The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p≤0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Empathy relies on brain systems that support the interaction between an observer’s mental state and cues about the others’ experience. Beyond the core brain areas typically activated in pain empathy studies (insular and anterior cingulate cortices), the diversity of paradigms used may reveal secondary networks that subserve other more specific processes. A coordinate-based meta-analysis of fMRI experiments on pain empathy was conducted to obtain activation likelihood estimates along three factors and seven conditions: visual cues (body parts, facial expressions), visuospatial (first-person, thirdperson), and cognitive (self-, stimuli-, other-oriented tasks) perspectives. The core network was found across cues and perspectives, and common activation was observed in higher-order visual areas. Body-parts distinctly activated areas related with sensorimotor processing (superior and inferior parietal lobules, anterior insula) while facial expression distinctly involved the inferior frontal gyrus. Self- compared to other-perspective produced distinct activations in the left insula while stimulus- versus other-perspective produced distinctive responses in the inferior frontal and parietal lobules, precentral gyrus, and cerebellum. Pain empathy relies on a core network which is modulated by several secondary networks. The involvement of the latter seems to depend on the visual cues available and the observer's mental state that can be influenced by specific instructions.

Evidence that patients with chronic pain selectively attend to pain-related stimuli presented in modified Stroop and dot-probe paradigms is mixed. The pain-related stimuli used in these studies have been primarily verbal in nature (i.e., words depicting themes of pain). The purpose of the present study was to determine whether patients with chronic pain, relative to healthy controls, show selective attention for pictures depicting painful faces. To do so, 170 patients with chronic pain and 40 age- and education-matched healthy control participants were tested using a dot-probe task in which painful, happy, and neutral facial expressions were presented. Selective attention was denoted using the mean reaction time and the bias index. Results indicated that, while both groups shifted attention away from happy faces (and towards neutral faces), only the control group shifted attention away from painful faces. Additional analyses were conducted on chronic pain participants after dividing them into groups on the basis of fear of pain/(re)injury. The results of these analyses revealed that while chronic pain patients with high and low levels of fear both shifted attention away from happy faces, those with low fear shifted attention away from painful faces, whereas those with high fear shifted attention towards painful faces. These results suggest that patients with chronic pain selectively attend to facial expressions of pain and, importantly, that the tendency to shift attention towards such stimuli is positively influenced by high fear of pain/(re)injury. Implications of the findings and future research directions are discussed.

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) is fraught with challenges, among which is the lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for the three main 3T MRI vendors: GE, Philips and Siemens. The protocol provides valuable metrics for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area (CSA) computation, multi-echo gradient echo for gray matter CSA, as well as magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. The spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects, as detailed in the companion paper [REF-DATA]. The spine generic protocol is open-access and its latest version can be found at: https://spinalcordmri.org/protocols. The protocol will serve as a valuable starting point for researchers and clinicians implementing new SC imaging initiatives. Note to the reviewer/editor/publisher: the companion paper is referred to as [REF-DATA]6/52 121 122dealing with cervical myelopathy and MS populations. Applications of the MethodThe proposed protocol is not geared towards a specific disease and it is suitable for imaging WM pathology (demyelination and Wallerian degeneration via axon/myelin-sensitive 122 https://mssociety.ca/about-ms-research/about-our-research-program/research-we-fund/canadian-prospect ive-cohort-study-to-understand-progression-in-ms-canproco 121 https://www.wingsforlife.com/us/research/imaging-spinal-cord-injury-and-assessing-its-predictive-value-th e-inspired-study-2675/ 9/52