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Brem S, Hoch MJ. Commentary: Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation Using a New Tool for Planning Brain Resections. Neurosurgery 2024; 95:e156-e158. [PMID: 38869302 DOI: 10.1227/neu.0000000000003065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Affiliation(s)
- Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia , Pennsylvania , USA
- Glioblastoma Translational Center of Excellence (TCE), Abramson Cancer Center, University of Pennsylvania, Philadelphia , Pennsylvania , USA
| | - Michael J Hoch
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia , Pennsylvania , USA
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van der A J, Lodema Y, Ottens TH, Schutter DJLG, Emmelot-Vonk MH, de Haan W, van Dellen E, Tendolkar I, Slooter AJC. DELirium treatment with Transcranial Electrical Stimulation (DELTES): study protocol for a multicentre, randomised, double-blind, sham-controlled trial. BMJ Open 2024; 14:e092165. [PMID: 39488424 PMCID: PMC11535714 DOI: 10.1136/bmjopen-2024-092165] [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: 08/07/2024] [Accepted: 10/15/2024] [Indexed: 11/04/2024] Open
Abstract
INTRODUCTION Delirium, a clinical manifestation of acute encephalopathy, is associated with extended hospitalisation, long-term cognitive dysfunction, increased mortality and high healthcare costs. Despite intensive research, there is still no targeted treatment. Delirium is characterised by electroencephalography (EEG) slowing, increased relative delta power and decreased functional connectivity. Recent studies suggest that transcranial alternating current stimulation (tACS) can entrain EEG activity, strengthen connectivity and improve cognitive functioning. Hence, tACS offers a potential treatment for augmenting EEG activity and reducing the duration of delirium. This study aims to evaluate the feasibility and assess the efficacy of tACS in reducing relative delta power. METHODS AND ANALYSIS A randomised, double-blind, sham-controlled trial will be conducted across three medical centres in the Netherlands. The study comprises two phases: a pilot phase (n=30) and a main study phase (n=129). Participants are patients aged 50 years and older who are diagnosed with delirium using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision criteria (DSM-5-TR), that persists despite treatment of underlying causes. During the pilot phase, participants will be randomised (1:1) to receive either standardised (10 Hz) tACS or sham tACS. In the main study phase, participants will be randomised to standardised tACS, sham tACS or personalised tACS, in which tACS settings are tailored to the participant. All participants will undergo daily 30 min of (sham) stimulation for up to 14 days or until delirium resolution or hospital discharge. Sixty-four-channel resting-state EEG will be recorded pre- and post the first tACS session, and following the final tACS session. Daily delirium assessments will be acquired using the Intensive Care Delirium Screening Checklist and Delirium Observation Screening Scale. The pilot phase will assess the percentage of completed tACS sessions and increased care requirements post-tACS. The primary outcome variable is change in relative delta EEG power. Secondary outcomes include (1) delirium duration and severity, (2) quantitative EEG measurements, (3) length of hospital stay, (4) cognitive functioning at 3 months post-tACS and (5) tACS treatment burden. Study recruitment started in April 2024 and is ongoing. ETHICS AND DISSEMINATION The study has been approved by the Medical Ethics Committee of the Utrecht University Medical Center and the Institutional Review Boards of all participating centres. Trial results will be disseminated via peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER NCT06285721.
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Affiliation(s)
- Julia van der A
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Yorben Lodema
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Thomas H Ottens
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Intensive Care Unit, HagaZiekenhuis, Den Haag, The Netherlands
| | | | | | - Willem de Haan
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU Medisch Centrum, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Neurology and Vrije Universiteit Brussel, UZ Brussel, Brussel, Belgium
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Radboud Universiteit, Nijmegen, The Netherlands
| | - Arjen J C Slooter
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Neurology and Vrije Universiteit Brussel, UZ Brussel, Brussel, Belgium
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van Dellen E. Precision psychiatry: predicting predictability. Psychol Med 2024; 54:1500-1509. [PMID: 38497091 DOI: 10.1017/s0033291724000370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
- Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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Luppi JJ, Schoonhoven DN, van Nifterick AM, Gouw AA, Hillebrand A, Scheltens P, Stam CJ, de Haan W. Oscillatory Activity of the Hippocampus in Prodromal Alzheimer’s Disease: A Source-Space Magnetoencephalography Study. J Alzheimers Dis 2022; 87:317-333. [PMID: 35311705 PMCID: PMC9198749 DOI: 10.3233/jad-215464] [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] [Indexed: 11/15/2022]
Abstract
Background: In Alzheimer’s disease (AD), oscillatory activity of the human brain slows down. However, oscillatory slowing varies between individuals, particularly in prodromal AD. Cortical oscillatory changes have shown suboptimal accuracy as diagnostic markers. We speculated that focusing on the hippocampus might prove more successful, particularly using magnetoencephalography (MEG) for capturing subcortical oscillatory activity. Objective: We explored MEG-based detection of hippocampal oscillatory abnormalities in prodromal AD patients. Methods: We acquired resting-state MEG data of 18 AD dementia patients, 18 amyloid-β-positive amnestic mild cognitive impairment (MCI, prodromal AD) patients, and 18 amyloid-β-negative persons with subjective cognitive decline (SCD). Oscillatory activity in 78 cortical regions and both hippocampi was reconstructed using beamforming. Between-group and hippocampal-cortical differences in spectral power were assessed. Classification accuracy was explored using ROC curves. Results: The MCI group showed intermediate power values between SCD and AD, except for the alpha range, where it was higher than both (p < 0.05 and p < 0.001). The largest differences between MCI and SCD were in the theta band, with higher power in MCI (p < 0.01). The hippocampi showed several unique group differences, such as higher power in the higher alpha band in MCI compared to SCD (p < 0.05). Classification accuracy (MCI versus SCD) was best for absolute theta band power in the right hippocampus (AUC = 0.87). Conclusion: In this MEG study, we detected oscillatory abnormalities of the hippocampi in prodromal AD patients. Moreover, hippocampus-based classification performed better than cortex-based classification. We conclude that a focus on hippocampal MEG may improve early detection of AD-related neuronal dysfunction.
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Affiliation(s)
- Janne J. Luppi
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Deborah N. Schoonhoven
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Willem de Haan
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
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Maestú F, de Haan W, Busche MA, DeFelipe J. Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev 2021; 69:101372. [PMID: 34029743 DOI: 10.1016/j.arr.2021.101372] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/16/2021] [Accepted: 05/19/2021] [Indexed: 02/08/2023]
Abstract
Our incomplete understanding of the link between Alzheimer's Disease pathology and symptomatology is a crucial obstacle for therapeutic success. Recently, translational studies have begun to connect the dots between protein alterations and deposition, brain network dysfunction and cognitive deficits. Disturbance of neuronal activity, and in particular an imbalance in underlying excitation/inhibition (E/I), appears early in AD, and can be regarded as forming a central link between structural brain pathology and cognitive dysfunction. While there are emerging (non-)pharmacological options to influence this imbalance, the complexity of human brain dynamics has hindered identification of an optimal approach. We suggest that focusing on the integration of neurophysiological aspects of AD at the micro-, meso- and macroscale, with the support of computational network modeling, can unite fundamental and clinical knowledge, provide a general framework, and suggest rational therapeutic targets.
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Human brain connectivity: Clinical applications for clinical neurophysiology. Clin Neurophysiol 2020; 131:1621-1651. [DOI: 10.1016/j.clinph.2020.03.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
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Abstract
Network neuroscience strives to understand the networks of the brain on all spatiotemporal scales and levels of observation. Current experimental and theoretical capabilities are beginning to facilitate a more holistic perspective, uniting these networks. This focus feature, "Bridging Scales and Levels," aims to document current research and looks to future progress towards this vision.
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Affiliation(s)
- Emma K Towlson
- Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA
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