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Ahrends C, Stevner A, Pervaiz U, Kringelbach ML, Vuust P, Woolrich MW, Vidaurre D. Data and model considerations for estimating time-varying functional connectivity in fMRI. Neuroimage 2022; 252:119026. [PMID: 35217207 PMCID: PMC9361391 DOI: 10.1016/j.neuroimage.2022.119026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 11/08/2022] Open
Abstract
Time-varying FC models sometimes fail to detect temporal changes in fMRI data. Between-subject and within-session FC variability affect model stasis. The choice of parcellation affects model stasis in real fMRI data. The number of observations and free parameters per state critically affect model stasis.
Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model's ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters per state that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model.
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Affiliation(s)
- C Ahrends
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, Aarhus C 8000, Denmark.
| | - A Stevner
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark
| | - U Pervaiz
- Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, United Kingdom
| | - M L Kringelbach
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, Aarhus C 8000, Denmark; Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom
| | - P Vuust
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, Aarhus C 8000, Denmark
| | - M W Woolrich
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom
| | - D Vidaurre
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Universitetsbyen 3, Aarhus C 8000, Denmark; Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom.
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Vidaurre D, Llera A, Smith SM, Woolrich MW. Behavioural relevance of spontaneous, transient brain network interactions in fMRI. Neuroimage 2021; 229:117713. [PMID: 33421594 PMCID: PMC7994296 DOI: 10.1016/j.neuroimage.2020.117713] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/26/2020] [Indexed: 12/19/2022] Open
Abstract
How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.
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Affiliation(s)
- D Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Health, Aarhus University, 8000 Denmark; Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK,.
| | - A Llera
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 Netherlands
| | - S M Smith
- Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
| | - M W Woolrich
- Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
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Becker R, Vidaurre D, Quinn AJ, Abeysuriya RG, Parker Jones O, Jbabdi S, Woolrich MW. Transient spectral events in resting state MEG predict individual task responses. Neuroimage 2020; 215:116818. [PMID: 32276062 DOI: 10.1016/j.neuroimage.2020.116818] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/27/2020] [Accepted: 03/26/2020] [Indexed: 01/12/2023] Open
Abstract
Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.
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Affiliation(s)
- R Becker
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK.
| | - D Vidaurre
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - A J Quinn
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - R G Abeysuriya
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - O Parker Jones
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - S Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - M W Woolrich
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
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Stevner ABA, Vidaurre D, Cabral J, Rapuano K, Nielsen SFV, Tagliazucchi E, Laufs H, Vuust P, Deco G, Woolrich MW, Van Someren E, Kringelbach ML. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun 2019; 10:1035. [PMID: 30833560 PMCID: PMC6399232 DOI: 10.1038/s41467-019-08934-3] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 02/11/2019] [Indexed: 12/02/2022] Open
Abstract
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep. Sleep is composed of a number of different stages, each associated with a different pattern of brain activity. Here, using a data-driven Hidden Markov Model (HMM) of fMRI data, the authors discover a more complex set of neural activity states underlying the conventional stages of non-REM sleep.
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Affiliation(s)
- A B A Stevner
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK. .,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark. .,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.
| | - D Vidaurre
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - J Cabral
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
| | - K Rapuano
- Department of Psychological and Brain Sciences, Dartmouth College, 03755, Hanover, NH, USA
| | - S F V Nielsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark
| | - E Tagliazucchi
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - H Laufs
- Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - P Vuust
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC, 3800, Australia
| | - M W Woolrich
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - E Van Someren
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Departments of Integrative Neurophysiology and Psychiatry GGZ-InGeest, Amsterdam Neuroscience, VU University and Medical Center, 1081 HV, Amsterdam, The Netherlands
| | - M L Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark.,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
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Vidaurre D, Rodríguez EE, Bielza C, Larrañaga P, Rudomin P. A new feature extraction method for signal classification applied to cord dorsum potential detection. J Neural Eng 2012; 9:056009. [PMID: 22929924 DOI: 10.1088/1741-2560/9/5/056009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.
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Affiliation(s)
- D Vidaurre
- Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain.
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Abstract
The elucidation of the genes involved in cell wall synthesis and assembly remains one of the biggest challenges of cell wall biology. Although traditional genetic approaches, using simple yet elegant screens, have identified components of the cell wall, many unknowns remain. Exhausting the genetic toolbox by performing sensitized screens, adopting chemical genetics or combining these with improved cell wall imaging, hold the promise of new gene discovery and function. With the recent introduction of next-generation sequencing technologies, it is now possible to quickly and efficiently map and clone genes of interest in record time. The combination of a classical genetics approach and cutting edge technology will propel cell wall biology in plants forward into the future.
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Affiliation(s)
- Danielle Vidaurre
- Department of Cell and Systems Biology, University of Toronto,Toronto, ON, Canada
| | - Dario Bonetta
- Faculty of Science, University of Ontario Institute of Technology,Oshawa, ON, Canada
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Austin RS, Vidaurre D, Stamatiou G, Breit R, Provart NJ, Bonetta D, Zhang J, Fung P, Gong Y, Wang PW, McCourt P, Guttman DS. Next-generation mapping of Arabidopsis genes. Plant J 2011; 67:715-25. [PMID: 21518053 DOI: 10.1111/j.1365-313x.2011.04619.x] [Citation(s) in RCA: 192] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Next-generation genomic sequencing technologies have made it possible to directly map mutations responsible for phenotypes of interest via direct sequencing. However, most mapping strategies proposed to date require some prior genetic analysis, which can be very time-consuming even in genetically tractable organisms. Here we present a de novo method for rapidly and robustly mapping the physical location of EMS mutations by sequencing a small pooled F₂ population. This method, called Next Generation Mapping (NGM), uses a chastity statistic to quantify the relative contribution of the parental mutant and mapping lines to each SNP in the pooled F₂ population. It then uses this information to objectively localize the candidate mutation based on its exclusive segregation with the mutant parental line. A user-friendly, web-based tool for performing NGM analysis is available at http://bar.utoronto.ca/NGM. We used NGM to identify three genes involved in cell-wall biology in Arabidopsis thaliana, and, in a power analysis, demonstrate success in test mappings using as few as ten F₂ lines and a single channel of Illumina Genome Analyzer data. This strategy can easily be applied to other model organisms, and we expect that it will also have utility in crops and any other eukaryote with a completed genome sequence.
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Affiliation(s)
- Ryan S Austin
- Centre for the Analysis of Genome Evolution and Function, University of Toronto, ON, Canada
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