1
|
Sabbagh D, Cartailler J, Touchard C, Joachim J, Mebazaa A, Vallée F, Gayat É, Gramfort A, Engemann DA. Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study. BJA Open 2023; 7:100145. [PMID: 37638087 PMCID: PMC10457469 DOI: 10.1016/j.bjao.2023.100145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/16/2023] [Indexed: 08/29/2023]
Abstract
Background Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk. Methods We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8-13 Hz) for age prediction: total power, alpha-band power (8-13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65). Results Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane. Conclusions Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.
Collapse
Affiliation(s)
- David Sabbagh
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
| | - Jérôme Cartailler
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Cyril Touchard
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Jona Joachim
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Alexandre Mebazaa
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Fabrice Vallée
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Étienne Gayat
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | | | - Denis A. Engemann
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| |
Collapse
|
2
|
Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, Sotiras A. A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. Patterns (N Y) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
Collapse
Affiliation(s)
- Robert J. Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M. Gatavins
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Undergraduate Neuroscience Program, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Denis A. Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Corresponding author
| |
Collapse
|
3
|
Vieira BH, Liem F, Dadi K, Engemann DA, Gramfort A, Bellec P, Craddock RC, Damoiseaux JS, Steele CJ, Yarkoni T, Langer N, Margulies DS, Varoquaux G. Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiol Aging 2022; 118:55-65. [PMID: 35878565 PMCID: PMC9853405 DOI: 10.1016/j.neurobiolaging.2022.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 01/24/2023]
Abstract
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.
Collapse
Affiliation(s)
- Bruno Hebling Vieira
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,Corresponding author. (B. Hebling Vieira)
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Denis A. Engemann
- UniversitéParis-Saclay, Inria, CEA, Palaiseau, France,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Pierre Bellec
- Functional Neuroimaging Unit, Geriatric Institute, University of Montreal, Montreal, Quebec, Canada
| | | | - Jessica S. Damoiseaux
- Institute of Gerontology and the Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas, Austin, TX, USA
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Daniel S. Margulies
- Cognitive Neuroanatomy Lab, Institut du Cerveau et de la Moelle épinière, Paris, France
| | | |
Collapse
|
4
|
Engemann DA, Mellot A, Höchenberger R, Banville H, Sabbagh D, Gemein L, Ball T, Gramfort A. A reusable benchmark of brain-age prediction from M/EEG resting-state signals. Neuroimage 2022; 262:119521. [PMID: 35905809 DOI: 10.1016/j.neuroimage.2022.119521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 07/04/2022] [Accepted: 07/25/2022] [Indexed: 01/02/2023] Open
Abstract
Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.
Collapse
Affiliation(s)
- Denis A Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland; Université Paris-Saclay, Inria, CEA, Palaiseau, France; Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.
| | | | | | - Hubert Banville
- Université Paris-Saclay, Inria, CEA, Palaiseau, France; Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - David Sabbagh
- Université Paris-Saclay, Inria, CEA, Palaiseau, France; Neuromedical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany
| | - Lukas Gemein
- Neurorobotics Lab, Computer Science Department - University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Köhler-Allee 80, 79110, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Tonio Ball
- Neurorobotics Lab, Computer Science Department - University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Köhler-Allee 80, 79110, Freiburg, Germany; InteraXon Inc., Toronto, Canada
| | | |
Collapse
|
5
|
Denissen S, Engemann DA, De Cock A, Costers L, Baijot J, Laton J, Penner IK, Grothe M, Kirsch M, D'hooghe MB, D'Haeseleer M, Dive D, De Mey J, Van Schependom J, Sima DM, Nagels G. Brain age as a surrogate marker for cognitive performance in multiple sclerosis. Eur J Neurol 2022; 29:3039-3049. [PMID: 35737867 PMCID: PMC9541923 DOI: 10.1111/ene.15473] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/04/2022] [Accepted: 06/15/2022] [Indexed: 11/28/2022]
Abstract
Background and purpose Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as ‘how old the brain looks’ and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age. Conclusions Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.
Collapse
Affiliation(s)
- S Denissen
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - D A Engemann
- Université Paris-Saclay, CEA, 1 Rue Honoré d'Estienne d'Orves, 91120, Palaiseau, France.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, D-04103, Leipzig, Germany
| | - A De Cock
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - L Costers
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - J Baijot
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - J Laton
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Nuffield Department of Clinical Neurosciences, University of Oxford, Headley Way, Headington, Oxford, OX3 9DU, United Kingdom
| | - I K Penner
- Cogito Center for Applied Neurocognition and Neuropsychological Research, Merowingerplatz 1, 40225, Düsseldorf, Germany.,Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - M Grothe
- Department of Neurology, University Medicine Greifswald, Ferdinand-Sauerbruchstraße, 17475, Greifswald, Germany
| | - M Kirsch
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine of Greifswald, Ferdinand-Sauerbruch-Straße, 17489, Greifswald, Germany
| | - M B D'hooghe
- National Multiple Sclerosis Center Melsbroek, Vereeckenstraat 44, 1820, Melsbroek, Belgium.,Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - M D'Haeseleer
- National Multiple Sclerosis Center Melsbroek, Vereeckenstraat 44, 1820, Melsbroek, Belgium
| | - D Dive
- Department of Neurology, University Hospital of Liege, Rue Grandfosse 31/33, 4130, Esneux, Belgium
| | - J De Mey
- Department of Radiology, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - J Van Schependom
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - D M Sima
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - G Nagels
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium.,St Edmund Hall, University of Oxford, Queen's Lane, Oxford, OX1 4AR, UK
| |
Collapse
|
6
|
Engemann DA, Kozynets O, Sabbagh D, Lemaître G, Varoquaux G, Liem F, Gramfort A. Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. eLife 2020; 9:e54055. [PMID: 32423528 PMCID: PMC7308092 DOI: 10.7554/elife.54055] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 05/09/2020] [Indexed: 12/14/2022] Open
Abstract
Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.
Collapse
Affiliation(s)
- Denis A Engemann
- Université Paris-Saclay, Inria, CEAPalaiseauFrance
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | | | - David Sabbagh
- Université Paris-Saclay, Inria, CEAPalaiseauFrance
- Inserm, UMRS-942, Paris Diderot UniversityParisFrance
- Department of Anaesthesiology and Critical Care, Lariboisière Hospital, Assistance Publique Hôpitaux de ParisParisFrance
| | | | | | - Franziskus Liem
- University Research Priority Program Dynamics of Healthy Aging, University of ZürichZürichSwitzerland
| | | |
Collapse
|
7
|
Sabbagh D, Ablin P, Varoquaux G, Gramfort A, Engemann DA. Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. Neuroimage 2020; 222:116893. [PMID: 32439535 DOI: 10.1016/j.neuroimage.2020.116893] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/17/2020] [Accepted: 04/27/2020] [Indexed: 01/22/2023] Open
Abstract
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.
Collapse
Affiliation(s)
- David Sabbagh
- Université Paris-Saclay, Inria, CEA, Palaiseau, France; Inserm, UMRS-942, Paris Diderot University, Paris, France; Department of Anaesthesiology and Critical Care, Lariboisière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France.
| | - Pierre Ablin
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | | | | | - Denis A Engemann
- Université Paris-Saclay, Inria, CEA, Palaiseau, France; Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.
| |
Collapse
|
8
|
Baillin F, Lefebvre A, Pedoux A, Beauxis Y, Engemann DA, Maruani A, Amsellem F, Kelso JAS, Bourgeron T, Delorme R, Dumas G. Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains. Front Psychiatry 2020; 11:510366. [PMID: 33324246 PMCID: PMC7725713 DOI: 10.3389/fpsyt.2020.510366] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 10/12/2020] [Indexed: 12/27/2022] Open
Abstract
The human dynamic clamp (HDC) is a human-machine interface designed on the basis of coordination dynamics for studying realistic social interaction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior "cooperative/competitive," human task "in-phase/anti-phase," diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions.
Collapse
Affiliation(s)
- Florence Baillin
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France.,Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Aline Lefebvre
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France.,Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Amandine Pedoux
- Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Yann Beauxis
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | - Denis A Engemann
- Parietal Project-Team, INRIA Saclay - Île de France, Palaiseau, France
| | - Anna Maruani
- Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Frédérique Amsellem
- Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - J A Scott Kelso
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States.,Intelligent Systems Research Centre, University of Ulster, Derry Londonderry, United Kingdom
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | - Richard Delorme
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France.,Child and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France.,Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States.,Department of Psychiatry, Université de Montréal, Montreal, QC, Canada.,CHU Sainte-Justine Centre de Recherche, Precision Psychiatry and Social Physiology Laboratory, Montreal, QC, Canada
| |
Collapse
|
9
|
Engemann DA, Raimondo F, King JR, Rohaut B, Louppe G, Faugeras F, Annen J, Cassol H, Gosseries O, Fernandez-Slezak D, Laureys S, Naccache L, Dehaene S, Sitt JD. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain 2019; 141:3179-3192. [PMID: 30285102 DOI: 10.1093/brain/awy251] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 08/20/2018] [Indexed: 11/13/2022] Open
Abstract
Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
Collapse
Affiliation(s)
- Denis A Engemann
- Parietal project-team, INRIA Saclay - Île de France, France.,Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif sur Yvette, France.,Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Federico Raimondo
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación FCEyN, UBA, Argentina.,CONICET - Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación, Godoy Cruz 2290, C1425FQB, Ciudad Autónoma de Buenos Aires, Argentina.,Sorbonne Universités, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| | - Jean-Rémi King
- Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif sur Yvette, France.,New York University, 6 Washington Place, New York, NY, USA.,Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Benjamin Rohaut
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Department of Neurology, Columbia University, New York, NY, USA
| | - Gilles Louppe
- New York University, 6 Washington Place, New York, NY, USA
| | - Frédéric Faugeras
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium
| | - Helena Cassol
- Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium
| | - Diego Fernandez-Slezak
- Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación FCEyN, UBA, Argentina.,CONICET - Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación, Godoy Cruz 2290, C1425FQB, Ciudad Autónoma de Buenos Aires, Argentina
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium
| | - Lionel Naccache
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Sorbonne Universités, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif sur Yvette, France.,Collège de France, Paris, France
| | - Jacobo D Sitt
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Sorbonne Universités, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| |
Collapse
|
10
|
Jas M, Larson E, Engemann DA, Leppäkangas J, Taulu S, Hämäläinen M, Gramfort A. A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices. Front Neurosci 2018; 12:530. [PMID: 30127712 PMCID: PMC6088222 DOI: 10.3389/fnins.2018.00530] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 07/16/2018] [Indexed: 11/13/2022] Open
Abstract
Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data often require subject-specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS vs. SSS, the use of a minimum norm inverse vs. LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.
Collapse
Affiliation(s)
- Mainak Jas
- Telecom ParisTech, Université Paris-Saclay, Paris, France
| | - Eric Larson
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Denis A Engemann
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INRIA, Université Paris-Saclay, Saclay, France
| | | | - Samu Taulu
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States.,Department of Physics, University of Washington, Seattle, WA, United States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Alexandre Gramfort
- Telecom ParisTech, Université Paris-Saclay, Paris, France.,NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INRIA, Université Paris-Saclay, Saclay, France
| |
Collapse
|
11
|
Raimondo F, Rohaut B, Demertzi A, Valente M, Engemann DA, Salti M, Fernandez Slezak D, Naccache L, Sitt JD. Brain-heart interactions reveal consciousness in noncommunicating patients. Ann Neurol 2017; 82:578-591. [PMID: 28892566 DOI: 10.1002/ana.25045] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Revised: 08/28/2017] [Accepted: 09/04/2017] [Indexed: 01/20/2023]
Abstract
OBJECTIVE We here aimed at characterizing heart-brain interactions in patients with disorders of consciousness. We tested how this information impacts data-driven classification between unresponsive and minimally conscious patients. METHODS A cohort of 127 patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS; n = 70) and minimally conscious state (MCS; n = 57) were presented with the local-global auditory oddball paradigm, which distinguishes 2 levels of processing: short-term deviation of local auditory regularities and global long-term rule violations. In addition to previously validated markers of consciousness extracted from electroencephalograms (EEG), we computed autonomic cardiac markers, such as heart rate (HR) and HR variability (HRV), and cardiac cycle phase shifts triggered by the processing of the auditory stimuli. RESULTS HR and HRV were similar in patients across groups. The cardiac cycle was not sensitive to the processing of local regularities in either the VS/UWS or MCS patients. In contrast, global regularities induced a phase shift of the cardiac cycle exclusively in the MCS group. The interval between the auditory stimulation and the following R peak was significantly shortened in MCS when the auditory rule was violated. When the information for the cardiac cycle modulations and other consciousness-related EEG markers were combined, single patient classification performance was enhanced compared to classification with solely EEG markers. INTERPRETATION Our work shows a link between residual cognitive processing and the modulation of autonomic somatic markers. These results open a new window to evaluate patients with disorders of consciousness via the embodied paradigm, according to which body-brain functions contribute to a holistic approach to conscious processing. Ann Neurol 2017;82:578-591.
Collapse
Affiliation(s)
- Federico Raimondo
- Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific and Technical Research Council-University of Buenos Aires, Buenos Aires, Argentina.,Brain and Spine Institute, Paris, France.,Pitié-Salpêtrière Faculty of Medicine, Pierre and Marie Curie University, Sorbonne Universities, Paris, France
| | - Benjamin Rohaut
- National Institute of Health and Medical Research, Paris, France.,Department of Neurology, Pitié-Salpêtrière Hospital Group, Public Hospital Network of Paris, Paris, France
| | - Athena Demertzi
- Brain and Spine Institute, Paris, France.,National Institute of Health and Medical Research, Paris, France
| | - Melanie Valente
- Brain and Spine Institute, Paris, France.,National Institute of Health and Medical Research, Paris, France
| | - Denis A Engemann
- Brain and Spine Institute, Paris, France.,National Institute of Health and Medical Research, Paris, France.,Parietal Project Team, French Institute for Research in Computer Science and Automation, Saclay-Ile de France, France.,Cognitive Neuroimaging Unit, Institute of Biomedical Imaging, Direction of Life Sciences, Alternative Energies and Atomic Energy Commission, National Institute of Health and Medical Research, University of Paris-Sud, University of Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
| | - Moti Salti
- Zlotowski Center for Neuroscience and Brain Imaging Research Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Diego Fernandez Slezak
- Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific and Technical Research Council-University of Buenos Aires, Buenos Aires, Argentina
| | - Lionel Naccache
- Brain and Spine Institute, Paris, France.,Pitié-Salpêtrière Faculty of Medicine, Pierre and Marie Curie University, Sorbonne Universities, Paris, France.,National Institute of Health and Medical Research, Paris, France.,Department of Neurology, Pitié-Salpêtrière Hospital Group, Public Hospital Network of Paris, Paris, France.,Department of Neurophysiology, Pitié-Salpêtrière Hospital Group, Public Hospital Network of Paris, Paris, France
| | - Jacobo D Sitt
- Brain and Spine Institute, Paris, France.,National Institute of Health and Medical Research, Paris, France
| |
Collapse
|
12
|
Abstract
We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.
Collapse
Affiliation(s)
- Mainak Jas
- LTCI, Télécom ParisTech, Université Paris-Saclay, France.
| | - Denis A Engemann
- Parietal project-team, INRIA Saclay - Ile de France, France; Cognitive Neuroimaging Unit, Neurospin, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France; Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France
| | - Yousra Bekhti
- LTCI, Télécom ParisTech, Université Paris-Saclay, France
| | - Federico Raimondo
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France; Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación, FCEyN, Universidad de Buenos Aires, Argentina; CONICET, Argentina; Sorbonne Universités, UPMC Univ Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| | | |
Collapse
|
13
|
Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 2016; 145:166-179. [PMID: 27989847 DOI: 10.1016/j.neuroimage.2016.10.038] [Citation(s) in RCA: 361] [Impact Index Per Article: 45.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 09/19/2016] [Accepted: 10/24/2016] [Indexed: 10/20/2022] Open
Abstract
Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within- and across-subject predictions, on multiple datasets -anatomical and functional MRI and MEG- and simulations. Theory and experiments outline that the popular "leave-one-out" strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be favorable to use sane defaults, in particular for non-sparse decoders.
Collapse
Affiliation(s)
- Gaël Varoquaux
- Parietal project-team, INRIA Saclay-ile de France, France; CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Pradeep Reddy Raamana
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada M6A 2E1; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada M5S 1A1
| | - Denis A Engemann
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France; Cognitive Neuroimaging Unit, INSERM, Université Paris-Sud and Université Paris-Saclay, 91191 Gif-sur-Yvette, France; Neuropsychology & Neuroimaging team INSERM UMRS 975, Brain and Spine Institute (ICM), Paris
| | - Andrés Hoyos-Idrobo
- Parietal project-team, INRIA Saclay-ile de France, France; CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Yannick Schwartz
- Parietal project-team, INRIA Saclay-ile de France, France; CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Bertrand Thirion
- Parietal project-team, INRIA Saclay-ile de France, France; CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| |
Collapse
|
14
|
Schleussner CF, Donges JF, Engemann DA, Levermann A. Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure. Sci Rep 2016; 6:30790. [PMID: 27510641 PMCID: PMC4980617 DOI: 10.1038/srep30790] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 07/11/2016] [Indexed: 11/09/2022] Open
Abstract
Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.
Collapse
Affiliation(s)
| | - Jonathan F Donges
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Denis A Engemann
- Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France.,Neuropsychology &Neuroimaging Team, INSERM UMRS 975, ICM, Paris, France
| | - Anders Levermann
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Lamont-Doherty Earth Observatory, Columbia University, New York, USA.,Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany
| |
Collapse
|
15
|
Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Hämäläinen M. MEG and EEG data analysis with MNE-Python. Front Neurosci 2013; 7:267. [PMID: 24431986 PMCID: PMC3872725 DOI: 10.3389/fnins.2013.00267] [Citation(s) in RCA: 1062] [Impact Index Per Article: 96.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 12/09/2013] [Indexed: 11/22/2022] Open
Abstract
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
Collapse
Affiliation(s)
- Alexandre Gramfort
- Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI Paris, France ; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School Charlestown MA, USA ; NeuroSpin, CEA Saclay Gif-sur-Yvette, France
| | - Martin Luessi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School Charlestown MA, USA
| | - Eric Larson
- Institute for Learning and Brain Sciences, University of Washington Seattle WA, USA
| | - Denis A Engemann
- Institute of Neuroscience and Medicine - Cognitive Neuroscience (INM-3) Forschungszentrum Juelich, Germany ; Brain Imaging Lab, Department of Psychiatry, University Hospital Cologne, Germany
| | - Daniel Strohmeier
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology Ilmenau, Germany
| | | | - Roman Goj
- Psychological Imaging Laboratory, Psychology, School of Natural Sciences, University of Stirling Stirling, UK
| | - Mainak Jas
- Department of Biomedical Engineering and Computational Science, Aalto University School of Science Espoo, Finland ; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science Espoo, Finland
| | - Teon Brooks
- Department of Psychology, New York University New York, NY, USA
| | - Lauri Parkkonen
- Department of Biomedical Engineering and Computational Science, Aalto University School of Science Espoo, Finland ; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science Espoo, Finland
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School Charlestown MA, USA ; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science Espoo, Finland
| |
Collapse
|
16
|
Köymen B, Lieven E, Engemann DA, Rakoczy H, Warneken F, Tomasello M. Children's Norm Enforcement in Their Interactions With Peers. Child Dev 2013; 85:1108-1122. [DOI: 10.1111/cdev.12178] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Bahar Köymen
- Max Planck Institute for Evolutionary Anthropology
| | - Elena Lieven
- Max Planck Institute for Evolutionary Anthropology
- The University of Manchester
| | - Denis A. Engemann
- Institute of Neuroscience and Medicine-Cognitive Neuroscience (INM-3)
- University Hospital of Cologne
| | | | | | | |
Collapse
|
17
|
Bzdok D, Langner R, Schilbach L, Engemann DA, Laird AR, Fox PT, Eickhoff SB. Segregation of the human medial prefrontal cortex in social cognition. Front Hum Neurosci 2013; 7:232. [PMID: 23755001 PMCID: PMC3665907 DOI: 10.3389/fnhum.2013.00232] [Citation(s) in RCA: 157] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Accepted: 05/14/2013] [Indexed: 11/13/2022] Open
Abstract
While the human medial prefrontal cortex (mPFC) is widely believed to be a key node of neural networks relevant for socio-emotional processing, its functional subspecialization is still poorly understood. We thus revisited the often assumed differentiation of the mPFC in social cognition along its ventral-dorsal axis. Our neuroinformatic analysis was based on a neuroimaging meta-analysis of perspective-taking that yielded two separate clusters in the ventral and dorsal mPFC, respectively. We determined each seed region's brain-wide interaction pattern by two complementary measures of functional connectivity: co-activation across a wide range of neuroimaging studies archived in the BrainMap database and correlated signal fluctuations during unconstrained ("resting") cognition. Furthermore, we characterized the functions associated with these two regions using the BrainMap database. Across methods, the ventral mPFC was more strongly connected with the nucleus accumbens, hippocampus, posterior cingulate cortex, and retrosplenial cortex, while the dorsal mPFC was more strongly connected with the inferior frontal gyrus, temporo-parietal junction, and middle temporal gyrus. Further, the ventral mPFC was selectively associated with reward related tasks, while the dorsal mPFC was selectively associated with perspective-taking and episodic memory retrieval. The ventral mPFC is therefore predominantly involved in bottom-up-driven, approach/avoidance-modulating, and evaluation-related processing, whereas the dorsal mPFC is predominantly involved in top-down-driven, probabilistic-scene-informed, and metacognition-related processing in social cognition.
Collapse
Affiliation(s)
- Danilo Bzdok
- Institute of Neuroscience and Medicine (INM-1), Research Center JülichJülich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine UniversityDüsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-1), Research Center JülichJülich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine UniversityDüsseldorf, Germany
| | - Leonhard Schilbach
- Max-Planck-Institute for Neurological ResearchCologne, Germany
- Department of Psychiatry, University of CologneCologne, Germany
| | - Denis A. Engemann
- Department of Psychiatry, University of CologneCologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center JülichJülich, Germany
| | - Angela R. Laird
- Department of Physics, Florida International UniversityMiami, FL, USA
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science CenterSan Antonio, TX, USA
- South Texas Veterans Administration Medical CenterSan Antonio, Texas
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Center JülichJülich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine UniversityDüsseldorf, Germany
| |
Collapse
|
18
|
Engemann DA, Bzdok D, Eickhoff SB, Vogeley K, Schilbach L. Games people play-toward an enactive view of cooperation in social neuroscience. Front Hum Neurosci 2012; 6:148. [PMID: 22675293 PMCID: PMC3365482 DOI: 10.3389/fnhum.2012.00148] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2012] [Accepted: 05/10/2012] [Indexed: 12/02/2022] Open
Abstract
The field of social neuroscience has made considerable progress in unraveling the neural correlates of human cooperation by making use of brain imaging methods. Within this field, neuroeconomic research has drawn on paradigms from experimental economics, such as the Prisoner's Dilemma (PD) and the Trust Game. These paradigms capture the topic of conflict in cooperation, while focusing strongly on outcome-related decision processes. Cooperation, however, does not equate with that perspective, but relies on additional psychological processes and events, including shared intentions and mutually coordinated joint action. These additional facets of cooperation have been successfully addressed by research in developmental psychology, cognitive science, and social philosophy. Corresponding neuroimaging data, however, is still sparse. Therefore, in this paper, we present a juxtaposition of these mutually related but mostly independent trends in cooperation research. We propose that the neuroscientific study of cooperation could benefit from paradigms and concepts employed in developmental psychology and social philosophy. Bringing both to a neuroimaging environment might allow studying the neural correlates of cooperation by using formal models of decision-making as well as capturing the neural responses that underlie joint action scenarios, thus, promising to advance our understanding of the nature of human cooperation.
Collapse
Affiliation(s)
- Denis A. Engemann
- Brain Imaging Lab, Clinic for Psychiatry and Psychotherapy, University Hospital of CologneCologne, Germany
- Jülich Research Centre, Institute of Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3)Jülich, Germany
| | - Danilo Bzdok
- Jülich Research Centre, Brain Network Modelling Group, Institute of Neuroscience and Medicine (INM-1)Jülich, Germany
- Department of Psychiatry Psychotherapy and Psychosomatics, University Hospital of AachenAachen, Germany
| | - Simon B. Eickhoff
- Jülich Research Centre, Brain Network Modelling Group, Institute of Neuroscience and Medicine (INM-1)Jülich, Germany
- Department of Psychiatry Psychotherapy and Psychosomatics, University Hospital of AachenAachen, Germany
- Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University DüsseldorfDüsseldorf, Germany
| | - Kai Vogeley
- Brain Imaging Lab, Clinic for Psychiatry and Psychotherapy, University Hospital of CologneCologne, Germany
- Jülich Research Centre, Institute of Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3)Jülich, Germany
| | - Leonhard Schilbach
- Brain Imaging Lab, Clinic for Psychiatry and Psychotherapy, University Hospital of CologneCologne, Germany
- Max-Planck-Institute for Neurological ResearchCologne, Germany
| |
Collapse
|