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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [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: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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Sousa D, Ferreira A, Rodrigues D, Pereira HC, Amaral J, Crisostomo J, Simoes M, Ribeiro M, Teixeira M, Castelo-Branco M. A neurophysiological signature of dynamic emotion recognition associated with social communication skills and cortical gamma-aminobutyric acid levels in children. Front Neurosci 2023; 17:1295608. [PMID: 38164245 PMCID: PMC10757932 DOI: 10.3389/fnins.2023.1295608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 11/30/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Emotion recognition is a core feature of social perception. In particular, perception of dynamic facial emotional expressions is a major feature of the third visual pathway. However, the classical N170 visual evoked signal does not provide a pure correlate of such processing. Indeed, independent component analysis has demonstrated that the N170 component is already active at the time of the P100, and is therefore distorted by early components. Here we implemented, a dynamic face emotional paradigm to isolate a more pure face expression selective N170. We searched for a neural correlate of perception of dynamic facial emotional expressions, by starting with a face baseline from which a facial expression evolved. This allowed for a specific facial expression contrast signal which we aimed to relate with social communication abilities and cortical gamma-aminobutyric acid (GABA) levels. Methods We recorded event-related potentials (ERPs) and Magnetic Resonance (MRS) measures in 35 typically developing (TD) children, (10-16 years) sex-matched, during emotion recognition of an avatar morphing/unmorphing from neutral to happy/sad expressions. This task allowed for the elimination of the contribution low-level visual components, in particular the P100, by morphing baseline isoluminant neutral faces into specific expressions, isolating dynamic emotion recognition. Therefore, it was possible to isolate a dynamic face sensitive N170 devoid of interactions with earlier components. Results We found delayed N170 and P300, with a hysteresis type of dependence on stimulus trajectory (morphing/unmorphing), with hemispheric lateralization. The delayed N170 is generated by an extrastriate source, which can be related to the third visual pathway specialized in biological motion processing. GABA levels in visual cortex were related with N170 amplitude and latency and predictive of worse social communication performance (SCQ scores). N170 latencies reflected delayed processing speed of emotional expressions and related to worse social communication scores. Discussion In sum, we found a specific N170 electrophysiological signature of dynamic face processing related to social communication abilities and cortical GABA levels. These findings have potential clinical significance supporting the hypothesis of a spectrum of social communication abilities and the identification of a specific face-expression sensitive N170 which can potentially be used in the development of diagnostic and intervention tools.
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Affiliation(s)
- Daniela Sousa
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Ana Ferreira
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Diana Rodrigues
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
| | - Helena Catarina Pereira
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Joana Amaral
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Joana Crisostomo
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Marco Simoes
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centre for Informatics and Systems, University of Coimbra, Coimbra, Portugal
| | - Mário Ribeiro
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
| | - Marta Teixeira
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research CIBIT, University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health ICNAS, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Department of Psychology, University of Maastricht, Maastricht, Netherlands
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [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: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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Singer N, Poker G, Dunsky-Moran N, Nemni S, Reznik Balter S, Doron M, Baker T, Dagher A, Zatorre RJ, Hendler T. Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation. Neuroimage 2023; 276:120183. [PMID: 37225112 PMCID: PMC10300238 DOI: 10.1016/j.neuroimage.2023.120183] [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: 11/20/2022] [Revised: 04/06/2023] [Accepted: 05/22/2023] [Indexed: 05/26/2023] Open
Abstract
Reward processing is essential for our mental-health and well-being. In the current study, we developed and validated a scalable, fMRI-informed EEG model for monitoring reward processing related to activation in the ventral-striatum (VS), a significant node in the brain's reward system. To develop this EEG-based model of VS-related activation, we collected simultaneous EEG/fMRI data from 17 healthy individuals while listening to individually-tailored pleasurable music - a highly rewarding stimulus known to engage the VS. Using these cross-modal data, we constructed a generic regression model for predicting the concurrently acquired Blood-Oxygen-Level-Dependent (BOLD) signal from the VS using spectro-temporal features from the EEG signal (termed hereby VS-related-Electrical Finger Print; VS-EFP). The performance of the extracted model was examined using a series of tests that were applied on the original dataset and, importantly, an external validation dataset collected from a different group of 14 healthy individuals who underwent the same EEG/FMRI procedure. Our results showed that the VS-EFP model, as measured by simultaneous EEG, predicted BOLD activation in the VS and additional functionally relevant regions to a greater extent than an EFP model derived from a different anatomical region. The developed VS-EFP was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating its functional relevance. These findings provide compelling evidence for the feasibility of using EEG alone to model neural activation related to the VS, paving the way for future use of this scalable neural probing approach in neural monitoring and self-guided neuromodulation.
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Affiliation(s)
- Neomi Singer
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Gilad Poker
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel
| | - Netta Dunsky-Moran
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Shlomi Nemni
- Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; School of Psychological Sciences, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Shira Reznik Balter
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel
| | - Maayan Doron
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Sackler School of Medicine, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Travis Baker
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Robert J Zatorre
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; International Laboratory for Brain, Music, and Sound Research (BRAMS), Canada
| | - Talma Hendler
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; School of Psychological Sciences, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; Sackler School of Medicine, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel.
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Ciccarelli G, Federico G, Mele G, Di Cecca A, Migliaccio M, Ilardi CR, Alfano V, Salvatore M, Cavaliere C. Simultaneous real-time EEG-fMRI neurofeedback: A systematic review. Front Hum Neurosci 2023; 17:1123014. [PMID: 37063098 PMCID: PMC10102573 DOI: 10.3389/fnhum.2023.1123014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/15/2023] [Indexed: 04/18/2023] Open
Abstract
Neurofeedback (NF) is a biofeedback technique that teaches individuals self-control of brain functions by measuring brain activations and providing an online feedback signal to modify emotional, cognitive, and behavioral functions. NF approaches typically rely on a single modality, such as electroencephalography (EEG-NF) or a brain imaging technique, such as functional magnetic resonance imaging (fMRI-NF). The introduction of simultaneous EEG-fMRI tools has opened up the possibility of combining the high temporal resolution of EEG with the high spatial resolution of fMRI, thereby increasing the accuracy of NF. However, only a few studies have actively combined both techniques. In this study, we conducted a systematic review of EEG-fMRI-NF studies (N = 17) to identify the potential and effectiveness of this non-invasive treatment for neurological conditions. The systematic review revealed a lack of homogeneity among the studies, including sample sizes, acquisition methods in terms of simultaneity of the two procedures (unimodal EEG-NF and fMRI-NF), therapeutic targets field, and the number of sessions. Indeed, because most studies are based on a single session of NF, it is difficult to draw any conclusions regarding the therapeutic efficacy of NF. Therefore, further research is needed to fully understand non-clinical and clinical potential of EEG-fMRI-NF.
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Russo AG, Lührs M, Di Salle F, Esposito F, Goebel R. Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis. J Neural Eng 2021; 18. [PMID: 33684900 DOI: 10.1088/1741-2552/abecc3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/08/2021] [Indexed: 11/12/2022]
Abstract
Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.
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Affiliation(s)
- Andrea G Russo
- Department of Political and Communication Sciences, University of Salerno, Fisciano (Salerno), Italy.,Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Michael Lührs
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy.,Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Department of Advanced Medical and Surgical Sciences,University of Campania 'Luigi Vanvitelli', Napoli,Italy
| | - Rainer Goebel
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
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