1
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Hausfeld L, Hamers IMH, Formisano E. FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes. Commun Biol 2024; 7:1217. [PMID: 39349723 PMCID: PMC11442455 DOI: 10.1038/s42003-024-06913-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/17/2024] [Indexed: 10/04/2024] Open
Abstract
Invasive and non-invasive electrophysiological measurements during "cocktail-party"-like listening indicate that neural activity in the human auditory cortex (AC) "tracks" the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl's gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal's sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the single speaker and concurrent speakers conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.
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Affiliation(s)
- Lars Hausfeld
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands.
- Maastricht Brain Imaging Centre, 6200 MD, Maastricht, The Netherlands.
| | - Iris M H Hamers
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Elia Formisano
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, 6200 MD, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology, Faculty of Science and Engineering, 6200 MD, Maastricht, The Netherlands
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2
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Hsieh JK, Prakash PR, Flint RD, Fitzgerald Z, Mugler E, Wang Y, Crone NE, Templer JW, Rosenow JM, Tate MC, Betzel R, Slutzky MW. Cortical sites critical to language function act as connectors between language subnetworks. Nat Commun 2024; 15:7897. [PMID: 39284848 PMCID: PMC11405775 DOI: 10.1038/s41467-024-51839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/15/2024] [Indexed: 09/20/2024] Open
Abstract
Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site's pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.
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Affiliation(s)
- Jason K Hsieh
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Prashanth R Prakash
- Department of Biomedical Engineering, Northwestern University, Chicago, IL, 60611, USA
| | - Robert D Flint
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Zachary Fitzgerald
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Emily Mugler
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yujing Wang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Jessica W Templer
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Joshua M Rosenow
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Matthew C Tate
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Cognitive Science Program, Program in Neuroscience, and Network Science Institute, Indiana University, Bloomington, IN, 47401, USA
| | - Marc W Slutzky
- Department of Biomedical Engineering, Northwestern University, Chicago, IL, 60611, USA.
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Neuroscience, Northwestern University, Chicago, IL, 60611, USA.
- Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, 60611, USA.
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3
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Kronberg G, Ceceli AO, Huang Y, Gaudreault PO, King SG, McClain N, Alia-Klein N, Goldstein RZ. Naturalistic drug cue reactivity in heroin use disorder: orbitofrontal synchronization as a marker of craving and recovery. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.02.23297937. [PMID: 37961156 PMCID: PMC10635268 DOI: 10.1101/2023.11.02.23297937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Movies captivate groups of individuals (the audience), especially if they contain themes of common motivational interest to the group. In drug addiction, a key mechanism is maladaptive motivational salience attribution whereby drug cues outcompete other reinforcers within the same environment or context. We predicted that while watching a drug-themed movie, where cues for drugs and other stimuli share a continuous narrative context, fMRI responses in individuals with heroin use disorder (iHUD) will preferentially synchronize during drug scenes. Results revealed such drug-biased synchronization in the orbitofrontal cortex (OFC), ventromedial and ventrolateral prefrontal cortex, and insula. After 15 weeks of inpatient treatment, there was a significant reduction in this drug-biased shared response in the OFC, which correlated with a concomitant reduction in dynamically-measured craving, suggesting synchronized OFC responses to a drug-themed movie as a neural marker of craving and recovery in iHUD.
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Affiliation(s)
- Greg Kronberg
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Ahmet O Ceceli
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Yuefeng Huang
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | | | - Sarah G King
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
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4
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Mandal AS, Wiener C, Assem M, Romero-Garcia R, Coelho P, McDonald A, Woodberry E, Morris RC, Price SJ, Duncan J, Santarius T, Suckling J, Hart MG, Erez Y. Tumour-infiltrated cortex participates in large-scale cognitive circuits. Cortex 2024; 173:1-15. [PMID: 38354669 PMCID: PMC10988771 DOI: 10.1016/j.cortex.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/03/2024] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
The extent to which tumour-infiltrated brain tissue contributes to cognitive function remains unclear. We tested the hypothesis that cortical tissue infiltrated by diffuse gliomas participates in large-scale cognitive circuits using a unique combination of intracranial electrocorticography (ECoG) and resting-state functional magnetic resonance (fMRI) imaging in four patients. We also assessed the relationship between functional connectivity with tumour-infiltrated tissue and long-term cognitive outcomes in a larger, overlapping cohort of 17 patients. We observed significant task-related high gamma (70-250 Hz) power modulations in tumour-infiltrated cortex in response to increased cognitive effort (i.e., switch counting compared to simple counting), implying preserved functionality of neoplastic tissue for complex tasks probing executive function. We found that tumour locations corresponding to task-responsive electrodes exhibited functional connectivity patterns that significantly co-localised with canonical brain networks implicated in executive function. Specifically, we discovered that tumour-infiltrated cortex with larger task-related high gamma power modulations tended to be more functionally connected to the dorsal attention network (DAN). Finally, we demonstrated that tumour-DAN connectivity is evident across a larger cohort of patients with gliomas and that it relates to long-term postsurgical outcomes in goal-directed attention. Overall, this study contributes convergent fMRI-ECoG evidence that tumour-infiltrated cortex participates in large-scale neurocognitive circuits that support executive function in health. These findings underscore the potential clinical utility of mapping large-scale connectivity of tumour-infiltrated tissue in the care of patients with diffuse gliomas.
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Affiliation(s)
- Ayan S Mandal
- Brain-Gene Development Lab, Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, USA; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK.
| | - Chemda Wiener
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel
| | - Moataz Assem
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Sevilla, Spain
| | | | - Alexa McDonald
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Emma Woodberry
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Robert C Morris
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Stephen J Price
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust, UK
| | - John Duncan
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Experimental Psychology, University of Oxford, UK
| | - Thomas Santarius
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust, UK; Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK; Cambridge and Peterborough NHS Foundation Trust, UK
| | - Michael G Hart
- St George's, University of London & St George's University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Neurosciences Research Centre, Cranmer Terrace, London, UK
| | - Yaara Erez
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel; Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK; Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
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5
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Carmichael DW, Vulliemoz S, Murta T, Chaudhary U, Perani S, Rodionov R, Rosa MJ, Friston KJ, Lemieux L. Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain. Bioengineering (Basel) 2024; 11:224. [PMID: 38534498 DOI: 10.3390/bioengineering11030224] [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: 10/16/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 03/28/2024] Open
Abstract
There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated. We acquired simultaneous ECoG-fMRI in the sensorimotor cortex of three patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was co-localised with fMRI measures across all subjects. The best model of fMRI changes across states was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than any others that were based either on the classical frequency bands or on summary measures of cross-spectral changes. The region-specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.
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Affiliation(s)
- David W Carmichael
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Serge Vulliemoz
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Epilepsy Unit, Neurology Department, University Hospital and University of Geneva, 1211 Geneva 14, Switzerland
| | - Teresa Murta
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Department of Bioengineering, Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
| | - Umair Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Suejen Perani
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Maria Joao Rosa
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Karl J Friston
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
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6
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Keles U, Dubois J, Le KJM, Tyszka JM, Kahn DA, Reed CM, Chung JM, Mamelak AN, Adolphs R, Rutishauser U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 2024; 11:214. [PMID: 38365977 PMCID: PMC10873379 DOI: 10.1038/s41597-024-03029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/31/2024] [Indexed: 02/18/2024] Open
Abstract
We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video "Bang! You're Dead" and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.
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Affiliation(s)
- Umit Keles
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Julien Dubois
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kevin J M Le
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - J Michael Tyszka
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - David A Kahn
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jeffrey M Chung
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ralph Adolphs
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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7
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Qin X, Yuan Y, Yu H, Yao Y, Li L. Acute Effect of Vagus Nerve Stimulation in Patients with Drug-Resistant Epilepsy: A Preliminary Exploration via Stereoelectroencephalogram. Neurosurg Clin N Am 2024; 35:105-118. [PMID: 38000834 DOI: 10.1016/j.nec.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
As the pathophysiological mechanisms of vagus nerve stimulation (VNS) causing individual differences in the vagal ascending network remains unclear, stereoelectroencephalography (SEEG) provides a unique platform to explore the brain networks affected by VNS and helps to understand the anti-seizure mechanism of VNS more comprehensively. This study presents a preliminary exploration of the acute effect of VNS. SEEG signals were collected to assess the acute effect of VNS on neural synchronization in patients with drug-resistant epilepsy, especially in epileptogenic networks. The results show that the better the efficacy of VNS, the wider the spread of desynchronization assessed by weighted phase lag index at a high frequency band caused by VNS. Future studies should focus on the association between the change in synchronization and the efficacy of VNS, exploring the possibility of synchronization as a biomarker for patient screening and parameter programming.
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Affiliation(s)
- Xiaoya Qin
- Precision Medicine & Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Yuan Yuan
- Precision Medicine & Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Huiling Yu
- National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Yi Yao
- Department of Functional Neurosurgery, Xiamen Humanity Hospital Affiliated to Fujian Medical University, Fujian, China; Surgery Division, Epilepsy Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China.
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China; IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, China.
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8
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Yin Q, Johnson EL, Ofen N. Neurophysiological mechanisms of cognition in the developing brain: Insights from intracranial EEG studies. Dev Cogn Neurosci 2023; 64:101312. [PMID: 37837918 PMCID: PMC10589793 DOI: 10.1016/j.dcn.2023.101312] [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: 07/04/2023] [Revised: 09/26/2023] [Accepted: 10/08/2023] [Indexed: 10/16/2023] Open
Abstract
The quest to understand how the development of the brain supports the development of complex cognitive functions is fueled by advances in cognitive neuroscience methods. Intracranial EEG (iEEG) recorded directly from the developing human brain provides unprecedented spatial and temporal resolution for mapping the neurophysiological mechanisms supporting cognitive development. In this paper, we focus on episodic memory, the ability to remember detailed information about past experiences, which improves from childhood into adulthood. We review memory effects based on broadband spectral power and emphasize the importance of isolating narrowband oscillations from broadband activity to determine mechanisms of neural coordination within and between brain regions. We then review evidence of developmental variability in neural oscillations and present emerging evidence linking the development of neural oscillations to the development of memory. We conclude by proposing that the development of oscillations increases the precision of neural coordination and may be an essential factor underlying memory development. More broadly, we demonstrate how recording neural activity directly from the developing brain holds immense potential to advance our understanding of cognitive development.
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Affiliation(s)
- Qin Yin
- Department of Psychology, Wayne State University, Detroit, MI, USA; Life-span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, USA
| | - Elizabeth L Johnson
- Departments of Medical Social Sciences and Pediatrics, Northwestern University, Chicago, IL, USA; Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Noa Ofen
- Department of Psychology, Wayne State University, Detroit, MI, USA; Life-span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, USA.
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9
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Gu Z, Jamison K, Sabuncu MR, Kuceyeski A. Human brain responses are modulated when exposed to optimized natural images or synthetically generated images. Commun Biol 2023; 6:1076. [PMID: 37872319 PMCID: PMC10593916 DOI: 10.1038/s42003-023-05440-7] [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: 05/10/2023] [Accepted: 10/10/2023] [Indexed: 10/25/2023] Open
Abstract
Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, anterior temporal lobe face area (aTLfaces) and fusiform body area 1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system.
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Affiliation(s)
- Zijin Gu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
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10
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Kocsis Z, Jenison RL, Taylor PN, Calmus RM, McMurray B, Rhone AE, Sarrett ME, Deifelt Streese C, Kikuchi Y, Gander PE, Berger JI, Kovach CK, Choi I, Greenlee JD, Kawasaki H, Cope TE, Griffiths TD, Howard MA, Petkov CI. Immediate neural impact and incomplete compensation after semantic hub disconnection. Nat Commun 2023; 14:6264. [PMID: 37805497 PMCID: PMC10560235 DOI: 10.1038/s41467-023-42088-7] [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: 11/18/2022] [Accepted: 09/28/2023] [Indexed: 10/09/2023] Open
Abstract
The human brain extracts meaning using an extensive neural system for semantic knowledge. Whether broadly distributed systems depend on or can compensate after losing a highly interconnected hub is controversial. We report intracranial recordings from two patients during a speech prediction task, obtained minutes before and after neurosurgical treatment requiring disconnection of the left anterior temporal lobe (ATL), a candidate semantic knowledge hub. Informed by modern diaschisis and predictive coding frameworks, we tested hypotheses ranging from solely neural network disruption to complete compensation by the indirectly affected language-related and speech-processing sites. Immediately after ATL disconnection, we observed neurophysiological alterations in the recorded frontal and auditory sites, providing direct evidence for the importance of the ATL as a semantic hub. We also obtained evidence for rapid, albeit incomplete, attempts at neural network compensation, with neural impact largely in the forms stipulated by the predictive coding framework, in specificity, and the modern diaschisis framework, more generally. The overall results validate these frameworks and reveal an immediate impact and capability of the human brain to adjust after losing a brain hub.
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Affiliation(s)
- Zsuzsanna Kocsis
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA.
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Rick L Jenison
- Departments of Neuroscience and Psychology, University of Wisconsin, Madison, WI, USA
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
- UCL Institute of Neurology, Queen Square, London, UK
| | - Ryan M Calmus
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Bob McMurray
- Department of Psychological and Brain Science, University of Iowa, Iowa City, IA, USA
| | - Ariane E Rhone
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | | | | | - Yukiko Kikuchi
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Phillip E Gander
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Joel I Berger
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | | | - Inyong Choi
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA, USA
| | | | - Hiroto Kawasaki
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | - Thomas E Cope
- Department of Clinical Neurosciences, Cambridge University, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, Cambridge University, Cambridge, UK
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Matthew A Howard
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | - Christopher I Petkov
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA.
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK.
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11
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Alwood JS, Mulavara AP, Iyer J, Mhatre SD, Rosi S, Shelhamer M, Davis C, Jones CW, Mao XW, Desai RI, Whitmire AM, Williams TJ. Circuits and Biomarkers of the Central Nervous System Relating to Astronaut Performance: Summary Report for a NASA-Sponsored Technical Interchange Meeting. Life (Basel) 2023; 13:1852. [PMID: 37763256 PMCID: PMC10532466 DOI: 10.3390/life13091852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Biomarkers, ranging from molecules to behavior, can be used to identify thresholds beyond which performance of mission tasks may be compromised and could potentially trigger the activation of countermeasures. Identification of homologous brain regions and/or neural circuits related to operational performance may allow for translational studies between species. Three discussion groups were directed to use operationally relevant performance tasks as a driver when identifying biomarkers and brain regions or circuits for selected constructs. Here we summarize small-group discussions in tables of circuits and biomarkers categorized by (a) sensorimotor, (b) behavioral medicine and (c) integrated approaches (e.g., physiological responses). In total, hundreds of biomarkers have been identified and are summarized herein by the respective group leads. We hope the meeting proceedings become a rich resource for NASA's Human Research Program (HRP) and the community of researchers.
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Affiliation(s)
| | | | - Janani Iyer
- Universities Space Research Association (USRA), Moffett Field, CA 94035, USA
| | | | - Susanna Rosi
- Department of Physical Therapy & Rehabilitation Science, University of California, San Francisco, CA 94110, USA
- Department of Neurological Surgery, University of California, San Francisco, CA 94110, USA
| | - Mark Shelhamer
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Catherine Davis
- Department of Pharmacology and Molecular Therapeutics, Uniformed Services University of the Health Sciences (USUHS), Bethesda, MD 20814, USA
| | - Christopher W. Jones
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xiao Wen Mao
- Department of Basic Sciences, Division of Biomedical Engineering Sciences (BMES), Loma Linda University Health, Loma Linda, CA 92354, USA
| | - Rajeev I. Desai
- Integrative Neurochemistry Laboratory, Behavioral Biology Program, McLean Hospital-Harvard Medical School, Belmont, MA 02478, USA
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12
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Axelrod V, Rozier C, Sohier E, Lehongre K, Adam C, Lambrecq V, Navarro V, Naccache L. Intracranial study in humans: Neural spectral changes during watching comedy movie of Charlie Chaplin. Neuropsychologia 2023; 185:108558. [PMID: 37061128 DOI: 10.1016/j.neuropsychologia.2023.108558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/01/2023] [Accepted: 04/04/2023] [Indexed: 04/17/2023]
Abstract
Humor plays a prominent role in our lives. Thus, understanding the cognitive and neural mechanisms of humor is particularly important. Previous studies that investigated neural substrates of humor used functional MRI and to a lesser extent EEG. In the present study, we conducted intracranial recording in human patients, enabling us to obtain the signal with high temporal precision from within specific brain locations. Our analysis focused on the temporal lobe and the surrounding areas, the temporal lobe was most densely covered in our recording. Thirteen patients watched a fragment of a Charlie Chaplin movie. An independent group of healthy participants rated the same movie fragment, helping us to identify the most funny and the least funny frames of the movie. We compared neural activity occurring during the most funny and least funny frames across frequencies in the range of 1-170 Hz. The most funny compared to least funny parts of the movie were associated with activity modulation in the broadband high-gamma (70-170 Hz; mostly activation) and to a lesser extent gamma band (40-69Hz; activation) and low frequencies (1-12 Hz, delta, theta, alpha bands; mostly deactivation). With regard to regional specificity, we found three types of brain areas: (I) temporal pole, middle and inferior temporal gyrus (both anterior and posterior) in which there was both activation in the high-gamma/gamma bands and deactivation in low frequencies; (II) ventral part of the temporal lobe such as the fusiform gyrus, in which there was mostly deactivation the low frequencies; (III) posterior temporal cortex and its environment, such as the middle occipital and the temporo-parietal junction, in which there was activation in the high-gamma/gamma band. Overall, our results suggest that humor appreciation might be achieved by neural activity across the frequency spectrum.
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Affiliation(s)
- Vadim Axelrod
- The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, 52900, Israel.
| | - Camille Rozier
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Elisa Sohier
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Katia Lehongre
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Claude Adam
- AP-HP, Epilepsy Unit, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France
| | - Virginie Lambrecq
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France; AP-HP, EEG Unit, Neurophysiology Department, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France; AP-HP, Epilepsy Unit, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France
| | - Vincent Navarro
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France; AP-HP, EEG Unit, Neurophysiology Department, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France; AP-HP, Epilepsy Unit, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France; AP-HP, Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
| | - Lionel Naccache
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS, AP-HP, Pitié-Salpêtrière Hospital, Paris, France; AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, 47-83 boulevard de l'Hôpital, Paris 75013, France
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13
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Barborica A, Mindruta I, López-Madrona VJ, Alario FX, Trébuchon A, Donos C, Oane I, Pistol C, Mihai F, Bénar CG. Studying memory processes at different levels with simultaneous depth and surface EEG recordings. Front Hum Neurosci 2023; 17:1154038. [PMID: 37082152 PMCID: PMC10110965 DOI: 10.3389/fnhum.2023.1154038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/06/2023] [Indexed: 04/07/2023] Open
Abstract
Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.
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Affiliation(s)
- Andrei Barborica
- Department of Physics, University of Bucharest, Bucharest, Romania
- *Correspondence: Andrei Barborica
| | - Ioana Mindruta
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
- Department of Neurology, Medical Faculty, Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
| | | | | | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | - Cristian Donos
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Irina Oane
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
| | | | - Felicia Mihai
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Christian G. Bénar
- Aix Marseille University, INSERM, INS, Institute of Neuroscience System, Marseille, France
- Christian G. Bénar
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14
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Anwar A, Radwan A, Zaky I, El Ayadi M, Youssef A. Resting state fMRI brain mapping in pediatric supratentorial brain tumors. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00713-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Functional mapping of eloquent brain areas is crucial for preoperative planning in patients with brain tumors. Resting state functional MRI (rs-fMRI) allows the localization of functional brain areas without the need for task performance, making it well-suited for the pediatric population. In this study the independent component analysis (ICA) rs-fMRI functional mapping results are reported in a group of 22 pediatric patients with supratentorial brain tumors. Additionally, the functional connectivity (FC) maps of the sensori-motor network (SMN) obtained using ICA and seed-based analysis (SBA) are compared.
Results
Different resting state networks (RSNs) were extracted using ICA with varying levels of sensitivity, notably, the SMN was identified in 100% of patients, followed by the Default mode network (DMN) (91%) and Language networks (80%). Additionally, FC maps of the SMN extracted by SBA were more extensive (mean volume = 25,288.36 mm3, standard deviation = 13,364.36 mm3) than those found on ICA (mean volume = 13,403.27 mm3, standard deviation = 9755.661 mm3). This was confirmed by statistical analysis using a Wilcoxon signed rank t test at p < 0.01.
Conclusions
Results clearly demonstrate the successful applicability of rs-fMRI for localizing different functional brain networks in the preoperative assessment of brain areas, and thus represent a further step in the integration of computational radiology research in a clinical setting.
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15
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Liu S, Zhang C, Meng C, Wang R, Jiang P, Cai H, Zhao W, Yu Y, Zhu J. Frequency-dependent genetic modulation of neuronal oscillations: a combined transcriptome and resting-state functional MRI study. Cereb Cortex 2022; 32:5132-5144. [PMID: 35106539 DOI: 10.1093/cercor/bhac003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/01/2022] [Accepted: 01/02/2022] [Indexed: 12/27/2022] Open
Abstract
Neuronal oscillations within certain frequency bands are assumed to associate with specific neural processes and cognitive functions. To examine this hypothesis, transcriptome-neuroimaging spatial correlation analysis was applied to resting-state functional magnetic resonance imaging data from 793 healthy individuals and gene expression data from the Allen Human Brain Atlas. We found that expression measures of 336 genes were correlated with fractional amplitude of low-frequency fluctuations (fALFF) in the slow-4 band (0.027-0.073 Hz), whereas there were no expression-fALFF correlations for the other frequency bands. Furthermore, functional enrichment analyses showed that these slow-4 fALFF-related genes were mainly enriched for ion channel, synaptic function, and neuronal system as well as many neuropsychiatric disorders. Specific expression analyses demonstrated that these genes were specifically expressed in brain tissue, in neurons, and during the late stage of cortical development. Concurrently, the fALFF-related genes were linked to multiple behavioral domains, including dementia, attention, and emotion. In addition, these genes could construct a protein-protein interaction network supported by 30 hub genes. Our findings of a frequency-dependent genetic modulation of spontaneous neuronal activity may support the concept that neuronal oscillations within different frequency bands capture distinct neurobiological processes from the perspective of underlying molecular mechanisms.
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Affiliation(s)
- Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Chun Meng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Department of Radiology, Anhui No.2 Provincial People's Hospital, Hefei 230041, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Ping Jiang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
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16
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Vecchio F, Nucci L, Pappalettera C, Miraglia F, Iacoviello D, Rossini PM. Time-frequency analysis of brain activity in response to directional and non-directional visual stimuli: an event related spectral perturbations (ERSP) study. J Neural Eng 2022; 19. [PMID: 36270505 DOI: 10.1088/1741-2552/ac9c96] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/21/2022] [Indexed: 01/11/2023]
Abstract
Objective.A large part of the cerebral cortex is dedicated to the processing of visual stimuli and there is still much to understand about such processing modalities and hierarchies. The main aim of the present study is to investigate the differences between directional visual stimuli (DS) and non-directional visual stimuli (n-DS) processing by time-frequency analysis of brain electroencephalographic activity during a visuo-motor task. Electroencephalography (EEG) data were divided into four regions of interest (ROIs) (frontal, central, parietal, occipital).Approach.The analysis of the visual stimuli processing was based on the combination of electroencephalographic recordings and time-frequency analysis. Event related spectral perturbations (ERSPs) were computed with spectrum analysis that allow to obtain the average time course of relative changes induced by the stimulus presentation in spontaneous EEG amplitude spectrum.Main results.Visual stimuli processing enhanced the same pattern of spectral modulation in all investigated ROIs with differences in amplitudes and timing. Additionally, statistically significant differences in occipital ROI between the DS and n-DS visual stimuli processing in theta, alpha and beta bands were found.Significance.These evidences suggest that ERSPs could be a useful tool to investigate the encoding of visual information in different brain regions. Because of their simplicity and their capability in the representation of brain activity, the ERSPs might be used as biomarkers of functional recovery for example in the rehabilitation of visual dysfunction and motor impairment following a stroke, as well as diagnostic tool of anomalies in brain functions in neurological diseases tailored to personalized treatments in clinical environment.
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Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Daniela Iacoviello
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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17
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Ebrahiminia F, Cichy RM, Khaligh-Razavi SM. A multivariate comparison of electroencephalogram and functional magnetic resonance imaging to electrocorticogram using visual object representations in humans. Front Neurosci 2022; 16:983602. [PMID: 36330341 PMCID: PMC9624066 DOI: 10.3389/fnins.2022.983602] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/23/2022] [Indexed: 09/07/2024] Open
Abstract
Today, most neurocognitive studies in humans employ the non-invasive neuroimaging techniques functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we aimed to understand the relation between EEG and fMRI data at the level of neural population codes using multivariate pattern analysis. In particular, we assessed whether this relation is affected when we change stimuli or introduce identity-preserving variations to them. For this, we recorded EEG and fMRI data separately from 21 healthy participants while participants viewed everyday objects in different viewing conditions, and then related the data to electrocorticogram (ECoG) data recorded for the same stimulus set from epileptic patients. The comparison of EEG and ECoG data showed that object category signals emerge swiftly in the visual system and can be detected by both EEG and ECoG at similar temporal delays after stimulus onset. The correlation between EEG and ECoG was reduced when object representations tolerant to changes in scale and orientation were considered. The comparison of fMRI and ECoG overall revealed a tighter relationship in occipital than in temporal regions, related to differences in fMRI signal-to-noise ratio. Together, our results reveal a complex relationship between fMRI, EEG, and ECoG signals at the level of population codes that critically depends on the time point after stimulus onset, the region investigated, and the visual contents used.
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Affiliation(s)
- Fatemeh Ebrahiminia
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | | | - Seyed-Mahdi Khaligh-Razavi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran
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18
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL Team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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19
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Bhaskara S, Sakorikar T, Chatterjee S, Shabari Girishan K, Pandya HJ. Recent advancements in Micro-engineered devices for surface and deep brain animal studies: A review. SENSING AND BIO-SENSING RESEARCH 2022. [DOI: 10.1016/j.sbsr.2022.100483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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20
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Dobrushina OR, Dobrynina LA, Arina GA, Kremneva EI, Novikova ES, Gubanova MV, Pechenkova EV, Suslina AD, Aristova VV, Trubitsyna VV, Krotenkova MV. Enhancing Brain Connectivity With Infra-Low Frequency Neurofeedback During Aging: A Pilot Study. Front Hum Neurosci 2022; 16:891547. [PMID: 35712529 PMCID: PMC9195620 DOI: 10.3389/fnhum.2022.891547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Aging is associated with decreased functional connectivity in the main brain networks, which can underlie changes in cognitive and emotional processing. Neurofeedback is a promising non-pharmacological approach for the enhancement of brain connectivity. Previously, we showed that a single session of infra-low frequency neurofeedback results in increased connectivity between sensory processing networks in healthy young adults. In the current pilot study, we aimed to evaluate the possibility of enhancing brain connectivity during aging with the use of infra-low frequency neurofeedback. Nine females aged 52 ± 7 years with subclinical signs of emotional dysregulation, including anxiety, mild depression, and somatoform symptoms, underwent 15 sessions of training. A resting-state functional MRI scan was acquired before and after the training. A hypothesis-free intrinsic connectivity analysis showed increased connectivity in regions in the bilateral temporal fusiform cortex, right supplementary motor area, left amygdala, left temporal pole, and cerebellum. Next, a seed-to-voxel analysis for the revealed regions was performed using the post- vs. pre-neurofeedback contrast. Finally, to explore the whole network of neurofeedback-related connectivity changes, the regions revealed by the intrinsic connectivity and seed-to-voxel analyses were entered into a network-based statistical analysis. An extended network was revealed, including the temporal and occipital fusiform cortex, multiple areas from the visual cortex, the right posterior superior temporal sulcus, the amygdala, the temporal poles, the superior parietal lobule, and the supplementary motor cortex. Clinically, decreases in alexithymia, depression, and anxiety levels were observed. Thus, infra-low frequency neurofeedback appears to be a promising method for enhancing brain connectivity during aging, and subsequent sham-controlled studies utilizing larger samples are feasible.
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Affiliation(s)
- Olga R. Dobrushina
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
- *Correspondence: Olga R. Dobrushina
| | | | - Galina A. Arina
- Faculty of Psychology, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Elena I. Kremneva
- Department of Radiology, Research Center of Neurology, Moscow, Russia
| | | | - Mariia V. Gubanova
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
| | | | | | - Vlada V. Aristova
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
- Faculty of Psychology, M.V. Lomonosov Moscow State University, Moscow, Russia
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21
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Arina GA, Dobrushina OR, Shvetsova ET, Osina ED, Meshkov GA, Aziatskaya GA, Trofimova AK, Efremova IN, Martunov SE, Nikolaeva VV. Infra-Low Frequency Neurofeedback in Tension-Type Headache: A Cross-Over Sham-Controlled Study. Front Hum Neurosci 2022; 16:891323. [PMID: 35669204 PMCID: PMC9164298 DOI: 10.3389/fnhum.2022.891323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Primary headaches are highly prevalent and represent a major cause of disability in young adults. Neurofeedback is increasingly used in the treatment of chronic pain; however, there are few studies investigating its efficacy in patients with headaches. We report the results of a cross-over sham-controlled study on the efficacy of neurofeedback in the prophylactic treatment of tension-type headache (TTH). Participants received ten sessions of infra-low frequency electroencephalographic neurofeedback and ten sessions of sham-neurofeedback, with the order of treatments being randomized. The study also included a basic psychotherapeutic intervention — a psychoeducational session performed before the main study phases and emotional support provided throughout the study period. The headache probability was modeled as a function of the neurofeedback and sham-neurofeedback sessions performed to date. As a result, we revealed a strong beneficial effect of neurofeedback and no influence of the sham sessions. The study supports the prophylactic use of infra-low frequency neurofeedback in patients with TTH. From a methodological point of view, we advocate for the explicit inclusion of psychotherapeutic components in neurofeedback study protocols.
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Affiliation(s)
- Galina A. Arina
- Faculty of Psychology, M. V. Lomonosov Moscow State University, Moscow, Russia
| | - Olga R. Dobrushina
- International Institute of Psychosomatic Health, Moscow, Russia
- Research Center of Neurology, Moscow, Russia
- *Correspondence: Olga R. Dobrushina,
| | | | - Ekaterina D. Osina
- Faculty of Psychology, M. V. Lomonosov Moscow State University, Moscow, Russia
| | | | | | - Alexandra K. Trofimova
- Federal State Budgetary Institution “Federal Center of Brain Research and Neurotechnologies” of the Federal Medical Biological Agency, Moscow, Russia
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22
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Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep 2021; 11:24155. [PMID: 34921176 PMCID: PMC8683494 DOI: 10.1038/s41598-021-03414-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/23/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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23
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Brief segments of neurophysiological activity enable individual differentiation. Nat Commun 2021; 12:5713. [PMID: 34588439 PMCID: PMC8481307 DOI: 10.1038/s41467-021-25895-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
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24
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Sahonero-Alvarez G, Singh AK, Sayrafian K, Bianchi L, Roman-Gonzalez A. A Functional BCI Model by the P2731 Working Group: Transducer. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
| | | | - Kamran Sayrafian
- Information Technology Laboratory, National Institute of Standards & Technology, Gaithersburg, USA
| | - Luigi Bianchi
- Civil Engineering and Computer Science Engineering Dept. Tor Vergata University of Rome, Rome, Italy
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25
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Zhang Y, Kim JH, Brang D, Liu Z. Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021; 19:100298. [PMID: 34423178 PMCID: PMC8376216 DOI: 10.1016/j.cobme.2021.100298] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Movies, audio stories, and virtual reality are increasingly used as stimuli for functional brain imaging. Such naturalistic paradigms are in sharp contrast to the tradition of experimental reductionism in neuroscience research. Being complex, dynamic, and diverse, naturalistic stimuli set up a more ecologically relevant condition and induce highly reproducible brain responses across a wide range of spatiotemporal scales. Here, we review recent technical advances and scientific findings on imaging the brain under naturalistic stimuli. Then we elaborate on the premise of using naturalistic paradigms for multi-scale, multi-modal, and high-throughput functional characterization of the human brain. We further highlight the growing potential of using deep learning models to infer neural information processing from brain responses to naturalistic stimuli. Lastly, we advocate large-scale collaborations to combine brain imaging and recording data across experiments, subjects, and labs that use the same set of naturalistic stimuli.
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Affiliation(s)
- Yizhen Zhang
- Department of Electrical Engineering and Computer Science, University of Michigan
| | - Jung-Hoon Kim
- Department of Biomedical Engineering, University of Michigan
- Weldon School of Biomedical Engineering, Purdue University
| | - David Brang
- Department of Psychology, University of Michigan
| | - Zhongming Liu
- Department of Electrical Engineering and Computer Science, University of Michigan
- Department of Biomedical Engineering, University of Michigan
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26
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Sanada T, Kapeller C, Jordan M, Grünwald J, Mitsuhashi T, Ogawa H, Anei R, Guger C. Multi-modal Mapping of the Face Selective Ventral Temporal Cortex-A Group Study With Clinical Implications for ECS, ECoG, and fMRI. Front Hum Neurosci 2021; 15:616591. [PMID: 33828468 PMCID: PMC8020907 DOI: 10.3389/fnhum.2021.616591] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/22/2021] [Indexed: 12/29/2022] Open
Abstract
Face recognition is impaired in patients with prosopagnosia, which may occur as a side effect of neurosurgical procedures. Face selective regions on the ventral temporal cortex have been localized with electrical cortical stimulation (ECS), electrocorticography (ECoG), and functional magnetic resonance imagining (fMRI). This is the first group study using within-patient comparisons to validate face selective regions mapping, utilizing the aforementioned modalities. Five patients underwent surgical treatment of intractable epilepsy and joined the study. Subdural grid electrodes were implanted on their ventral temporal cortices to localize seizure foci and face selective regions as part of the functional mapping protocol. Face selective regions were identified in all patients with fMRI, four patients with ECoG, and two patients with ECS. From 177 tested electrode locations in the region of interest (ROI), which is defined by the fusiform gyrus and the inferior temporal gyrus, 54 face locations were identified by at least one modality in all patients. fMRI mapping showed the highest detection rate, revealing 70.4% for face selective locations, whereas ECoG and ECS identified 64.8 and 31.5%, respectively. Thus, 28 face locations were co-localized by at least two modalities, with detection rates of 89.3% for fMRI, 85.7% for ECoG and 53.6 % for ECS. All five patients had no face recognition deficits after surgery, even though five of the face selective locations, one obtained by ECoG and the other four by fMRI, were within 10 mm to the resected volumes. Moreover, fMRI included a quite large volume artifact on the ventral temporal cortex in the ROI from the anatomical structures of the temporal base. In conclusion, ECS was not sensitive in several patients, whereas ECoG and fMRI even showed activation within 10 mm to the resected volumes. Considering the potential signal drop-out in fMRI makes ECoG the most reliable tool to identify face selective locations in this study. A multimodal approach can improve the specificity of ECoG and fMRI, while simultaneously minimizing the number of required ECS sessions. Hence, all modalities should be considered in a clinical mapping protocol entailing combined results of co-localized face selective locations.
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Affiliation(s)
- Takahiro Sanada
- Department of Neurosurgery, Nayoro City General Hospital, Nayoro, Japan.,Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Christoph Kapeller
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Guger Technologies OG, Graz, Austria
| | - Michael Jordan
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Guger Technologies OG, Graz, Austria
| | - Johannes Grünwald
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Guger Technologies OG, Graz, Austria
| | - Takumi Mitsuhashi
- Department of Neurosurgery, Juntendo University, Tokyo, Japan.,Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, United States
| | - Hiroshi Ogawa
- Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ryogo Anei
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Christoph Guger
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Guger Technologies OG, Graz, Austria
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27
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Li Q, Gao J, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent. Front Bioeng Biotechnol 2020; 8:1006. [PMID: 33015003 PMCID: PMC7506120 DOI: 10.3389/fbioe.2020.01006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/31/2020] [Indexed: 11/23/2022] Open
Abstract
Epileptiform discharges are of fundamental importance in understanding the physiology of epilepsy. To aid in the clinical diagnosis, classification, prognosis, and treatment of epilepsy, it is important to develop automated computer programs to distinguish epileptiform discharges from normal electroencephalogram (EEG). This is a challenging task as clinically used scalp EEG often contains a lot of noise and motion artifacts. The challenge is even greater if one wishes to develop explainable rather than black-box based approaches. To take on this challenge, we propose to use a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We analyzed 640 multi-channel EEG segments, each 4 s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We found that features from SDLE were very effective in distinguishing epileptiform discharges from normal EEG. Using Random Forest Classifier (RF) and Support Vector Machines (SVM), the proposed approach with different features from SDLE robustly achieves an accuracy exceeding 99% in distinguishing epileptiform discharges from normal control ones. A single parameter, which is the ratio of the spectral energy of EEG signals and the SDLE and quantifies the regularity or predictability of the EEG signals, is introduced to better understand the high accuracy in the classification. It is found that this regularity is considerably greater for epileptiform discharges than for normal controls. Robustly having high accuracy in distinguishing epileptiform discharges from normal controls irrespective of which classification scheme being used, the proposed approach has the potential to be used widely in a clinical setting.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, Beijing, China.,International College, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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28
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Fahimi Hnazaee M, Wittevrongel B, Khachatryan E, Libert A, Carrette E, Dauwe I, Meurs A, Boon P, Van Roost D, Van Hulle MM. Localization of deep brain activity with scalp and subdural EEG. Neuroimage 2020; 223:117344. [PMID: 32898677 DOI: 10.1016/j.neuroimage.2020.117344] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 01/11/2023] Open
Abstract
To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.
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Affiliation(s)
| | - Benjamin Wittevrongel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Elvira Khachatryan
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Arno Libert
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Evelien Carrette
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Ine Dauwe
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Alfred Meurs
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Paul Boon
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Dirk Van Roost
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Marc M Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
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29
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Nentwich M, Ai L, Madsen J, Telesford QK, Haufe S, Milham MP, Parra LC. Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI. Neuroimage 2020; 218:117001. [PMID: 32492509 PMCID: PMC7457369 DOI: 10.1016/j.neuroimage.2020.117001] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/21/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023] Open
Abstract
A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.
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Affiliation(s)
- Maximilian Nentwich
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA
| | - Jens Madsen
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Michael P Milham
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Lucas C Parra
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA.
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30
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Li Q, Gao J, Zhang Z, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective. Front Physiol 2020; 11:828. [PMID: 32903770 PMCID: PMC7438848 DOI: 10.3389/fphys.2020.00828] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/22/2020] [Indexed: 01/03/2023] Open
Abstract
Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter H extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- International College, Guangxi University, Nanning, Guangxi, China
| | - Ziwen Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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31
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Maffei A. Spectrally resolved EEG intersubject correlation reveals distinct cortical oscillatory patterns during free‐viewing of affective scenes. Psychophysiology 2020; 57:e13652. [DOI: 10.1111/psyp.13652] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 06/08/2020] [Accepted: 07/06/2020] [Indexed: 01/10/2023]
Affiliation(s)
- Antonio Maffei
- Department of General Psychology University of Padua Padua Italy
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32
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Dobrushina OR, Vlasova RM, Rumshiskaya AD, Litvinova LD, Mershina EA, Sinitsyn VE, Pechenkova EV. Modulation of Intrinsic Brain Connectivity by Implicit Electroencephalographic Neurofeedback. Front Hum Neurosci 2020; 14:192. [PMID: 32655386 PMCID: PMC7324903 DOI: 10.3389/fnhum.2020.00192] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/28/2020] [Indexed: 12/01/2022] Open
Abstract
Despite the increasing popularity of neurofeedback, its mechanisms of action are still poorly understood. This study aims to describe the processes underlying implicit electroencephalographic neurofeedback. Fifty-two healthy volunteers were randomly assigned to a single session of infra-low frequency neurofeedback or sham neurofeedback, with electrodes over the right middle temporal gyrus and the right inferior parietal lobule. They observed a moving rocket, the speed of which was modulated by the waveform derived from a band-limited infra-low frequency filter. Immediately before and after the session, the participants underwent a resting-state fMRI. Network-based statistical analysis was applied, comparing post- vs. pre-session and real vs. sham neurofeedback conditions. As a result, two phenomena were observed. First, we described a brain circuit related to the implicit neurofeedback process itself, consisting of the lateral occipital cortex, right dorsolateral prefrontal cortex, left orbitofrontal cortex, right ventral striatum, and bilateral dorsal striatum. Second, we found increased connectivity between key regions of the salience, language, and visual networks, which is indicative of integration in sensory processing. Thus, it appears that a single session of implicit infra-low frequency electroencephalographic neurofeedback leads to significant changes in intrinsic brain connectivity.
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Affiliation(s)
- Olga R Dobrushina
- Third Neurological Department, Research Center of Neurology, Moscow, Russia.,International Institute of Psychosomatic Health, Moscow, Russia
| | - Roza M Vlasova
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States
| | | | - Liudmila D Litvinova
- Radiology Department, Federal Center of Treatment and Rehabilitation, Moscow, Russia
| | - Elena A Mershina
- Medical Research and Educational Center, Lomonosov Moscow State University, Moscow, Russia
| | - Valentin E Sinitsyn
- Medical Research and Educational Center, Lomonosov Moscow State University, Moscow, Russia
| | - Ekaterina V Pechenkova
- Laboratory for Cognitive Research, National Research University Higher School of Economics, Moscow, Russia
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33
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Berezutskaya J, Freudenburg ZV, Güçlü U, van Gerven MAJ, Ramsey NF. Brain-optimized extraction of complex sound features that drive continuous auditory perception. PLoS Comput Biol 2020; 16:e1007992. [PMID: 32614826 PMCID: PMC7363106 DOI: 10.1371/journal.pcbi.1007992] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 07/15/2020] [Accepted: 05/27/2020] [Indexed: 01/01/2023] Open
Abstract
Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match the neural representation of sound. Here, we postulate that constructing a data-driven neural model of auditory perception, with a minimum of theoretical assumptions about the relevant sound features, could provide an alternative approach and possibly a better match to the neural responses. We collected electrocorticography recordings from six patients who watched a long-duration feature film. The raw movie soundtrack was used to train an artificial neural network model to predict the associated neural responses. The model achieved high prediction accuracy and generalized well to a second dataset, where new participants watched a different film. The extracted bottom-up features captured acoustic properties that were specific to the type of sound and were associated with various response latency profiles and distinct cortical distributions. Specifically, several features encoded speech-related acoustic properties with some features exhibiting shorter latency profiles (associated with responses in posterior perisylvian cortex) and others exhibiting longer latency profiles (associated with responses in anterior perisylvian cortex). Our results support and extend the current view on speech perception by demonstrating the presence of temporal hierarchies in the perisylvian cortex and involvement of cortical sites outside of this region during audiovisual speech perception.
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Affiliation(s)
- Julia Berezutskaya
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Zachary V. Freudenburg
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Umut Güçlü
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Marcel A. J. van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Nick F. Ramsey
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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34
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Chen Y, Farivar R. Natural scene representations in the gamma band are prototypical across subjects. Neuroimage 2020; 221:117010. [PMID: 32505697 DOI: 10.1016/j.neuroimage.2020.117010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 05/01/2020] [Accepted: 05/29/2020] [Indexed: 11/15/2022] Open
Abstract
Prototypical brain responses describe similarity in neural representations between subjects in response to a natural stimulus. During natural movie viewing, for example, inter-subject correlation (ISC) measured by fMRI is high in visual areas (Hasson et al., 2004). But the electrophysiological basis for this fMRI ISC has been controversial. Previous reports have only found ISC in low frequency bands-below 12 Hz (Chang et al., 2015). These findings stand in contrast to reports that gamma band oscillations-30 to 90 Hz-are highly stimulus-driven in visual cortex (Perry et al., 2015). To resolve this discrepancy, we carried out both ISC estimation and a novel inter-subject representational correlation analysis across six frequency bands extracted from MEG data of 24 subjects who each viewed four 5-min clips of an underwater documentary. Region-of-interest-based and vertex-based temporal ISC estimates confirmed that low-frequency bands are significantly synchronized in visual areas and that gamma band has low temporal correlation. We also found the representational geometry of movie scenes were related to structural statistics from the stimuli. Crucially, our results show that the gamma band oscillations also reflect prototypical brain response in scene representations formed in response to naturalistic stimuli as revealed by inter-subject representational correlation.
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Affiliation(s)
- Yiran Chen
- McGill Vision Research, McGill University, Montreal, QC, Canada; Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
| | - Reza Farivar
- McGill Vision Research, McGill University, Montreal, QC, Canada; Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
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35
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Imhof MA, Schmälzle R, Renner B, Schupp HT. Strong health messages increase audience brain coupling. Neuroimage 2020; 216:116527. [PMID: 31954843 DOI: 10.1016/j.neuroimage.2020.116527] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 12/03/2019] [Accepted: 01/07/2020] [Indexed: 10/25/2022] Open
Abstract
Mass media messaging is central for health communication. The success of these efforts, however, depends on whether health messages resonate with their target audiences. Here, we used electroencephalography (EEG) to capture brain responses of young adults - an important target group for alcohol prevention - while they viewed real-life video messages of varying perceived message effectiveness about risky alcohol use. We found that strong messages, which were rated to be more effective, prompted enhanced inter-subject correlation (ISC). In further analyses, we linked ISC to subsequent drinking behavior change and used time-resolved EEG-ISC to model functional neuroimaging data (fMRI) of an independent audience. The EEG measure was not only related to sensory-perceptual brain regions, but also to regions previously related to successful messaging, i.e., cortical midline regions and the insula. The findings suggest EEG-ISC as a marker for audience engagement and effectiveness of naturalistic health messages, which could quantify the impact of mass communication within the brains of small target audiences.
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Affiliation(s)
- Martin A Imhof
- Department of Psychology, University of Konstanz, 78457, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78457, Konstanz, Germany.
| | - Ralf Schmälzle
- Department of Communication, Michigan State University, East Lansing, MI, 48824, USA
| | - Britta Renner
- Department of Psychology, University of Konstanz, 78457, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78457, Konstanz, Germany
| | - Harald T Schupp
- Department of Psychology, University of Konstanz, 78457, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78457, Konstanz, Germany
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Abstract
Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.
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Affiliation(s)
- Nick F Ramsey
- Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
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37
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Semantic and perceptual priming activate partially overlapping brain networks as revealed by direct cortical recordings in humans. Neuroimage 2019; 203:116204. [DOI: 10.1016/j.neuroimage.2019.116204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 08/19/2019] [Accepted: 09/17/2019] [Indexed: 01/20/2023] Open
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38
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Marino M, Arcara G, Porcaro C, Mantini D. Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network. Front Neurosci 2019; 13:1060. [PMID: 31636535 PMCID: PMC6788217 DOI: 10.3389/fnins.2019.01060] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 09/20/2019] [Indexed: 12/16/2022] Open
Abstract
Hemodynamic fluctuations in the default mode network (DMN), observed through functional magnetic resonance imaging (fMRI), have been linked to electrophysiological oscillations detected by electroencephalography (EEG). It has been reported that, among the electrophysiological oscillations, those in the alpha frequency range (8–13 Hz) are the most dominant during resting state. We hypothesized that DMN spatial configuration closely depends on the specific neuronal oscillations considered, and that alpha oscillations would mainly correlate with increased blood oxygen-level dependent (BOLD) signal in the DMN. To test this hypothesis, we used high-density EEG (hdEEG) data simultaneously collected with fMRI scanning in 20 healthy volunteers at rest. We first detected the DMN from source reconstructed hdEEG data for multiple frequency bands, and we then mapped the correlation between temporal profile of hdEEG-derived DMN activity and fMRI–BOLD signals on a voxel-by-voxel basis. In line with our hypothesis, we found that the correlation map associated with alpha oscillations, more than with any other frequency bands, displayed a larger overlap with DMN regions. Overall, our study provided further evidence for a primary role of alpha oscillations in supporting DMN functioning. We suggest that simultaneous EEG–fMRI may represent a powerful tool to investigate the neurophysiological basis of human brain networks.
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Affiliation(s)
- Marco Marino
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Giorgio Arcara
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy.,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.,Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
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39
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Nastase SA, Gazzola V, Hasson U, Keysers C. Measuring shared responses across subjects using intersubject correlation. Soc Cogn Affect Neurosci 2019; 14:667-685. [PMID: 31099394 PMCID: PMC6688448 DOI: 10.1093/scan/nsz037] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/10/2019] [Accepted: 05/13/2019] [Indexed: 12/18/2022] Open
Abstract
Our capacity to jointly represent information about the world underpins our social experience. By leveraging one individual's brain activity to model another's, we can measure shared information across brains-even in dynamic, naturalistic scenarios where an explicit response model may be unobtainable. Introducing experimental manipulations allows us to measure, for example, shared responses between speakers and listeners or between perception and recall. In this tutorial, we develop the logic of intersubject correlation (ISC) analysis and discuss the family of neuroscientific questions that stem from this approach. We also extend this logic to spatially distributed response patterns and functional network estimation. We provide a thorough and accessible treatment of methodological considerations specific to ISC analysis and outline best practices.
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Affiliation(s)
- Samuel A Nastase
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, USA
| | - Valeria Gazzola
- Social Brain Lab, Netherlands Institute for Neuroscience, KNAW, 105BA Amsterdam, The Netherlands
- Department of Psychology, University of Amsterdam, 1018 WV Amsterdam, The Netherlands
| | - Uri Hasson
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, USA
| | - Christian Keysers
- Social Brain Lab, Netherlands Institute for Neuroscience, KNAW, 105BA Amsterdam, The Netherlands
- Department of Psychology, University of Amsterdam, 1018 WV Amsterdam, The Netherlands
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40
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Li Q, Liu G, Yuan G, Wang G, Wu Z, Zhao X. DC Shifts-fMRI: A Supplement to Event-Related fMRI. Front Comput Neurosci 2019; 13:37. [PMID: 31244636 PMCID: PMC6581730 DOI: 10.3389/fncom.2019.00037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 05/21/2019] [Indexed: 11/13/2022] Open
Abstract
Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.
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Affiliation(s)
- Qiang Li
- Education Science College, Guizhou Normal College, Guiyang, China
| | - Guangyuan Liu
- College of Electronic and Information Engineering, Southwest University, Chongqing, China.,Chongqing Collaborative Innovation Center for Brain Science, Southwest University, Chongqing, China
| | - Guangjie Yuan
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Gaoyuan Wang
- College of Music, Southwest University, Chongqing, China
| | - Zonghui Wu
- Southwest University Hospital, Southwest University, Chongqing, China
| | - Xingcong Zhao
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
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41
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Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement. PLoS One 2019; 14:e0214507. [PMID: 30921406 PMCID: PMC6438528 DOI: 10.1371/journal.pone.0214507] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/14/2019] [Indexed: 01/10/2023] Open
Abstract
Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (~36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows.
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42
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Petroni A, Cohen SS, Ai L, Langer N, Henin S, Vanderwal T, Milham MP, Parra LC. The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex. eNeuro 2018; 5:ENEURO.0244-17.2017. [PMID: 29379880 PMCID: PMC5786826 DOI: 10.1523/eneuro.0244-17.2017] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 12/08/2017] [Accepted: 12/14/2017] [Indexed: 11/21/2022] Open
Abstract
Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample (n = 114) of human children and adults with ages ranging from 5 to 44 yr, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the intersubject correlation of evoked electroencephalographic responses. Young males also had less-variable responses than young females. These results were replicated in an independent cohort (n = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least during the passive viewing of real-world stimuli.
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Affiliation(s)
- Agustin Petroni
- Department of Biomedical Engineering, City College of New York, New York, NY 10031
| | - Samantha S. Cohen
- Department of Biomedical Engineering, City College of New York, New York, NY 10031
- Department of Psychology, the Graduate Center of the City University of New York, New York, NY 10016
| | - Lei Ai
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022
| | - Nicolas Langer
- Department of Biomedical Engineering, City College of New York, New York, NY 10031
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022
- Methods of Plasticity Research, Department of Psychology, University of Zurich, 8050, Switzerland
| | - Simon Henin
- Department of Biomedical Engineering, City College of New York, New York, NY 10031
| | | | - Michael P. Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Lucas C. Parra
- Department of Biomedical Engineering, City College of New York, New York, NY 10031
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