Hemispheric asymmetries in resting-state EEG and fMRI are related to approach and avoidance behaviour, but not to eating behaviour or BMI

Much of our behaviour is driven by two motivational dimensions-approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting-state EEG studies: Approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right-brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self-reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour and BMI. We analysed three samples: Sample 1 (n = 117) containing EEG and fMRI data from lean participants, and Samples 2 (n = 89) and 3 (n = 152) containing fMRI data from lean, overweight and obese participants. In Sample 1, approach behaviour in women was related to EEG, but not to fMRI hemispheric asymmetries. In Sample 2, approach/avoidance behaviours were related to fMRI hemispheric asymmetries. Finally, hemispheric asymmetries were not related to either BMI or eating behaviour in any of the samples. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI.Peer reviewe

Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control

Resting state neuronal activity in EEG/MEG recordings is primarily characterized by the presence of alpha oscillations (approx. 8–12 Hz). However, their functional significance and link to cognitive task performance remains elusive. We investigated resting state neuronal activity and its relation to task performance by assessing traditional measures of alpha activity (power and individual alpha peak frequency) and dynamic properties of the signal measured by long-range temporal correlations (LRTC). Multichannel EEG was recorded at rest in 82 healthy male adults and compared to their cognitive performance, measured by tests involving executive functions, working memory, short- and long-term memory demands. Our results showed that attention-span scores positively correlated with alpha power at rest, with corresponding neuronal sources located primarily in the left-hemispheric anterior cingulate cortex, parietal regions, and bilateral suplementary motor areas. Furthermore, better working memory performance was related to increased LRTC of alpha oscillations at rest in the right hemispheric fronto-parietal, temporal, and occipital regions. Our findings suggest that resting state neuronal activity may reflect properties of brain networks that are functionally relevant for cognitive task performance. While alpha power measured at rest might relate to tasks that employ sustained inhibitory control, LRTC are suggested to reflect the capacity of neuronal networks to perform tasks that require phasic attention and quick adaptation to changing task demands

Consistency of EEG source localization and connectivity estimates

As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure

The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

The human cortex is characterized by local morphological features such as cortical thickness, myelin content, and gene expression that change along the posterior-anterior axis. We investigated if some of these structural gradients are associated with a similar gradient in a prominent feature of brain activity - namely the frequency of oscillations. In resting-state MEG recordings from healthy participants (N = 187) using mixed effect models, we found that the dominant peak frequency in a brain area decreases significantly along the posterior-anterior axis following the global hierarchy from early sensory to higher order areas. This spatial gradient of peak frequency was significantly anticorrelated with that of cortical thickness, representing a proxy of the cortical hierarchical level. This result indicates that the dominant frequency changes systematically and globally along the spatial and hierarchical gradients and establishes a new structure-function relationship pertaining to brain oscillations as a core organization that may underlie hierarchical specialization in the brain