Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI.
Structural and functional connectivity (SC and FC) have received much attention over the last decade, as they offer unique insight into the coordination of brain functioning. They are often assessed independently with three imaging modalities: SC using diffusion-weighted imaging (DWI), FC using functional magnetic resonance imaging (fMRI), and magnetoencephalography/electroencephalography (MEG/EEG). DWI provides information about white matter organization, allowing the reconstruction of fiber bundles. fMRI uses blood-oxygenation level-dependent (BOLD) contrast to indirectly map neuronal activation. MEG and EEG are direct measures of neuronal activity, as they are sensitive to the synchronous inputs in pyramidal neurons. Seminal studies have targeted either the electrophysiological substrate of BOLD or the anatomical basis of FC. However, multimodal comparisons have been scarcely performed, and the relation between SC, fMRI-FC, and MEG-FC is still unclear. Here we present a systematic comparison of SC, resting state fMRI-FC, and MEG-FC between cortical regions, by evaluating their similarities at three different scales: global network, node, and hub distribution. We obtained strong similarities between the three modalities, especially for the following pairwise combinations: SC and fMRI-FC; SC and MEG-FC at theta, alpha, beta and gamma bands; and fMRI-FC and MEG-FC in alpha and beta. Furthermore, highest node similarity was found for regions of the default mode network and primary motor cortex, which also presented the highest hubness score. Distance was partially responsible for these similarities since it biased all three connectivity estimates, but not the unique contributor, since similarities remained after controlling for distance.
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.
Pilar
P
Garcés
Department of Industrial Engineering, Institute of Biomedical Technology (ITB-CIBINCAN), Universidad De La Laguna, Avda. Astrofísico Fco. Sánchez S/N, La Laguna, Tenerife, 38205, Spain.
Ernesto
E
Pereda
Department of Electronics Technology, Universidad Rey Juan Carlos, C/Tulipán S/N, Móstoles, Madrid, 28933, Spain.
Juan A
JA
Hernández-Tamames
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.|Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
Francisco
F
Del-Pozo
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.|Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
Fernando
F
Maestú
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.|CINAC, HM Puerta del Sur, Hospitales de Madrid, 28938 Móstoles, and CEU-San Pablo University, 28003, Madrid, Spain.
José Ángel
JÁ
Pineda-Pardo
D000328:Adult; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D056324:Diffusion Tensor Imaging; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D008297:Male; D008959:Models, Neurological; D064847:Multimodal Imaging; D009434:Neural Pathways / Q000098:blood supply* / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D055815:Young Adult
DWI; MEG; fMRI; functional connectivity; structural connectivity