Slow-5 dynamic functional connectivity reflects the capacity to sustain cognitive performance during pain.

Some individuals are more distracted by pain during a cognitive task than others, representing poor pain coping. We have characterized individuals as A-type (attention dominates) or P-type (pain dominates) based on how pain interferes with task speed. The ability to optimize behavior during pain may relate to the flexibility in communication at rest between the dorsolateral prefrontal cortex (DLPFC) of the executive control network, and the anterior mid-cingulate cortex (aMCC) of the salience network (SN) - regions involved in cognitive-interference. The aMCC and aIns (SN hub) also signify pain salience; flexible communication at rest between them possibly allowing prioritizing task performance during pain. We tested the hypotheses that pain-induced changes in task performance are related to resting-state dynamic functional connectivity (dFC) between these region pairs (DLPFC-aMCC; aMCC-aIns). We found that 1) pain reduces task consistency/speed in P-type individuals, but enhances performance in A-type individuals, 2) task consistency is related to the FC dynamics within DLPFC-aMCC and aMCC-aIns pairs, 3) brain-behavior relationships are driven by dFC within the slow-5 (0.01-0.027Hz) frequency band, and 4) dFC across the brain decreases at higher frequencies. Our findings point to neural communication dynamics at rest as being associated with prioritizing task performance over pain.

Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI.

BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2).

This article proposes the image intraclass correlation (I2C2) coefficient as a global measure of reliability for imaging studies. The I2C2 generalizes the classic intraclass correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: regional analysis of volumes in normalized space imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting-state functional magnetic resonance imaging (fMRI), and fractional anisotropy in an area surrounding the corpus callosum via diffusion tensor imaging. Notably, resting-state fMRI brain activation maps are found to have low reliability, ranging from .2 to .4. Software and data are available to provide easy access to the proposed methods.