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Wilson LE, Castanheira JDS, Kinder BL, Baillet S. Model selection for spectral parameterization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.606216. [PMID: 39149403 PMCID: PMC11326208 DOI: 10.1101/2024.08.01.606216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.
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Affiliation(s)
| | | | | | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
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52
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Stieger JR, Pinheiro-Chagas P, Fang Y, Li J, Lusk Z, Perry CM, Girn M, Contreras D, Chen Q, Huguenard JR, Spreng RN, Edlow BL, Wagner AD, Buch V, Parvizi J. Cross-regional coordination of activity in the human brain during autobiographical self-referential processing. Proc Natl Acad Sci U S A 2024; 121:e2316021121. [PMID: 39078679 PMCID: PMC11317603 DOI: 10.1073/pnas.2316021121] [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: 09/15/2023] [Accepted: 06/10/2024] [Indexed: 07/31/2024] Open
Abstract
For the human brain to operate, populations of neurons across anatomical structures must coordinate their activity within milliseconds. To date, our understanding of such interactions has remained limited. We recorded directly from the hippocampus (HPC), posteromedial cortex (PMC), ventromedial/orbital prefrontal cortex (OFC), and the anterior nuclei of the thalamus (ANT) during two experiments of autobiographical memory processing that are known from decades of neuroimaging work to coactivate these regions. In 31 patients implanted with intracranial electrodes, we found that the presentation of memory retrieval cues elicited a significant increase of low frequency (LF < 6 Hz) activity followed by cross-regional phase coherence of this LF activity before select populations of neurons within each of the four regions increased high-frequency (HF > 70 Hz) activity. The power of HF activity was modulated by memory content, and its onset followed a specific temporal order of ANT→HPC/PMC→OFC. Further, we probed cross-regional causal effective interactions with repeated electrical pulses and found that HPC stimulations cause the greatest increase in LF-phase coherence across all regions, whereas the stimulation of any region caused the greatest LF-phase coherence between that particular region and ANT. These observations support the role of the ANT in gating, and the HPC in synchronizing, the activity of cortical midline structures when humans retrieve self-relevant memories of their past. Our findings offer a fresh perspective, with high temporal fidelity, about the dynamic signaling and underlying causal connections among distant regions when the brain is actively involved in retrieving self-referential memories from the past.
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Affiliation(s)
- James R. Stieger
- Laboratory of Behavioral and Cognitive Neuroscience, Human Intracranial Cognitive Electrophysiology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA94305
| | - Pedro Pinheiro-Chagas
- Laboratory of Behavioral and Cognitive Neuroscience, Human Intracranial Cognitive Electrophysiology Program, Stanford University School of Medicine, Stanford, CA94305
| | - Ying Fang
- School of Psychology, South China Normal University, Guangzhou510631, China
| | - Jian Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Zoe Lusk
- Laboratory of Behavioral and Cognitive Neuroscience, Human Intracranial Cognitive Electrophysiology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA94305
| | - Claire M. Perry
- Laboratory of Behavioral and Cognitive Neuroscience, Human Intracranial Cognitive Electrophysiology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA94305
| | - Manesh Girn
- Montreal Neurological Institute, Department Neurology and Neurosurgery, McGill University, Montreal, QCH3G 1A4, Canada
| | - Diego Contreras
- Department of Neuroscience, University of Pennsylvania, School of Medicine, Philadelphia, PA19104
| | - Qi Chen
- School of Psychology, South China Normal University, Guangzhou510631, China
| | - John R. Huguenard
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford, CA94305
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department Neurology and Neurosurgery, McGill University, Montreal, QCH3G 1A4, Canada
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Anthony D. Wagner
- Wu Tsai Neurosciences Institute, Stanford, CA94305
- Department of Psychology, Stanford University, Stanford, CA94305
| | - Vivek Buch
- Department of Neurosurgery, Stanford University, Stanford School of Medicine, Stanford, CA94305
| | - Josef Parvizi
- Laboratory of Behavioral and Cognitive Neuroscience, Human Intracranial Cognitive Electrophysiology Program, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford, CA94305
- Department of Neurosurgery, Stanford University, Stanford School of Medicine, Stanford, CA94305
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53
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Herzberg MP, Nielsen AN, Luby J, Sylvester CM. Measuring neuroplasticity in human development: the potential to inform the type and timing of mental health interventions. Neuropsychopharmacology 2024:10.1038/s41386-024-01947-7. [PMID: 39103496 DOI: 10.1038/s41386-024-01947-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/17/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
Neuroplasticity during sensitive periods, the molecular and cellular process of enduring neural change in response to external stimuli during windows of high environmental sensitivity, is crucial for adaptation to expected environments and has implications for psychiatry. Animal research has characterized the developmental sequence and neurobiological mechanisms that govern neuroplasticity, yet gaps in our ability to measure neuroplasticity in humans limit the clinical translation of these principles. Here, we present a roadmap for the development and validation of neuroimaging and electrophysiology measures that index neuroplasticity to begin to address these gaps. We argue that validation of measures to track neuroplasticity in humans will elucidate the etiology of mental illness and inform the type and timing of mental health interventions to optimize effectiveness. We outline criteria for evaluating putative neuroimaging measures of plasticity in humans including links to neurobiological mechanisms shown to govern plasticity in animal models, developmental change that reflects heightened early life plasticity, and prediction of neural and/or behavior change. These criteria are applied to three putative measures of neuroplasticity using electroencephalography (gamma oscillations, aperiodic exponent of power/frequency) or functional magnetic resonance imaging (amplitude of low frequency fluctuations). We discuss the use of these markers in psychiatry, envision future uses for clinical and developmental translation, and suggest steps to address the limitations of the current putative neuroimaging measures of plasticity. With additional work, we expect these markers will significantly impact mental health and be used to characterize mechanisms, devise new interventions, and optimize developmental trajectories to reduce psychopathology risk.
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Affiliation(s)
- Max P Herzberg
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA.
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Joan Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO, USA
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54
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Cox KM, Kase D, Znati T, Turner RS. Detecting rhythmic spiking through the power spectra of point process model residuals. J Neural Eng 2024; 21:046041. [PMID: 38986461 PMCID: PMC11299538 DOI: 10.1088/1741-2552/ad6188] [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: 03/25/2024] [Revised: 06/21/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective. Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ('RP', the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established 'shuffling' procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach. In a novel 'residuals' method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the precedingnrmilliseconds. Finally, we compute the PSD of the model's residuals.Main results. We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey-in which alpha-beta oscillations (8-30 Hz) were anticipated-the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance. These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.
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Affiliation(s)
- Karin M Cox
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States of America
| | - Daisuke Kase
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States of America
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
- Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
| | - Taieb Znati
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Robert S Turner
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States of America
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
- Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
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55
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Maschke C, O'Byrne J, Colombo MA, Boly M, Gosseries O, Laureys S, Rosanova M, Jerbi K, Blain-Moraes S. Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity. Commun Biol 2024; 7:946. [PMID: 39103539 DOI: 10.1038/s42003-024-06613-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024] Open
Abstract
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects' PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.
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Affiliation(s)
- Charlotte Maschke
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Jordan O'Byrne
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, QC, Canada
| | | | - Melanie Boly
- Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, WI, USA
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du cerveau, CHU of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- CERVO Brain Research Centre, Laval University, Laval, QC, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Karim Jerbi
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, QC, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, QC, Canada
- Centre UNIQUE (Union Neurosciences & Intelligence Artificielle), Montréal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada.
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada.
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56
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Aksenov A, Renaud-D’Ambra M, Volpert V, Beuter A. Phase-shifted tACS can modulate cortical alpha waves in human subjects. Cogn Neurodyn 2024; 18:1575-1592. [PMID: 39104698 PMCID: PMC11297852 DOI: 10.1007/s11571-023-09997-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 07/13/2023] [Accepted: 08/06/2023] [Indexed: 08/07/2024] Open
Abstract
In the present study, we investigated traveling waves induced by transcranial alternating current stimulation in the alpha frequency band of healthy subjects. Electroencephalographic data were recorded in 12 healthy subjects before, during, and after phase-shifted stimulation with a device combining both electroencephalographic and stimulation capacities. In addition, we analyzed the results of numerical simulations and compared them to the results of identical analysis on real EEG data. The results of numerical simulations indicate that imposed transcranial alternating current stimulation induces a rotating electric field. The direction of waves induced by stimulation was observed more often during at least 30 s after the end of stimulation, demonstrating the presence of aftereffects of the stimulation. Results suggest that the proposed approach could be used to modulate the interaction between distant areas of the cortex. Non-invasive transcranial alternating current stimulation can be used to facilitate the propagation of circulating waves at a particular frequency and in a controlled direction. The results presented open new opportunities for developing innovative and personalized transcranial alternating current stimulation protocols to treat various neurological disorders. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09997-1.
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Affiliation(s)
| | | | - Vitaly Volpert
- Institute Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, France
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57
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Parr AC, Sydnor VJ, Calabro FJ, Luna B. Adolescent-to-adult gains in cognitive flexibility are adaptively supported by reward sensitivity, exploration, and neural variability. Curr Opin Behav Sci 2024; 58:101399. [PMID: 38826569 PMCID: PMC11138371 DOI: 10.1016/j.cobeha.2024.101399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Cognitive flexibility exhibits dynamic changes throughout development, with different forms of flexibility showing dissociable developmental trajectories. In this review, we propose that an adolescent-specific mode of flexibility in the face of changing environmental contingencies supports the emergence of adolescent-to-adult gains in cognitive shifting efficiency. We first describe how cognitive shifting abilities monotonically improve from childhood to adulthood, accompanied by increases in brain state flexibility, neural variability, and excitatory/inhibitory balance. We next summarize evidence supporting the existence of a dopamine-driven, adolescent peak in flexible behavior that results in reward seeking, undirected exploration, and environmental sampling. We propose a neurodevelopmental framework that relates these adolescent behaviors to the refinement of neural phenotypes relevant to mature cognitive flexibility, and thus highlight the importance of the adolescent period in fostering healthy neurocognitive trajectories.
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Affiliation(s)
- Ashley C. Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 14213, USA
| | - Valerie J. Sydnor
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 14213, USA
| | - Finnegan J. Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 14213, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 14213, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh PA, 14213, USA
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58
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Stanyard RA, Mason D, Ellis C, Dickson H, Short R, Batalle D, Arichi T. Aperiodic and Hurst EEG exponents across early human brain development: A systematic review. Dev Cogn Neurosci 2024; 68:101402. [PMID: 38917647 PMCID: PMC11254951 DOI: 10.1016/j.dcn.2024.101402] [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: 02/20/2024] [Revised: 04/12/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
In electroencephalographic (EEG) data, power-frequency slope exponents (1/f_β) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/fβ evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0-26 yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing ≥1 1/f measures and ≥10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (Nparticipants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.
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Affiliation(s)
- R A Stanyard
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
| | - D Mason
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - C Ellis
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - H Dickson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - R Short
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - D Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - T Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, United Kingdom; Department of Bioengineering, Imperial College London, United Kingdom
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59
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Park SE, Chung J, Lee J, Kim MJB, Kim J, Jeon HJ, Kim H, Woo C, Kim H, Lee SA. Digital assessment of cognitive-affective biases related to mental health. PLOS DIGITAL HEALTH 2024; 3:e0000595. [PMID: 39208388 PMCID: PMC11361731 DOI: 10.1371/journal.pdig.0000595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/28/2024] [Indexed: 09/04/2024]
Abstract
With an increasing societal need for digital therapy solutions for poor mental health, we face a corresponding rise in demand for scientifically validated digital contents. In this study we aimed to lay a sound scientific foundation for the development of brain-based digital therapeutics to assess and monitor cognitive effects of social and emotional bias across diverse populations and age-ranges. First, we developed three computerized cognitive tasks using animated graphics: 1) an emotional flanker task designed to test attentional bias, 2) an emotional go-no-go task to measure bias in memory and executive function, and 3) an emotional social evaluation task to measure sensitivity to social judgments. Then, we confirmed the generalizability of our results in a wide range of samples (children (N = 50), young adults (N = 172), older adults (N = 39), online young adults (N=93), and depression patients (N = 41)) using touchscreen and online computer-based tasks, and devised a spontaneous thought generation task that was strongly associated with, and therefore could potentially serve as an alternative to, self-report scales. Using PCA, we extracted five components that represented different aspects of cognitive-affective function (emotional bias, emotional sensitivity, general accuracy, and general/social attention). Next, a gamified version of the above tasks was developed to test the feasibility of digital cognitive training over a 2-week period. A pilot training study utilizing this application showed decreases in emotional bias in the training group (that were not observed in the control group), which was correlated with a reduction in anxiety symptoms. Using a 2-channel wearable EEG system, we found that frontal alpha and gamma power were associated with both emotional bias and its reduction across the 2-week training period.
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Affiliation(s)
- Sang-Eon Park
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jisu Chung
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jeonghyun Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Minwoo JB Kim
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jinhee Kim
- School of Psychology, Korea University, Seoul, Republic of Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyungsook Kim
- Hanyang Digital Healthcare Center, Hanyang University, Seoul, Republic of Korea
| | - Choongwan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hackjin Kim
- School of Psychology, Korea University, Seoul, Republic of Korea
| | - Sang Ah Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
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60
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Pigorini A, Avanzini P, Barborica A, Bénar CG, David O, Farisco M, Keller CJ, Manfridi A, Mikulan E, Paulk AC, Roehri N, Subramanian A, Vulliémoz S, Zelmann R. Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity. J Neurosci Methods 2024; 408:110160. [PMID: 38734149 DOI: 10.1016/j.jneumeth.2024.110160] [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: 12/18/2023] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.
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Affiliation(s)
- Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Pietro Avanzini
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | | | - Christian-G Bénar
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Olivier David
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Michele Farisco
- Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, P.O. Box 256, Uppsala, SE 751 05, Sweden; Science and Society Unit Biogem, Biology and Molecular Genetics Institute, Via Camporeale snc, Ariano Irpino, AV 83031, Italy
| | - Corey J Keller
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Alfredo Manfridi
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Angelique C Paulk
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicolas Roehri
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Ajay Subramanian
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Rina Zelmann
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Wang P, Dai W, Liu H, Liu H, Xu Y. Fenobam modulates distinct electrophysiological mechanisms for regulating excessive gamma oscillations in the striatum of dyskinetic rats. Exp Neurol 2024; 378:114833. [PMID: 38782350 DOI: 10.1016/j.expneurol.2024.114833] [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: 01/22/2024] [Revised: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024]
Abstract
Gamma oscillations have been frequently observed in levodopa-induced dyskinesia (LID), manifest as broadband (60-120 Hz) and narrowband (80-110 Hz) gamma activity in cortico-striatal projection. We investigated the electrophysiological mechanisms and correlation of gamma oscillations with dyskinesia severity, while assessing the administration of fenobam, a selective metabotropic glutamate receptor 5 (mGluR5) antagonist, in regulating dyskinesia-associated gamma activity. We conducted simultaneous electrophysiological recordings in Striatum (Str) and primary motor cortex (M1), together with Abnormal Involuntary Movement Scale scoring (AIMs). Phase-amplitude coupling (PAC), power, coherence, and Granger causality analyses were conducted for electrophysiological data. The findings demonstrated increased beta oscillations with directionality from M1 to Str in parkinsonian state. During on-state dyskinesia, elevated broadband gamma activity was modulated by the phase of theta activity in Str, while M1 → Str gamma causality mediated narrowband gamma oscillations in Str. Striatal gamma power (both periodic and aperiodic power), periodic power, peak frequency, and PAC at 80 min (corresponding to the peak dyskinesia) after repeated levodopa injections across recording days (day 30, 33, 36, 39, and 42) increased progressively, correlating with total AIMs. Additionally, a time-dependent parabolic trend of PAC, peak frequency and gamma power was observed after levodopa injection on day 42 from 20 to 120 min, which also correlated with corresponding AIMs. Fenobam effectively alleviates dyskinesia, suppresses enhanced gamma oscillations in the M1-Str directionality, and reduces PAC in Str. The temporal characteristics of gamma oscillations provide parameters for classifying LID severity. Antagonizing striatal mGluR5, a promising therapeutic target for dyskinesia, exerts its effects by modulating gamma activity.
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Affiliation(s)
- Pengfei Wang
- Department of Otology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weina Dai
- School of Basic Medical Science, Sanquan College of Xinxiang Medical University, Henan Province, China
| | - Hongbin Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; NHC Key Laboratory of Prevention and treatment of Cerebrovascular Disease, Henan Key Laboratory of Cerebrovascular Diseases of Zhengzhou University, Zhengzhou, China
| | - Han Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; NHC Key Laboratory of Prevention and treatment of Cerebrovascular Disease, Henan Key Laboratory of Cerebrovascular Diseases of Zhengzhou University, Zhengzhou, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; NHC Key Laboratory of Prevention and treatment of Cerebrovascular Disease, Henan Key Laboratory of Cerebrovascular Diseases of Zhengzhou University, Zhengzhou, China.
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Schantell M, John JA, Coutant AT, Okelberry HJ, Horne LK, Glesinger R, Springer SD, Mansouri A, May‐Weeks PE, Wilson TW. Chronic cannabis use alters the spontaneous and oscillatory gamma dynamics serving cognitive control. Hum Brain Mapp 2024; 45:e26787. [PMID: 39023178 PMCID: PMC11256138 DOI: 10.1002/hbm.26787] [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: 09/28/2023] [Revised: 06/21/2024] [Accepted: 06/30/2024] [Indexed: 07/20/2024] Open
Abstract
Regular cannabis use is associated with cortex-wide changes in spontaneous and oscillatory activity, although the functional significance of such changes remains unclear. We hypothesized that regular cannabis use would suppress spontaneous gamma activity in regions serving cognitive control and scale with task performance. Participants (34 cannabis users, 33 nonusers) underwent an interview regarding their substance use history and completed the Eriksen flanker task during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain and virtual sensors were extracted from the peak voxels of the grand-averaged oscillatory interference maps to quantify spontaneous gamma activity during the pre-stimulus baseline period. We then assessed group-level differences in spontaneous and oscillatory gamma activity, and their relationship with task performance and cannabis use metrics. Both groups exhibited a significant behavioral flanker interference effect, with slower responses during incongruent relative to congruent trials. Mixed-model ANOVAs indicated significant gamma-frequency neural interference effects in the left frontal eye fields (FEF) and left temporoparietal junction (TPJ). Further, a group-by-condition interaction was detected in the left FEF, with nonusers exhibiting stronger gamma oscillations during incongruent relative to congruent trials and cannabis users showing no difference. In addition, spontaneous gamma activity was sharply suppressed in cannabis users relative to nonusers in the left FEF and TPJ. Finally, spontaneous gamma activity in the left FEF and TPJ was associated with task performance across all participants, and greater cannabis use was associated with weaker spontaneous gamma activity in the left TPJ of the cannabis users. Regular cannabis use was associated with weaker spontaneous gamma in the TPJ and FEF. Further, the degree of use may be proportionally related to the degree of suppression in spontaneous activity in the left TPJ.
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Affiliation(s)
- Mikki Schantell
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
| | - Jason A. John
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Anna T. Coutant
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Hannah J. Okelberry
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Lucy K. Horne
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Ryan Glesinger
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Seth D. Springer
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
| | - Amirsalar Mansouri
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
| | | | - Tony W. Wilson
- Institute for Human NeuroscienceBoys Town National Research HospitalBoys TownNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
- Department of Pharmacology and NeuroscienceCreighton UniversityOmahaNebraskaUSA
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Gao Y, Roessner V, Stock AK, Mückschel M, Colzato L, Hommel B, Beste C. Catecholaminergic Modulation of Metacontrol Is Reflected by Changes in Aperiodic EEG Activity. Int J Neuropsychopharmacol 2024; 27:pyae033. [PMID: 39096235 PMCID: PMC11348007 DOI: 10.1093/ijnp/pyae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 08/02/2024] [Indexed: 08/05/2024] Open
Abstract
BACKGROUND "Metacontrol" describes the ability to maintain an optimal balance between cognitive control styles that are either more persistent or more flexible. Recent studies have shown a link between metacontrol and aperiodic EEG patterns. The present study aimed to gain more insight into the neurobiological underpinnings of metacontrol by using methylphenidate (MPH), a compound known to increase postsynaptic catecholamine levels and modulate cortical noise. METHODS In a double-blind, randomized, placebo-controlled study design, we investigated the effect of MPH (0.5 mg/kg) on aperiodic EEG activity during a flanker task in a sample of n = 25 neurotypical adults. To quantify cortical noise, we employed the fitting oscillations and one over f algorithm. RESULTS Compared with placebo, MPH increased the aperiodic exponent, suggesting that it reduces cortical noise in 2 ways. First, it did so in a state-like fashion, as the main effect of the drug was visible and significant in both pre-trial and within-trial periods. Second, the electrode-specific analyses showed that the drug also affects specific processes by dampening the downregulation of noise in conditions requiring more control. CONCLUSIONS Our findings suggest that the aperiodic exponent provides a neural marker of metacontrol states and changes therein. Further, we propose that the effectiveness of medications targeting catecholaminergic signaling can be evaluated by studying changes of cortical noise, fostering the idea of using the quantification of cortical noise as an indicator in pharmacological treatment.
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Affiliation(s)
- Yang Gao
- School of Psychology, Shandong Normal University, Jinan, China
| | - Veit Roessner
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Moritz Mückschel
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Lorenza Colzato
- School of Psychology, Shandong Normal University, Jinan, China
| | - Bernhard Hommel
- School of Psychology, Shandong Normal University, Jinan, China
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
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Lum JAG, Barham MP, Hill AT. Pupillometry reveals resting state alpha power correlates with individual differences in adult auditory language comprehension. Cortex 2024; 177:1-14. [PMID: 38821014 DOI: 10.1016/j.cortex.2024.02.019] [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: 11/06/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 06/02/2024]
Abstract
Although individual differences in adult language processing are well-documented, the neural basis of this variability remains largely unexplored. The current study addressed this gap in the literature by examining the relationship between resting state alpha activity and individual differences in auditory language comprehension. Alpha oscillations modulate cortical excitability, facilitating efficient information processing in the brain. While resting state alpha oscillations have been tied to individual differences in cognitive performance, their association with auditory language comprehension is less clear. Participants in the study were 80 healthy adults with a mean age of 25.8 years (SD = 7.2 years). Resting state alpha activity was acquired using electroencephalography while participants looked at a benign stimulus for 3 min. Participants then completed a language comprehension task that involved listening to 'syntactically simple' subject-relative clause sentences and 'syntactically complex' object-relative clause sentences. Pupillometry measured real-time processing demand changes, with larger pupil dilation indicating increased processing loads. Replicating past research, comprehending object relative clauses, compared to subject relative clauses, was associated with lower accuracy, slower reaction times, and larger pupil dilation. Resting state alpha power was found to be positively correlated with the pupillometry data. That is, participants with higher resting state alpha activity evidenced larger dilation during sentence comprehension. This effect was more pronounced for the 'complex' object sentences compared to the 'simple' subject sentences. These findings suggest the brain's capacity to generate a robust resting alpha rhythm contributes to variability in processing demands associated with auditory language comprehension, especially when faced with challenging syntactic structures. More generally, the study demonstrates that the intrinsic functional architecture of the brain likely influences individual differences in language comprehension.
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Affiliation(s)
- Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia.
| | - Michael P Barham
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia
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Kozma C, Schroeder G, Owen T, de Tisi J, McEvoy AW, Miserocchi A, Duncan J, Wang Y, Taylor PN. Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra. J Neurosci Methods 2024; 408:110180. [PMID: 38795977 DOI: 10.1016/j.jneumeth.2024.110180] [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: 02/15/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes. NEW METHODS We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG). RESULTS Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.
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Affiliation(s)
- Csaba Kozma
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Gabrielle Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - John Duncan
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
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66
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Smith MK, Grabowecky M, Suzuki S. Dynamic Formation of a Posterior-to-Anterior Peak-Alpha-Frequency Gradient Driven by Two Distinct Processes. eNeuro 2024; 11:ENEURO.0273-24.2024. [PMID: 39142821 PMCID: PMC11373881 DOI: 10.1523/eneuro.0273-24.2024] [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: 06/22/2024] [Accepted: 07/22/2024] [Indexed: 08/16/2024] Open
Abstract
Peak-alpha frequency varies across individuals and mental states, but it also forms a negative gradient from posterior to anterior regions in association with increases in cortical thickness and connectivity, reflecting a cortical hierarchy in temporal integration. Tracking the spatial standard deviation of peak-alpha frequency in scalp EEG, we observed that a posterior-to-anterior gradient dynamically formed and dissolved. Periods of high spatial standard deviation yielded robustly negative posterior-to-anterior gradients-the "gradient state"-while periods of low spatial standard deviation yielded globally converged peak-alpha frequency-the "uniform state." The state variations were characterized by a combination of slow (0.3-0.5 Hz) oscillations and random-walk-like fluctuations. They were relatively independently correlated with peak-alpha frequency variations in anterior regions and peak-alpha power variations in central regions driven by posterior regions (together accounting for ∼50% of the state variations), suggesting that two distinct mechanisms modulate the state variations: an anterior mechanism that directly adjusts peak-alpha frequencies and a posterior-central mechanism that indirectly adjusts them by influencing synchronization. The state variations likely reflect general operations as their spatiotemporal characteristics remained unchanged while participants engaged in a variety of tasks (breath focus, vigilance, working memory, mental arithmetic, and generative thinking) with their eyes closed or watched a silent nature video. The ongoing state variations may dynamically balance two global processing modes, one that facilitates greater temporal integration (and potentially also information influx) toward anterior regions in the gradient state and the other that facilitates flexible global communication (via phase locking) in the uniform state.
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Affiliation(s)
- Max Kailler Smith
- Department of Psychology, Northwestern University, Evanston, Illinois 60208
| | - Marcia Grabowecky
- Department of Psychology and Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois 60208
| | - Satoru Suzuki
- Department of Psychology and Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois 60208
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Lewis-Healey E, Tagliazucchi E, Canales-Johnson A, Bekinschtein TA. Breathwork-induced psychedelic experiences modulate neural dynamics. Cereb Cortex 2024; 34:bhae347. [PMID: 39191666 DOI: 10.1093/cercor/bhae347] [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: 04/02/2024] [Revised: 08/01/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024] Open
Abstract
Breathwork is an understudied school of practices involving intentional respiratory modulation to induce an altered state of consciousness (ASC). We simultaneously investigate the phenomenological and neural dynamics of breathwork by combining Temporal Experience Tracing, a quantitative methodology that preserves the temporal dynamics of subjective experience, with low-density portable EEG devices. Fourteen novice participants completed a course of up to 28 breathwork sessions-of 20, 40, or 60 min-in 28 days, yielding a neurophenomenological dataset of 301 breathwork sessions. Using hypothesis-driven and data-driven approaches, we found that "psychedelic-like" subjective experiences were associated with increased neural Lempel-Ziv complexity during breathwork. Exploratory analyses showed that the aperiodic exponent of the power spectral density-but not oscillatory alpha power-yielded similar neurophenomenological associations. Non-linear neural features, like complexity and the aperiodic exponent, neurally map both a multidimensional data-driven composite of positive experiences, and hypothesis-driven aspects of psychedelic-like experience states such as high bliss.
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Affiliation(s)
- Evan Lewis-Healey
- Cambridge Consciousness and Cognition Lab, Department of Psychology, Downing Place, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
| | - Enzo Tagliazucchi
- Consciousness, Culture and Complexity Lab, Department of Physics, Pabellón I, University of Buenos Aires, 1428, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, 7910000, Chile
- Cognitive Neuroscience Center, Universidad de San Andrés, Vito Dumas 284, B1644BID Victoria, Provincia de Buenos Aires, Argentina
| | - Andres Canales-Johnson
- Cambridge Consciousness and Cognition Lab, Department of Psychology, Downing Place, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
- The Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, 3460000, Talca, Chile
| | - Tristan A Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, Downing Place, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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Suresh T, Iwane F, Zhang M, McElmurry M, Manesiya M, Freedberg MV, Hussain SJ. Motor sequence learning-induced corticospinal plasticity is biased towards sensorimotor mu rhythm peak phases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606022. [PMID: 39211097 PMCID: PMC11361050 DOI: 10.1101/2024.07.31.606022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Motor cortical (M1) transcranial magnetic stimulation (TMS) increases corticospinal output and improves motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that mechanisms supporting motor learning may be most active during mu trough phases. If so, learning-related corticospinal plasticity should be most evident during mu trough phases. Healthy adults were assigned to either a sequence or control group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the control group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude increases after SRTT acquisition differed between groups. MEP amplitude changes from baseline to 30 minutes after SRTT acquisition more strongly differed across phases in the control relative to the sequence group, with the control group showing smaller increases in peak- than trough-specific MEPs. Contrary to our original hypothesis, results revealed that sequence learning recruits peak- rather than trough-specific neurophysiological mechanisms. Overall, these findings suggest that mu peak phases may provide protected time windows for motor memory consolidation and demonstrate the presence of a mu phase-dependent motor learning mechanism in the human brain. Significance statement Recent work suggests that the neurophysiological mechanisms supporting motor learning may be most active during sensorimotor mu rhythm trough phases. Here, we evaluated this possibility by measuring mu phase-dependent corticospinal plasticity induced by motor sequence learning. Results provide first evidence that motor sequence learning produced corticospinal plasticity that was more pronounced during mu peak than trough phases, demonstrating the presence of a phase-dependent learning mechanism within the human motor system.
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69
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Liang X, Wang R, Wu H, Ma Y, Liu C, Gao Y, Yu D, Ning X. A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG. Bioengineering (Basel) 2024; 11:773. [PMID: 39199731 PMCID: PMC11351447 DOI: 10.3390/bioengineering11080773] [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: 06/12/2024] [Revised: 07/27/2024] [Accepted: 07/29/2024] [Indexed: 09/01/2024] Open
Abstract
Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.
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Affiliation(s)
- Xiaoyu Liang
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hefei National Laboratory, Hefei 230088, China
| | - Ruonan Wang
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Huanqi Wu
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Yuyu Ma
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Changzeng Liu
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Yang Gao
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China
- National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China
| | - Dexin Yu
- Shandong Key Laboratory: Magnetic Field-Free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan 250012, China;
| | - Xiaolin Ning
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (X.L.); (R.W.); (H.W.); (Y.M.); (C.L.); (Y.G.)
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hefei National Laboratory, Hefei 230088, China
- Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China
- National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China
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Agnihotri SK, Cai J. Investigating the Effects of Transcranial Alternating Current Stimulation on Cortical Oscillations and Network Dynamics. Brain Sci 2024; 14:767. [PMID: 39199461 PMCID: PMC11353238 DOI: 10.3390/brainsci14080767] [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: 07/03/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 09/01/2024] Open
Abstract
Transcranial electrical brain stimulation techniques like transcranial direct current (tDCS) and transcranial alternating current (tACS) have emerged as potential tools for treating neurological diseases by modulating cortical excitability. These techniques deliver small electric currents to the brain non-invasively through electrodes on the scalp. tDCS uses constant direct current which weakly alters the membrane voltage of cortical neurons, while tACS utilizes alternating current to target and enhance cortical oscillations, though the underlying mechanisms are not fully understood more specifically. To elucidate how tACS perturbs endogenous network dynamics, we simulated spiking neuron network models. We identified distinct roles of the depolarizing and hyperpolarizing phases in driving network activity towards and away from the strong nonlinearity provided by pyramidal neurons. Exploring resonance effects, we found matching tACS frequency to the network's endogenous resonance frequency creates greater entrainment. Based on this, we developed an algorithm to determine the network's endogenous frequency, phase, and amplitude, then deliver optimized tACS to entrain network oscillations. Together, these computational results provide mechanistic insight into the effects of tACS on network dynamics and could inform future closed-loop tACS systems that dynamically tune stimulation parameters to ongoing brain activity.
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Bernardo D, Xie X, Verma P, Kim J, Liu V, Numis AL, Wu Y, Glass HC, Yap PT, Nagarajan SS, Raj A. Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model. ARXIV 2024:arXiv:2405.02524v3. [PMID: 39040639 PMCID: PMC11261974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.
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Affiliation(s)
- Danilo Bernardo
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Kim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Virginia Liu
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adam L. Numis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hannah C. Glass
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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72
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Vaz A, Wathen C, Miranda S, Thomas R, Darlington T, Jabarkheel R, Tomlinson S, Arena J, Bond K, Salwi S, Ajmera S, Bachschmid-Romano L, Gugger J, Sandsmark D, Diaz-Arrastia R, Schuster J, Ramayya AG, Cajigas I, Pesaran B, Chen HI, Petrov D. Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604293. [PMID: 39091808 PMCID: PMC11291083 DOI: 10.1101/2024.07.19.604293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.
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Affiliation(s)
- Alex Vaz
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Connor Wathen
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephen Miranda
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rachel Thomas
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Timothy Darlington
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rashad Jabarkheel
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Samuel Tomlinson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John Arena
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kamila Bond
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sanjana Salwi
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sonia Ajmera
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - James Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle Sandsmark
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Schuster
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ashwin G Ramayya
- Department of Neurosurgery, Stanford University, Palo Alto, CA, 94305, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bijan Pesaran
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - H Isaac Chen
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Dmitriy Petrov
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
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73
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Figueira JSB, Chapman EA, Ayomen EN, Keil A, Mathews CA. Stimulus-related oscillatory brain activity discriminates hoarding disorder from OCD and healthy controls. Biol Psychol 2024; 192:108848. [PMID: 39048018 DOI: 10.1016/j.biopsycho.2024.108848] [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: 03/01/2024] [Revised: 07/18/2024] [Accepted: 07/20/2024] [Indexed: 07/27/2024]
Abstract
Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring.
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Affiliation(s)
- Jessica Sanches Braga Figueira
- Department of Psychology, University of Florida, Gainesville, FL, USA; Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, FL, USA
| | | | - Estelle N Ayomen
- Department of Psychiatry, University of Florida, Gainesville, FL, USA; Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, FL, USA
| | - Andreas Keil
- Department of Psychology, University of Florida, Gainesville, FL, USA; Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, FL, USA
| | - Carol A Mathews
- Department of Psychiatry, University of Florida, Gainesville, FL, USA; Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, FL, USA.
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74
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Kramer MA, Chu CJ. A General, Noise-Driven Mechanism for the 1/f-Like Behavior of Neural Field Spectra. Neural Comput 2024; 36:1643-1668. [PMID: 39028955 DOI: 10.1162/neco_a_01682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/25/2024] [Indexed: 07/21/2024]
Abstract
Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo without requiring a specific biological mechanism or collective critical behavior.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, and Center for Systems Neuroscience, Boston University, Boston, MA 02214, U.S.A.
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A.
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75
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Marchant S, van der Vaart M, Pillay K, Baxter L, Bhatt A, Fitzgibbon S, Hartley C, Slater R. A machine learning artefact detection method for single-channel infant event-related potential studies. J Neural Eng 2024; 21:046021. [PMID: 38925111 PMCID: PMC11250100 DOI: 10.1088/1741-2552/ad5c04] [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: 10/13/2023] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 06/28/2024]
Abstract
Objective. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant-for example because the clinical vulnerability of the infants limits access for recording. Current artefact-detection methods that perform well on adult data and resting-state and multi-channel data in infants are not suitable for this application. The aim of this study was to create and test an automated method of detecting artefact in single-channel 1500 ms epochs of infant EEG.Approach. A total of 410 epochs of EEG were used, collected from 160 infants of 28-43 weeks postmenstrual age. This dataset-which was balanced to include epochs of background activity and responses to visual, auditory, tactile and noxious stimuli-was presented to seven independent raters, who independently labelled the epochs according to whether or not they were able to visually identify artefacts. The data was split into a training set (340 epochs) and an independent test set (70 epochs). A random forest model was trained to identify epochs as either artefact or not artefact.Main results. This model performs well, achieving a balanced accuracy of 0.81, which is as good as manual review of data. Accuracy was not significantly related to the infant age or type of stimulus.Significance. This method provides an objective tool for automated artefact rejection for short epoch, single-channel EEG in neonates and could increase the utility of EEG in neonates in both the clinical and research setting.
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Affiliation(s)
- Simon Marchant
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | | | - Kirubin Pillay
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Aomesh Bhatt
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Sean Fitzgibbon
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Caroline Hartley
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
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76
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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [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: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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77
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Zioga I, Kenett YN, Giannopoulos A, Luft CDB. The role of alpha oscillations in free- and goal-directed semantic associations. Hum Brain Mapp 2024; 45:e26770. [PMID: 38970217 PMCID: PMC11226545 DOI: 10.1002/hbm.26770] [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: 08/22/2023] [Revised: 05/13/2024] [Accepted: 06/13/2024] [Indexed: 07/08/2024] Open
Abstract
Alpha oscillations are known to play a central role in several higher-order cognitive functions, especially selective attention, working memory, semantic memory, and creative thinking. Nonetheless, we still know very little about the role of alpha in the generation of more remote semantic associations, which is key to creative and semantic cognition. Furthermore, it remains unclear how these oscillations are shaped by the intention to "be creative," which is the case in most creativity tasks. We aimed to address these gaps in two experiments. In Experiment 1, we compared alpha oscillatory activity (using a method which distinguishes genuine oscillatory activity from transient events) during the generation of free associations which were more vs. less distant from a given concept. In Experiment 2, we replicated these findings and also compared alpha oscillatory activity when people were generating free associations versus associations with the instruction to be creative (i.e. goal-directed). We found that alpha was consistently higher during the generation of more distant semantic associations, in both experiments. This effect was widespread, involving areas in both left and right hemispheres. Importantly, the instruction to be creative seems to increase alpha phase synchronisation from left to right temporal brain areas, suggesting that intention to be creative changed the flux of information in the brain, likely reflecting an increase in top-down control of semantic search processes. We conclude that goal-directed generation of remote associations relies on top-down mechanisms compared to when associations are freely generated.
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Affiliation(s)
- Ioanna Zioga
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Yoed N. Kenett
- Faculty of Data and Decision Sciences, Technion—Israel Institute of TechnologyHaifaIsrael
| | - Anastasios Giannopoulos
- School of Electrical and Computer EngineeringNational Technical University of Athens (NTUA) AthensAthensGreece
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78
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Thiele C, Rufener KS, Repplinger S, Zaehle T, Ruhnau P. Transcranial temporal interference stimulation (tTIS) influences event-related alpha activity during mental rotation. Psychophysiology 2024:e14651. [PMID: 38997805 DOI: 10.1111/psyp.14651] [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: 10/31/2023] [Revised: 05/14/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
Non-invasive brain stimulation techniques offer therapeutic potential for neurological and psychiatric disorders. However, current methods are often limited in their stimulation depth. The novel transcranial temporal interference stimulation (tTIS) aims to overcome this limitation by non-invasively targeting deeper brain regions. In this study, we aimed to evaluate the efficacy of tTIS in modulating alpha activity during a mental rotation task. The effects of tTIS were compared with transcranial alternating current stimulation (tACS) and a sham control. Participants were randomly assigned to a tTIS, tACS, or sham group. They performed alternating blocks of resting and mental rotation tasks before, during, and after stimulation. During the stimulation blocks, participants received 20 min of stimulation adjusted to their individual alpha frequency (IAF). We assessed shifts in resting state alpha power, event-related desynchronization (ERD) of alpha activity during mental rotation, as well as resulting improvements in behavioral performance. Our results indicate tTIS and tACS to be effective in modulating cortical alpha activity during mental rotation, leading to an increase in ERD from pre- to poststimulation as well as compared to sham stimulation. However, this increase in ERD was not correlated with enhanced mental rotation performance, and resting state alpha power remained unchanged. Our findings underscore the complex nature of tTIS and tACS efficacy, indicating that stimulation effects are more observable during active cognitive tasks, while their impacts are less pronounced on resting neuronal systems.
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Affiliation(s)
- Carsten Thiele
- Department of Neurology, Otto-von-Guericke-University, University Clinic of Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
| | - Katharina S Rufener
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine of Childhood and Adolescents, Otto-von-Guericke-University, University Clinic of Magdeburg, Magdeburg, Germany
| | - Stefan Repplinger
- Department of Neurology, Otto-von-Guericke-University, University Clinic of Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
| | - Tino Zaehle
- Department of Neurology, Otto-von-Guericke-University, University Clinic of Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
| | - Philipp Ruhnau
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
- School of Psychology and Humanities, University of Central Lancashire, Preston, UK
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79
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Larsh TR, Gilbert DL, Vadivelu S, Binder DK, Pedapati EV, Wu SW. Post Deep Brain Stimulation Time Course of Aperiodic Activity in Childhood and Young Adult Dystonia. Mov Disord Clin Pract 2024. [PMID: 38989718 DOI: 10.1002/mdc3.14159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/16/2024] [Accepted: 06/18/2024] [Indexed: 07/12/2024] Open
Affiliation(s)
- Travis R Larsh
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Donald L Gilbert
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Sudhakar Vadivelu
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Devin K Binder
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, California, USA
| | - Ernest V Pedapati
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Steve W Wu
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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80
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Zhu H, Michalak AJ, Merricks EM, Agopyan-Miu AHCW, Jacobs J, Hamberger MJ, Sheth SA, McKhann GM, Feldstein N, Schevon CA, Hillman EMC. Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.600416. [PMID: 39026706 PMCID: PMC11257469 DOI: 10.1101/2024.07.08.600416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.
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Affiliation(s)
- Hongkun Zhu
- Department of Biomedical Engineering, Columbia University
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Andrew J Michalak
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Marla J Hamberger
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Neil Feldstein
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Catherine A Schevon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Elizabeth M C Hillman
- Department of Biomedical Engineering, Columbia University
- Department of Radiology, Columbia University Medical Center, New York, 10032, New York, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
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81
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Wilkinson CL, Yankowitz LD, Chao JY, Gutiérrez R, Rhoades JL, Shinnar S, Purdon PL, Nelson CA. Developmental trajectories of EEG aperiodic and periodic components in children 2-44 months of age. Nat Commun 2024; 15:5788. [PMID: 38987558 PMCID: PMC11237135 DOI: 10.1038/s41467-024-50204-4] [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: 08/11/2023] [Accepted: 07/02/2024] [Indexed: 07/12/2024] Open
Abstract
The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2-44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.
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Affiliation(s)
- Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Lisa D Yankowitz
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jerry Y Chao
- Department of Anesthesiology, Montefiore Medical Center, Children's Hospital at Montefiore, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rodrigo Gutiérrez
- Departamento de Anestesia y Medicina Perioperatoria, Hospital Clínico de la Universidad de Chile, Santiago, Chile
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeff L Rhoades
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Shlomo Shinnar
- The Saul R. Korey Department of Neurology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard Graduate School of Education, Cambridge, MA, USA
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82
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Kapanaiah SKT, Rosenbrock H, Hengerer B, Kätzel D. Neural effects of dopaminergic compounds revealed by multi-site electrophysiology and interpretable machine-learning. Front Pharmacol 2024; 15:1412725. [PMID: 39045050 PMCID: PMC11263031 DOI: 10.3389/fphar.2024.1412725] [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: 04/05/2024] [Accepted: 06/10/2024] [Indexed: 07/25/2024] Open
Abstract
Background Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis. Methods We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown. Results Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamo-hippocampal network entails a larger share of D1-as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs. Conclusion Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies.
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Affiliation(s)
| | - Holger Rosenbrock
- Boehringer Ingelheim Pharma GmbH & Co. KG, Div. Research Germany, Ingelheim, Germany
| | - Bastian Hengerer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Div. Research Germany, Ingelheim, Germany
| | - Dennis Kätzel
- Institute of Applied Physiology, Ulm University, Ulm, Germany
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83
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Duma GM, Cuozzo S, Wilson L, Danieli A, Bonanni P, Pellegrino G. Excitation/Inhibition balance relates to cognitive function and gene expression in temporal lobe epilepsy: a high density EEG assessment with aperiodic exponent. Brain Commun 2024; 6:fcae231. [PMID: 39056027 PMCID: PMC11272395 DOI: 10.1093/braincomms/fcae231] [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: 02/19/2024] [Revised: 05/22/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
Patients with epilepsy are characterized by a dysregulation of excitation/inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here, we exploited the exponent of the aperiodic component of the power spectrum of the electroencephalography (EEG) signal, as a non-invasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 67 patients with temporal lobe epilepsy and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between patients with epilepsy and controls. Spearman's correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from the Allen Human Brain Atlas. Patients with temporal lobe epilepsy showed a significantly larger exponent, corresponding to inhibition-directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short-term verbal memory. Limited to patients with temporal lobe epilepsy, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2 and PDYN genes. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a close relationship between altered E/I patterns, cognition and genetics.
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Affiliation(s)
- Gian Marco Duma
- Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, 31015, Conegliano, Italy
| | - Simone Cuozzo
- Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, 31015, Conegliano, Italy
| | - Luc Wilson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alberto Danieli
- Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, 31015, Conegliano, Italy
| | - Paolo Bonanni
- Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, 31015, Conegliano, Italy
| | - Giovanni Pellegrino
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London N6A5C1, Canada
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84
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Montemurro S, Borek D, Marinazzo D, Zago S, Masina F, Napoli E, Filippini N, Arcara G. Aperiodic component of EEG power spectrum and cognitive performance are modulated by education in aging. Sci Rep 2024; 14:15111. [PMID: 38956186 PMCID: PMC11220063 DOI: 10.1038/s41598-024-66049-2] [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: 11/30/2023] [Accepted: 06/26/2024] [Indexed: 07/04/2024] Open
Abstract
Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.
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Affiliation(s)
- Sonia Montemurro
- Department of Philosophy, Sociology, Pedagogy and Applied Psychology, FISPPA, University of Padova, Padua, Italy.
| | - Daniel Borek
- Department of Data-Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | - Daniele Marinazzo
- Department of Data-Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | - Sara Zago
- IRCCS San Camillo Hospital, Venice, Italy
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85
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Tröndle M, Langer N. Decomposing neurophysiological underpinnings of age-related decline in visual working memory. Neurobiol Aging 2024; 139:30-43. [PMID: 38593526 DOI: 10.1016/j.neurobiolaging.2024.03.004] [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: 05/26/2023] [Revised: 03/17/2024] [Accepted: 03/17/2024] [Indexed: 04/11/2024]
Abstract
Exploring the neural basis of age-related decline in working memory is vital in our aging society. Previous electroencephalographic studies suggested that the contralateral delay activity (CDA) may be insensitive to age-related decline in lateralized visual working memory (VWM) performance. Instead, recent evidence indicated that task-induced alpha power lateralization decreases in older age. However, the relationship between alpha power lateralization and age-related decline of VWM performance remains unknown, and recent studies have questioned the validity of these findings due to confounding factors of the aperiodic signal. Using a sample of 134 participants, we replicated the age-related decrease of alpha power lateralization after adjusting for the aperiodic signal. Critically, the link between task performance and alpha power lateralization was found only when correcting for aperiodic signal biases. Functionally, these findings suggest that age-related declines in VWM performance may be related to the decreased ability to prioritize relevant over irrelevant information. Conversely, CDA amplitudes were stable across age groups, suggesting a distinct neural mechanism possibly related to preserved VWM encoding or early maintenance.
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Affiliation(s)
- Marius Tröndle
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland.
| | - Nicolas Langer
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland
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86
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. EBioMedicine 2024; 105:105201. [PMID: 38908100 PMCID: PMC11253223 DOI: 10.1016/j.ebiom.2024.105201] [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: 12/04/2023] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/24/2024] Open
Abstract
BACKGROUND Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).
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Affiliation(s)
| | - Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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87
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Marsh B, Navas-Zuloaga MG, Rosen BQ, Sokolov Y, Delanois JE, Gonzalez OC, Krishnan GP, Halgren E, Bazhenov M. Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain. PLoS Comput Biol 2024; 20:e1012245. [PMID: 39028760 PMCID: PMC11290683 DOI: 10.1371/journal.pcbi.1012245] [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: 02/06/2024] [Revised: 07/31/2024] [Accepted: 06/11/2024] [Indexed: 07/21/2024] Open
Abstract
Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.
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Affiliation(s)
- Brianna Marsh
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Neuroscience Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | - M. Gabriela Navas-Zuloaga
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Burke Q. Rosen
- Neuroscience Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | - Yury Sokolov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Jean Erik Delanois
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, United States of America
| | - Oscar C. Gonzalez
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Eric Halgren
- Neuroscience Graduate Program, University of California San Diego, La Jolla, California, United States of America
- Departments of Radiology and Neuroscience, University of California San Diego, La Jolla, California, United States of America
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Neuroscience Graduate Program, University of California San Diego, La Jolla, California, United States of America
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88
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Kanth ST, Ray S. Gamma Responses to Colored Natural Stimuli Can Be Predicted from Local Low-Level Stimulus Features. eNeuro 2024; 11:ENEURO.0417-23.2024. [PMID: 39054054 PMCID: PMC11277289 DOI: 10.1523/eneuro.0417-23.2024] [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: 10/17/2023] [Revised: 05/17/2024] [Accepted: 07/01/2024] [Indexed: 07/27/2024] Open
Abstract
The role of gamma rhythm (30-80 Hz) in visual processing is debated; stimuli like gratings and hue patches generate strong gamma, but many natural images do not. Could image gamma responses be predicted by approximating images as gratings or hue patches? Surprisingly, this question remains unanswered, since the joint dependence of gamma on multiple features is poorly understood. We recorded local field potentials and electrocorticogram from two female monkeys while presenting natural images and parametric stimuli varying along several feature dimensions. Gamma responses to different grating/hue features were separable, allowing for a multiplicative model based on individual features. By fitting a hue patch to the image around the receptive field, this simple model could predict gamma responses to chromatic images across scales with reasonably high accuracy. Our results provide a simple "baseline" model to predict gamma from local image properties, against which more complex models of natural vision can be tested.
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Affiliation(s)
- Sidrat Tasawoor Kanth
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India
- Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India
| | - Supratim Ray
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India
- Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India
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89
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Lin Y, Huang S, Mao J, Li M, Haihambo N, Wang F, Liang Y, Chen W, Han C. The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device. Neuroimage 2024; 294:120637. [PMID: 38714216 DOI: 10.1016/j.neuroimage.2024.120637] [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: 01/29/2024] [Revised: 03/31/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024] Open
Abstract
In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.
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Affiliation(s)
- Yuchen Lin
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shaojia Huang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Jidong Mao
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Fang Wang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Yuping Liang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Wufang Chen
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Chuanliang Han
- School of Biomedical Sciences and Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
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90
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Akbarian F, Rossi C, Costers L, D'hooghe MB, D'haeseleer M, Nagels G, Van Schependom J. Stimulus-related modulation in the 1/f spectral slope suggests an impaired inhibition during a working memory task in people with multiple sclerosis. Mult Scler 2024; 30:1036-1046. [PMID: 38767227 DOI: 10.1177/13524585241253777] [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] [Indexed: 05/22/2024]
Abstract
BACKGROUND An imbalance of excitatory and inhibitory synaptic transmission in multiple sclerosis (MS) may lead to cognitive impairment, such as impaired working memory. The 1/f slope of electroencephalography/magnetoencephalography (EEG/MEG) power spectra is shown to be a non-invasive proxy of excitation/inhibition balance. A flatter slope is associated with higher excitation/lower inhibition. OBJECTIVES To assess the 1/f slope modulation induced by stimulus and its association with behavioral and cognitive measures. METHODS We analyzed MEG recordings of 38 healthy controls (HCs) and 79 people with multiple sclerosis (pwMS) while performing an n-back task including target and distractor stimuli. Target trials require an answer, while distractor trials do not. We computed the 1/f spectral slope through the fitting oscillations and one over f (FOOOF) algorithm within the time windows 1 second before and after each stimulus presentation. RESULTS We observed a flatter 1/f slope after distractor stimuli in pwMS compared to HCs. The 1/f slope was significantly steeper after stimulus for both HCs and pwMS and was significantly correlated with reaction times. This modulation in 1/f slope was significantly correlated with visuospatial memory assessed by the BVMT-R test. CONCLUSION Our results suggest possible inhibitory mechanism deficits in pwMS during a working memory task.
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Affiliation(s)
- Fahimeh Akbarian
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Chiara Rossi
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Lars Costers
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium; icometrix, Leuven, Belgium
| | | | - Miguel D'haeseleer
- National MS Center Melsbroek, Melsbroek, Belgium; Department of Neurology, UZ Brussel, Brussels, Belgium
| | - Guy Nagels
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Neurology, UZ Brussel, Brussels, Belgium
- St Edmund Hall, University of Oxford, Oxford, UK
| | - Jeroen Van Schependom
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
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91
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Pauls KAM, Nurmi P, Ala-Salomäki H, Renvall H, Kujala J, Liljeström M. Human sensorimotor resting state beta events and aperiodic activity show good test-retest reliability. Clin Neurophysiol 2024; 163:244-254. [PMID: 38820994 DOI: 10.1016/j.clinph.2024.03.021] [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: 09/22/2023] [Revised: 03/04/2024] [Accepted: 03/20/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVE Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases. METHODS We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition. RESULTS Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%. CONCLUSIONS Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients. SIGNIFICANCE Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity.
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Affiliation(s)
- K Amande M Pauls
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neurology, Helsinki University Hospital and Department of Clinical Neurosciences (Neurology), University of Helsinki, 00029 Helsinki, Finland.
| | - Pietari Nurmi
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Heidi Ala-Salomäki
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Hanna Renvall
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Jan Kujala
- Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Mia Liljeström
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland; Aalto NeuroImaging, Aalto University, 00076 Aalto, Finland
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92
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Galvan CM, Spies RD, Milone DH, Peterson V. Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2346-2355. [PMID: 38900612 DOI: 10.1109/tnsre.2024.3417311] [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: 06/22/2024]
Abstract
Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.
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93
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Clements CC, Engelstad AM, Wilkinson CL, Hyde C, Hartney M, Simmons A, Tager-Flusberg H, Jeste S, Nelson CA. Resting state EEG in young children with Tuberous Sclerosis Complex. RESEARCH SQUARE 2024:rs.3.rs-4543112. [PMID: 38978564 PMCID: PMC11230505 DOI: 10.21203/rs.3.rs-4543112/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Tuberous Sclerosis Complex (TSC) manifests behaviorally with features of autism, epilepsy, and intellectual disability. Resting state electroencephalography (EEG) offers a window into neural oscillatory activity and may serve as an intermediate biomarker between gene expression and behavioral manifestations. Such a biomarker could be useful in clinical trials as an endpoint or predictor of treatment response. However, seizures and antiepileptic medications also affect resting neural oscillatory activity and could undermine the utility of resting state EEG features as biomarkers in neurodevelopmental disorders such as TSC. Methods This paper compares resting state EEG features in a cross-sectional cohort of young children with TSC (n=49, ages 12-37 months) to 49 age- and sex-matched typically developing controls. Within children with TSC, associations were examined between resting state EEG features, seizure severity composite score, and use of GABA agonists. Results Compared to matched typically developing controls, children with TSC showed significantly greater alpha and beta power in permutation cluster analyses iterated across a broad frequency range (2-50Hz). Children with TSC also showed significantly greater aperiodic offset after power spectra were parameterized using SpecParam into aperiodic and periodic components. Within children with TSC, greater seizure severity was significantly related to increased periodic peak beta power. Use of GABA agonists was also independently and significantly associated with increased periodic peak beta power; the interaction between seizure severity and GABA agonist use had no significant effect on peak beta power. Conclusions The elevated peak beta power observed in children with TSC compared to matched typically developing controls may be driven by both seizures and GABA agonist use. It is recommended to collect seizure and mediation data alongside EEG data for clinical trials. These results highlight the challenge of using resting state EEG features as biomarkers in trials with neurodevelopmental disabilities when epilepsy and anti-epileptic medication are common.
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94
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Catalano LT, Reavis EA, Wynn JK, Green MF. Peak Alpha Frequency in Schizophrenia, Bipolar Disorder, and Healthy Volunteers: Associations With Visual Information Processing and Cognition. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00162-9. [PMID: 38909899 DOI: 10.1016/j.bpsc.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Schizophrenia (SCZ) and bipolar disorder (BD) are associated with information processing abnormalities, including visual perceptual and cognitive impairments, that impact daily functioning. Recent work with healthy samples suggests that peak alpha frequency (PAF) is an electrophysiological index of visual information processing speed that is correlated with cognitive ability. There is evidence that PAF is slowed in SCZ, but it remains unclear whether PAF is reduced in BD or whether slower PAF is associated with impaired visual perception and cognition in these clinical disorders. METHODS We recorded resting-state brain activity (both eyes open and closed) with electroencephalography in 90 participants with SCZ, 62 participants with BD, and 69 healthy control participants. Most participants also performed a visual perception task (backward masking) and cognitive testing (MATRICS Concensus Cognitive Battery). RESULTS We replicated previous findings of reduced PAF in patients with SCZ compared with healthy control participants. In contrast, PAF in patients with BD did not differ significantly from that in healthy control participants. Furthermore, PAF was significantly correlated with performance on the perceptual and cognitive measures in SCZ but not BD. PAF was also correlated with visual perception in the healthy control group and showed a trend-level correlation with cognition. CONCLUSIONS Together, these results suggest that PAF deficits characterize SCZ, but not BD, and that individual differences in PAF are related to abnormalities in visual information processing and cognition in SCZ.
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Affiliation(s)
- Lauren T Catalano
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California.
| | - Eric A Reavis
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California
| | - Jonathan K Wynn
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California
| | - Michael F Green
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California
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95
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Bergosh M, Medvidovic S, Zepeda N, Crown L, Ipe J, Debattista L, Romero L, Amjadi E, Lam T, Hakopian E, Choi W, Wu K, Lo JYT, Lee DJ. Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment. Front Neurosci 2024; 18:1389096. [PMID: 38966758 PMCID: PMC11222339 DOI: 10.3389/fnins.2024.1389096] [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: 02/20/2024] [Accepted: 05/07/2024] [Indexed: 07/06/2024] Open
Abstract
Introduction Both ketamine (KET) and medial prefrontal cortex (mPFC) deep brain stimulation (DBS) are emerging therapies for treatment-resistant depression, yet our understanding of their electrophysiological mechanisms and biomarkers is incomplete. This study investigates aperiodic and periodic spectral parameters, and the signal complexity measure sample entropy, within mPFC local field potentials (LFP) in a chronic corticosterone (CORT) depression model after ketamine and/or mPFC DBS. Methods Male rats were intraperitoneally administered CORT or vehicle for 21 days. Over the last 7 days, animals receiving CORT were treated with mPFC DBS, KET, both, or neither; then tested across an array of behavioral tasks for 9 days. Results We found that the depression-like behavioral and weight effects of CORT correlated with a decrease in aperiodic-adjusted theta power (5-10 Hz) and an increase in sample entropy during the administration phase, and an increase in theta peak frequency and a decrease in the aperiodic exponent once the depression-like phenotype had been induced. The remission-like behavioral effects of ketamine alone correlated with a post-treatment increase in the offset and exponent, and decrease in sample entropy, both immediately and up to eight days post-treatment. The remission-like behavioral effects of mPFC DBS alone correlated with an immediate decrease in sample entropy, an immediate and sustained increase in low gamma (20-50 Hz) peak width and aperiodic offset, and sustained improvements in cognitive function. Failure to fully induce remission-like behavior in the combinatorial treatment group correlated with a failure to suppress an increase in sample entropy immediately after treatment. Conclusion Our findings therefore support the potential of periodic theta parameters as biomarkers of depression-severity; and periodic low gamma parameters and cognitive measures as biomarkers of mPFC DBS treatment efficacy. They also support sample entropy and the aperiodic spectral parameters as potential cross-modal biomarkers of depression severity and the therapeutic efficacy of mPFC DBS and/or ketamine. Study of these biomarkers is important as objective measures of disease severity and predictive measures of therapeutic efficacy can be used to personalize care and promote the translatability of research across studies, modalities, and species.
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Affiliation(s)
- Matthew Bergosh
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sasha Medvidovic
- Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nancy Zepeda
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Lindsey Crown
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jennifer Ipe
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Lauren Debattista
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Luis Romero
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Eimon Amjadi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tian Lam
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Erik Hakopian
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
| | - Wooseong Choi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Kevin Wu
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jack Yu Tung Lo
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Darrin Jason Lee
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
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96
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Guth TA, Brandt A, Reinacher PC, Schulze-Bonhage A, Jacobs J, Kunz L. Theta-phase locking of single neurons during human spatial memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.20.599841. [PMID: 38948829 PMCID: PMC11212943 DOI: 10.1101/2024.06.20.599841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The precise timing of single-neuron activity in relation to local field potentials may support various cognitive functions. Extensive research in rodents, along with some evidence in humans, suggests that single-neuron activity at specific phases of theta oscillations plays a crucial role in memory processes. Our fundamental understanding of such theta-phase locking in humans and its dependency on basic electrophysiological properties of the local field potential is still limited, however. Here, using single-neuron recordings in epilepsy patients performing a spatial memory task, we thus aimed at improving our understanding of factors modulating theta-phase locking in the human brain. Combining a generalized-phase approach for frequency-adaptive theta-phase estimation with time-resolved spectral parameterization, our results show that theta-phase locking is a strong and prevalent phenomenon across human medial temporal lobe regions, both during spatial memory encoding and retrieval. Neuronal theta-phase locking increased during periods of elevated theta power, when clear theta oscillations were present, and when aperiodic activity exhibited steeper slopes. Theta-phase locking was similarly strong during successful and unsuccessful memory, and most neurons activated at similar theta phases between encoding and retrieval. Some neurons changed their preferred theta phases between encoding and retrieval, in line with the idea that different memory processes are separated within the theta cycle. Together, these results help disentangle how different properties of local field potentials and memory states influence theta-phase locking of human single neurons. This contributes to a better understanding of how interactions between single neurons and local field potentials may support human spatial memory.
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Affiliation(s)
- Tim A. Guth
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Epilepsy Center, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Armin Brandt
- Epilepsy Center, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter C. Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Fraunhofer Institute for Laser Technology, Aachen, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, USA
| | - Lukas Kunz
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
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97
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Lo CW, Meyer L. Chunk boundaries disrupt dependency processing in an AG: Reconciling incremental processing and discrete sampling. PLoS One 2024; 19:e0305333. [PMID: 38889141 PMCID: PMC11185458 DOI: 10.1371/journal.pone.0305333] [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: 03/18/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
Language is rooted in our ability to compose: We link words together, fusing their meanings. Links are not limited to neighboring words but often span intervening words. The ability to process these non-adjacent dependencies (NADs) conflicts with the brain's sampling of speech: We consume speech in chunks that are limited in time, containing only a limited number of words. It is unknown how we link words together that belong to separate chunks. Here, we report that we cannot-at least not so well. In our electroencephalography (EEG) study, 37 human listeners learned chunks and dependencies from an artificial grammar (AG) composed of syllables. Multi-syllable chunks to be learned were equal-sized, allowing us to employ a frequency-tagging approach. On top of chunks, syllable streams contained NADs that were either confined to a single chunk or crossed a chunk boundary. Frequency analyses of the EEG revealed a spectral peak at the chunk rate, showing that participants learned the chunks. NADs that cross boundaries were associated with smaller electrophysiological responses than within-chunk NADs. This shows that NADs are processed readily when they are confined to the same chunk, but not as well when crossing a chunk boundary. Our findings help to reconcile the classical notion that language is processed incrementally with recent evidence for discrete perceptual sampling of speech. This has implications for language acquisition and processing as well as for the general view of syntax in human language.
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Affiliation(s)
- Chia-Wen Lo
- Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lars Meyer
- Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- University Clinic Münster, Münster, Germany
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98
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Liu X, Guang J, Glowinsky S, Abadi H, Arkadir D, Linetsky E, Abu Snineh M, León JF, Israel Z, Wang W, Bergman H. Subthalamic nucleus input-output dynamics are correlated with Parkinson's burden and treatment efficacy. NPJ Parkinsons Dis 2024; 10:117. [PMID: 38879564 PMCID: PMC11180194 DOI: 10.1038/s41531-024-00737-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/31/2024] [Indexed: 06/19/2024] Open
Abstract
The subthalamic nucleus (STN) is pivotal in basal ganglia function in health and disease. Micro-electrode recordings of >25,000 recording sites from 146 Parkinson's patients undergoing deep brain stimulation (DBS) allowed differentiation between subthalamic input, represented by local field potential (LFP), and output, reflected in spike discharge rate (SPK). As with many natural systems, STN neuronal activity exhibits power-law dynamics characterized by the exponent α. We, therefore, dissected STN data into aperiodic and periodic components using the Fitting Oscillations & One Over F (FOOOF) tool. STN LFP showed significantly higher aperiodic exponents than SPK. Additionally, SPK beta oscillations demonstrated a downward frequency shift compared to LFP. Finally, the STN aperiodic and spiking parameters explained a significant fraction of the variance of the burden and treatment efficacy of Parkinson's disease. The unique STN input-output dynamics may clarify its role in Parkinson's physiology and can be utilized in closed-loop DBS therapy.
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Affiliation(s)
- Xiaowei Liu
- Department of Neurosurgery, West China Hospital, West China School of Medicine, Sichuan University, Guoxue Lane No. 37, Chengdu, 610041, Sichuan, China
- The Edmond and Lily Safra Center for Brain Science, The Hebrew University, Jerusalem, Israel
| | - Jing Guang
- The Edmond and Lily Safra Center for Brain Science, The Hebrew University, Jerusalem, Israel
| | - Stefanie Glowinsky
- The Edmond and Lily Safra Center for Brain Science, The Hebrew University, Jerusalem, Israel
| | - Hodaya Abadi
- The Edmond and Lily Safra Center for Brain Science, The Hebrew University, Jerusalem, Israel
| | - David Arkadir
- Department of Neurology, Hadassah University Hospital, Jerusalem, Israel
| | - Eduard Linetsky
- Department of Neurology, Hadassah University Hospital, Jerusalem, Israel
| | - Muneer Abu Snineh
- Department of Neurology, Hadassah University Hospital, Jerusalem, Israel
| | - Juan F León
- Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel
| | - Zvi Israel
- Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel
| | - Wei Wang
- Department of Neurosurgery, West China Hospital, West China School of Medicine, Sichuan University, Guoxue Lane No. 37, Chengdu, 610041, Sichuan, China
| | - Hagai Bergman
- The Edmond and Lily Safra Center for Brain Science, The Hebrew University, Jerusalem, Israel.
- Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel.
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel.
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99
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Chatburn A, Lushington K, Cross ZR. Considerations towards a neurobiologically-informed EEG measurement of sleepiness. Brain Res 2024; 1841:149088. [PMID: 38879143 DOI: 10.1016/j.brainres.2024.149088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/30/2024] [Accepted: 06/12/2024] [Indexed: 06/18/2024]
Abstract
Sleep is a daily experience across humans and other species, yet our understanding of how and why we sleep is presently incomplete. This is particularly prevalent in research examining the neurophysiological measurement of sleepiness in humans, where several electroencephalogram (EEG) phenomena have been linked with prolonged wakefulness. This leaves researchers without a solid basis for the measurement of homeostatic sleep need and complicates our understanding of the nature of sleep. Recent theoretical and technical advances may allow for a greater understanding of the neurobiological basis of homeostatic sleep need: this may result from increases in neuronal excitability and shifts in excitation/inhibition balance in neuronal circuits and can potentially be directly measured via the aperiodic component of the EEG. Here, we review the literature on EEG-derived markers of sleepiness in humans and argue that changes in these electrophysiological markers may actually result from neuronal activity represented by changes in aperiodic markers. We argue for the use of aperiodic markers derived from the EEG in predicting sleepiness and suggest areas for future research based on these.
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Affiliation(s)
- Alex Chatburn
- Cognitive Neuroscience Laboratory, University of South Australia, Adelaide, Australia.
| | - Kurt Lushington
- Cognitive Neuroscience Laboratory, University of South Australia, Adelaide, Australia; Centre for Behaviour-Brain-Body: Justice and Society Unit, University of South Australia, Adelaide, South Australia, Australia
| | - Zachariah R Cross
- Cognitive Neuroscience Laboratory, University of South Australia, Adelaide, Australia; Feinberg School of Medicine, Northwestern University, USA
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100
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Das P, He M, Purdon PL. A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.26.550594. [PMID: 37546851 PMCID: PMC10402019 DOI: 10.1101/2023.07.26.550594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.
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Affiliation(s)
- Proloy Das
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305
| | - Mingjian He
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305
- epartment of Psychology, Stanford University, Stanford, CA 94305
| | - Patrick L. Purdon
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA 94305
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