Functional Connectivity Lateralisation Shift of Resting State Networks is Linked to Visuospatial Memory and White Matter Microstructure in Relapsing–Remitting Multiple Sclerosis

Laterality patterns of resting state networks (RSN) change in various neuropsychiatric conditions. Multiple sclerosis (MS) causes neuro-cognitive symptoms involving dysfunctional large-scale brain networks. Yet, whether healthy laterality patterns of RSNs are maintained in MS and whether altered laterality patterns explain disease symptoms has not been explicitly investigated. We analysed functional MRI and diffusion tensor imaging data from 24 relapsing–remitting MS patients and 25 healthy participants. We performed group-level independent component analysis and used dual regression to estimate individual versions of well-established RSNs. Voxelwise laterality indices were calculated for each RSN. Group differences were assessed via a general linear model-based approach. The relationship between functional laterality and white matter microstructural asymmetry was assessed using Tract-Based Spatial Statistics. Spearman’s correlation was calculated between laterality indices and Brief International Cognitive Assessment for Multiple Sclerosis scores. Functional laterality of the dorsal attention network showed a significant leftward shift in the MS group in the posterior intraparietal sulcus (p < 0.033). Default-mode network laterality showed a significant leftward shift in the MS group in the angular gyrus (p < 0.005). Diminished dorsal attention network laterality was associated with increased fractional anisotropy asymmetry in the superior longitudinal fasciculus (p < 0.02). In the default-mode network, leftward laterality of the angular gyrus was associated with higher BVMT-R scores (R = − 0.52, p < 0.023). Our results confirm previous descriptions of RSN dysfunction in relapsing–remitting MS and show that altered functional connectivity lateralisation patterns of RSNs might contibute to cognitive performance and structural remodellation even in patients with mild clinical symptoms.

It is Not (Always) the Mismatch That Beats You—On the Relationship Between Interaction of Early and Recent Life Stress and Emotion Regulation, an fMRI Study

Stress may impact the ability to effectively regulate emotions. To study the impact of stressful experiences in early and recent life on emotion regulation, we examined the relationship between early life stress, recent stress, and brain activation during cognitive reappraisal. We investigated two regulation goals: the decrease and increase of emotional response to both negative and positive stimuli. Furthermore, two models of stress consequences were examined: the cumulative and match/mismatch models. A total of 83 participants (Mage = 21.66) took part in the study. There was an interaction between cumulative stress and stimuli valence in the cuneus, superior lateral occipital cortex, superior parietal lobule, supramarginal gyrus extending to superior temporal gyrus, and precentral gyrus extending to supplementary motor area. Interaction between mismatched stress index and stimuli valence was found in the left hippocampus, left insula extending to the orbitofrontal cortex and amygdala, and in a cluster including the anterior cingulate cortex, superior frontal gyrus, and frontal pole. Furthermore, there were differences between the effects of cumulative and mismatched stress indices on brain activation during reappraisal of positive but not negative stimuli. Results indicate that cumulative stress and match/mismatch approaches are both useful for explaining brain activation during reappraisal. This finding is important for our understanding of the multifaceted impact of stress on emotion regulation.

Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

Meditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in $$\alpha$$ α , $$\beta$$ β , $$\gamma$$ γ , and $$\theta$$ θ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.