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.

Assessment of data acquisition parameters, and analysis techniques for noise reduction in spinal cord fMRI data.

PURPOSE: The purpose of this work is to characterize the noise in spinal cord functional MRI, assess current methods aimed at reducing noise, and optimize imaging parameters. METHODS: Functional MRI data were acquired at multiple echo times and the contrast-to-noise ratio (CNR) was calculated. Independently, the repetition time was systematically varied with and without parallel imaging, to maximize BOLD sensitivity and minimize type I errors. Noise in the images was characterized by examining the frequency spectrum, and investigating whether autocorrelations exist. The efficacy of several physiological noise reduction methods in both null (no stimuli) and task (thermal pain paradigm) data was also assessed. Finally, our previous normalization methods were extended. RESULTS: The echo time with the highest functional CNR at 3 Tesla is at roughly 75msec. Parallel imaging reduced the variance and the presence of autocorrelations, however the BOLD response in task data was more robust in data acquired without parallel imaging. Model-free based approaches further increased the detection of active voxels in the task data. Finally, inter-subject registration was improved. CONCLUSIONS: Results from this study provide a rigorous characterization of the properties of the noise and assessment of data acquisition and analysis methods for spinal cord and brainstem fMRI.

Advanced MR imaging techniques and characterization of residual anatomy.

Advances in technology in recent decades have contributed to rapid developments in non-invasive methods for imaging human anatomy, and advanced imaging methods are now one of the primary tools for clinical diagnosis after neurological trauma or disease. Here we review the current and upcoming capabilities of one of the most rapidly developing methods, magnetic resonance imaging (MRI). The underlying theory is introduced so that the reasons for the strengths, weaknesses, and future expectations of this method, can be explained. Current techniques for imaging anatomical changes, inflammation, and changes in white matter, axonal integrity, blood flow and function, are reviewed. Applications for specific purposes of assessing traumatic injury in the brain or spinal cord, and for multiple-sclerosis are also presented, and are used as examples of how the advanced techniques are being used in practice.