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Cui R, Hao X, Huang P, He M, Ma W, Gong D, Yao D. Behavioral state-dependent associations between EEG temporal correlations and depressive symptoms. Psychiatry Res Neuroimaging 2024; 341:111811. [PMID: 38583274 DOI: 10.1016/j.pscychresns.2024.111811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/21/2024] [Accepted: 03/11/2024] [Indexed: 04/09/2024]
Abstract
Previous studies have shown abnormal long-range temporal correlations in neuronal oscillations among individuals with Major Depressive Disorders, occurring during both resting states and transitions between resting and task states. However, the understanding of this effect in preclinical individuals with depression remains limited. This study investigated the association between temporal correlations of neuronal oscillations and depressive symptoms during resting and task states in preclinical individuals, specifically focusing on male action video gaming experts. Detrended fluctuation analysis (DFA), Lifetimes, and Waitingtimes were employed to explore temporal correlations across long-range and short-range scales. The results indicated widespread changes from the resting state to the task state across all frequency bands and temporal scales. Rest-task DFA changes in the alpha band exhibited a negative correlation with depressive scores at most electrodes. Significant positive correlations between DFA values and depressive scores were observed in the alpha band during the resting state but not in the task state. Similar patterns of results emerged concerning maladaptive negative emotion regulation strategies. Additionally, short-range temporal correlations in the alpha band echoed the DFA results. These findings underscore the state-dependent relationships between temporal correlations of neuronal oscillations and depressive symptoms, as well as maladaptive emotion regulation strategies, in preclinical individuals.
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Affiliation(s)
- Ruifang Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyang Hao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pei Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Mengling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weiyi Ma
- School of Human Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Diankun Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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2
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Klar P, Çatal Y, Langner R, Huang Z, Northoff G. Scale-free dynamics of core-periphery topography. Hum Brain Mapp 2022; 44:1997-2017. [PMID: 36579661 PMCID: PMC9980897 DOI: 10.1002/hbm.26187] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/15/2022] [Accepted: 12/11/2022] [Indexed: 12/30/2022] Open
Abstract
The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.
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Affiliation(s)
- Philipp Klar
- Medical Faculty, C. & O. Vogt‐Institute for Brain ResearchHeinrich Heine University of DüsseldorfDüsseldorfGermany
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Robert Langner
- Institute of Systems NeuroscienceHeinrich Heine University DusseldorfDusseldorfGermany,Institute of Neuroscience and MedicineBrain & Behaviour (INM‐7), Research Centre JülichJülichGermany
| | - Zirui Huang
- Department of AnesthesiologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA,Center for Consciousness ScienceUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada,Centre for Cognition and Brain DisordersHangzhou Normal UniversityHangzhouZhejiangChina
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3
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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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4
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Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
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Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
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5
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Sieberg CB, Lebel A, Silliman E, Holmes S, Borsook D, Elman I. Left to themselves: Time to target chronic pain in childhood rare diseases. Neurosci Biobehav Rev 2021; 126:276-288. [PMID: 33774086 PMCID: PMC8738995 DOI: 10.1016/j.neubiorev.2021.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/02/2021] [Accepted: 03/04/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Chronic pain is prevalent among patients with rare diseases (RDs). However, little is understood about how biopsychosocial mechanisms may be integrated in the unique set of clinical features and therapeutic challenges inherent in their pain conditions. METHODS This review presents examples of major categories of RDs with particular pain conditions. In addition, we provide translational evidence on clinical and scientific rationale for psychosocially- and neurodevelopmentally-informed treatment of pain in RD patients. RESULTS Neurobiological and functional overlap between various RD syndromes and pain states suggests amalgamation and mutual modulation of the respective conditions. Emotional sequelae could be construed as an emotional homologue of physical pain mediated via overlapping brain circuitry. Given their clearly defined genetic and molecular etiologies, RDs may serve as heuristic models for unraveling pathophysiological processes inherent in chronic pain. CONCLUSIONS Systematic evaluation of chronic pain in patients with RD contributes to sophisticated insight into both pain and their psychosocial correlates, which could transform treatment.
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Affiliation(s)
- Christine B Sieberg
- Biobehavioral Pediatric Pain Lab, Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, 02115, USA; Center for Pain and the Brain (P.A.I.N Group), Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
| | - Alyssa Lebel
- Center for Pain and the Brain (P.A.I.N Group), Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Anesthesiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Erin Silliman
- Biobehavioral Pediatric Pain Lab, Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, 02115, USA; Division of Graduate Medical Sciences, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Scott Holmes
- Center for Pain and the Brain (P.A.I.N Group), Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Anesthesiology, Harvard Medical School, Boston, MA, 02115, USA
| | - David Borsook
- Center for Pain and the Brain (P.A.I.N Group), Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Anesthesiology, Harvard Medical School, Boston, MA, 02115, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Igor Elman
- Center for Pain and the Brain (P.A.I.N Group), Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, 02139, USA
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6
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Cruz G, Grent-'t-Jong T, Krishnadas R, Palva JM, Palva S, Uhlhaas PJ. Long range temporal correlations (LRTCs) in MEG-data during emerging psychosis: Relationship to symptoms, medication-status and clinical trajectory. Neuroimage Clin 2021; 31:102722. [PMID: 34130193 PMCID: PMC8209846 DOI: 10.1016/j.nicl.2021.102722] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/30/2021] [Accepted: 06/04/2021] [Indexed: 12/24/2022]
Abstract
Long-Range Temporal Correlations (LRTCs) index the capacity of the brain to optimally process information. Previous research has shown that patients with chronic schizophrenia present altered LRTCs at alpha and beta oscillations. However, it is currently unclear at which stage of schizophrenia aberrant LRTCs emerge. To address this question, we investigated LRTCs in resting-state magnetoencephalographic (MEG) recordings obtained from patients with affective disorders and substance abuse (clinically at low-risk of psychosis, CHR-N), patients at clinical high-risk of psychosis (CHR-P) (n = 115), as well as patients with a first episode (FEP) (n = 25). Matched healthy controls (n = 47) served as comparison group. LRTCs were obtained for frequencies from 4 to 40 Hz and correlated with clinical and neuropsychological data. In addition, we examined the relationship between LRTCs and transition to psychosis in CHR-P participants, and the relationship between LRTC and antipsychotic medication in FEP participants. Our results show that participants from the clinical groups have similar LRTCs to controls. In addition, LRTCs did not correlate with clinical and neurocognitive variables across participants nor did LRTCs predict transition to psychosis. Therefore, impaired LRTCs do not reflect a feature in the clinical trajectory of psychosis. Nevertheless, reduced LRTCs in the beta-band over posterior sensors of medicated FEP participants indicate that altered LRTCs may appear at the onset of the illness. Future studies are needed to elucidate the role of anti-psychotic medication in altered LRTCs.
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Affiliation(s)
- Gabriela Cruz
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
| | - Tineke Grent-'t-Jong
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Rajeev Krishnadas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - J Matias Palva
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Neuroscience Centre, Helsinki Institute of Life Science, University of Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland
| | - Satu Palva
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Neuroscience Centre, Helsinki Institute of Life Science, University of Helsinki, Finland
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
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7
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Scaling behaviour in music and cortical dynamics interplay to mediate music listening pleasure. Sci Rep 2019; 9:17700. [PMID: 31776389 PMCID: PMC6881362 DOI: 10.1038/s41598-019-54060-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 11/08/2019] [Indexed: 01/17/2023] Open
Abstract
The pleasure of music listening regulates daily behaviour and promotes rehabilitation in healthcare. Human behaviour emerges from the modulation of spontaneous timely coordinated neuronal networks. Too little is known about the physical properties and neurophysiological underpinnings of music to understand its perception, its health benefit and to deploy personalized or standardized music-therapy. Prior studies revealed how macroscopic neuronal and music patterns scale with frequency according to a 1/fα relationship, where a is the scaling exponent. Here, we examine how this hallmark in music and neuronal dynamics relate to pleasure. Using electroencephalography, electrocardiography and behavioural data in healthy subjects, we show that music listening decreases the scaling exponent of neuronal activity and-in temporal areas-this change is linked to pleasure. Default-state scaling exponents of the most pleased individuals were higher and approached those found in music loudness fluctuations. Furthermore, the scaling in selective regions and timescales and the average heart rate were largely proportional to the scaling of the melody. The scaling behaviour of heartbeat and neuronal fluctuations were associated during music listening. Our results point to a 1/f resonance between brain and music and a temporal rescaling of neuronal activity in the temporal cortex as mechanisms underlying music appreciation.
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8
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Harrison SJ, Hough M, Schmid K, Groff BR, Stergiou N. When Coordinating Finger Tapping to a Variable Beat the Variability Scaling Structure of the Movement and the Cortical BOLD Signal are Both Entrained to the Auditory Stimuli. Neuroscience 2018; 392:203-218. [PMID: 29958941 PMCID: PMC8091912 DOI: 10.1016/j.neuroscience.2018.06.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 06/12/2018] [Accepted: 06/18/2018] [Indexed: 01/13/2023]
Abstract
Rhythmic actions are characterizable as a repeating invariant pattern of movement together with variability taking the form of cycle-to-cycle fluctuations. Variability in behavioral measures is atypically random, and often exhibits serial temporal dependencies and statistical self-similarity in the scaling of variability magnitudes across timescales. Self-similar (i.e. fractal) variability scaling is evident in measures of both brain and behavior. Variability scaling structure can be quantified via the scaling exponent (α) from detrended fluctuation analysis (DFA). Here we study the task of coordinating thumb-finger tapping to the beats of constructed auditory stimuli. We test the hypothesis that variability scaling evident in tap-to-tap intervals as well as in the fluctuations of cortical hemodynamics will become entrained to (i.e. drawn toward) manipulated changes in the variability scaling of a stimulus's beat-to-beat intervals. Consistent with this hypothesis, manipulated changes of the exponent α of the experimental stimuli produced corresponding changes in the exponent α of both tap-to-tap intervals and cortical hemodynamics. The changes in hemodynamics were observed in both motor and sensorimotor cortical areas in the contralateral hemisphere. These results were observed only for the longer timescales of the detrended fluctuation analysis used to measure the exponent α. These findings suggest that complex auditory stimuli engage both brain and behavior at the level of variability scaling structures.
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Affiliation(s)
- Steven J Harrison
- Department of Kinesiology, University of Connecticut, United States.
| | - Michael Hough
- Department of Biomechanics, University of Nebraska at Omaha, United States
| | - Kendra Schmid
- Department of Biostatistics, University of Nebraska Medical Center, United States
| | - Boman R Groff
- Department of Biomechanics, University of Nebraska at Omaha, United States
| | - Nicholas Stergiou
- Department of Biomechanics, University of Nebraska at Omaha, United States
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9
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La Rocca D, Zilber N, Abry P, van Wassenhove V, Ciuciu P. Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics. J Neurosci Methods 2018; 309:175-187. [DOI: 10.1016/j.jneumeth.2018.09.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/16/2022]
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10
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Huk A, Bonnen K, He BJ. Beyond Trial-Based Paradigms: Continuous Behavior, Ongoing Neural Activity, and Natural Stimuli. J Neurosci 2018; 38:7551-7558. [PMID: 30037835 PMCID: PMC6113904 DOI: 10.1523/jneurosci.1920-17.2018] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 07/02/2018] [Accepted: 07/07/2018] [Indexed: 11/21/2022] Open
Abstract
The vast majority of experiments examining perception and behavior are conducted using experimental paradigms that adhere to a rigid trial structure: each trial consists of a brief and discrete series of events and is regarded as independent from all other trials. The assumptions underlying this structure ignore the reality that natural behavior is rarely discrete, brain activity follows multiple time courses that do not necessarily conform to the trial structure, and the natural environment has statistical structure and dynamics that exhibit long-range temporal correlation. Modern advances in statistical modeling and analysis offer tools that make it feasible for experiments to move beyond rigid independent and identically distributed trial structures. Here we review literature that serves as evidence for the feasibility and advantages of moving beyond trial-based paradigms to understand the neural basis of perception and cognition. Furthermore, we propose a synthesis of these efforts, integrating the characterization of natural stimulus properties with measurements of continuous neural activity and behavioral outputs within the framework of sensory-cognitive-motor loops. Such a framework provides a basis for the study of natural statistics, naturalistic tasks, and/or slow fluctuations in brain activity, which should provide starting points for important generalizations of analytical tools in neuroscience and subsequent progress in understanding the neural basis of perception and cognition.
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Affiliation(s)
- Alexander Huk
- Center for Perceptual Systems,
- Institute for Neuroscience
- Departments of Psychology and Neuroscience, The University of Texas at Austin, Austin 78712, Texas, and
| | | | - Biyu J He
- Departments of Neurology, Neuroscience and Physiology, and Radiology, Neuroscience Institute, New York University Langone Medical Center, New York, New York 10016
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11
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Neural Integration of Stimulus History Underlies Prediction for Naturalistically Evolving Sequences. J Neurosci 2018; 38:1541-1557. [PMID: 29311143 DOI: 10.1523/jneurosci.1779-17.2017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 02/02/2023] Open
Abstract
Forming valid predictions about the environment is crucial to survival. However, whether humans are able to form valid predictions about natural stimuli based on their temporal statistical regularities remains unknown. Here, we presented subjects with tone sequences with pitch fluctuations that, over time, capture long-range temporal dependence structures prevalent in natural stimuli. We found that subjects were able to exploit such naturalistic statistical regularities to make valid predictions about upcoming items in a sequence. Magnetoencephalography (MEG) recordings revealed that slow, arrhythmic cortical dynamics tracked the evolving pitch sequence over time such that neural activity at a given moment was influenced by the pitch of up to seven previous tones. Importantly, such history integration contained in neural activity predicted the expected pitch of the upcoming tone, providing a concrete computational mechanism for prediction. These results establish humans' ability to make valid predictions based on temporal regularities inherent in naturalistic stimuli and further reveal the neural mechanisms underlying such predictive computation.SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how the brain predicts upcoming events in the environment. To date, this question has primarily been addressed in experiments using relatively simple stimulus sequences. Here, we studied predictive processing in the human brain using auditory tone sequences that exhibit temporal statistical regularities similar to those found in natural stimuli. We observed that humans are able to form valid predictions based on such complex temporal statistical regularities. We further show that neural response to a given tone in the sequence reflects integration over the preceding tone sequence and that this history dependence forms the foundation for prediction. These findings deepen our understanding of how humans form predictions in an ecologically valid environment.
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12
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Simola J, Zhigalov A, Morales-Muñoz I, Palva JM, Palva S. Critical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task. Sci Rep 2017; 7:2909. [PMID: 28588303 PMCID: PMC5460255 DOI: 10.1038/s41598-017-02750-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 04/18/2017] [Indexed: 12/11/2022] Open
Abstract
Fluctuations with power-law scaling and long-range temporal correlations (LRTCs) are characteristic to human psychophysical performance. Systems operating in a critical state exhibit such LRTCs, but phenomenologically similar fluctuations and LRTCs may also be caused by slow decay of the system’s memory without the system being critical. Theoretically, criticality endows the system with the greatest representational capacity and flexibility in state transitions. Without criticality, however, slowly decaying system memory would predict inflexibility. We addressed these contrasting predictions of the ‘criticality’ and ‘long-memory’ candidate mechanisms of human behavioral LRTCs by using a Go/NoGo task wherein the commission errors constitute a measure of cognitive flexibility. Response time (RT) fluctuations in this task exhibited power-law frequency scaling, autocorrelations, and LRTCs. We show here that the LRTC scaling exponents, quantifying the strength of long-range correlations, were negatively correlated with the commission error rates. Strong LRTCs hence parallel optimal cognitive flexibility and, in line with the criticality hypothesis, indicate a functionally advantageous state. This conclusion was corroborated by a positive correlation between the LRTC scaling exponents and executive functions measured with the Rey-Osterrieth Complex Figure test. Our results hence support the notion that LRTCs arise from critical dynamics that is functionally significant for human cognitive performance.
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Affiliation(s)
- Jaana Simola
- Helsinki Institute for Lifesciences, Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4), FI-00014, Helsinki, Finland.
| | - Alexander Zhigalov
- Helsinki Institute for Lifesciences, Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4), FI-00014, Helsinki, Finland
| | - Isabel Morales-Muñoz
- Helsinki Institute for Lifesciences, Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4), FI-00014, Helsinki, Finland
| | - J Matias Palva
- Helsinki Institute for Lifesciences, Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4), FI-00014, Helsinki, Finland
| | - Satu Palva
- Helsinki Institute for Lifesciences, Neuroscience Center, University of Helsinki, P.O. Box 56 (Viikinkaari 4), FI-00014, Helsinki, Finland.
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