Segerdahl, A. R. | bioRxiv Science

bioRxiv2021

Neural dynamics of parametrically modulated human mechanical pain determined using whole-brain quantitative perfusion imaging.

Arterial spin labelling (ASL) FMRI is a powerful tool to noninvasively image tonic and ongoing pain states in both healthy participants and patients. We used ASL to image the neural correlates of extended, parametrically modulated mechanical pain in healthy human participants. The aims of this study were to: i) assess if force-calibrated pin-prick probes could safely and robustly evoke tonic mechanical pain; ii) determine the neural correlates of the parametric changes in both the "force" of the stimulus and the "intensity" of the perception that this elicits using ASL; and iii) provide an initial assessment of the capacity for ALFF to differentiate painful versus non-painful tonic stimuli based on changes in the dynamics of the evoked signal. Our data confirm that it is possible to employ a stimulus force-locked design to induce robust, well maintained ongoing mechanical pain and to observe significant changes in rCBF relative to underlying component processes such as monitoring graded changes in the force applied to the skin (dACC, aMCC, pMCC, PCC, SI, SII, putamen, thalamus and the insula (anterior and posterior subsections); ipsilateral amygdala and hypothalamus; and the contralateral DLPFC) and tracking changes in the perceived intensity of the experience (: bilateral dACC, aMCC, pMCC, PCC, thalamus, SII and the cerebellum; and contralateral SI, insula (including the dpIns). Further exploration of the data using analyses targeting the spectral frequency aspects of the rCBF signal observed reveals that a collection of regions (e.g. the contralateral VLPFC, inferior frontal gyrus, insula (anterior, mid and posterior subsections), SII, putamen, OFC, amygdala, and the hippocampus) exhibit unique perfusion dynamics during extended painful stimulation compared to non-painful touch. Results from this study provide further validation for the application of ASL to image experimental pain in healthy human subjects while interrogation of the data offers unique insight into the dynamic signal changes underlying the perception of a tonic mechanical pain experience.

neuroscience↗

bioRxiv2019

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) [Harrison et al. 2015] to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. Using simulated data, resting-state data from 1,000 subjects collected as part of the Human Connectome Project [Van Essen et al. 2013], and an analysis of 14 subjects in a variety of continuous task-states [Kieliba et al. 2019], we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

neuroscience↗