole MW. Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior. PLoS Biol 2022; 20:e3001686. [PMID: 35980898 PMCID: PMC9387855 DOI: 10.1371/journal.pbio.3001686] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/24/2022] [Indexed: 11/21/2022] Open
Abstract
How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.
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Affiliation(s)
- Ravi D. Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Julia L. Hamilton
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Emily C. Winfield
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Nicole Lalta
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey, United States of America
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
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Shamo: A Tool for Electromagnetic Modeling, Simulation and Sensitivity Analysis of the Head. Neuroinformatics 2022; 20:811-824. [PMID: 35266105 DOI: 10.1007/s12021-022-09574-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/10/2022] [Indexed: 12/31/2022]
Abstract
Accurate electromagnetic modeling of the head of a subject is of main interest in the fields of source reconstruction and brain stimulation. Those processes rely heavily on the quality of the model and, even though the geometry of the tissues can be extracted from magnetic resonance images (MRI) or computed tomography (CT), their physical properties such as the electrical conductivity are difficult to measure with non intrusive techniques. In this paper, we propose a tool to assess the uncertainty in the model parameters, the tissue conductivity, as well as compute a parametric forward models for electroencephalography (EEG) and transcranial direct current stimulation (tDCS) current distribution.
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40
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Zhao M, Bonassi G, Samogin J, Taberna GA, Porcaro C, Pelosin E, Avanzino L, Mantini D. Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging. Front Neurosci 2022; 16:912075. [PMID: 35720696 PMCID: PMC9204106 DOI: 10.3389/fnins.2022.912075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.
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Affiliation(s)
- Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, Genoa, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | | | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies—National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- *Correspondence: Dante Mantini,
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41
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Hund SJ, Brown BR, Lemale CL, Menon PG, Easley KA, Dreier JP, Jones SC. Numerical Simulation of Concussive-Generated Cortical Spreading Depolarization to Optimize DC-EEG Electrode Spacing for Noninvasive Visual Detection. Neurocrit Care 2022; 37:67-82. [PMID: 35233716 PMCID: PMC9262830 DOI: 10.1007/s12028-021-01430-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 12/29/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Cortical spreading depolarization (SD) is a propagating depolarization wave of neurons and glial cells in the cerebral gray matter. SD occurs in all forms of severe acute brain injury, as documented by using invasive detection methods. Based on many experimental studies of mechanical brain deformation and concussion, the occurrence of SDs in human concussion has often been hypothesized. However, this hypothesis cannot be confirmed in humans, as SDs can only be detected with invasive detection methods that would require either a craniotomy or a burr hole to be performed on athletes. Typical electroencephalography electrodes, placed on the scalp, can help detect the possible presence of SD but have not been able to accurately and reliably identify SDs. METHODS To explore the possibility of a noninvasive method to resolve this hurdle, we developed a finite element numerical model that simulates scalp voltage changes that are induced by a brain surface SD. We then compared our simulation results with retrospectively evaluated data in patients with aneurysmal subarachnoid hemorrhage from Drenckhahn et al. (Brain 135:853, 2012). RESULTS The ratio of peak scalp to simulated peak cortical voltage, Vscalp/Vcortex, was 0.0735, whereas the ratio from the retrospectively evaluated data was 0.0316 (0.0221, 0.0527) (median [1st quartile, 3rd quartile], n = 161, p < 0.001, one sample Wilcoxon signed-rank test). These differing values provide validation because their differences can be attributed to differences in shape between concussive SDs and aneurysmal subarachnoid hemorrhage SDs, as well as the inherent limitations in human study voltage measurements. This simulated scalp surface potential was used to design a virtual scalp detection array. Error analysis and visual reconstruction showed that 1 cm is the optimal electrode spacing to visually identify the propagating scalp voltage from a cortical SD. Electrode spacings of 2 cm and above produce distorted images and high errors in the reconstructed image. CONCLUSIONS Our analysis suggests that concussive (and other) SDs can be detected from the scalp, which could confirm SD occurrence in human concussion, provide concussion diagnosis on the basis of an underlying physiological mechanism, and lead to noninvasive SD detection in the setting of severe acute brain injury.
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Affiliation(s)
- Samuel J Hund
- CerebroScope, SciencePlusPlease LLC, Pittsburgh, PA, USA
- SimulationSolutions, Pittsburgh, PA, USA
| | | | - Coline L Lemale
- Center for Stroke Research, Charité, - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Department of Experimental Neurology, Charité, - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Prahlad G Menon
- CerebroScope, SciencePlusPlease LLC, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kirk A Easley
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jens P Dreier
- Center for Stroke Research, Charité, - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Department of Experimental Neurology, Charité, - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Department of Neurology, Charité, - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
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42
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Garcés P, Baumeister S, Mason L, Chatham CH, Holiga S, Dukart J, Jones EJH, Banaschewski T, Baron-Cohen S, Bölte S, Buitelaar JK, Durston S, Oranje B, Persico AM, Beckmann CF, Bougeron T, Dell'Acqua F, Ecker C, Moessnang C, Charman T, Tillmann J, Murphy DGM, Johnson M, Loth E, Brandeis D, Hipp JF. Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis. Mol Autism 2022; 13:22. [PMID: 35585637 PMCID: PMC9118870 DOI: 10.1186/s13229-022-00500-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/06/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.
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Affiliation(s)
- Pilar Garcés
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland.
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Luke Mason
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Christopher H Chatham
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Stefan Holiga
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Emily J H Jones
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Simon Baron-Cohen
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, UK
| | - Sven Bölte
- Department of Women's and Children's Health, Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Karolinska Institutet and Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Sarah Durston
- Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bob Oranje
- Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Antonio M Persico
- Interdepartmental Program "Autism 0-90", "G. Martino" University Hospital, University of Messina, Messina, Italy
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Thomas Bougeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | - Flavio Dell'Acqua
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Christine Ecker
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Carolin Moessnang
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Tony Charman
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Julian Tillmann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Mark Johnson
- Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
| | - Eva Loth
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Joerg F Hipp
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
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Neudorfer C, Kroneberg D, Al-Fatly B, Goede L, Kübler D, Faust K, van Rienen U, Tietze A, Picht T, Herrington TM, Middlebrooks EH, Kühn A, Schneider GH, Horn A. Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences. Ann Neurol 2022; 91:613-628. [PMID: 35165921 DOI: 10.1002/ana.26326] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 02/09/2022] [Accepted: 02/11/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVE With a growing appreciation for interindividual anatomical variability and patient-specific brain connectivity, advanced imaging sequences offer the opportunity to directly visualize anatomical targets for deep brain stimulation (DBS). The lack of quantitative evidence demonstrating their clinical utility, however, has hindered their broad implementation in clinical practice. METHODS Using fast gray matter acquisition T1 inversion recovery (FGATIR) sequences, the present study identified a thalamic hypointensity that holds promise as a visual marker in DBS. To validate the clinical utility of the identified hypointensity, we retrospectively analyzed 65 patients (26 female, mean age = 69.1 ± 12.7 years) who underwent DBS in the treatment of essential tremor. We characterized its neuroanatomical substrates and evaluated the hypointensity's ability to predict clinical outcome using stimulation volume modeling and voxelwise mapping. Finally, we determined whether the hypointensity marker could predict symptom improvement on a patient-specific level. RESULTS Anatomical characterization suggested that the identified hypointensity constituted the terminal part of the dentatorubrothalamic tract. Overlap between DBS stimulation volumes and the hypointensity in standard space significantly correlated with tremor improvement (R2 = 0.16, p = 0.017) and distance to hotspots previously reported in the literature (R2 = 0.49, p = 7.9e-4). In contrast, the amount of variance explained by other anatomical atlas structures was reduced. When accounting for interindividual neuroanatomical variability, the predictive power of the hypointensity increased further (R2 = 0.37, p = 0.002). INTERPRETATION Our findings introduce and validate a novel imaging-based marker attainable from FGATIR sequences that has the potential to personalize and inform targeting and programming in DBS for essential tremor. ANN NEUROL 2022;91:613-628.
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Affiliation(s)
- Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany.,MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR), MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Kroneberg
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Bassam Al-Fatly
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Lukas Goede
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Dorothee Kübler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Katharina Faust
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.,Department Life, Light, and Matter, University of Rostock, Rostock, Germany.,Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany
| | - Anna Tietze
- Institute of Neuroradiology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Thomas Picht
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Boston, MA.,Department of Neurology, Harvard Medical School, Boston, MA
| | - Erik H Middlebrooks
- Department of Radiology, Mayo Clinic, Jacksonville, FL.,Department of Neurosurgery, Mayo Clinic, Jacksonville, FL
| | - Andrea Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin and Humboldt University of Berlin, Berlin, Germany.,MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR), MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
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Zhao M, Bonassi G, Samogin J, Taberna GA, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. Frequency-dependent modulation of neural oscillations across the gait cycle. Hum Brain Mapp 2022; 43:3404-3415. [PMID: 35384123 PMCID: PMC9248303 DOI: 10.1002/hbm.25856] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/08/2022] [Accepted: 03/22/2022] [Indexed: 12/14/2022] Open
Abstract
Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.
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Affiliation(s)
- Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Chiavari, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | | | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, Genova, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
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45
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Conte S, Richards JE. Cortical Source Analysis of Event-Related Potentials: A Developmental Approach. Dev Cogn Neurosci 2022; 54:101092. [PMID: 35231872 PMCID: PMC8885610 DOI: 10.1016/j.dcn.2022.101092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/03/2022] Open
Abstract
Cortical source analysis of electroencephalographic (EEG) signals has become an important tool in the analysis of brain activity. The aim of source analysis is to reconstruct the cortical generators (sources) of the EEG signal recorded on the scalp. The quality of the source reconstruction relies on the accuracy of the forward problem, and consequently the inverse problem. An accurate forward solution is obtained when an appropriate imaging modality (i.e., structural magnetic resonance imaging - MRI) is used to describe the head geometry, precise electrode locations are identified with 3D maps of the sensor positions on the scalp, and realistic conductivity values are determined for each tissue type of the head model. Together these parameters contribute to the definition of realistic head models. Here, we describe the steps necessary to reconstruct the cortical generators of the EEG signal recorded on the scalp. We provide an example of source reconstruction of event-related potentials (ERPs) during a face-processing task performed by a 6-month-old infant. We discuss the adjustments necessary to perform source analysis with measures different from the ERPs. The proposed pipeline can be applied to the investigation of different cognitive tasks in both younger and older participants.
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46
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Zhao M, Bonassi G, Guarnieri R, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data. J Neural Eng 2021; 18. [PMID: 34874319 DOI: 10.1088/1741-2552/ac4084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/06/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.
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Affiliation(s)
- Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy
| | - Roberto Guarnieri
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Icometrix, 3012 Leuven, Belgium
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, 16132 Genova, Italy.,Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, 3001 Leuven, Belgium
| | - Laura Avanzino
- Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, 16132 Genoa, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
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47
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Treder MS, Charest I, Michelmann S, Martín-Buro MC, Roux F, Carceller-Benito F, Ugalde-Canitrot A, Rollings DT, Sawlani V, Chelvarajah R, Wimber M, Hanslmayr S, Staresina BP. The hippocampus as the switchboard between perception and memory. Proc Natl Acad Sci U S A 2021; 118:e2114171118. [PMID: 34880133 PMCID: PMC8685930 DOI: 10.1073/pnas.2114171118] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2021] [Indexed: 12/30/2022] Open
Abstract
Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process.
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Affiliation(s)
- Matthias S Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, United Kingdom
| | - Ian Charest
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
- cerebrUM, Département de Psychologie, Université de Montréal, Montreal, QC H2V 259, Canada
| | - Sebastian Michelmann
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Department of Psychology, Princeton University, Princeton, NJ 08540
| | - María Carmen Martín-Buro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology 28223 Madrid, Spain
- Faculty of Health Sciences, King Juan Carlos University 28933 Madrid, Spain
| | - Frédéric Roux
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | | | - Arturo Ugalde-Canitrot
- Epilepsy Monitoring Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz 28046 Madrid, Spain
- School of Medicine, Universidad Francisco de Vitoria 28223 Madrid, Spain
| | - David T Rollings
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
- Complex Epilepsy and Surgery Service, Neurophysiology Department, Queen Elizabeth Hospital, Birmingham B15 2GW, United Kingdom
| | - Vijay Sawlani
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
- Complex Epilepsy and Surgery Service, Neuroradiology Department, Queen Elizabeth Hospital, Birmingham B15 2GW, United Kingdom
| | - Ramesh Chelvarajah
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
- Complex Epilepsy and Surgery Service, Neurosurgery Department, Queen Elizabeth Hospital, Birmingham B15 2GW, United Kingdom
| | - Maria Wimber
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Simon Hanslmayr
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Bernhard P Staresina
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom;
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
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48
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Song S, Nordin AD. Mobile Electroencephalography for Studying Neural Control of Human Locomotion. Front Hum Neurosci 2021; 15:749017. [PMID: 34858154 PMCID: PMC8631362 DOI: 10.3389/fnhum.2021.749017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2021] [Indexed: 01/09/2023] Open
Abstract
Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.
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Affiliation(s)
- Seongmi Song
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
| | - Andrew D Nordin
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Texas A&M Institute for Neuroscience, College Station, TX, United States
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49
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Conte S, Richards JE. The Influence of the Head Model Conductor on the Source Localization of Auditory Evoked Potentials. Brain Topogr 2021; 34:793-812. [PMID: 34570330 PMCID: PMC8647205 DOI: 10.1007/s10548-021-00871-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/12/2021] [Indexed: 11/28/2022]
Abstract
The accuracy of EEG source analysis reconstruction improves when a realistic head volume conductor is modeled. In this study we investigated how the progressively more complex head representations influence the spatial localization of auditory-evoked potentials (AEPs). Fourteen young-adult participants with normal hearing performed the AEP task. Individualized head models were obtained from structural MRI and diffusion-weighted imaging scans collected in a separate session. AEPs were elicited by 1 k Hz and 4 k Hz tone bursts during a passive-listening tetanizing paradigm. We compared the amplitude of the N1 and P2 components before and after 4 min of tetanic-stimulation with 1 k Hz sounds. Current density reconstruction values of both components were investigated in the primary auditory cortex and adjacent areas. Furthermore, we compared the signal topography and magnitude obtained with 10 different head models on the EEG forward solution. Starting from the simplest model (scalp, skull, brain), we investigated the influence of modeling the CSF, distinguishing between GM and WM conductors, and including anisotropic WM values. We localized the activity of AEPs within the primary auditory cortex, but not in adjacent areas. The inclusion of the CSF compartment had the strongest influence on the source reconstruction, whereas white matter anisotropy led to a smaller improvement. We conclude that individualized realistic head models provide the best solution for the forward solution when modeling the CSF conductor.
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Affiliation(s)
- Stefania Conte
- Department of Psychology, University of South Carolina, Columbia, USA.
| | - John E Richards
- Department of Psychology, University of South Carolina, Columbia, USA
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50
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Zaky MH, Shoorangiz R, Poudel GR, Yang L, Jones RD. Investigating the neural signature of microsleeps using EEG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6293-6296. [PMID: 34892552 DOI: 10.1109/embc46164.2021.9630401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A microsleep (MS) is a complete lapse of responsiveness due to an episode of brief sleep (≲ 15 s) with eyes partially or completely closed. MSs are highly correlated with the risk of car accidents, severe injuries, and death. To investigate EEG changes during MSs, we used a 2D continuous visuomotor tracking (CVT) task and eye-video to identify MSs in 20 subjects performing the 50-min task. Following pre-processing, FFT spectral analysis was used to calculate the activity in the EEG delta, theta, alpha, beta, and gamma bands, followed by eLORETA for source reconstruction. A group statistical analysis was performed to compare the change in activity over EEG bands of an MS to its baseline. After correction for multiple comparisons, we found maximum increases in delta, theta, and alpha activities over the frontal lobe, and beta over the parietal and occipital lobes. There were no significant changes in the gamma band, and no significant decreases in any band. Our results are in agreement with previous studies which reported increased alpha activity in MSs. However, this is the first study to have reported increased beta activity during MSs, which, due to the usual association of beta activity with wakefulness, was unexpected.
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