1
|
Astle DE, Luckhoo H, Woolrich M, Kuo BC, Nobre AC, Scerif G. The Neural Dynamics of Fronto-Parietal Networks in Childhood Revealed using Magnetoencephalography. Cereb Cortex 2014; 25:3868-76. [PMID: 25410426 PMCID: PMC4585520 DOI: 10.1093/cercor/bhu271] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [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] [Indexed: 01/28/2023] Open
Abstract
Our ability to hold information in mind is limited, requires a high degree of cognitive control, and is necessary for many subsequent cognitive processes. Children, in particular, are highly variable in how, trial-by-trial, they manage to recruit cognitive control in service of memory. Fronto-parietal networks, typically recruited under conditions where this cognitive control is needed, undergo protracted development. We explored, for the first time, whether dynamic changes in fronto-parietal activity could account for children's variability in tests of visual short-term memory (VSTM). We recorded oscillatory brain activity using magnetoencephalography (MEG) as 9- to 12-year-old children and adults performed a VSTM task. We combined temporal independent component analysis (ICA) with general linear modeling to test whether the strength of fronto-parietal activity correlated with VSTM performance on a trial-by-trial basis. In children, but not adults, slow frequency theta (4–7 Hz) activity within a right lateralized fronto-parietal network in anticipation of the memoranda predicted the accuracy with which those memory items were subsequently retrieved. These findings suggest that inconsistent use of anticipatory control mechanism contributes significantly to trial-to-trial variability in VSTM maintenance performance.
Collapse
Affiliation(s)
- Duncan E Astle
- MRC Cognition and Brain Sciences Unit, Cambridge, UK Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Henry Luckhoo
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
| | - Bo-Cheng Kuo
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK Department of Experimental Psychology, University of Oxford, Oxford, UK Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Gaia Scerif
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| |
Collapse
|
2
|
Cabral J, Luckhoo H, Woolrich M, Joensson M, Mohseni H, Baker A, Kringelbach ML, Deco G. Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage 2014; 90:423-35. [PMID: 24321555 DOI: 10.1016/j.neuroimage.2013.11.047] [Citation(s) in RCA: 174] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2013] [Revised: 11/20/2013] [Accepted: 11/27/2013] [Indexed: 12/13/2022] Open
|
3
|
Nakagawa TT, Woolrich M, Luckhoo H, Joensson M, Mohseni H, Kringelbach ML, Jirsa V, Deco G. How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest. Neuroimage 2013; 87:383-94. [PMID: 24246492 DOI: 10.1016/j.neuroimage.2013.11.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 09/23/2013] [Accepted: 11/05/2013] [Indexed: 10/26/2022] Open
Abstract
In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific task has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a large scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal patterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as in MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of the brain has been identified as being a key to the observed functional network connectivity, but the mechanisms behind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the importance of the connectome in shaping network dynamics, while the importance of delays and noise differ between studies and depend on the models' specific dynamics. In the current study, we present a spiking neuron network model that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrum of group resting-state MEG recordings. We studied how well the model captured the inter-node correlation structure of the alpha-band power envelopes for different delays between brain areas, and found that the model performs best for propagation delays inside the physiological range (5-10 m/s). Delays also shift the transition from noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the asynchronous fMRI state, delays are important to consider in the presence of oscillation.
Collapse
Affiliation(s)
- Tristan T Nakagawa
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
| | - Mark Woolrich
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Henry Luckhoo
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Morten Joensson
- Department of Psychiatry, University of Oxford, Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Denmark
| | - Hamid Mohseni
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Denmark
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Universitè, 13005 Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona 08010, Spain
| |
Collapse
|
4
|
Woolrich MW, Baker A, Luckhoo H, Mohseni H, Barnes G, Brookes M, Rezek I. Dynamic state allocation for MEG source reconstruction. Neuroimage 2013; 77:77-92. [PMID: 23545283 PMCID: PMC3898887 DOI: 10.1016/j.neuroimage.2013.03.036] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 03/15/2013] [Accepted: 03/21/2013] [Indexed: 11/16/2022] Open
Abstract
Our understanding of the dynamics of neuronal activity in the human brain remains limited, due in part to a lack of adequate methods for reconstructing neuronal activity from noninvasive electrophysiological data. Here, we present a novel adaptive time-varying approach to source reconstruction that can be applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. The method is underpinned by a Hidden Markov Model (HMM), which infers the points in time when particular states re-occur in the sensor space data. HMM inference finds short-lived states on the scale of 100ms. Intriguingly, this is on the same timescale as EEG microstates. The resulting state time courses can be used to intelligently pool data over these distinct and short-lived periods in time. This is used to compute time-varying data covariance matrices for use in beamforming, resulting in a source reconstruction approach that can tune its spatial filtering properties to those required at different points in time. Proof of principle is demonstrated with simulated data, and we demonstrate improvements when the method is applied to MEG.
Collapse
Affiliation(s)
- Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, Oxford, UK.
| | | | | | | | | | | | | |
Collapse
|
5
|
Nakagawa TT, Luckhoo H, Woolrich M, Joensson M, Mohseni H, Kringelbach M, Jirsa V, Deco G. Modeling Alpha-Band Functional Connectivity for MEG Resting State Data: Oscillations and Delays in a Spiking Neuron Model. BMC Neurosci 2013. [PMCID: PMC3704861 DOI: 10.1186/1471-2202-14-s1-p99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
|
6
|
Hindriks R, Woolrich M, Kringelbach M, Luckhoo H, Joensson M, Mohseni H, Deco G. Role of anatomical pathways in shaping posterior alpha oscillations in the resting human brain. BMC Neurosci 2013. [PMCID: PMC3704852 DOI: 10.1186/1471-2202-14-s1-p98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
|
7
|
Brookes MJ, Liddle EB, Hale JR, Woolrich MW, Luckhoo H, Liddle PF, Morris PG. Task induced modulation of neural oscillations in electrophysiological brain networks. Neuroimage 2012; 63:1918-30. [PMID: 22906787 DOI: 10.1016/j.neuroimage.2012.08.012] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2012] [Revised: 08/02/2012] [Accepted: 08/05/2012] [Indexed: 11/24/2022] Open
Abstract
In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.
Collapse
Affiliation(s)
- M J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, UK.
| | | | | | | | | | | | | |
Collapse
|
8
|
Mohseni HR, Woolrich MW, Kringelbach ML, Luckhoo H, Smith PP, Aziz TZ. Fusion of Magnetometer and Gradiometer Sensors of MEG in the Presence of Multiplicative Error. IEEE Trans Biomed Eng 2012; 59:1951-61. [DOI: 10.1109/tbme.2012.2195001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
9
|
Luckhoo H, Hale JR, Stokes MG, Nobre AC, Morris PG, Brookes MJ, Woolrich MW. Inferring task-related networks using independent component analysis in magnetoencephalography. Neuroimage 2012; 62:530-41. [PMID: 22569064 PMCID: PMC3387383 DOI: 10.1016/j.neuroimage.2012.04.046] [Citation(s) in RCA: 102] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2011] [Revised: 04/18/2012] [Accepted: 04/23/2012] [Indexed: 12/01/2022] Open
Abstract
A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8–20 Hz frequency range, temporally down-sampled with windows of 1–4 s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.
Collapse
Affiliation(s)
- H Luckhoo
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK.
| | | | | | | | | | | | | |
Collapse
|