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Cho H, Benjaber M, Alexis Gkogkidis C, Buchheit M, Ruiz-Rodriguez JF, Grannan BL, Weaver KE, Ko AL, Cramer SC, Ojemann JG, Denison T, Herron JA. Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3625-3635. [PMID: 39264785 PMCID: PMC11485249 DOI: 10.1109/tnsre.2024.3459801] [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: 09/14/2024]
Abstract
With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.
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Jamil Z, Saisanen L, Demjan M, Reijonen J, Julkunen P. The Effect of Stimulation Intensity, Sampling Frequency, and Sample Synchronization in TMS-EEG on the TMS Pulse Artifact Amplitude and Duration. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2612-2620. [PMID: 39024076 DOI: 10.1109/tnsre.2024.3429176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) possesses diagnostic and therapeutic benefits. However, TMS provokes a large pulse artifact that momentarily obscures the cortical response, presenting a significant challenge for EEG data interpretation. We examined how stimulation intensity (SI), EEG sampling frequency (Fs) and synchronization of stimulation with EEG sampling influence the amplitude and duration of the pulse artifact. In eight healthy subjects, single-pulse TMS was administered to the primary motor cortex, due to its well-documented responsiveness to TMS. We applied two different SIs (90% and 120% of resting motor threshold, representing the commonly used subthreshold and suprathreshold levels) and Fs (conventional 5 kHz and high frequency 20 kHz) both with TMS synchronized with the EEG sampling and the conventional non-synchronized setting. Aside from removal of the DC-offset and epoching, no preprocessing was performed to the data. Using a random forest regression model, we identified that Fs had the largest impact on both the amplitude and duration of the pulse artifact, with median variable importance values of 1.444 and 1.327, respectively, followed by SI (0.964 and 1.083) and sampling synchronization (0.223 and 0.248). This indicated that Fs and SI are crucial for minimizing prediction error and thus play a pivotal role in accurately characterizing the pulse artifact. The results of this study enable focusing some of the study design parameters to minimize TMS pulse artifact, which is essential for both enhancing the reliability of clinical TMS-EEG applications and improving the overall integrity and interpretability of TMS-EEG data.
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Ding Z, Wang Y, Niu Z, Ouyang G, Li X. The effect of EEG microstate on the characteristics of TMS-EEG. Comput Biol Med 2024; 173:108332. [PMID: 38555703 DOI: 10.1016/j.compbiomed.2024.108332] [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: 01/28/2024] [Revised: 02/26/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS The N100 component of microstate C group (-2.376 μV) was significantly higher (p = 0.003) than of microstate D group (-1.739 μV), and the P180 component of microstate D group (2.482 μV) was significantly higher (p = 0.024) than of microstate B group (1.766 μV) and slightly higher (p = 0.058) than of microstate C group (1.863 μV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.
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Affiliation(s)
- Zhaohuan Ding
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai, 519031, China
| | - Zikang Niu
- Aviation Psychology Research Office, Air Force Medical Center, Beijing, 100142, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
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Chirumamilla VC, Hitchings L, Mulkey SB, Anwar T, Baker R, Larry Maxwell G, De Asis-Cruz J, Kapse K, Limperopoulos C, du Plessis A, Govindan RB. Association of brain functional connectivity with neurodevelopmental outcomes in healthy full-term newborns. Clin Neurophysiol 2024; 160:68-74. [PMID: 38412745 DOI: 10.1016/j.clinph.2024.02.009] [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: 09/26/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 02/29/2024]
Abstract
OBJECTIVE To study the association between neurodevelopmental outcomes and functional brain connectivity (FBC) in healthy term infants. METHODS This is a retrospective study of prospectively collected High-density electroencephalography (HD-EEG) from newborns within 72 hours from birth. Developmental assessments were performed at two years of age using the Bayley Scales of Infant Development-III (BSID-III) measuring cognitive, language, motor, and socio-emotional scores. The FBC was calculated using phase synchronization analysis of source signals in delta, theta, alpha, beta, and gamma frequency bands and its association with neurodevelopmental score was assessed with stepwise regression. RESULTS 47/163 had both HD-EEG and BSID-III scores. The FBC of frontal region was associated with cognitive score in the theta band (corrected p, regression coefficients range: p < 0.01, 1.66-1.735). Language scores were significantly associated with connectivity in all frequency bands, predominantly in the left hemisphere (p < 0.01, -2.74-2.40). The FBC of frontal and occipital brain regions of both hemispheres was related to motor score and socio-emotional development in theta, alpha, and gamma frequency bands (p < 0.01, -2.16-2.97). CONCLUSIONS Functional connectivity of higher-order processing is already present at term age. SIGNIFICANCE The FBC might be used to guide interventions for optimizing subsequent neurodevelopment even in low-risk newborns.
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Affiliation(s)
- Venkata C Chirumamilla
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, United States
| | - Laura Hitchings
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, United States
| | - Sarah B Mulkey
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, United States; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
| | - Tayyba Anwar
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States; Department of Neurology, Children's National Hospital, Washington, DC, United States
| | - Robin Baker
- Inova Women's and Children's Hospital, Fairfax, VA, United States; Fairfax Neonatal Associates, Fairfax, VA, United States
| | - G Larry Maxwell
- Inova Women's and Children's Hospital, Fairfax, VA, United States
| | | | - Kushal Kapse
- Developing Brain Institute, Children's National Hospital, Washington, DC, United States
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, Washington, DC, United States; Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, United States
| | - Adre du Plessis
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, United States; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
| | - R B Govindan
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, United States; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
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Sack AT, Paneva J, Küthe T, Dijkstra E, Zwienenberg L, Arns M, Schuhmann T. Target Engagement and Brain State Dependence of Transcranial Magnetic Stimulation: Implications for Clinical Practice. Biol Psychiatry 2024; 95:536-544. [PMID: 37739330 DOI: 10.1016/j.biopsych.2023.09.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 08/31/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023]
Abstract
Transcranial magnetic stimulation (TMS) is capable of noninvasively inducing lasting neuroplastic changes when applied repetitively across multiple treatment sessions. In recent years, repetitive TMS has developed into an established evidence-based treatment for various neuropsychiatric disorders such as depression. Despite significant advancements in our understanding of the mechanisms of action of TMS, there is still much to learn about how these mechanisms relate to the clinical effects observed in patients. If there is one thing about TMS that we know for sure, it is that TMS effects are state dependent. In this review, we describe how the effects of TMS on brain networks depend on various factors, including cognitive brain state, oscillatory brain state, and recent brain state history. These states play a crucial role in determining the effects of TMS at the moment of stimulation and are therefore directly linked to what is referred to as target engagement in TMS therapy. There is no control over target engagement without considering the different brain state dependencies of our TMS intervention. Clinical TMS protocols are largely ignoring this fundamental principle, which may explain the large variability and often still limited efficacy of TMS treatments. We propose that after almost 30 years of research on state dependency of TMS, it is time to change standard clinical practice by taking advantage of this fundamental principle. Rather than ignoring TMS state dependency, we can use it to our clinical advantage to improve the effectiveness of TMS treatments.
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Affiliation(s)
- Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Brain + Nerve Center, Maastricht University Medical Center, Maastricht, the Netherlands.
| | - Jasmina Paneva
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Tara Küthe
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Eva Dijkstra
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Heart and Brain Group, Brainclinics Foundation, Nijmegen, the Netherlands; Neurowave, Amsterdam, the Netherlands
| | - Lauren Zwienenberg
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Heart and Brain Group, Brainclinics Foundation, Nijmegen, the Netherlands; Synaeda Psycho Medisch Centrum, Leeuwarden, the Netherlands
| | - Martijn Arns
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Brain + Nerve Center, Maastricht University Medical Center, Maastricht, the Netherlands; Heart and Brain Group, Brainclinics Foundation, Nijmegen, the Netherlands
| | - Teresa Schuhmann
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
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Abstract
In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus but by the nature of the brain that is receiving the stimulus at that instant in time. Over the past decades, therapeutic brain stimulation has typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neurotechnological advancements have enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instance in time) with sufficient temporal precision in the range of milliseconds using feedforward algorithms applied to electroencephalography time-series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies have shown that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here, we survey the evidence that this is also possible at the systems level of the human cortex using electroencephalography-informed transcranial magnetic stimulation. We critically discuss opportunities and difficulties in developing brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than is achievable with conventional fixed protocols. The same real-time electroencephalography-informed transcranial magnetic stimulation technology will allow closing of the loop by recording the effects of stimulation. This information may enable stimulation protocol adaptation that maximizes treatment response. This way, brain states control brain stimulation, thereby introducing a paradigm shift from open-loop to closed-loop stimulation.
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Affiliation(s)
- Christoph Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute for Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany.
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
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Kim B, Erickson BA, Fernandez-Nunez G, Rich R, Mentzelopoulos G, Vitale F, Medaglia JD. EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio. eNeuro 2023; 10:ENEURO.0050-23.2023. [PMID: 37558464 PMCID: PMC10481640 DOI: 10.1523/eneuro.0050-23.2023] [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: 02/13/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 08/11/2023] Open
Abstract
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | | | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, Pennsylvania 19146
| | - John D Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
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8
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Zrenner B, Zrenner C, Balderston N, Blumberger DM, Kloiber S, Laposa JM, Tadayonnejad R, Trevizol AP, Zai G, Feusner JD. Toward personalized circuit-based closed-loop brain-interventions in psychiatry: using symptom provocation to extract EEG-markers of brain circuit activity. Front Neural Circuits 2023; 17:1208930. [PMID: 37671039 PMCID: PMC10475600 DOI: 10.3389/fncir.2023.1208930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
Symptom provocation is a well-established component of psychiatric research and therapy. It is hypothesized that specific activation of those brain circuits involved in the symptomatic expression of a brain pathology makes the relevant neural substrate accessible as a target for therapeutic interventions. For example, in the treatment of obsessive-compulsive disorder (OCD), symptom provocation is an important part of psychotherapy and is also performed prior to therapeutic brain stimulation with transcranial magnetic stimulation (TMS). Here, we discuss the potential of symptom provocation to isolate neurophysiological biomarkers reflecting the fluctuating activity of relevant brain networks with the goal of subsequently using these markers as targets to guide therapy. We put forward a general experimental framework based on the rapid switching between psychiatric symptom states. This enable neurophysiological measures to be derived from EEG and/or TMS-evoked EEG measures of brain activity during both states. By subtracting the data recorded during the baseline state from that recorded during the provoked state, the resulting contrast would ideally isolate the specific neural circuits differentially activated during the expression of symptoms. A similar approach enables the design of effective classifiers of brain activity from EEG data in Brain-Computer Interfaces (BCI). To obtain reliable contrast data, psychiatric state switching needs to be achieved multiple times during a continuous recording so that slow changes of brain activity affect both conditions equally. This is achieved easily for conditions that can be controlled intentionally, such as motor imagery, attention, or memory retention. With regard to psychiatric symptoms, an increase can often be provoked effectively relatively easily, however, it can be difficult to reliably and rapidly return to a baseline state. Here, we review different approaches to return from a provoked state to a baseline state and how these may be applied to different symptoms occurring in different psychiatric disorders.
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Affiliation(s)
- Brigitte Zrenner
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
- University Psychiatry Hospital, University of Tübingen, Tübingen, Germany
| | - Christoph Zrenner
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- University Neurology Hospital, University of Tübingen, Tübingen, Germany
| | - Nicholas Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel M. Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Stefan Kloiber
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Judith M. Laposa
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Reza Tadayonnejad
- TMS Clinical and Research Service, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States
| | - Alisson Paulino Trevizol
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Gwyneth Zai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Jamie D. Feusner
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States
- Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
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Ding Z, Guan L, He W, Gu H, Wang Y, Li X. Spatial characteristics of closed-loop TMS-EEG with occipital alpha-phase synchronized. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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10
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Chirumamilla VC, Hitchings L, Mulkey SB, Anwar T, Baker R, Larry Maxwell G, De Asis-Cruz J, Kapse K, Limperopoulos C, du Plessis A, Govindan RB. Functional brain network properties of healthy full-term newborns quantified by scalp and source-reconstructed EEG. Clin Neurophysiol 2023; 147:72-80. [PMID: 36731349 PMCID: PMC9975070 DOI: 10.1016/j.clinph.2023.01.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/20/2022] [Accepted: 01/01/2023] [Indexed: 01/24/2023]
Abstract
OBJECTIVE Identifying the functional brain network properties of term low-risk newborns using high-density EEG (HD-EEG) and comparing these properties with those of established functional magnetic resonance image (fMRI) - based networks. METHODS HD-EEG was collected from 113 low-risk term newborns before delivery hospital discharge and within 72 hours of birth. Functional brain networks were reconstructed using coherence at the scalp and source levels in delta, theta, alpha, beta, and gamma frequency bands. These networks were characterized for the global and local network architecture. RESULTS Source-level networks in all the frequency bands identified the presence of the efficient small world (small-world propensity (SWP) > 0.6) architecture with four distinct modules linked by hub regions and rich-club (coefficient > 1) topology. The modular regions included primary, association, limbic, paralimbic, and subcortical regions, which have been demonstrated in fMRI studies. In contrast, scalp-level networks did not display consistent small world architecture (SWP < 0.6), and also identified only 2-3 modules in each frequency band.The modular regions of the scalp-network primarily included frontal and occipital regions. CONCLUSIONS Our findings show that EEG sources in low-risk newborns corroborate fMRI-based connectivity results. SIGNIFICANCE EEG source analysis characterizes functional connectivity at the bedside of low-risk newborn infants soon after birth.
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Affiliation(s)
| | - Laura Hitchings
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA
| | - Sarah B Mulkey
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Tayyba Anwar
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, Children's National Hospital, Washington, DC, USA
| | - Robin Baker
- Inova Women's and Children's Hospital, Fairfax, VA, USA; Fairfax Neonatal Associates, Fairfax, VA, USA
| | | | | | - Kushal Kapse
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA; Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | - Adre du Plessis
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - R B Govindan
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
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11
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Poorganji M, Zomorrodi R, Zrenner C, Bansal A, Hawco C, Hill AT, Hadas I, Rajji TK, Chen R, Zrenner B, Voineskos D, Blumberger DM, Daskalakis ZJ. Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG. BIOSENSORS 2023; 13:220. [PMID: 36831986 PMCID: PMC9953459 DOI: 10.3390/bios13020220] [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: 10/11/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation.
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Affiliation(s)
- Mohsen Poorganji
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Christoph Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Aiyush Bansal
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Aron T. Hill
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC 3125, Australia
| | - Itay Hadas
- Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093-0603, USA
| | - Tarek K. Rajji
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Robert Chen
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Brigitte Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Daphne Voineskos
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Zafiris J. Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093-0603, USA
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12
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Strafella R, Chen R, Rajji TK, Blumberger DM, Voineskos D. Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review. Front Hum Neurosci 2022; 16:940759. [PMID: 35992942 PMCID: PMC9387384 DOI: 10.3389/fnhum.2022.940759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/28/2022] [Indexed: 11/28/2022] Open
Abstract
Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.
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Affiliation(s)
- Rebecca Strafella
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Robert Chen
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tarek K. Rajji
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
| | - Daniel M. Blumberger
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Daphne Voineskos
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Daphne Voineskos
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13
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Chirumamilla VC, Hitchings L, Mulkey SB, Anwar T, Baker R, Larry Maxwell G, De Asis-Cruz J, Kapse K, Limperopoulos C, du Plessis A, Govindan R. Electroencephalogram in low-risk term newborns predicts neurodevelopmental metrics at age two years. Clin Neurophysiol 2022; 140:21-28. [DOI: 10.1016/j.clinph.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 12/01/2022]
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14
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Tabarelli D, Brancaccio A, Zrenner C, Belardinelli P. Functional Connectivity States of Alpha Rhythm Sources in the Human Cortex at Rest: Implications for Real-Time Brain State Dependent EEG-TMS. Brain Sci 2022; 12:348. [PMID: 35326304 PMCID: PMC8946162 DOI: 10.3390/brainsci12030348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 02/13/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Alpha is the predominant rhythm of the human electroencephalogram, but its function, multiple generators and functional coupling patterns are still relatively unknown. In this regard, alpha connectivity patterns can change between different cortical generators depending on the status of the brain. Therefore, in the light of the communication through coherence framework, an alpha functional network depends on the functional coupling patterns in a determined state. This notion has a relevance for brain-state dependent EEG-TMS because, beyond the local state, a network connectivity overview at rest could provide further and more comprehensive information for the definition of 'instantaneous state' at the stimulation moment, rather than just the local state around the stimulation site. For this reason, we studied functional coupling at rest in 203 healthy subjects with MEG data. Sensor signals were source localized and connectivity was studied at the Individual Alpha Frequency (IAF) between three different cortical areas (occipital, parietal and prefrontal). Two different and complementary phase-coherence metrices were used. Our results show a consistent connectivity between parietal and prefrontal regions whereas occipito-prefrontal connectivity is less marked and occipito-parietal connectivity is extremely low, despite physical closeness. We consider our results a relevant add-on for informed, individualized real-time brain state dependent stimulation, with possible contributions to novel, personalized non-invasive therapeutic approaches.
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Affiliation(s)
- Davide Tabarelli
- Center for Mind/Brain Sciences—CIMeC, University of Trento, I-38123 Trento, Italy; (D.T.); (A.B.)
| | - Arianna Brancaccio
- Center for Mind/Brain Sciences—CIMeC, University of Trento, I-38123 Trento, Italy; (D.T.); (A.B.)
| | - Christoph Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4, Canada;
| | - Paolo Belardinelli
- Center for Mind/Brain Sciences—CIMeC, University of Trento, I-38123 Trento, Italy; (D.T.); (A.B.)
- Department of Neurology & Stroke, University of Tübingen, D-72070 Tübingen, Germany
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15
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Bridging the gap: TMS-EEG from Lab to Clinic. J Neurosci Methods 2022; 369:109482. [PMID: 35041855 DOI: 10.1016/j.jneumeth.2022.109482] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/09/2022] [Accepted: 01/13/2022] [Indexed: 01/06/2023]
Abstract
The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has reached technological maturity and has been an object of significant scientific interest for over two decades. Ιn parallel, accumulating evidence highlights the potential of TMS-EEG as a useful tool in the field of clinical neurosciences. Nevertheless, its clinical utility has not yet been established, partly because technical and methodological limitations have created a gap between an evolving scientific tool and standard clinical practice. Here we review some of the identified gaps that still prevent TMS-EEG moving from science laboratories to clinical practice. The principal and partly overlapping gaps include: 1) complex and laborious application, 2) difficulty in obtaining high-quality signals, 3) suboptimal accuracy and reliability, and 4) insufficient understanding of the neurobiological substrate of the responses. All these four aspects need to be satisfactorily addressed for the method to become clinically applicable and enter the diagnostic and therapeutic arena. In the current review, we identify steps that might be taken to address these issues and discuss promising recent studies providing tools to aid bridging the gaps.
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16
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Wodeyar A, Schatza M, Widge AS, Eden UT, Kramer MA. A state space modeling approach to real-time phase estimation. eLife 2021; 10:e68803. [PMID: 34569936 PMCID: PMC8536256 DOI: 10.7554/elife.68803] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 09/24/2021] [Indexed: 11/14/2022] Open
Abstract
Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.
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Affiliation(s)
- Anirudh Wodeyar
- Mathematics and Statistics, Boston UniversityBostonUnited States
| | - Mark Schatza
- Department of Psychiatry, University of MinnesotaMinneapolisUnited States
| | - Alik S Widge
- Department of Psychiatry, University of MinnesotaMinneapolisUnited States
| | - Uri T Eden
- Mathematics and Statistics, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
| | - Mark A Kramer
- Mathematics and Statistics, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
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