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Wu Y, Gao H, Zhang C, Ma X, Zhu X, Wu S, Lin L. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review. Tomography 2024; 10:1238-1262. [PMID: 39195728 PMCID: PMC11359833 DOI: 10.3390/tomography10080093] [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: 07/18/2024] [Revised: 08/09/2024] [Accepted: 08/09/2024] [Indexed: 08/29/2024] Open
Abstract
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
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Affiliation(s)
| | | | | | | | | | | | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (H.G.); (C.Z.); (X.M.); (X.Z.); (S.W.)
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Noroozi M, Gholami M, Sadeghsalehi H, Behzadi S, Habibzadeh A, Erabi G, Sadatmadani SF, Diyanati M, Rezaee A, Dianati M, Rasoulian P, Khani Siyah Rood Y, Ilati F, Hadavi SM, Arbab Mojeni F, Roostaie M, Deravi N. Machine and deep learning algorithms for classifying different types of dementia: A literature review. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-15. [PMID: 39087520 DOI: 10.1080/23279095.2024.2382823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
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Affiliation(s)
- Masoud Noroozi
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohammadreza Gholami
- Department of Electrical and Computer Engineering, Tarbiat Modares Univeristy, Tehran, Iran
| | - Hamidreza Sadeghsalehi
- Department of Artificial Intelligence in Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Saleh Behzadi
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Adrina Habibzadeh
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
- USERN Office, Fasa University of Medical Sciences, Fasa, Iran
| | - Gisou Erabi
- Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran
| | | | - Mitra Diyanati
- Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Aryan Rezaee
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Dianati
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Pegah Rasoulian
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yashar Khani Siyah Rood
- Faculty of Engineering, Computer Engineering, Islamic Azad University of Bandar Abbas, Bandar Abbas, Iran
| | - Fatemeh Ilati
- Student Research Committee, Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran
| | | | - Fariba Arbab Mojeni
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Minoo Roostaie
- School of Medicine, Islamic Azad University Tehran Medical Branch, Tehran, Iran
| | - Niloofar Deravi
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Bomatter P, Paillard J, Garces P, Hipp J, Engemann DA. Machine learning of brain-specific biomarkers from EEG. EBioMedicine 2024; 106:105259. [PMID: 39106531 DOI: 10.1016/j.ebiom.2024.105259] [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: 01/10/2024] [Revised: 07/05/2024] [Accepted: 07/11/2024] [Indexed: 08/09/2024] Open
Abstract
BACKGROUND Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). METHODS We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. FINDINGS Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. INTERPRETATION We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. FUNDING All authors have been working for F. Hoffmann-La Roche Ltd.
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Affiliation(s)
- Philipp Bomatter
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Joseph Paillard
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Pilar Garces
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jörg Hipp
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Denis-Alexander Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
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Tetereva A, Pat N. Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals. eLife 2024; 12:RP87297. [PMID: 38869938 PMCID: PMC11175613 DOI: 10.7554/elife.87297] [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] [Indexed: 06/14/2024] Open
Abstract
One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.
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Affiliation(s)
- Alina Tetereva
- Department of Psychology, University of OtagoDunedinNew Zealand
| | - Narun Pat
- Department of Psychology, University of OtagoDunedinNew Zealand
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Tetereva A, Knodt AR, Melzer TR, van der Vliet W, Gibson B, Hariri AR, Whitman ET, Li J, Deng J, Ireland D, Ramrakha S, Pat N. Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.589404. [PMID: 38746222 PMCID: PMC11092590 DOI: 10.1101/2024.05.03.589404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.
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Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [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: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
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Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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Jockwitz C, Krämer C, Dellani P, Caspers S. Differential predictability of cognitive profiles from brain structure in older males and females. GeroScience 2024; 46:1713-1730. [PMID: 37730943 PMCID: PMC10828131 DOI: 10.1007/s11357-023-00934-y] [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: 07/27/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large sample of older adults (1000BRAINS; N = 634; age range 55-85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, Caspers S, Jockwitz C. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience 2024; 46:283-308. [PMID: 37308769 PMCID: PMC10828156 DOI: 10.1007/s11357-023-00831-4] [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: 03/02/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023] Open
Abstract
Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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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] [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.
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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
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Xing W, Gao W, Zhao Z, Xu X, Bu H, Su H, Mao G, Chen J. Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. J Transl Med 2023; 21:492. [PMID: 37480074 PMCID: PMC10362762 DOI: 10.1186/s12967-023-04321-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 07/01/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Diet may influence biological aging and the discrepancy (∆age) between a subject's biological age (BA) and chronological age (CA). We aimed to investigate the correlation of dietary flavonoids with the ∆age of organs (heart, kidney, liver) and the whole body. METHOD A total of 3193 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2007-2008 and 2017-2018. Dietary flavonoids intake was assessed using 24-h dietary recall method. Multiple linear regression analysis was performed to evaluate the association of dietary flavonoids intake with the ∆age of organs (heart, kidney, liver) and the whole body. BA was computed based on circulating biomarkers, and the resulting ∆age was tested as an outcome in linear regression analysis. RESULTS The ∆age of the whole body, heart, and liver was inversely associated with higher flavonoids intake (the whole body ∆age β = - 0.58, cardiovascular ∆age β = - 0.96, liver ∆age β = - 3.19) after adjustment for variables. However, higher flavonoids intake positively related to renal ∆age (β = 0.40) in participants with chronic kidney disease (CKD). Associations were influenced by population characteristics, such as age, health behavior, or chronic diseases. Anthocyanidins, isoflavones and flavones had the strongest inverse associations between the whole body ∆age and cardiovascular ∆age among all the flavonoids subclasses. CONCLUSION Flavonoids intake positively contributes to delaying the biological aging process, especially in the heart, and liver organ, which may be beneficial for reducing the long-term risk of cardiovascular or liver disease.
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Affiliation(s)
- Wenmin Xing
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China
| | - Wenyan Gao
- School of Pharmacy, Hangzhou Medical College, Hangzhou, China
| | - Zhenlei Zhao
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China
| | - Xiaogang Xu
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China
| | - Hongyan Bu
- School of Pharmacy, Hangzhou Medical College, Hangzhou, China
| | - Huili Su
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China.
| | - Genxiang Mao
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China.
| | - Jun Chen
- Department of Geriatrics, Zhejiang Provincial Key Laboratory of Geriatrics, Zhejiang Hospital, No. 1229, Gudun Road, 310013, Hangzhou, China.
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12
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Veyrié A, Noreña A, Sarrazin JC, Pezard L. Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking. BIOLOGY 2023; 12:967. [PMID: 37508397 PMCID: PMC10376775 DOI: 10.3390/biology12070967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
In informational masking paradigms, the successful segregation between the target and masker creates auditory perceptual awareness. The dynamics of the build-up of auditory perception is based on a set of interactions between bottom-up and top-down processes that generate neuronal modifications within the brain network activity. These neural changes are studied here using event-related potentials (ERPs), entropy, and integrated information, leading to several measures applied to electroencephalogram signals. The main findings show that the auditory perceptual awareness stimulated functional activation in the fronto-temporo-parietal brain network through (i) negative temporal and positive centro-parietal ERP components; (ii) an enhanced processing of multi-information in the temporal cortex; and (iii) an increase in informational content in the fronto-central cortex. These different results provide information-based experimental evidence about the functional activation of the fronto-temporo-parietal brain network during auditory perceptual awareness.
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Affiliation(s)
- Alexandre Veyrié
- Centre National de la Recherche Scientifique (UMR 7291), Laboratoire de Neurosciences Cognitives, Aix-Marseille Université, 13331 Marseille, France
- ONERA, The French Aerospace Lab, 13300 Salon de Provence, France
| | - Arnaud Noreña
- Centre National de la Recherche Scientifique (UMR 7291), Laboratoire de Neurosciences Cognitives, Aix-Marseille Université, 13331 Marseille, France
| | | | - Laurent Pezard
- Centre National de la Recherche Scientifique (UMR 7291), Laboratoire de Neurosciences Cognitives, Aix-Marseille Université, 13331 Marseille, France
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13
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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 (NEW YORK, N.Y.) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [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.
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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
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14
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Tanglay O, Dadario NB, Chong EHN, Tang SJ, Young IM, Sughrue ME. Graph Theory Measures and Their Application to Neurosurgical Eloquence. Cancers (Basel) 2023; 15:556. [PMID: 36672504 PMCID: PMC9857081 DOI: 10.3390/cancers15020556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.
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Affiliation(s)
- Onur Tanglay
- UNSW School of Clinical Medicine, Faulty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ 08901, USA
| | - Elizabeth H. N. Chong
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Si Jie Tang
- School of Medicine, University of California Davis, Sacramento, CA 95817, USA
| | - Isabella M. Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
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15
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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16
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Has Silemek AC, Nolte G, Pöttgen J, Engel AK, Heesen C, Gold SM, Stellmann JP. Topological reorganization of brain network might contribute to the resilience of cognitive functioning in mildly disabled relapsing remitting multiple sclerosis. J Neurosci Res 2023; 101:143-161. [PMID: 36263462 DOI: 10.1002/jnr.25135] [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: 10/18/2021] [Revised: 09/28/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022]
Abstract
Multiple sclerosis (MS) is an inflammatory and demyelinating disease which leads to impairment in several functional systems including cognition. Alteration of brain networks is linked to disability and its progression. However, results are mostly cross-sectional and yet contradictory as putative adaptive and maladaptive mechanisms were found. Here, we aimed to explore longitudinal reorganization of brain networks over 2-years by combining diffusion tensor imaging (DTI), resting-state functional MRI (fMRI), magnetoencephalography (MEG), and a comprehensive neuropsychological-battery. In 37 relapsing-remitting MS (RRMS) and 39 healthy-controls, cognition remained stable over-time. We reconstructed network models based on the three modalities and analyzed connectivity in relation to the hierarchical topology and functional subnetworks. Network models were compared across modalities and in their association with cognition using linear-mixed-effect-regression models. Loss of hub connectivity and global reduction was observed on a structural level over-years (p < .010), which was similar for functional MEG-networks but not for fMRI-networks. Structural hub connectivity increased in controls (p = .044), suggesting a physiological mechanism of healthy aging. Despite a general loss in structural connectivity in RRMS, hub connectivity was preserved (p = .002) over-time in default-mode-network (DMN). MEG-networks were similar to DTI and weakly correlated with fMRI in MS (p < .050). Lower structural (β between .23-.33) and both lower (β between .40-.59) and higher functional connectivity (β = -.54) in DMN was associated with poorer performance in attention and memory in RRMS (p < .001). MEG-networks involved no association with cognition. Here, cognitive stability despite ongoing neurodegeneration might indicate a resilience mechanism of DMN hubs mimicking a physiological reorganization observed in healthy aging.
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Affiliation(s)
- Arzu Ceylan Has Silemek
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix-Marseille Université, CNRS, CRMBM, UMR 7339, Marseille, France
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17
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Krämer C, Stumme J, da Costa Campos L, Rubbert C, Caspers J, Caspers S, Jockwitz C. Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach. Netw Neurosci 2023; 7:122-147. [PMID: 37339286 PMCID: PMC10270720 DOI: 10.1162/netn_a_00275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/22/2022] [Indexed: 09/22/2023] Open
Abstract
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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18
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Pat N, Wang Y, Anney R, Riglin L, Thapar A, Stringaris A. Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Hum Brain Mapp 2022; 43:5520-5542. [PMID: 35903877 PMCID: PMC9704790 DOI: 10.1002/hbm.26027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 06/22/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of 'opportunistic stacking' allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework.
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Affiliation(s)
- Narun Pat
- Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Yue Wang
- Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Richard Anney
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
| | - Lucy Riglin
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
| | - Anita Thapar
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
| | - Argyris Stringaris
- Division of Psychiatry, Department of Clinical, Educational and Health PsychologyUniversity College LondonLondonUK
- Department of PsychiatryNational and Kapodistrian University of AthensAthensGreece
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19
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Mononen T, Kujala J, Liljeström M, Leppäaho E, Kaski S, Salmelin R. The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study. Front Neurosci 2022; 16:1019572. [PMID: 36408411 PMCID: PMC9669574 DOI: 10.3389/fnins.2022.1019572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Abstract
Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.
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Affiliation(s)
- Tommi Mononen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- Aalto NeuroImaging, Aalto University, Espoo, Finland
- Department of Computer Science, Aalto University, Espoo, Finland
- Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- *Correspondence: Tommi Mononen,
| | - Jan Kujala
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- Aalto NeuroImaging, Aalto University, Espoo, Finland
- BioMag Laboratory, Helsinki University Hospital, Helsinki, Finland
| | - Eemeli Leppäaho
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- Aalto NeuroImaging, Aalto University, Espoo, Finland
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20
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Capturing Brain-Cognition Relationship: Integrating Task-Based fMRI Across Tasks Markedly Boosts Prediction and Test-Retest Reliability. Neuroimage 2022; 263:119588. [PMID: 36057404 DOI: 10.1016/j.neuroimage.2022.119588] [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: 05/25/2022] [Revised: 08/13/2022] [Accepted: 08/23/2022] [Indexed: 11/22/2022] Open
Abstract
Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r=.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.
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21
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Besson P, Rogalski E, Gill NP, Zhang H, Martersteck A, Bandt SK. Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging. Front Aging Neurosci 2022; 14:895535. [PMID: 36081894 PMCID: PMC9445244 DOI: 10.3389/fnagi.2022.895535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.
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Affiliation(s)
- Pierre Besson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nathan P. Gill
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Hui Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Adam Martersteck
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - S. Kathleen Bandt
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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22
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Millar PR, Luckett PH, Gordon BA, Benzinger TLS, Schindler SE, Fagan AM, Cruchaga C, Bateman RJ, Allegri R, Jucker M, Lee JH, Mori H, Salloway SP, Yakushev I, Morris JC, Ances BM. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. Neuroimage 2022; 256:119228. [PMID: 35452806 PMCID: PMC9178744 DOI: 10.1016/j.neuroimage.2022.119228] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 12/29/2022] Open
Abstract
"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University, St. Louis, MO 63110, USA.
| | - Patrick H Luckett
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical School, Abenoku, Osaka, 545-8585, Japan, Nagaoka Sutoku University
| | | | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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23
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Lutkenhoff ES, Nigri A, Rossi Sebastiano D, Sattin D, Visani E, Rosazza C, D'Incerti L, Bruzzone MG, Franceschetti S, Leonardi M, Ferraro S, Monti MM. EEG Power spectra and subcortical pathology in chronic disorders of consciousness. Psychol Med 2022; 52:1491-1500. [PMID: 32962777 DOI: 10.1017/s003329172000330x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete. METHODS Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018. RESULTS In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis. CONCLUSIONS These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Rossi Sebastiano
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Sattin
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Elisa Visani
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Cristina Rosazza
- Scientific Direction, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Ludovico D'Incerti
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Silvana Franceschetti
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China: On the behalf of the Coma Research Center, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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24
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Vaghari D, Kabir E, Henson RN. Late combination shows that MEG adds to MRI in classifying MCI versus controls. Neuroimage 2022; 252:119054. [PMID: 35247546 PMCID: PMC8987738 DOI: 10.1016/j.neuroimage.2022.119054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/20/2022] [Accepted: 03/01/2022] [Indexed: 12/12/2022] Open
Abstract
Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.
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Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsanollah Kabir
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
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25
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van Loon W, de Vos F, Fokkema M, Szabo B, Koini M, Schmidt R, de Rooij M. Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification. Front Neurosci 2022; 16:830630. [PMID: 35546881 PMCID: PMC9082949 DOI: 10.3389/fnins.2022.830630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.
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Affiliation(s)
- Wouter van Loon
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands
| | - Frank de Vos
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden, Netherlands
| | - Marjolein Fokkema
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands
| | - Botond Szabo
- Department of Decision Sciences, Bocconi University, Milan, Italy.,Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy
| | - Marisa Koini
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Mark de Rooij
- Department of Methodology and Statistics, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden, Netherlands
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26
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 550] [Impact Index Per Article: 275.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Shan L, Huang H, Zhang Z, Wang Y, Gu F, Lu M, Zhou W, Jiang Y, Dai J. Mapping the emergence of visual consciousness in the human brain via brain-wide intracranial electrophysiology. Innovation (N Y) 2022; 3:100243. [PMID: 35519511 PMCID: PMC9065914 DOI: 10.1016/j.xinn.2022.100243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/12/2022] [Indexed: 10/25/2022] Open
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Chyzhyk D, Varoquaux G, Milham M, Thirion B. How to remove or control confounds in predictive models, with applications to brain biomarkers. Gigascience 2022; 11:giac014. [PMID: 35277962 PMCID: PMC8917515 DOI: 10.1093/gigascience/giac014] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 09/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers. RESULTS Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds. CONCLUSIONS Cross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.
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Affiliation(s)
- Darya Chyzhyk
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Gaël Varoquaux
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Bertrand Thirion
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
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Niechcial MA, Vaportzis E, Gow AJ. Genes Versus Lifestyles: Exploring Beliefs About the Determinants of Cognitive Ageing. Front Psychol 2022; 13:838323. [PMID: 35310240 PMCID: PMC8931720 DOI: 10.3389/fpsyg.2022.838323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022] Open
Abstract
Genetic and lifestyle factors contribute to cognitive ageing. However, the extent to which the public attribute changes in thinking skills to either genetic or lifestyle factors is largely unknown. This may be important if it impacts engagement in activities deemed beneficial to thinking skills. This study, therefore, explored people's beliefs about determinants of cognitive ageing and whether those beliefs were associated with engagement in potentially beneficial behaviours. Data were collected through a United Kingdom-wide survey of people aged 40 and over. Participants completed questions about their beliefs regarding cognitive ageing, and specifically the extent to which they believed lifestyle or genetic factors influence those changes, and their engagement in specific behaviours that may be cognitively beneficial. Responses from 3,130 individuals (94.0% of the survey sample) were analysed using chi-square tests of independence, principal component analysis and ANCOVAs to investigate whether their attribution of genetic or lifestyle determinants were associated with their beliefs about cognitive ageing and their participation in brain health-related behaviours. Most respondents (62.2%) believed genes and lifestyle contribute equally to age-related changes in cognitive skills. Respondents who believed genetic factors were more influential were less likely to expect cognitive skills might be improved or maintained with age, less sure what behaviours might be associated with brain health, and less likely to engage in behaviours comprising mental challenge/novelty supported as beneficial for brain health. From this United Kingdom-wide survey about beliefs regarding potential determinants of cognitive ageing, some of our respondents' views were not aligned with the findings from ageing research. It is important for the public to know how to keep their brains healthy. Our results indicate a need for clearer messaging highlighting the role of lifestyle factors for brain health.
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Affiliation(s)
- Malwina A. Niechcial
- Department of Psychology, Centre for Applied Behavioural Sciences, School of Social Sciences, Heriot-Watt University, Edinburgh, United Kingdom
| | | | - Alan J. Gow
- Department of Psychology, Centre for Applied Behavioural Sciences, School of Social Sciences, Heriot-Watt University, Edinburgh, United Kingdom
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30
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Robust learning from corrupted EEG with dynamic spatial filtering. Neuroimage 2022; 251:118994. [PMID: 35181552 DOI: 10.1016/j.neuroimage.2022.118994] [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: 09/29/2021] [Revised: 02/03/2022] [Accepted: 02/11/2022] [Indexed: 11/20/2022] Open
Abstract
Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.
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31
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Sadaghiani S, Brookes MJ, Baillet S. Connectomics of human electrophysiology. Neuroimage 2022; 247:118788. [PMID: 34906715 PMCID: PMC8943906 DOI: 10.1016/j.neuroimage.2021.118788] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/03/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022] Open
Abstract
We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.
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Affiliation(s)
- Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana-Champaign, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, IL, United States
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG72RD, United Kingdom
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Venkadesh S, Van Horn JD. Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods. Front Neurosci 2021; 15:752332. [PMID: 34776853 PMCID: PMC8585845 DOI: 10.3389/fnins.2021.752332] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/13/2021] [Indexed: 11/24/2022] Open
Abstract
The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.
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Affiliation(s)
- Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States.,School of Data Science, University of Virginia, Charlottesville, VA, United States
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Dadi K, Varoquaux G, Houenou J, Bzdok D, Thirion B, Engemann D. Population modeling with machine learning can enhance measures of mental health. Gigascience 2021; 10:giab071. [PMID: 34651172 PMCID: PMC8559220 DOI: 10.1093/gigascience/giab071] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/14/2021] [Accepted: 09/22/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? RESULTS Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. CONCLUSION Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.
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Affiliation(s)
- Kamalaker Dadi
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
| | - Gaël Varoquaux
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Montréal Neurological Institute, McGill University, Montreal,
QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal,
QC, Canada
| | - Josselin Houenou
- CEA, NeuroSpin, Psychiatry Team, UNIACT Lab, Université Paris
Saclay, France
- APHP, Mondor University Hospitals, Psychiatry Department,
INSERM U955 Team 15 “Translational Psychiatry,” Créteil, France
| | - Danilo Bzdok
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Mila - Quebec Artificial Intelligence Institute, Montreal,
QC, Canada
- Department of Biomedical Engineering, Montreal Neurological Institute,
Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Bertrand Thirion
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
| | - Denis Engemann
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain
Sciences, Germany
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34
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Drobinin V, Van Gestel H, Helmick CA, Schmidt MH, Bowen CV, Uher R. The developmental brain age is associated with adversity, depression, and functional outcomes among adolescents. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:406-414. [PMID: 34555562 DOI: 10.1016/j.bpsc.2021.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Most psychiatric disorders emerge in the second decade of life. In the present study we examined whether environmental adversity, developmental antecedents, major depressive disorder (MDD), and functional impairment correlate with deviation from normative brain development in adolescence. METHODS We trained a brain age prediction model using 189 structural MRI brain features in 1299 typically developing adolescents (age range 9-19 years old, M = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (M = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (M = 13.6, SD = 2.82). We tested associations between the brain-age-gap and adversity, early antecedents, depression, and functional impairment. RESULTS We accurately predicted chronological age in typically developing adolescents (mean absolute error (MAE) = 1.53 years). The model generalized to the validation set (MAE = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (MAE = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intra class correlation = 0.94). Experience of environmental advertises (β = 0.18, 95% CI [0.04, 0.31], p = 0.02), diagnosis of MDD (β = 0.61, 95% CI [0.23, 0.99], p = 0.01) and functional impairment (β = 0.16, 95% CI [0.05, 0.27], p = 0.01) were associated with a positive brain-age-gap. CONCLUSIONS Risk factors, diagnosis, and impact of mental illness are associated with an older appearing brain during development.
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Affiliation(s)
| | | | - Carl A Helmick
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Matthias H Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Chris V Bowen
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
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35
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Multivariate semi-blind deconvolution of fMRI time series. Neuroimage 2021; 241:118418. [PMID: 34303793 DOI: 10.1016/j.neuroimage.2021.118418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 07/17/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022] Open
Abstract
Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.
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36
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Sorrentino P, Rucco R, Lardone A, Liparoti M, Troisi Lopez E, Cavaliere C, Soricelli A, Jirsa V, Sorrentino G, Amico E. Clinical connectome fingerprints of cognitive decline. Neuroimage 2021; 238:118253. [PMID: 34116156 DOI: 10.1016/j.neuroimage.2021.118253] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 12/29/2022] Open
Abstract
Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.
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Affiliation(s)
- Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Rosaria Rucco
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | - Anna Lardone
- Department of Social and Developmental Psychology, University of Rome "Sapienza, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | | | | | - Andrea Soricelli
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; IRCCS SDN, Naples, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Giuseppe Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; Hermitage Capodimonte Clinic, Naples, Italy.
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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Quiñones I, Amoruso L, Pomposo Gastelu IC, Gil-Robles S, Carreiras M. What Can Glioma Patients Teach Us about Language (Re)Organization in the Bilingual Brain: Evidence from fMRI and MEG. Cancers (Basel) 2021; 13:2593. [PMID: 34070619 PMCID: PMC8198785 DOI: 10.3390/cancers13112593] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/20/2021] [Accepted: 05/20/2021] [Indexed: 12/15/2022] Open
Abstract
Recent evidence suggests that the presence of brain tumors (e.g., low-grade gliomas) triggers language reorganization. Neuroplasticity mechanisms called into play can transfer linguistic functions from damaged to healthy areas unaffected by the tumor. This phenomenon has been reported in monolingual patients, but much less is known about the neuroplasticity of language in the bilingual brain. A central question is whether processing a first or second language involves the same or different cortical territories and whether damage results in diverse recovery patterns depending on the language involved. This question becomes critical for preserving language areas in bilingual brain-tumor patients to prevent involuntary pathological symptoms following resection. While most studies have focused on intraoperative mapping, here, we go further, reporting clinical cases for five bilingual patients tested before and after tumor resection, using a novel multimethod approach merging neuroimaging information from fMRI and MEG to map the longitudinal reshaping of the language system. Here, we present four main findings. First, all patients preserved linguistic function in both languages after surgery, suggesting that the surgical intervention with intraoperative language mapping was successful in preserving cortical and subcortical structures necessary for brain plasticity at the functional level. Second, we found reorganization of the language network after tumor resection in both languages, mainly reflected by a shift of activity to right hemisphere nodes and the recruitment of ipsilesional left nodes. Third, we found that this reorganization varied according to the language involved, indicating that L1 and L2 follow different reshaping patterns after surgery. Fourth, oscillatory longitudinal effects were correlated with BOLD laterality changes in superior parietal and middle frontal areas. These findings may reflect that neuroplasticity impacts on the compensatory involvement of executive control regions, supporting the allocation of cognitive resources as a consequence of increased attentional demands. Furthermore, these results hint at the complementary role of this neuroimaging approach in language mapping, with fMRI offering excellent spatial localization and MEG providing optimal spectrotemporal resolution.
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Affiliation(s)
- Ileana Quiñones
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.A.); (M.C.)
| | - Lucia Amoruso
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.A.); (M.C.)
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
| | | | - Santiago Gil-Robles
- BioCruces Research Institute, 48015 Bilbao, Spain;
- Department of Neurosurgery, Hospital Quironsalud, 28223 Madrid, Spain
| | - Manuel Carreiras
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.A.); (M.C.)
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
- Department of Basque Language and Communication, University of the Basque Country, UPV/EHU, 48940 Bilbao, Spain
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Increased brain atrophy and lesion load is associated with stronger lower alpha MEG power in multiple sclerosis patients. NEUROIMAGE-CLINICAL 2021; 30:102632. [PMID: 33770549 PMCID: PMC8022249 DOI: 10.1016/j.nicl.2021.102632] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 02/05/2021] [Accepted: 03/11/2021] [Indexed: 12/30/2022]
Abstract
In multiple sclerosis, the interplay of neurodegeneration, demyelination and inflammation leads to changes in neurophysiological functioning. This study aims to characterize the relation between reduced brain volumes and spectral power in multiple sclerosis patients and matched healthy subjects. During resting-state eyes closed, we collected magnetoencephalographic data in 67 multiple sclerosis patients and 47 healthy subjects, matched for age and gender. Additionally, we quantified different brain volumes through magnetic resonance imaging (MRI). First, a principal component analysis of MRI-derived brain volumes demonstrates that atrophy can be largely described by two components: one overall degenerative component that correlates strongly with different cognitive tests, and one component that mainly captures degeneration of the cortical grey matter that strongly correlates with age. A multimodal correlation analysis indicates that increased brain atrophy and lesion load is accompanied by increased spectral power in the lower alpha (8-10 Hz) in the temporoparietal junction (TPJ). Increased lower alpha power in the TPJ was further associated with worse results on verbal and spatial working memory tests, whereas an increased lower/upper alpha power ratio was associated with slower information processing speed. In conclusion, multiple sclerosis patients with increased brain atrophy, lesion and thalamic volumes demonstrated increased lower alpha power in the TPJ and reduced cognitive abilities.
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Xifra-Porxas A, Ghosh A, Mitsis GD, Boudrias MH. Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques. Neuroimage 2021; 231:117822. [PMID: 33549751 DOI: 10.1016/j.neuroimage.2021.117822] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 11/30/2022] Open
Abstract
Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18-88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.
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Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada; Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada
| | - Arna Ghosh
- Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada; Integrated Program in Neuroscience, McGill University, Montréal, Canada
| | | | - Marie-Hélène Boudrias
- Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada; School of Physical and Occupational Therapy, McGill University, Montréal, Canada.
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40
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High-resolution connectomic fingerprints: Mapping neural identity and behavior. Neuroimage 2021; 229:117695. [PMID: 33422711 DOI: 10.1016/j.neuroimage.2020.117695] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/16/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.
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41
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Kuo CY, Tai TM, Lee PL, Tseng CW, Chen CY, Chen LK, Lee CK, Chou KH, See S, Lin CP. Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework. Front Psychiatry 2021; 12:626677. [PMID: 33833699 PMCID: PMC8021919 DOI: 10.3389/fpsyt.2021.626677] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/22/2021] [Indexed: 01/02/2023] Open
Abstract
Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R 2 = 0.88; support vector regression, MAE = 4.42 years, R 2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R 2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.
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Affiliation(s)
- Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | | | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | | | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Simon See
- NVIDIA AI Technology Center, NVIDIA, Taipei, Taiwan
| | - Ching-Po Lin
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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42
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Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnaes D, Richard G, de Lange AMG, Lund MJ, Norbom L, Agartz I, Melle I, Naerland T, Selbaek G, Persson K, Nordvik JE, Schwarz E, Andreassen OA, Kaufmann T, Westlye LT. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp 2020; 42:1714-1726. [PMID: 33340180 PMCID: PMC7978139 DOI: 10.1002/hbm.25323] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/20/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
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Affiliation(s)
- Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Wibeke Nordhøy
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Daniel S Quintana
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnaes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Department of Psychiatry, University of Oxford, Oxford, UK
| | - Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Norbom
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Terje Naerland
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Geir Selbaek
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | | | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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43
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Touchard C, Cartailler J, Levé C, Serrano J, Sabbagh D, Manquat E, Joachim J, Mateo J, Gayat E, Engemann D, Vallée F. Propofol Requirement and EEG Alpha Band Power During General Anesthesia Provide Complementary Views on Preoperative Cognitive Decline. Front Aging Neurosci 2020; 12:593320. [PMID: 33328973 PMCID: PMC7729157 DOI: 10.3389/fnagi.2020.593320] [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: 08/10/2020] [Accepted: 11/05/2020] [Indexed: 11/15/2022] Open
Abstract
Background: Although cognitive decline (CD) is associated with increased post-operative morbidity and mortality, routinely screening patients remains difficult. The main objective of this prospective study is to use the EEG response to a Propofol-based general anesthesia (GA) to reveal CD. Methods: 42 patients with collected EEG and Propofol target concentration infusion (TCI) during GA had a preoperative cognitive assessment using MoCA. We evaluated the performance of three variables to detect CD (MoCA < 25 points): age, Propofol requirement to induce unconsciousness (TCI at SEF95: 8–13 Hz) and the frontal alpha band power (AP at SEF95: 8–13 Hz). Results: The 17 patients (40%) with CD were significantly older (p < 0.001), had lower TCI (p < 0.001), and AP (p < 0.001). We found using logistic models that TCI and AP were the best set of variables associated with CD (AUC: 0.89) and performed better than age (p < 0.05). Propofol TCI had a greater impact on CD probability compared to AP, although both were complementary in detecting CD. Conclusion: TCI and AP contribute additively to reveal patient with preoperative cognitive decline. Further research on post-operative cognitive trajectory are necessary to confirm the interest of intra operative variables in addition or as a substitute to cognitive evaluation.
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Affiliation(s)
- Cyril Touchard
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Charlotte Levé
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - José Serrano
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - David Sabbagh
- Université Paris-Saclay, Inria, CEA Palaiseau, France
| | - Elsa Manquat
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Jona Joachim
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Joaquim Mateo
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France
| | - Etienne Gayat
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Denis Engemann
- Université Paris-Saclay, Inria, CEA Palaiseau, France.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fabrice Vallée
- Department of Anesthesiology and Intensive Care, Lariboisière - Saint Louis Hospitals, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France.,Université Paris-Saclay, Inria, CEA Palaiseau, France
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44
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Banville H, Chehab O, Hyvarinen A, Engemann D, Gramfort A. Uncovering the structure of clinical EEG signals with self-supervised learning. J Neural Eng 2020; 18. [PMID: 33181507 DOI: 10.1088/1741-2552/abca18] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/12/2020] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. APPROACH We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. MAIN RESULTS Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. SIGNIFICANCE We demonstrate the benefit of SSL approaches on EEG data. Our results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data.
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45
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Couvy-Duchesne B, Faouzi J, Martin B, Thibeau-Sutre E, Wild A, Ansart M, Durrleman S, Dormont D, Burgos N, Colliot O. Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Front Psychiatry 2020; 11:593336. [PMID: 33384629 PMCID: PMC7770104 DOI: 10.3389/fpsyt.2020.593336] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/20/2020] [Indexed: 12/14/2022] Open
Abstract
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
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Affiliation(s)
- Baptiste Couvy-Duchesne
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Johann Faouzi
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Benoît Martin
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Elina Thibeau-Sutre
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Adam Wild
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Manon Ansart
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Stanley Durrleman
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Didier Dormont
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neuroradiology, Paris, France
| | - Ninon Burgos
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Olivier Colliot
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
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