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Yan B, Peng Y, Zhang Y, Zhang Y, Zhang H, Cao Y, Sun C, Ding M. From simulation to clinic: Assessing the required channel count for effective clinical use of OPM-MEG systems. Neuroimage 2025; 314:121262. [PMID: 40347999 DOI: 10.1016/j.neuroimage.2025.121262] [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: 02/06/2025] [Revised: 04/20/2025] [Accepted: 05/08/2025] [Indexed: 05/14/2025] Open
Abstract
The channel count in an Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG) system plays a pivotal role in determining its overall performance. While existing research consistently highlights that a greater number of channels enhances system capabilities, practical constraints such as sensor placement on the head, inter-channel interference, and cost-efficiency impose limitations on channel scalability. Additionally, the optimal channel count required for clinical applications of OPM-MEG remains unclear. In this study, we systematically investigate the impact of channel count on OPM-MEG performance by integrating simulations, phantom experiments, and human MEG experiments. Four configurations with varying channel counts (16, 32, 64, and 128) are evaluated. Specifically, systems with fewer channels (e.g., 16 channels) encounter significant challenges in meeting the demands of clinical MEG applications. In contrast, a 64-channel OPM-MEG system demonstrates performance metrics-such as signal-to-noise ratio (SNR) and localization accuracy-that are comparable to those of a 306-channel Superconducting Quantum Interference Device MEG (SQUID-MEG) system. Notably, a 128-channel OPM-MEG system surpasses the 306-channel SQUID-MEG system, achieving superior results. This work provides a detailed exploration of the relationship between channel count and OPM-MEG system performance, analyzing how many channels of the OPM-MEG system are suitable for clinical applications. By combining simulation-based evaluations with empirical measurements, we found that it is crucial to carefully select the appropriate number of channels based on the specific usage requirements in clinical applications.
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Affiliation(s)
- Bing Yan
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Yuming Peng
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Yixiang Zhang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Yun Zhang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Haonan Zhang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Yifu Cao
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Chang Sun
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China
| | - Ming Ding
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, PR China.
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Gaubert S, Garces P, Hipp J, Bruña R, Lopéz ME, Maestu F, Vaghari D, Henson R, Paquet C, Engemann DA. Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia. EBioMedicine 2025; 114:105659. [PMID: 40153923 PMCID: PMC11995804 DOI: 10.1016/j.ebiom.2025.105659] [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: 07/25/2024] [Revised: 01/13/2025] [Accepted: 03/06/2025] [Indexed: 04/01/2025] Open
Abstract
BACKGROUND Developing non-invasive and affordable biomarkers to detect Alzheimer's disease (AD) at a prodromal stage is essential, particularly in the context of new disease-modifying therapies. Mild cognitive impairment (MCI) is a critical stage preceding dementia, but not all patients with MCI will progress to AD. This study explores the potential of magnetoencephalography (MEG) to predict cognitive decline from MCI to AD dementia. METHODS We analysed resting-state MEG data from the BioFIND dataset including 117 patients with MCI among whom 64 developed AD dementia (AD progression), while 53 remained cognitively stable (stable MCI), using spectral analysis. Logistic regression models estimated the additive explanation of selected clinical, MEG, and MRI variables for AD progression risk. We then built a high-dimensional classification model to combine all modalities and variables of interest. FINDINGS MEG 16-38Hz spectral power, particularly over parieto-occipital magnetometers, was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The data-driven classification model confirmed, among other factors, the complementary information of MEG covariance (AUC = 0.74, SD = 0.13) and MRI cortical volumes (AUC = 0.77, SD = 0.14) to predict AD progression. Combining all inputs led to markedly improved classification scores (AUC = 0.81, SD = 0.12). INTERPRETATION These findings highlight the potential of spectral power and covariance as robust non-invasive electrophysiological biomarkers to predict AD progression, complementing other diagnostic measures, including cognitive scores and MRI. FUNDING This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).
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Affiliation(s)
- Sinead Gaubert
- Université Paris Cité, Inserm UMRS 1144 Therapeutic Optimization in Neuropsychopharmacology, Paris, France; Cognitive Neurology Center, GHU.Nord APHP Hôpital Lariboisière Fernand Widal, Paris, France.
| | - 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
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28223, Madrid, Spain; Department of Radiology, Rehabilitation and Physiotherapy, School of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Maria Eugenia Lopéz
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28223, Madrid, Spain; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Maestu
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28223, Madrid, Spain; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | | | - Richard Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, UK; Department of Psychiatry, University of Cambridge, UK
| | - Claire Paquet
- Université Paris Cité, Inserm UMRS 1144 Therapeutic Optimization in Neuropsychopharmacology, Paris, France; Cognitive Neurology Center, GHU.Nord APHP Hôpital Lariboisière Fernand Widal, Paris, France
| | - 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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Ahrends C, Woolrich MW, Vidaurre D. Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel. eLife 2025; 13:RP95125. [PMID: 39887179 PMCID: PMC11785372 DOI: 10.7554/elife.95125] [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: 02/01/2025] Open
Abstract
Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual's brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual's time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.
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Affiliation(s)
- Christine Ahrends
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Diego Vidaurre
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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Liu Y, Wang L, Ning X, Gao Y, Wang D. Enhancing early Alzheimer's disease classification accuracy through the fusion of sMRI and rsMEG data: a deep learning approach. Front Neurosci 2024; 18:1480871. [PMID: 39633895 PMCID: PMC11615070 DOI: 10.3389/fnins.2024.1480871] [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: 08/14/2024] [Accepted: 10/24/2024] [Indexed: 12/07/2024] Open
Abstract
Objective Early detection and prediction of Alzheimer's Disease are paramount for elucidating neurodegenerative processes and enhancing cognitive resilience. Structural Magnetic Resonance Imaging (sMRI) provides insights into brain morphology, while resting-state Magnetoencephalography (rsMEG) elucidates functional aspects. However, inherent disparities between these multimodal neuroimaging modalities pose challenges to the effective integration of multimodal features. Approach To address these challenges, we propose a deep learning-based multimodal classification framework for Alzheimer's disease, which harnesses the fusion of pivotal features from sMRI and rsMEG to augment classification precision. Utilizing the BioFIND dataset, classification trials were conducted on 163 Mild Cognitive Impairment cases and 144 cognitively Healthy Controls. Results The study findings demonstrate that the InterFusion method, combining sMRI and rsMEG data, achieved a classification accuracy of 0.827. This accuracy significantly surpassed the accuracies obtained by rsMEG only at 0.710 and sMRI only at 0.749. Moreover, the evaluation of different fusion techniques revealed that InterFusion outperformed both EarlyFusion with an accuracy of 0.756 and LateFusion with an accuracy of 0.801. Additionally, the study delved deeper into the role of different frequency band features of rsMEG in fusion by analyzing six frequency bands, thus expanding the diagnostic scope. Discussion These results highlight the value of integrating resting-state rsMEG and sMRI data in the early diagnosis of Alzheimer's disease, demonstrating significant potential in the field of neuroscience diagnostics.
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Affiliation(s)
- Yuchen Liu
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
- Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, China
| | - Ling Wang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
| | - Xiaolin Ning
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
- Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, China
- Hefei National Laboratory, Hefei, China
| | - Yang Gao
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
- Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, China
| | - Defeng Wang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
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Mandal PK, Mahto RV. Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs. SENSORS (BASEL, SWITZERLAND) 2023; 23:8192. [PMID: 37837027 PMCID: PMC10574860 DOI: 10.3390/s23198192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.
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Affiliation(s)
- Paul K. Mandal
- Department of Computer Science, University of Texas, Austin, TX 78712, USA
| | - Rakeshkumar V. Mahto
- Department of Electrical and Computer Engineering, California State University, Fullerton, CA 92831, USA;
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Martínez‐Cañada P, Perez‐Valero E, Minguillon J, Pelayo F, López‐Gordo MA, Morillas C. Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12477. [PMID: 37662693 PMCID: PMC10474329 DOI: 10.1002/dad2.12477] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023]
Abstract
INTRODUCTION Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. METHODS Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. RESULTS We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. DISCUSSION Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.
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Affiliation(s)
- Pablo Martínez‐Cañada
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Eduardo Perez‐Valero
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Jesus Minguillon
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Francisco Pelayo
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Miguel A. López‐Gordo
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Christian Morillas
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
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Adams NE, Jafarian A, Perry A, Rouse MA, Shaw AD, Murley AG, Cope TE, Bevan-Jones WR, Passamonti L, Street D, Holland N, Nesbitt D, Hughes LE, Friston KJ, Rowe JB. Neurophysiological consequences of synapse loss in progressive supranuclear palsy. Brain 2023; 146:2584-2594. [PMID: 36514918 PMCID: PMC10232290 DOI: 10.1093/brain/awac471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.
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Affiliation(s)
- Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Alistair Perry
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Alexander D Shaw
- Washington Singer Laboratories, University of Exeter, Exeter EX4 4QG, UK
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Negin Holland
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - David Nesbitt
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge CB2 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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