1
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Provenza NR, Reddy S, Allam AK, Rajesh SV, Diab N, Reyes G, Caston RM, Katlowitz KA, Gandhi AD, Bechtold RA, Dang HQ, Najera RA, Giridharan N, Kabotyanski KE, Momin F, Hasen M, Banks GP, Mickey BJ, Kious BM, Shofty B, Hayden BY, Herron JA, Storch EA, Patel AB, Goodman WK, Sheth SA. Disruption of neural periodicity predicts clinical response after deep brain stimulation for obsessive-compulsive disorder. Nat Med 2024:10.1038/s41591-024-03125-0. [PMID: 38997607 DOI: 10.1038/s41591-024-03125-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 06/11/2024] [Indexed: 07/14/2024]
Abstract
Recent advances in surgical neuromodulation have enabled chronic and continuous intracranial monitoring during everyday life. We used this opportunity to identify neural predictors of clinical state in 12 individuals with treatment-resistant obsessive-compulsive disorder (OCD) receiving deep brain stimulation (DBS) therapy ( NCT05915741 ). We developed our neurobehavioral models based on continuous neural recordings in the region of the ventral striatum in an initial cohort of five patients and tested and validated them in a held-out cohort of seven additional patients. Before DBS activation, in the most symptomatic state, theta/alpha (9 Hz) power evidenced a prominent circadian pattern and a high degree of predictability. In patients with persistent symptoms (non-responders), predictability of the neural data remained consistently high. On the other hand, in patients who improved symptomatically (responders), predictability of the neural data was significantly diminished. This neural feature accurately classified clinical status even in patients with limited duration recordings, indicating generalizability that could facilitate therapeutic decision-making.
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Affiliation(s)
- Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, USA
| | - Sandesh Reddy
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Anthony K Allam
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Sameer V Rajesh
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nabeel Diab
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Gabriel Reyes
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Rose M Caston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Kalman A Katlowitz
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Ajay D Gandhi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Raphael A Bechtold
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Huy Q Dang
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Ricardo A Najera
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nisha Giridharan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | | | - Faiza Momin
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Mohammed Hasen
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Brian J Mickey
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brent M Kious
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Ben Shofty
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Benjamin Y Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey A Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Eric A Storch
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Ankit B Patel
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Wayne K Goodman
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.
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2
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Zioga I, Zhou YJ, Weissbart H, Martin AE, Haegens S. Alpha and Beta Oscillations Differentially Support Word Production in a Rule-Switching Task. eNeuro 2024; 11:ENEURO.0312-23.2024. [PMID: 38490743 PMCID: PMC10988358 DOI: 10.1523/eneuro.0312-23.2024] [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: 08/23/2023] [Revised: 01/26/2024] [Accepted: 02/22/2024] [Indexed: 03/17/2024] Open
Abstract
Research into the role of brain oscillations in basic perceptual and cognitive functions has suggested that the alpha rhythm reflects functional inhibition while the beta rhythm reflects neural ensemble (re)activation. However, little is known regarding the generalization of these proposed fundamental operations to linguistic processes, such as speech comprehension and production. Here, we recorded magnetoencephalography in participants performing a novel rule-switching paradigm. Specifically, Dutch native speakers had to produce an alternative exemplar from the same category or a feature of a given target word embedded in spoken sentences (e.g., for the word "tuna", an exemplar from the same category-"seafood"-would be "shrimp", and a feature would be "pink"). A cue indicated the task rule-exemplar or feature-either before (pre-cue) or after (retro-cue) listening to the sentence. Alpha power during the working memory delay was lower for retro-cue compared with that for pre-cue in the left hemispheric language-related regions. Critically, alpha power negatively correlated with reaction times, suggestive of alpha facilitating task performance by regulating inhibition in regions linked to lexical retrieval. Furthermore, we observed a different spatiotemporal pattern of beta activity for exemplars versus features in the right temporoparietal regions, in line with the proposed role of beta in recruiting neural networks for the encoding of distinct categories. Overall, our study provides evidence for the generalizability of the role of alpha and beta oscillations from perceptual to more "complex, linguistic processes" and offers a novel task to investigate links between rule-switching, working memory, and word production.
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Affiliation(s)
- Ioanna Zioga
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 EN, The Netherlands
- Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
| | - Ying Joey Zhou
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 EN, The Netherlands
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Oxford, United Kingdom
| | - Hugo Weissbart
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 EN, The Netherlands
| | - Andrea E Martin
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 EN, The Netherlands
- Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
| | - Saskia Haegens
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 EN, The Netherlands
- Department of Psychiatry, Columbia University, New York, New York 10032
- Division of Systems Neuroscience, New York State Psychiatric Institute, New York, New York 10032
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3
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Argyelan M, Fenoy AJ. Objective Measure of a Subjective Experience: A Real-Time Biomarker of Mood Status? Biol Psychiatry 2023; 94:438-439. [PMID: 37611982 DOI: 10.1016/j.biopsych.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 08/25/2023]
Affiliation(s)
- Miklos Argyelan
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.
| | - Albert J Fenoy
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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4
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Xiao J, Provenza NR, Asfouri J, Myers J, Mathura RK, Metzger B, Adkinson JA, Allawala AB, Pirtle V, Oswalt D, Shofty B, Robinson ME, Mathew SJ, Goodman WK, Pouratian N, Schrater PR, Patel AB, Tolias AS, Bijanki KR, Pitkow X, Sheth SA. Decoding Depression Severity From Intracranial Neural Activity. Biol Psychiatry 2023; 94:445-453. [PMID: 36736418 PMCID: PMC10394110 DOI: 10.1016/j.biopsych.2023.01.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. METHODS We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. RESULTS Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. CONCLUSIONS The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.
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Affiliation(s)
- Jiayang Xiao
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; Department of Neuroscience, Baylor College of Medicine, Houston, Texas
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Joseph Asfouri
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas
| | - John Myers
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Raissa K Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Brian Metzger
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Joshua A Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | | | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Denise Oswalt
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Ben Shofty
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Meghan E Robinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Sanjay J Mathew
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas
| | - Wayne K Goodman
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, Texas
| | - Paul R Schrater
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota; Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Ankit B Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas.
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5
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Karatygin NA, Korobeinikova II, Pertsov SS. Correlation between Cognitive Performance and Interhemispheric Asymmetry of EEG Alpha Rhythm during Lateralized Presentation of Visual Information against the Background of Optical Stimulation. Bull Exp Biol Med 2023; 175:295-299. [PMID: 37566249 DOI: 10.1007/s10517-023-05855-6] [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: 01/23/2023] [Indexed: 08/12/2023]
Abstract
The effect of lateralized optical stimulation with a frequency of 10 Hz on the effectiveness of cognitive task performance (n-back test) was studied in 33 healthy subjects (right-handed men). Test visual information was presented to the right or left visual hemifield under normal conditions and against the background of optical stimulation with a frequency of 10 Hz. The absolute values of the spectral power of the high (10-13 Hz) subrange of alpha-rhythm of EEG (SPα2) were calculated. When test information was sent to the right hemisphere against the background of stimulation, an increase in task performance was revealed in subjects with low SPα2. This was accompanied by an increase in SPα2 in some cortical areas of the contralateral (left) hemisphere and, as a result, an increase in left-side dominance of SPα2. The findings indicate the possibility of using lateralized optical stimulation to improve cognitive task performance, in particular, by changing the interhemispheric asymmetry of the EEG alpha2-rhythm.
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Affiliation(s)
- N A Karatygin
- P. K. Anokhin Research Institute of Normal Physiology, Moscow, Russia.
| | - I I Korobeinikova
- P. K. Anokhin Research Institute of Normal Physiology, Moscow, Russia
| | - S S Pertsov
- P. K. Anokhin Research Institute of Normal Physiology, Moscow, Russia
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6
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McSweeney M, Morales S, Valadez EA, Buzzell GA, Yoder L, Fifer WP, Pini N, Shuffrey LC, Elliott AJ, Isler JR, Fox NA. Age-related trends in aperiodic EEG activity and alpha oscillations during early- to middle-childhood. Neuroimage 2023; 269:119925. [PMID: 36739102 DOI: 10.1016/j.neuroimage.2023.119925] [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: 06/12/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Age-related structural and functional changes that occur during brain development are critical for cortical development and functioning. Previous electroencephalography (EEG) and magnetoencephalography (MEG) studies have highlighted the utility of power spectra analyses and have uncovered age-related trends that reflect perceptual, cognitive, and behavioural states as well as their underlying neurophysiology. The aim of the current study was to investigate age-related change in aperiodic and periodic alpha activity across a large sample of pre- and school-aged children (N = 502, age range 4 -11-years-of-age). Power spectra were extracted from baseline EEG recordings (eyes closed, eyes open) for each participant and parameterized into aperiodic activity to derive the offset and exponent parameters and periodic alpha oscillatory activity to derive the alpha peak frequency and the associated power estimates. Multilevel models were run to investigate age-related trends and condition-dependent changes for each of these measures. We found quadratic age-related effects for both the aperiodic offset and exponent. In addition, we observed increases in periodic alpha peak frequency as a function of age. Aperiodic measures and periodic alpha power were larger in magnitude during eyes closed compared to the eyes open baseline condition. Taken together, these results advance our understanding of the maturational patterns/trajectories of brain development during early- to middle-childhood.
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Affiliation(s)
- Marco McSweeney
- Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA.
| | - Santiago Morales
- Department of Psychology, University of Southern California, USA
| | - Emilio A Valadez
- Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA
| | - George A Buzzell
- Department of Psychology and the Center for Children and Families, Florida International University, USA
| | - Lydia Yoder
- Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA
| | - William P Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; Department of Paediatrics, Columbia University Irving Medical Center, New York, USA; Division of Developmental Neuroscience, New York State Psychiatric Institute, USA
| | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; Division of Developmental Neuroscience, New York State Psychiatric Institute, USA
| | - Lauren C Shuffrey
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; Division of Developmental Neuroscience, New York State Psychiatric Institute, USA
| | - Amy J Elliott
- Avera Research Institute, USA; Department of Paediatrics, University of South Dakota School of Medicine, USA
| | - Joseph R Isler
- Department of Paediatrics, Columbia University Irving Medical Center, New York, USA
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA
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7
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Zhai Y, Li Y, Zhou S, Zhang C, Luo E, Tang C, Xie K. Mental fatigue decreases complexity: Evidence from multiscale entropy analysis of instantaneous frequency variation in alpha rhythm. Front Hum Neurosci 2022; 16:906735. [PMID: 36393985 PMCID: PMC9643441 DOI: 10.3389/fnhum.2022.906735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/13/2022] [Indexed: 08/17/2023] Open
Abstract
Mental fatigue (MF) jeopardizes performance and safety through a variety of cognitive impairments and according to the complexity loss theory, should represent "complexity loss" in electroencephalogram (EEG). However, the studies are few and inconsistent concerning the relationship between MF and loss of complexity, probably because of the susceptibility of brain waves to noise. In this study, MF was induced in thirteen male college students by a simulated flight task. Before and at the end of the task, spontaneous EEG and auditory steady-state response (ASSR) were recorded and instantaneous frequency variation (IFV) in alpha rhythm was extracted and analyzed by multiscale entropy (MSE) analysis. The results show that there were significant differences in IFV in alpha rhythm either from spontaneous EEG or from ASSR for all subjects. Therefore, the proposed method can be effective in revealing the complexity loss caused by MF in spontaneous EEG and ASSR, which may serve as a promising analyzing method to mark mild mental impairments.
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Affiliation(s)
- Yawen Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Yan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Shengyi Zhou
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- Air Force Hospital of Western Theater Command, Chengdu, China
| | - Chenxu Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Erping Luo
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Chi Tang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Kangning Xie
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
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8
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Rodriguez-Larios J, ElShafei A, Wiehe M, Haegens S. Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex. eNeuro 2022; 9:ENEURO.0159-22.2022. [PMID: 36171059 PMCID: PMC9536853 DOI: 10.1523/eneuro.0159-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/20/2022] [Accepted: 07/12/2022] [Indexed: 11/21/2022] Open
Abstract
Oscillatory activity in the human brain is dominated by posterior alpha oscillations (8-14 Hz), which have been shown to be functionally relevant in a wide variety of cognitive tasks. Although posterior alpha oscillations are commonly considered a single oscillator anchored at an individual alpha frequency (IAF; ∼10 Hz), previous work suggests that IAF reflects a spatial mixture of different brain rhythms. In this study, we assess whether Independent Component Analysis (ICA) can disentangle functionally distinct posterior alpha rhythms in the context of visual short-term memory retention. Magnetoencephalography (MEG) was recorded in 33 subjects while performing a visual working memory task. Group analysis at sensor level suggested the existence of a single posterior alpha oscillator that increases in power and decreases in frequency during memory retention. Conversely, single-subject analysis of independent components revealed the existence of two dissociable alpha rhythms: one that increases in power during memory retention (Alpha1) and another one that decreases in power (Alpha2). Alpha1 and Alpha2 rhythms were differentially modulated by the presence of visual distractors (Alpha1 increased in power while Alpha2 decreased) and had an opposite relationship with accuracy (positive for Alpha1 and negative for Alpha2). In addition, Alpha1 rhythms showed a lower peak frequency, a narrower peak width, a greater relative peak amplitude and a more central source than Alpha2 rhythms. Together, our results demonstrate that modulations in posterior alpha oscillations during short-term memory retention reflect the dynamics of at least two distinct brain rhythms with different functions and spatiospectral characteristics.Significance statementAlpha oscillations are the most prominent feature of the human brain's electrical activity, and consist of rhythmic neuronal activity in posterior parts of the cortex. Alpha is usually considered a single brain rhythm that changes in power and frequency to support cognitive operations. We here show that posterior alpha entails at least two dissociable rhythms with distinct functions and characteristics. These findings could solve previous inconsistencies in the literature regarding the direction of task-related alpha power/frequency modulations and their relation to cognitive performance. In addition, the existence of two distinct posterior alpha rhythms could have important consequences for the design of neurostimulation protocols aimed at modulating alpha oscillations and subsequently cognition.
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Affiliation(s)
- Julio Rodriguez-Larios
- Dept. of Psychiatry, Columbia University, New York, USA, NY 10032
- Div. of Systems Neuroscience, New York State Psychiatric Institute, New York, USA, NY 10032
| | - Alma ElShafei
- Donders Institute for Brain, Cognition & Behavior, Radboud University, Nijmegen, The Netherlands, 6525 EN
| | - Melanie Wiehe
- Donders Institute for Brain, Cognition & Behavior, Radboud University, Nijmegen, The Netherlands, 6525 EN
| | - Saskia Haegens
- Dept. of Psychiatry, Columbia University, New York, USA, NY 10032
- Div. of Systems Neuroscience, New York State Psychiatric Institute, New York, USA, NY 10032
- Donders Institute for Brain, Cognition & Behavior, Radboud University, Nijmegen, The Netherlands, 6525 EN
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9
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Keil A, Bernat EM, Cohen MX, Ding M, Fabiani M, Gratton G, Kappenman ES, Maris E, Mathewson KE, Ward RT, Weisz N. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology 2022; 59:e14052. [PMID: 35398913 PMCID: PMC9717489 DOI: 10.1111/psyp.14052] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 01/29/2023]
Abstract
Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.
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Affiliation(s)
- Andreas Keil
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Edward M. Bernat
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Michael X. Cohen
- Radboud University and University Medical Center, Nijmegen, the Netherlands
| | - Mingzhou Ding
- J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Emily S. Kappenman
- Department of Psychology, San Diego State University, San Diego, California, USA
| | - Eric Maris
- Donders Institute for Brain, Cognition, and Behaviour & Faculty of Social Sciences Radboud University, Nijmegen, the Netherlands
| | - Kyle E. Mathewson
- Department of Psychology, Faculty of Science, University of Alberta, Edmonton, Alberta, Canada
| | - Richard T. Ward
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Nathan Weisz
- Psychology, University of Salzburg, Salzburg, Austria,Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
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10
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Weisz N, Keil A. Introduction to the special issue of human oscillatory brain activity: Methods, models, and mechanisms. Psychophysiology 2022; 59:e14038. [DOI: 10.1111/psyp.14038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Nathan Weisz
- Department of Psychology University of Salzburg Salzburg Austria
- Neuroscience Institute Christian Doppler University Hospital, Paracelsus Medical University Salzburg Austria
| | - Andreas Keil
- Department of Psychology, Center for the Study of Emotion and Attention University of Florida Gainesville Florida USA
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