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Singhal S, Ghosh P, Kumar N, Banerjee A. Parametric separation of phase-locked and non-phase-locked activity. J Neurophysiol 2023; 129:199-210. [PMID: 36541609 DOI: 10.1152/jn.00467.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components: "phase-locked" and "non-phase-locked" to the stimulus. Phase-locked activity is often implicitly studied as event-related potentials (ERPs), and the trial-averaged estimates-evoked potentials (EP) considered both time-locked and phase-locked to the stimulus. The non-phase-locked activity, on the other hand, refers to an increase in power in a narrow band or broadband frequencies in the signal emerging at variable phases from stimulus initiation. Both components are understood to stem from different neuronal mechanisms; hence, accurately characterizing them is of immense importance to neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a novel concurrent phaser method (CPM) that simultaneously decomposes the two components. First, we establish that the single-trial separation of phase-locked and non-phase-locked power is an ill-posed problem. Second, using simulations where ground truth validation is possible, we elucidate how the estimation of non-phase-locked power gets biased by phase-locked power in the state-of-the-art averaging method and ways to resolve the issue using CPM. Next, we use two experimental EEG datasets-audio oddball and auditory steady-state responses (ASSR) to show that empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity. Thus, using ground truth validation from simulations and demonstration in real experimental scenarios, the efficacy of the proposed CPM is established.NEW & NOTEWORTHY Parametric models for estimation of phase-locked and non-phase-locked brain signals reveals how estimation of non-phase-locked component is biased by the variability of phase-locked component and at the level of single trial becomes an ill-posed problem. Furthermore, the modeling framework delimits the boundaries where traditional averaging approach can be trusted to estimate the phase-locked and non-phase-locked components.
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Affiliation(s)
- Shubham Singhal
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Priyanka Ghosh
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Neeraj Kumar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
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2
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Oliveira-Silva P, Maia L, Coutinho J, Moreno AF, Penalba L, Frank B, Soares JM, Sampaio A, Gonçalves ÓF. Nodes of the default mode network implicated in the quality of empathic responses: A clinical perspective of the empathic response. Int J Clin Health Psychol 2023; 23:100319. [PMID: 36168601 PMCID: PMC9485908 DOI: 10.1016/j.ijchp.2022.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/25/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Patrícia Oliveira-Silva
- HNL - Human Neurobehavioral Laboratory, CEDH – Research Centre for Human Development, Faculty of Education and Psychology, Universidade Católica Portuguesa, Porto, Portugal
- Corresponding author.
| | - Liliana Maia
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Joana Coutinho
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Ana Filipa Moreno
- HNL - Human Neurobehavioral Laboratory, CEDH – Research Centre for Human Development, Faculty of Education and Psychology, Universidade Católica Portuguesa, Porto, Portugal
| | - Lucia Penalba
- HNL - Human Neurobehavioral Laboratory, CEDH – Research Centre for Human Development, Faculty of Education and Psychology, Universidade Católica Portuguesa, Porto, Portugal
| | | | - José Miguel Soares
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, ICVS, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal, Portugal
| | - Adriana Sampaio
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Óscar F. Gonçalves
- Proaction Lab, CINEICC – Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
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3
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Uddin LQ. Bring the Noise: Reconceptualizing Spontaneous Neural Activity. Trends Cogn Sci 2020; 24:734-746. [PMID: 32600967 PMCID: PMC7429348 DOI: 10.1016/j.tics.2020.06.003] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/04/2020] [Accepted: 06/05/2020] [Indexed: 12/17/2022]
Abstract
Definitions of what constitutes the 'signal of interest' in neuroscience can be controversial, due in part to continuously evolving notions regarding the significance of spontaneous neural activity. This review highlights how the challenge of separating brain signal from noise has led to new conceptualizations of brain functional organization at both the micro- and macroscopic level. Recent debates in the functional neuroimaging community surrounding artifact removal processes have revived earlier discussions surrounding how to appropriately isolate and measure neuronal signals against a background of noise from various sources. Insights from electrophysiological studies and computational modeling can inform current theory and data analytic practices in human functional neuroimaging, given that signal and noise may be inextricably linked in the brain.
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Affiliation(s)
- Lucina Q Uddin
- Department of Psychology, University of Miami, PO Box 248185-0751, Coral Gables, FL 33124, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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4
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Parker DB, Razlighi QR. Task-evoked Negative BOLD Response and Functional Connectivity in the Default Mode Network are Representative of Two Overlapping but Separate Neurophysiological Processes. Sci Rep 2019; 9:14473. [PMID: 31597927 PMCID: PMC6785640 DOI: 10.1038/s41598-019-50483-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 08/30/2019] [Indexed: 01/21/2023] Open
Abstract
The topography of the default mode network (DMN) can be obtained with one of two different functional magnetic resonance imaging (fMRI) methods: either from the spontaneous but organized synchrony of the low-frequency fluctuations in resting-state fMRI (rs-fMRI), known as "functional connectivity", or from the consistent and robust deactivations in task-based fMRI (tb-fMRI), here referred to as the "negative BOLD response" (NBR). These two methods are fundamentally different, but their results are often used interchangeably to describe the brain's resting-state, baseline, or intrinsic activity. While the DMN was initially defined by consistent task-based decreases in blood flow in a set of specific brain regions using PET imaging, recently nearly all studies on the DMN employ functional connectivity in rs-fMRI. In this study, we first show the high level of spatial overlap between NBR and functional connectivity of the DMN extracted from the same tb-fMRI scan; then, we demonstrate that the NBR in putative DMN regions can be significantly altered without causing any change in their overlapping functional connectivity. Furthermore, we present evidence that in the DMN, the NBR is more closely related to task performance than the functional connectivity. We conclude that the NBR and functional connectivity of the DMN reflect two separate but overlapping neurophysiological processes, and thus should be differentiated in studies investigating brain-behavior relationships in both healthy and diseased populations. Our findings further raise the possibility that the macro-scale networks of the human brain might internally exhibit a hierarchical functional architecture.
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Affiliation(s)
- David B Parker
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Qolamreza R Razlighi
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
- Department of Neurology, College of Physicians and Surgeons, Columbia University Medial Center, New York, NY, 10032, USA.
- Taub Institute for research on Alzheimer's disease and the aging brain, Columbia University Medical Center, New York, NY, 10032, USA.
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5
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Sander TH, Zhou B. Linking neuroimaging signals to behavioral responses in single cases: Challenges and opportunities. Psych J 2016; 5:161-9. [DOI: 10.1002/pchj.143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 08/09/2016] [Accepted: 08/09/2016] [Indexed: 11/08/2022]
Affiliation(s)
| | - Bin Zhou
- Key Laboratory of Behavioral Sciences, Institute of Psychology; Chinese Academy of Sciences; Beijing China
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6
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Li F, Liu T, Wang F, Li H, Gong D, Zhang R, Jiang Y, Tian Y, Guo D, Yao D, Xu P. Relationships between the resting-state network and the P3: Evidence from a scalp EEG study. Sci Rep 2015; 5:15129. [PMID: 26456594 PMCID: PMC4601035 DOI: 10.1038/srep15129] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 09/17/2015] [Indexed: 01/01/2023] Open
Abstract
The P3 is an important event-related potential that can be used to identify neural activity related to the cognitive processes of the human brain. However, the relationships, especially the functional correlations, between resting-state brain activity and the P3 have not been well established. In this study, we investigated the relationships between P3 properties (i.e., amplitude and latency) and resting-state brain networks. The results indicated that P3 amplitude was significantly correlated with resting-state network topology, and in general, larger P3 amplitudes could be evoked when the resting-state brain network was more efficient. However, no significant relationships were found for the corresponding P3 latency. Additionally, the long-range connections between the prefrontal/frontal and parietal/occipital brain regions, which represent the synchronous activity of these areas, were functionally related to the P3 parameters, especially P3 amplitude. The findings of the current study may help us better understand inter-subject variation in the P3, which may be instructive for clinical diagnosis, cognitive neuroscience studies, and potential subject selection for brain-computer interface applications.
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Affiliation(s)
- Fali Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tiejun Liu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.,Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Fei Wang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - He Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Diankun Gong
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Rui Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Jiang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yin Tian
- College of Bio-information, ChongQing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Daqing Guo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.,Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.,Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.,Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China
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7
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Ritter P, Born J, Brecht M, Dinse HR, Heinemann U, Pleger B, Schmitz D, Schreiber S, Villringer A, Kempter R. State-dependencies of learning across brain scales. Front Comput Neurosci 2015; 9:1. [PMID: 25767445 PMCID: PMC4341560 DOI: 10.3389/fncom.2015.00001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 01/06/2015] [Indexed: 01/09/2023] Open
Abstract
Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.
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Affiliation(s)
- Petra Ritter
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité University Medicine Berlin Berlin, Germany ; Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany
| | - Jan Born
- Department of Medical Psychology and Behavioral Neurobiology & Center for Integrative Neuroscience (CIN), University of Tübingen Tübingen, Germany
| | - Michael Brecht
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany
| | - Hubert R Dinse
- Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University Bochum Bochum, Germany ; Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum Bochum, Germany
| | - Uwe Heinemann
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany
| | - Burkhard Pleger
- Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
| | - Dietmar Schmitz
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany ; Neuroscience Research Center NWFZ, Charité University Medicine Berlin Berlin, Germany ; Max-Delbrück Center for Molecular Medicine, MDC Berlin, Germany ; Center for Neurodegenerative Diseases (DZNE) Berlin, Germany
| | - Susanne Schreiber
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biology, Institute for Theoretical Biology (ITB), Humboldt-Universität zu Berlin Berlin, Germany
| | - Arno Villringer
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany ; Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
| | - Richard Kempter
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biology, Institute for Theoretical Biology (ITB), Humboldt-Universität zu Berlin Berlin, Germany
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8
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Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual–auditory cortices and default-mode network. Neuroimage 2013; 76:362-72. [DOI: 10.1016/j.neuroimage.2013.02.070] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Revised: 01/21/2013] [Accepted: 02/25/2013] [Indexed: 11/22/2022] Open
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9
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Hahn T, Dresler T, Pyka M, Notebaert K, Fallgatter AJ. Local synchronization of resting-state dynamics encodes Gray's trait Anxiety. PLoS One 2013; 8:e58336. [PMID: 23520499 PMCID: PMC3592924 DOI: 10.1371/journal.pone.0058336] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Accepted: 02/02/2013] [Indexed: 11/20/2022] Open
Abstract
The Behavioral Inhibition System (BIS) as defined within the Reinforcement Sensitivity Theory (RST) modulates reactions to stimuli indicating aversive events. Gray's trait Anxiety determines the extent to which stimuli activate the BIS. While studies have identified the amygdala-septo-hippocampal circuit as the key-neural substrate of this system in recent years and measures of resting-state dynamics such as randomness and local synchronization of spontaneous BOLD fluctuations have recently been linked to personality traits, the relation between resting-state dynamics and the BIS remains unexplored. In the present study, we thus examined the local synchronization of spontaneous fMRI BOLD fluctuations as measured by Regional Homogeneity (ReHo) in the hippocampus and the amygdala in twenty-seven healthy subjects. Correlation analyses showed that Gray's trait Anxiety was significantly associated with mean ReHo in both the amygdala and the hippocampus. Specifically, Gray's trait Anxiety explained 23% and 17% of resting-state ReHo variance in the left amygdala and the left hippocampus, respectively. In summary, we found individual differences in Gray's trait Anxiety to be associated with ReHo in areas previously associated with BIS functioning. Specifically, higher ReHo in resting-state neural dynamics corresponded to lower sensitivity to punishment scores both in the amygdala and the hippocampus. These findings corroborate and extend recent findings relating resting-state dynamics and personality while providing first evidence linking properties of resting-state fluctuations to Gray's BIS.
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Affiliation(s)
- Tim Hahn
- Department of Cognitive Psychology II, Johann Wolfgang Goethe University, Frankfurt am Main, Germany.
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10
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Nierhaus T, Gundlach C, Goltz D, Thiel SD, Pleger B, Villringer A. Internal ventilation system of MR scanners induces specific EEG artifact during simultaneous EEG-fMRI. Neuroimage 2013; 74:70-6. [PMID: 23435207 DOI: 10.1016/j.neuroimage.2013.02.016] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 01/18/2013] [Accepted: 02/03/2013] [Indexed: 10/27/2022] Open
Abstract
During simultaneous EEG-fMRI acquisition, the EEG signal suffers from tremendous artifacts caused by the scanner "environment". Particularly, gradient artifacts and the ballistocardiogram have been well characterized, along with methods to eliminate them. Here, we describe another systematic artifact in the EEG signal, which is induced by the internal ventilation system of Siemens TRIO and VERIO MR scanners. A ventilation-level dependent vibration induces specific peaks in the frequency spectrum of the EEG. These frequency peaks are in the range of physiologically relevant brain rhythms (gamma frequency range), and thus interfere with their reliable acquisition. This ventilation dependent artifact was most prominent on the electrodes placed directly on the subject's head, so it is not sufficient to simply place the EEG's amplifier outside the scanner tube. Instead, the ventilator must be switched off to fully eliminate the ventilator's artificial manipulation of EEG recordings. Without the internal ventilator system being on, the temperature within the scanner tube may rise, thus requiring shorter scanning sessions or an additional external ventilation system.
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Affiliation(s)
- Till Nierhaus
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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11
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Telkemeyer S, Rossi S, Nierhaus T, Steinbrink J, Obrig H, Wartenburger I. Acoustic processing of temporally modulated sounds in infants: evidence from a combined near-infrared spectroscopy and EEG study. Front Psychol 2011; 1:62. [PMID: 21716574 PMCID: PMC3110620 DOI: 10.3389/fpsyg.2011.00062] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2010] [Accepted: 03/25/2011] [Indexed: 11/15/2022] Open
Abstract
Speech perception requires rapid extraction of the linguistic content from the acoustic signal. The ability to efficiently process rapid changes in auditory information is important for decoding speech and thereby crucial during language acquisition. Investigating functional networks of speech perception in infancy might elucidate neuronal ensembles supporting perceptual abilities that gate language acquisition. Interhemispheric specializations for language have been demonstrated in infants. How these asymmetries are shaped by basic temporal acoustic properties is under debate. We recently provided evidence that newborns process non-linguistic sounds sharing temporal features with language in a differential and lateralized fashion. The present study used the same material while measuring brain responses of 6 and 3 month old infants using simultaneous recordings of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). NIRS reveals that the lateralization observed in newborns remains constant over the first months of life. While fast acoustic modulations elicit bilateral neuronal activations, slow modulations lead to right-lateralized responses. Additionally, auditory-evoked potentials and oscillatory EEG responses show differential responses for fast and slow modulations indicating a sensitivity for temporal acoustic variations. Oscillatory responses reveal an effect of development, that is, 6 but not 3 month old infants show stronger theta-band desynchronization for slowly modulated sounds. Whether this developmental effect is due to increasing fine-grained perception for spectrotemporal sounds in general remains speculative. Our findings support the notion that a more general specialization for acoustic properties can be considered the basis for lateralization of speech perception. The results show that concurrent assessment of vascular based imaging and electrophysiological responses have great potential in the research on language acquisition.
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Affiliation(s)
- Silke Telkemeyer
- Languages of Emotion Cluster of Excellence, Freie Universität BerlinBerlin, Germany
- Department of Cognitive Psychology, Humboldt-Universität BerlinBerlin, Germany
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
- Department of Linguistics, University of PotsdamPotsdam, Germany
| | - Sonja Rossi
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
- Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, University HospitalLeipzig, Germany
| | - Till Nierhaus
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
- Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, University HospitalLeipzig, Germany
| | - Jens Steinbrink
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
| | - Hellmuth Obrig
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
| | - Isabell Wartenburger
- Languages of Emotion Cluster of Excellence, Freie Universität BerlinBerlin, Germany
- Berlin NeuroImaging Center, Department of Neurology, Charité University MedicineBerlin, Germany
- Department of Linguistics, University of PotsdamPotsdam, Germany
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12
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Ostwald D, Porcaro C, Bagshaw AP. Voxel-wise information theoretic EEG-fMRI feature integration. Neuroimage 2010; 55:1270-86. [PMID: 21167287 DOI: 10.1016/j.neuroimage.2010.12.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2010] [Revised: 12/07/2010] [Accepted: 12/08/2010] [Indexed: 11/18/2022] Open
Abstract
We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data pre-processing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG pre-processing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-fMRI activity dependence. More advanced EEG data pre-processing rendered the feature distributions more stimulus-informative, but did not alter the EEG-fMRI activity and conditional dependencies.
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Affiliation(s)
- Dirk Ostwald
- School of Psychology, University of Birmingham, UK.
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