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Ngo HVV, Oster H, Andreou C, Obleser J. Circadian rhythms in auditory hallucinations and psychosis. Acta Physiol (Oxf) 2023; 237:e13944. [PMID: 36744985 DOI: 10.1111/apha.13944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/07/2023]
Abstract
Circadian rhythms are imprinted in all organisms and influence virtually all aspects of physiology and behavior in adaptation to the 24-h day-night cycle. This recognition of a circadian timekeeping system permeating essentially all healthy functioning of body and mind quickly leads to the realization that, in turn, human ailments should be probed for the degree to which they are rooted in or marked by disruptions and dysregulations of circadian clock functions in the human body. In this review, we will focus on psychosis as a key mental illness and foremost one of its cardinal symptoms: auditory hallucinations. We will discuss recent empirical evidence and conceptual advances probing the potential role of circadian disruption in auditory hallucinations. Moreover, a dysbalance in excitation and inhibition within cortical networks, which in turn drive a disinhibition of dopaminergic signaling, will be highlighted as central physiological mechanism. Finally, we will propose two avenues for experimentally intervening on the circadian influences to potentially alleviate hallucinations in psychotic disorders.
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Affiliation(s)
- Hong-Viet V Ngo
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Henrik Oster
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
- Institute of Neurobiology, University of Lübeck, Lübeck, Germany
| | - Christina Andreou
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
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Lendner JD, Harler U, Daume J, Engel AK, Zöllner C, Schneider TR, Fischer M. Oscillatory and aperiodic neuronal activity in working memory following anesthesia. Clin Neurophysiol 2023; 150:79-88. [PMID: 37028144 DOI: 10.1016/j.clinph.2023.03.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 02/07/2023] [Accepted: 03/02/2023] [Indexed: 03/28/2023]
Abstract
OBJECTIVE Anesthesia and surgery are associated with cognitive impairment, particularly memory deficits. So far, electroencephalography markers of perioperative memory function remain scarce. METHODS We included male patients >60 years scheduled for prostatectomy under general anesthesia. We obtained neuropsychological assessments and a visual match-to-sample working memory task with simultaneous 62-channel scalp electroencephalography 1 day before and 2 to 3 days after surgery. RESULTS Twenty-six patients completed both pre- and postoperative sessions. Compared with preoperative performance, verbal learning deteriorated after anesthesia (California Verbal Learning Test total recall; t25 = -3.25, p = 0.015, d = -0.902), while visual working memory performance showed a dissociation between match and mismatch accuracy (match*session F1,25 = 3.866, p = 0.060). Better verbal learning was associated with an increase of aperiodic brain activity (total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), whereas visual working memory accuracy was tracked by oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (matches: p < 0.001, mismatches: p = 0.022). CONCLUSIONS Oscillatory and aperiodic brain activity in scalp electroencephalography track distinct features of perioperative memory function. SIGNIFICANCE Aperiodic activity provides a potential electroencephalographic biomarker to identify patients at risk for postoperative cognitive impairments.
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Wiest C, Morgante F, Torrecillos F, Pogosyan A, He S, Baig F, Bertaina I, Hart MG, Edwards MJ, Pereira EA, Tan H. Subthalamic Nucleus Stimulation-Induced Local Field Potential Changes in Dystonia. Mov Disord 2023; 38:423-434. [PMID: 36562479 PMCID: PMC7614354 DOI: 10.1002/mds.29302] [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: 11/11/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Subthalamic nucleus (STN) stimulation is an effective treatment for Parkinson's disease and induced local field potential (LFP) changes that have been linked with clinical improvement. STN stimulation has also been used in dystonia although the internal globus pallidus is the standard target where theta power has been suggested as a physiomarker for adaptive stimulation. OBJECTIVE We aimed to explore if enhanced theta power was also present in STN and if stimulation-induced spectral changes that were previously reported for Parkinson's disease would occur in dystonia. METHODS We recorded LFPs from 7 patients (12 hemispheres) with isolated craniocervical dystonia whose electrodes were placed such that inferior, middle, and superior contacts covered STN, zona incerta, and thalamus. RESULTS We did not observe prominent theta power in STN at rest. STN stimulation induced similar spectral changes in dystonia as in Parkinson's disease, such as broadband power suppression, evoked resonant neural activity (ERNA), finely-tuned gamma oscillations, and an increase in aperiodic exponents in STN-LFPs. Both power suppression and ERNA localize to STN. Based on this, single-pulse STN stimulation elicits evoked neural activities with largest amplitudes in STN, which are relayed to the zona incerta and thalamus with changing characteristics as the distance from STN increases. CONCLUSIONS Our results show that STN stimulation-induced spectral changes are a nondisease-specific response to high-frequency stimulation, which can serve as placement markers for STN. This broadens the scope of STN stimulation and makes it an option for other disorders with excessive oscillatory peaks in STN. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Francesca Morgante
- Neurosciences Research CentreMolecular and Clinical Sciences Institute, St. George's, University of LondonLondonUnited Kingdom
| | - Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Fahd Baig
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
- Neurosciences Research CentreMolecular and Clinical Sciences Institute, St. George's, University of LondonLondonUnited Kingdom
| | - Ilaria Bertaina
- Neurosciences Research CentreMolecular and Clinical Sciences Institute, St. George's, University of LondonLondonUnited Kingdom
| | - Michael G. Hart
- Neurosciences Research CentreMolecular and Clinical Sciences Institute, St. George's, University of LondonLondonUnited Kingdom
| | - Mark J. Edwards
- Institute of Psychiatry, Psychology and NeurosciencesKing's College LondonLondonUnited Kingdom
| | - Erlick A. Pereira
- Neurosciences Research CentreMolecular and Clinical Sciences Institute, St. George's, University of LondonLondonUnited Kingdom
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical NeurosciencesJohn Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
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Wiest C, Torrecillos F, Pogosyan A, Bange M, Muthuraman M, Groppa S, Hulse N, Hasegawa H, Ashkan K, Baig F, Morgante F, Pereira EA, Mallet N, Magill PJ, Brown P, Sharott A, Tan H. The aperiodic exponent of subthalamic field potentials reflects excitation/inhibition balance in Parkinsonism. eLife 2023; 12:e82467. [PMID: 36810199 PMCID: PMC10005762 DOI: 10.7554/elife.82467] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023] Open
Abstract
Periodic features of neural time-series data, such as local field potentials (LFPs), are often quantified using power spectra. While the aperiodic exponent of spectra is typically disregarded, it is nevertheless modulated in a physiologically relevant manner and was recently hypothesised to reflect excitation/inhibition (E/I) balance in neuronal populations. Here, we used a cross-species in vivo electrophysiological approach to test the E/I hypothesis in the context of experimental and idiopathic Parkinsonism. We demonstrate in dopamine-depleted rats that aperiodic exponents and power at 30-100 Hz in subthalamic nucleus (STN) LFPs reflect defined changes in basal ganglia network activity; higher aperiodic exponents tally with lower levels of STN neuron firing and a balance tipped towards inhibition. Using STN-LFPs recorded from awake Parkinson's patients, we show that higher exponents accompany dopaminergic medication and deep brain stimulation (DBS) of STN, consistent with untreated Parkinson's manifesting as reduced inhibition and hyperactivity of STN. These results suggest that the aperiodic exponent of STN-LFPs in Parkinsonism reflects E/I balance and might be a candidate biomarker for adaptive DBS.
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Affiliation(s)
- Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Manuel Bange
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Natasha Hulse
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Harutomo Hasegawa
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Fahd Baig
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Francesca Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Erlick A Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Nicolas Mallet
- Institut des Maladies Neurodégénératives, CNRS UMR5293, Université de BordeauxBordeauxFrance
| | - Peter J Magill
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
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55
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Darmani G, Drummond NM, Ramezanpour H, Saha U, Hoque T, Udupa K, Sarica C, Zeng K, Cortez Grippe T, Nankoo JF, Bergmann TO, Hodaie M, Kalia SK, Lozano AM, Hutchison WD, Fasano A, Chen R. Long-Term Recording of Subthalamic Aperiodic Activities and Beta Bursts in Parkinson's Disease. Mov Disord 2023; 38:232-243. [PMID: 36424835 DOI: 10.1002/mds.29276] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Local field potentials (LFPs) represent the summation of periodic (oscillations) and aperiodic (fractal) signals. Although previous studies showed changes in beta band oscillations and burst characteristics of the subthalamic nucleus (STN) in Parkinson's disease (PD), how aperiodic activity in the STN is related to PD pathophysiology is unknown. OBJECTIVES The study aimed to characterize the long-term effects of STN-deep brain stimulation (DBS) and dopaminergic medications on aperiodic activities and beta bursts. METHODS A total of 10 patients with PD participated in this longitudinal study. Simultaneous bilateral STN-LFP recordings were conducted in six separate visits during a period of 18 months using the Activa PC + S device in the off and on dopaminergic medication states. We used irregular-resampling auto-spectral analysis to separate oscillations and aperiodic components (exponent and offset) in the power spectrum of STN-LFP signals in beta band. RESULTS Our results revealed a systematic increase in both the exponent and the offset of the aperiodic spectrum over 18 months following the DBS implantation, independent of the dopaminergic medication state of patients with PD. In contrast, beta burst durations and amplitudes were stable over time and were suppressed by dopaminergic medications. CONCLUSIONS These findings indicate that oscillations and aperiodic activities reflect at least partially distinct yet complementary neural mechanisms, which should be considered in the design of robust biomarkers to optimize adaptive DBS. Given the link between increased gamma-aminobutyric acidergic (GABAergic) transmission and higher aperiodic activity, our findings suggest that long-term STN-DBS may relate to increased inhibition in the basal ganglia. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ghazaleh Darmani
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Neil M Drummond
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | - Utpal Saha
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Tasnuva Hoque
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Kaviraja Udupa
- Department of Neurophysiology, National Institute of Mental Health & Neurosciences, Bengaluru, India
| | - Can Sarica
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Ke Zeng
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | | | - Til Ole Bergmann
- Neuroimaging Center, Johannes Gutenberg University Medical Center, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Mojgan Hodaie
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Suneil K Kalia
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Andres M Lozano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - William D Hutchison
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Alfonso Fasano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Robert Chen
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
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56
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Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology 2023; 60:e14259. [PMID: 36700291 DOI: 10.1111/psyp.14259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 12/14/2022] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Xinlin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China
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57
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Johnston PR, McIntosh AR, Meltzer JA. Spectral slowing in chronic stroke reflects abnormalities in both periodic and aperiodic neural dynamics. Neuroimage Clin 2022; 37:103277. [PMID: 36495856 PMCID: PMC9758570 DOI: 10.1016/j.nicl.2022.103277] [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: 09/16/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022]
Abstract
Decades of electrophysiological work have demonstrated the presence of "spectral slowing" in stroke patients - a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal models, it has yet to be explained in terms of the pathophysiological processes of stroke. To do so requires clear understanding of the neural dynamics that these differences represent, acknowledging the often overlooked fact that spectral power reflects more than just the amplitude of neural oscillations. To accomplish this, we used a combination of frequency domain and time domain measures to disambiguate and quantify periodic (oscillatory) and aperiodic (non-oscillatory) neural dynamics in resting state magnetoencephalography (MEG) recordings from chronic stroke patients. We found that abnormally elevated low frequency power in these patients was best explained by a steepening of the aperiodic component of the power spectrum, rather than an enhancement of low frequency oscillations, as is often assumed. However, genuine oscillatory activity at higher frequencies was also found to be abnormal, with patients showing alpha slowing and diminished oscillatory activity in the beta band. These aperiodic and periodic abnormalities were found to covary, and could be detected even in the un-lesioned hemisphere, however they were most prominent in perilesional tissue, where their magnitude was predictive of cognitive impairment. This work redefines spectral slowing as a pattern of changes involving both aperiodic and periodic neural dynamics and narrows the gap in understanding between non-invasive markers of dysfunctional tissue and disease processes responsible for altered neural dynamics.
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Affiliation(s)
- Phillip R Johnston
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada.
| | - Anthony R McIntosh
- Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada; Institute for Neuroscience and Neurotechnology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada
| | - Jed A Meltzer
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada; Department of Speech-Language Pathology, University of Toronto, 500 University Avenue, Toronto, ON M5G 1V7, Canada
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58
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Arnett AB, Peisch V, Levin AR. The role of aperiodic spectral slope in event-related potentials and cognition among children with and without attention deficit hyperactivity disorder. J Neurophysiol 2022; 128:1546-1554. [PMID: 36382902 PMCID: PMC9902214 DOI: 10.1152/jn.00295.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022] Open
Abstract
Aperiodic spectral slope is a measure of spontaneous neural oscillatory activity that is believed to support regulation of brain responses to environmental stimuli. Compared to typically developing (TD) control participants, children with attention deficit hyperactivity disorder (ADHD) have been shown to have flatter aperiodic spectral slope at rest as well as attenuated event-related potential (ERP) amplitudes in response to environmental stimuli. A small body of research suggests that aperiodic slope may also explain differences in behavioral responses. In this study, we examine associations between prestimulus aperiodic slope, stimulus characteristics, environmental demands, and neural as well as behavioral responses to these stimuli. Furthermore, we evaluate whether ADHD diagnostic status moderates these associations. Seventy-nine children with ADHD and 27 TD school-age children completed two visual ERP experiments with predictable alternating presentations of task-relevant and task-irrelevant stimuli. Aperiodic slope was extracted from prestimulus time windows. Prestimulus aperiodic slope was steeper for the TD relative to ADHD group, driven by task-relevant rather than task-irrelevant stimuli. For both groups, the aperiodic slope was steeper during a task with lower cognitive demand and before trials in which they responded correctly. Aperiodic slope did not mediate the association between ADHD diagnosis and attenuated P300 amplitude. The aperiodic spectral slope is dynamic and changes in anticipation of varying stimulus categories to support performance. The aperiodic slope and P300 amplitude reflect distinct cognitive processes. Background neural oscillations, captured via aperiodic slope, support cognitive behavioral control and should be included in etiological models of ADHD.NEW & NOTEWORTHY This study constitutes the first investigation of associations between aperiodic spectral slope and three aspects of neurocognition: event-related potential (ERP) amplitudes, cognitive load, and task performance. We find that background oscillatory activity is dynamic, shifting in anticipation of varying levels of task relevance and in response to increasing cognitive load. Moreover, we report that aperiodic activity and ERPs constitute distinct neurophysiological processes. Children with attention deficit hyperactivity disorder (ADHD) show reduced aperiodic dynamics in addition to attenuated ERP amplitudes.
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Affiliation(s)
- Anne B Arnett
- Division of Developmental Medicine, Boston Children's Hospital, Boston, Massachusetts
- Pediatrics, Harvard Medical School, Boston, Massachusetts
| | - Virginia Peisch
- Division of Developmental Medicine, Boston Children's Hospital, Boston, Massachusetts
| | - April R Levin
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
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59
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Finley AJ, Angus DJ, van Reekum CM, Davidson RJ, Schaefer SM. Periodic and aperiodic contributions to theta-beta ratios across adulthood. Psychophysiology 2022; 59:e14113. [PMID: 35751645 PMCID: PMC9532351 DOI: 10.1111/psyp.14113] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/15/2022] [Accepted: 05/04/2022] [Indexed: 12/31/2022]
Abstract
The ratio of fronto-central theta (4-7 Hz) to beta oscillations (13-30 Hz), known as the theta-beta ratio, is negatively correlated with attentional control, reinforcement learning, executive function, and age. Although theta-beta ratios have been found to decrease with age in adolescents and young adults, theta has been found to increase with age in older adults. Moreover, age-related decrease in individual alpha peak frequency and flattening of the 1/f aperiodic component may artifactually inflate the association between theta-beta ratio and age. These factors lead to an incomplete understanding of how theta-beta ratio varies across the lifespan and the extent to which variation is due to a conflation of aperiodic and periodic activity. We conducted a partially preregistered analysis examining the cross-sectional associations between age and resting canonical fronto-central theta-beta ratio, individual alpha peak frequency, and aperiodic component (n = 268; age 36-84, M = 55.8, SD = 11.0). Age was negatively associated with theta-beta ratios, individual peak alpha frequencies, and the aperiodic exponent. The correlation between theta-beta ratios and age remained after controlling for individual peak alpha frequencies, but was nonsignificant when controlling for the aperiodic exponent. Aperiodic exponent fully mediated the relationship between theta-beta ratio and age, although beta remained significantly associated with age after controlling for theta, individual peak alpha, and aperiodic exponent. Results replicate previous observations and show age-related decreases in theta-beta ratios are not due to age-related decrease in individual peak alpha frequencies but primarily explained by flattening of the aperiodic component with age.
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Affiliation(s)
- Anna J. Finley
- Center for Healthy MindsUniversity of WisconsinMadisonWisconsinUSA
| | - Douglas J. Angus
- School of PsychologyBond UniversityGold CoastQueenslandAustralia
| | - Carien M. van Reekum
- School of Psychology and Clinical Language SciencesUniversity of ReadingReadingUK
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60
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Gyurkovics M, Clements GM, Low KA, Fabiani M, Gratton G. Stimulus-Induced Changes in 1/ f-like Background Activity in EEG. J Neurosci 2022; 42:7144-7151. [PMID: 35970561 PMCID: PMC9480870 DOI: 10.1523/jneurosci.0414-22.2022] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/21/2022] Open
Abstract
Research into the nature of 1/f-like, nonoscillatory electrophysiological activity has grown exponentially in recent years in cognitive neuroscience. The shape of this activity has been linked to the balance between excitatory and inhibitory neural circuits, which is thought to be important for information processing. However, to date, it is not known whether the presentation of a stimulus induces changes in the parameters of 1/f activity in scalp recordings, separable from event-related potentials (ERPs). Here, we analyzed event-related broadband changes in human EEG both before and after removing ERPs to demonstrate their confounding effect, and to establish whether there are genuine stimulus-induced changes in 1/f Using data from a passive and an active auditory task (n = 23, 61% female), we found that the shape of the post-event spectra between 2 and 25 Hz differed significantly from the pre-event spectra even after removing the frequency-content of ERPs. Further, a significant portion of this difference could be accounted for by a rotational shift in 1/f activity, manifesting as an increase in low and a decrease in high frequencies. Importantly, the magnitude of this rotational shift was related to the attentional demands of the task. This change in 1/f is consistent with increased inhibition following stimulus onset, and likely reflects a disruption of ongoing excitatory activity proportional to processing demands. Finally, these findings contradict the central assumption of baseline normalization strategies in time-frequency analyses, namely, that background EEG activity is stationary across time. As such, they have far-reaching consequences relevant for several subfields of neuroscience.SIGNIFICANCE STATEMENT Interest in the functional role of the 1/f-like background brain activity has been growing exponentially in neuroscience. Yet, no study to date has demonstrated a clear relationship between information processing and 1/f activity by investigating event-related effects on its parameters in noninvasive recordings of neural activity. Here, we demonstrate, for the first time, that stimuli induce rotational changes in 1/f activity, detectable at lower frequencies and independent from the occurrence of event-related potentials. These findings suggest the presence of large-scale inhibition following stimulus onset, largest when the stimulus is novel, and indicate that the assumption of stationary background activity in the analysis of neural oscillations is untenable. These results have far-reaching consequences that cut across several subfields of neuroscience.
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Affiliation(s)
- Máté Gyurkovics
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61801
| | - Grace M Clements
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61801
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61820
| | - Kathy A Low
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61801
| | - Monica Fabiani
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61801
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61820
| | - Gabriele Gratton
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61801
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois 61820
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61
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Wilson LE, da Silva Castanheira J, Baillet S. Time-resolved parameterization of aperiodic and periodic brain activity. eLife 2022; 11:e77348. [PMID: 36094163 PMCID: PMC9467511 DOI: 10.7554/elife.77348] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time-resolved manner. First, we demonstrate, with naturalistic synthetic data, SPRiNT's capacity to reliably recover time-varying spectral features. We emphasize SPRiNT's specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state EEG data (n=178) and relate the observed changes in aperiodic parameters over time to participants' demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour.
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Affiliation(s)
- Luc Edward Wilson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | | | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
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62
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Cross ZR, Corcoran AW, Schlesewsky M, Kohler MJ, Bornkessel-Schlesewsky I. Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning. J Cogn Neurosci 2022; 34:1630-1649. [PMID: 35640095 DOI: 10.1162/jocn_a_01878] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.
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63
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Pietrelli M, Samaha J, Postle BR. Spectral Distribution Dynamics across Different Attentional Priority States. J Neurosci 2022; 42:4026-4041. [PMID: 35387871 PMCID: PMC9097778 DOI: 10.1523/jneurosci.2318-21.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 11/21/2022] Open
Abstract
Anticipatory covert spatial attention improves performance on tests of visual detection and discrimination, and shifts are accompanied by decreases and increases of α band power at electroencephalography (EEG) electrodes corresponding to the attended and unattended location, respectively. Although the increase at the unattended location is often interpreted as an active mechanism (e.g., inhibiting processing at the unattended location), most experiments cannot rule out the alternative possibility that it is a secondary consequence of selection elsewhere. To adjudicate between these accounts, we designed a Posner-style visual cueing task in which male and female human participants made orientation judgments of targets appearing at one of four locations: up, down, right, or left. Critically, trials were blocked such that within a block the locations along one meridian alternated in status between attended and unattended, and targets never appeared at the other two, making them irrelevant. Analyses of the concurrently measured EEG signal were conducted on "traditional" narrowband α (8-14 Hz), as well as on two components resulting from the decomposition of this signal: "periodic" α; and the slope of the aperiodic 1/f-like component. Although data from right-left blocks replicated the familiar pattern of lateralized asymmetry in narrowband α power, with neither α signal could we find evidence for any difference in the time course at unattended versus irrelevant locations, an outcome consistent with the secondary-consequence interpretation of attention-related dynamics in the α band. Additionally, 1/f slope was shallower at attended and unattended locations, relative to irrelevant, suggesting a tonic adjustment of physiological state.SIGNIFICANCE STATEMENT Visual spatial attention, the prioritization of one location in the visual field, is critical for guiding behavior in cluttered environments. Although influential theories posit an important role for α band oscillations in the inhibition of processing at unattended locations, we used a novel procedure to find evidence for an alternative interpretation: selection of one location may simply result in a return to physiological baseline at all others. In addition to determining one way that attention does not work (important for future progress in this field), we also discovered novel evidence for one way that it does work: by modifying the tonic physiological state (indexed by an aperiodic component of the electroencephalography (EEG)] at locations where spatial selection is likely to occur.
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Affiliation(s)
- Mattia Pietrelli
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin 53719
| | - Jason Samaha
- Department of Psychology, University of California, Santa Cruz, California 95064
| | - Bradley R Postle
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin 53719
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin 53706
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64
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Karalunas SL, Ostlund BD, Alperin BR, Figuracion M, Gustafsson HC, Deming EM, Foti D, Antovich D, Dude J, Nigg J, Sullivan E. Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development. Dev Psychobiol 2022; 64:e22228. [PMID: 35312046 PMCID: PMC9707315 DOI: 10.1002/dev.22228] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/20/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022]
Abstract
The aperiodic exponent of the electroencephalogram (EEG) power spectrum has received growing attention as a physiological marker of neurodevelopmental psychopathology, including attention-deficit/hyperactivity disorder (ADHD). However, its use as a marker of ADHD risk across development, and particularly in very young children, is limited by unknown reliability, difficulty in aligning canonical band-based measures across development periods, and unclear effects of treatment in later development. Here, we investigate the internal consistency of the aperiodic EEG power spectrum slope and its association with ADHD risk in both infants (n = 69, 1-month-old) and adolescents (n = 262, ages 11-17 years). Results confirm good to excellent internal consistency in infancy and adolescence. In infancy, a larger aperiodic exponent was associated with greater family history of ADHD. In contrast, in adolescence, ADHD diagnosis was associated with a smaller aperiodic exponent, but only in children with ADHD who had not received stimulant medication treatment. Results suggest that disruptions in cortical development associated with ADHD risk may be detectable shortly after birth via this approach. Together, findings imply a dynamic developmental shift in which the developmentally normative flattening of the EEG power spectrum is exaggerated in ADHD, potentially reflecting imbalances in cortical excitation and inhibition that could contribute to long-lasting differences in brain connectivity.
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Affiliation(s)
- Sarah L Karalunas
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Brendan D Ostlund
- Department of Psychology, Pennsylvania State University, State College, Pennsylvania, USA
| | - Brittany R Alperin
- Department of Psychology, University of Richmond, Richmond, Virginia, USA
| | - McKenzie Figuracion
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Hanna C Gustafsson
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Erika Michiko Deming
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Dan Foti
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Dylan Antovich
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Jason Dude
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Joel Nigg
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Elinor Sullivan
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
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65
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Ostlund B, Donoghue T, Anaya B, Gunther KE, Karalunas SL, Voytek B, Pérez-Edgar KE. Spectral parameterization for studying neurodevelopment: How and why. Dev Cogn Neurosci 2022; 54:101073. [PMID: 35074579 PMCID: PMC8792072 DOI: 10.1016/j.dcn.2022.101073] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 12/07/2021] [Accepted: 01/15/2022] [Indexed: 12/12/2022] Open
Abstract
A growing body of literature suggests that the explicit parameterization of neural power spectra is important for the appropriate physiological interpretation of periodic and aperiodic electroencephalogram (EEG) activity. In this paper, we discuss why parameterization is an imperative step for developmental cognitive neuroscientists interested in cognition and behavior across the lifespan, as well as how parameterization can be readily accomplished with an automated spectral parameterization ("specparam") algorithm (Donoghue et al., 2020a). We provide annotated code for power spectral parameterization, via specparam, in Jupyter Notebook and R Studio. We then apply this algorithm to EEG data in childhood (N = 60; Mage = 9.97, SD = 0.95) to illustrate its utility for developmental cognitive neuroscientists. Ultimately, the explicit parameterization of EEG power spectra may help us refine our understanding of how dynamic neural communication contributes to normative and aberrant cognition across the lifespan. Data and annotated analysis code for this manuscript are available on GitHub as a supplement to the open-access specparam toolbox.
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Affiliation(s)
- Brendan Ostlund
- Department of Psychology, The Pennsylvania State University, USA.
| | - Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego, USA
| | - Berenice Anaya
- Department of Psychology, The Pennsylvania State University, USA
| | - Kelley E Gunther
- Department of Psychology, The Pennsylvania State University, USA
| | | | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, USA; Neurosciences Graduate Program, University of California, San Diego, USA
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66
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Hill AT, Clark GM, Bigelow FJ, Lum JAG, Enticott PG. Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Dev Cogn Neurosci 2022; 54:101076. [PMID: 35085871 PMCID: PMC8800045 DOI: 10.1016/j.dcn.2022.101076] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/10/2022] [Accepted: 01/21/2022] [Indexed: 11/27/2022] Open
Abstract
The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for the emergence and maturation of a range of social and cognitive processes. Here, we utilised both eyes open and eyes closed resting-state electroencephalography (EEG) to examine maturational changes in both oscillatory (i.e., periodic) and non-oscillatory (aperiodic, '1/f-like') activity in a large cohort of participants ranging from 4-to-12 years of age (N = 139, average age=9.41 years, SD=1.95). The EEG signal was parameterised into aperiodic and periodic components, and linear regression models were used to evaluate if chronological age could predict aperiodic exponent and offset, as well as well as peak frequency and power within the alpha and beta ranges. Exponent and offset were found to both decrease with age, while aperiodic-adjusted alpha peak frequency increased with age; however, there was no association between age and peak frequency for the beta band. Age was also unrelated to aperiodic-adjusted spectral power within either the alpha or beta bands, despite both frequency ranges being correlated with the aperiodic signal. Overall, these results highlight the capacity for both periodic and aperiodic features of the EEG to elucidate age-related functional changes within the developing brain.
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Affiliation(s)
- Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia.
| | - Gillian M Clark
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Felicity J Bigelow
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
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67
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Pfeffer T, Keitel C, Kluger DS, Keitel A, Russmann A, Thut G, Donner TH, Gross J. Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing. eLife 2022; 11:e71890. [PMID: 35133276 PMCID: PMC8853659 DOI: 10.7554/elife.71890] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.
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Affiliation(s)
- Thomas Pfeffer
- Universitat Pompeu Fabra, Center for Brain and Cognition, Computational Neuroscience GroupBarcelonaSpain
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Christian Keitel
- University of Stirling, PsychologyStirlingUnited Kingdom
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Daniel S Kluger
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, MalmedywegMuensterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMuensterGermany
| | - Anne Keitel
- University of Dundee, PsychologyDundeeUnited Kingdom
| | - Alena Russmann
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Gregor Thut
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Tobias H Donner
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Joachim Gross
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, MalmedywegMuensterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMuensterGermany
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68
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Pathania A, Euler MJ, Clark M, Cowan R, Duff K, Lohse KR. Resting EEG spectral slopes are associated with age-related differences in information processing speed. Biol Psychol 2022; 168:108261. [PMID: 34999166 DOI: 10.1016/j.biopsycho.2022.108261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 01/03/2022] [Accepted: 01/05/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND Previous research has shown the slope of the EEG power spectrum differentiates between older and younger adults in various experimental cognitive tasks. We extend that work, assessing the relation between the EEG power spectrum and performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHODS Twenty-one younger and twenty-three older adults completed the RBANS with EEG data collected at rest. Using spectral parameterization, we tested the mediating effect of the spectral slope on differences in subsequent cognitive task performance. RESULTS Older adults performed reliably worse on the RBANS overall, and on the Attention and Delayed Memory domains specifically. However, evidence of mediation was only found for the Coding subtest. CONCLUSIONS The slope of the resting EEG power spectrum mediated age-related differences in cognition, but only in a task requiring speeded processing. Mediation was not statistically significant for delayed memory, even though age-related differences were present.
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Affiliation(s)
- A Pathania
- Department of Health and Kinesiology, University of Utah
| | - M J Euler
- Department of Psychology, University of Utah
| | - M Clark
- Department of Health and Kinesiology, University of Utah
| | - R Cowan
- Department of Health and Kinesiology, University of Utah
| | - K Duff
- Department of Neurology, University of Utah
| | - K R Lohse
- Department of Health and Kinesiology, University of Utah; Program in Physical Therapy and Department of Neurology, Washington University School of Medicine in Saint Louis
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69
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Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations. Neuroinformatics 2022; 20:991-1012. [PMID: 35389160 PMCID: PMC9588478 DOI: 10.1007/s12021-022-09581-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2022] [Indexed: 12/31/2022]
Abstract
Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.
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