Abstract
Background:
Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.
New method:
We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.
Results:
We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer’s disease.
Conclusions:
Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.
New method:
We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.
Results:
We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer’s disease.
Conclusions:
Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
| Original language | English |
|---|---|
| Article number | 108991 |
| Journal | Journal of Neuroscience Methods |
| Volume | 348 |
| Early online date | 9 Nov 2020 |
| DOIs | |
| Publication status | Published (in print/issue) - 15 Jan 2021 |
Bibliographical note
Funding Information:This work was supported by the EU’s INTERREG VA Programme , managed by the Special EU Programmes Body (SEUPB) (J.M.S.-B., P.L.M. and K.W.-L.), the Northern Ireland Functional Brain Mapping Project ( 1303/101154803 ) funded by Invest NI and Ulster University (S.Y., G.P. and K.W.-L.), the Spanish Ministry of Economy and Competitiveness ( PSI2009-14415-C03-01 ) and Madrid Neurocenter (M.E.L., R.B. and F.M.), Alzheimer’s Research UK (ARUK) Pump Priming Awards (D.P.F, S.T., G.P., P.L.M. and K.W.-L.), and Medical College of Wisconsin (V.Y.). P. M. and K.W.-L. received additional support from Ulster University Research Challenge Fund, and Global Challenges Research Fund, and K.W.-L. from COST Action Open Multiscale Systems Medicine (OpenMultiMed) supported by COST (European Cooperation in Science and Technology). The views and opinions expressed in this paper do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).
Funding Information:
This work was supported by the EU's INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB) (J.M.S.-B. P.L.M. and K.W.-L.), the Northern Ireland Functional Brain Mapping Project (1303/101154803) funded by Invest NI and Ulster University (S.Y. G.P. and K.W.-L.), the Spanish Ministry of Economy and Competitiveness (PSI2009-14415-C03-01) and Madrid Neurocenter (M.E.L. R.B. and F.M.), Alzheimer's Research UK (ARUK) Pump Priming Awards (D.P.F, S.T. G.P. P.L.M. and K.W.-L.), and Medical College of Wisconsin (V.Y.). P. M. and K.W.-L. received additional support from Ulster University Research Challenge Fund, and Global Challenges Research Fund, and K.W.-L. from COST Action Open Multiscale Systems Medicine (OpenMultiMed) supported by COST (European Cooperation in Science and Technology). The views and opinions expressed in this paper do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).
Publisher Copyright:
© 2020 The Authors
Keywords
- functional connectivity
- cluster permutation statistics
- nonparametric statistics
- multiple comparison correction
- EEG and MEG biomarkers
- Alzheimer’s disease
- Functional connectivity
- Cluster permutation statistics
- Multiple comparison correction
- Alzheimer's disease
- Nonparametric statistics