Abstract-Some meditation techniques teach the practitioner to achieve the state of mental silence. The aim of this study was to investigate brain regions that are associated with their volume and functional connectivity (FC) with the depth of mental silence in long-term practitioners of Sahaja Yoga Meditation. Twenty-three longterm practitioners of this meditation were scanned using Magnetic Resonance Imaging. In order to identify the neural correlates of the depth of mental silence, we tested which gray matter volumes (GMV) were correlated with the depth of mental silence and which regions these areas were functionally connected to under a meditation condition. GMV in medial prefrontal cortex including rostral anterior cingulate cortex were positively correlated with the subjective perception of the depth of mental silence inside the scanner. Furthermore, there was significantly increased FC between this area and bilateral anterior insula/putamen during a meditation-state specifically, while decreased connectivity with the right thalamus/parahippocampal gyrus was present during the meditation-state and the resting-state. The capacity of long-term meditators to establish a durable state of mental silence inside an MRI scanner was associated with larger gray matter volume in a medial frontal region that is crucial for topdown cognitive, emotion and attention control. This is furthermore corroborated by increased FC of this region during the meditation-state with bilateral anterior insula/putamen, which are important for interoception, emotion, and attention regulation. The findings hence suggest that the depth of mental silence is associated with medial fronto-insular-striatal networks that are crucial for top-down attention and emotional control.

Previous behavioral research points to a positive relationship between maternal touch and early social development. Here, we explored the brain correlates of this relationship. The frequency of maternal touch was recorded for 43 five-year-old children during a 10 min standardized play session. Additionally, all children completed a resting-state functional magnetic resonance imaging session. Investigating the default mode network revealed a positive relation between the frequency of maternal touch and activity in the right posterior superior temporal sulcus (pSTS) extending into the temporo-parietal junction. Using this effect as a seed in a functional connectivity analysis identified a network including extended bilateral regions along the temporal lobe, bilateral frontal cortex, and left insula. Compared with children with low maternal touch, children with high maternal touch showed additional connectivity with the right dorso-medial prefrontal cortex. Together these results support the notion that childhood tactile experiences shape the developing “social brain” with a particular emphasis on a network involved in mentalizing.

In neuroimaging, the multiplicity issue may sneak into data analysis through several channels, affecting expected false positive rates (FPRs; type I errors) in diverse ways. One widely recognized aspect of multiplicity, multiple testing, occurs when the investigator fits a separate model for each voxel in the brain. However, multiplicity also occurs when the investigator conducts multiple comparisons within a model, tests two tails of a t-test separately when prior information is unavailable about the directionality, and branches in the analytic pipelines. The current practice of handling multiple testing through controlling the overall FPR in neuroimaging under the null hypothesis significance testing (NHST) paradigm excessively penalizes the statistical power with inflated type II errors. More fundamentally, the adoption of dichotomous decisions through sharp thresholding under NHST may not be appropriate when the null hypothesis itself is not pragmatically relevant because the effect of interest takes a continuum instead of discrete values and is not expected to be null in most brain regions.When the noise inundates the signal, two different types of error are more relevant than the concept of FPR: incorrect sign (type S) and incorrect magnitude (type M).In light of these considerations, we introduce a different strategy using Bayesian hierarchical modeling (BHM) to achieve two goals: 1) improving modeling efficiency via one integrative (instead of many separate) model and dissolving the multiple testing issue, and 2) turning the focus of conventional NHST on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BHM may simultaneously achieve increased spatial specificity and inference efficiency. The benefits of BHM are illustrated in model performance and quality checking using an experimental dataset. In addition, BHM offers an alternative, confirmatory, or complementary approach to the conventional whole brain analysis under NHST, and promotes results reporting in totality and transparency. The methodology also avoids sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The modeling approach with its auxiliary tools will be available as part of the AFNI suite for general use.

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

Resting-state functional magnetic resonance imaging is a powerful technique to study the whole-brain neural connectivity that underlies cognitive systems. The present study aimed to define the changes in neural connectivity in their relation to language development. Longitudinal resting-state functional data were acquired from a cohort of preschool children at age 5 and one year later, and changes in functional connectivity were correlated with language performance in sentence comprehension. For this, degree centrality, a voxel-based network measure, was used to assess age-related differences in connectivity at the whole-brain level. Increases in connectivity with age were found selectively in a cluster within the left posterior superior temporal gyrus and sulcus (STG/STS). In order to further specify the connection changes, a secondary seed-based functional connectivity analysis on this very cluster was performed. The correlations between resting-state functional connectivity (RSFC) and language performance revealed developmental effects with age and, importantly, also dependent on the advancement in sentence comprehension ability over time. In children with greater advancement in language abilities, the behavioral improvement was positively correlated with RSFC increase between left posterior STG/STS and other regions of the language network, i.e., left and right inferior frontal cortex. The age-related changes observed in this study provide evidence for alterations in the language network as language develops and demonstrates the viability of this approach for the investigation of normal and aberrant language development.