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Background Autonomic dysregulation may lead to blunted sympathetic reactivity in chronic pain states. Autonomic responses are controlled by the central autonomic network (CAN). Little research has examined sympathetic reactivity and associations with brain CAN structures in the presence of chronic pain; thus, the present study aims to investigate how chronic pain influences sympathetic reactivity and associations with CAN brain region volumes. Methods Sympathetic reactivity was measured as change in skin conductance level (ΔSCL) between a resting reference period and walking periods for typical and complex walking tasks (obstacle and dual-task). Participants included 31 people with (n = 19) and without (n = 12) chronic musculoskeletal pain. Structural 3 T MRI was used to determine gray matter volume associations with ΔSCL in regions of the CAN (i.e., brainstem, amygdala, insula, and anterior cingulate cortex). Results ΔSCL varied across walking tasks (main effect p = 0.036), with lower ΔSCL in chronic pain participants compared to controls across trials 2 and 3 under the obstacle walking condition. ΔSCL during typical walking was associated with multiple CAN gray matter volumes, including brainstem, bilateral insula, amygdala, and right caudal anterior cingulate cortex (p’s < 0.05). The difference in ΔSCL from typical-to-obstacle walking were associated with volumes of the midbrain segment of the brainstem and anterior segment of the circular sulcus of the insula (p’s < 0.05), with no other significant associations. The difference in ΔSCL from typical-to-dual task walking was associated with the bilateral caudal anterior cingulate cortex, and left rostral cingulate cortex (p’s < 0.05). Conclusions Sympathetic reactivity is blunted during typical and complex walking tasks in persons with chronic pain. Additionally, blunted sympathetic reactivity is associated with CAN brain structure, with direction of association dependent on brain region. These results support the idea that chronic pain may negatively impact typical autonomic responses needed for walking performance via its potential impact on the brain.

Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the brain, which are defined from the analysis of functional data lack a definitive justification consistent with the anatomical structure of the brain. In this work we show that a possible connection may naturally rest on the idea that information flows in the brain through a neural message-passing dynamics between macroscopic structures, like those defined by the human connectome (HC). In our model, each brain region in the HC is assumed to have a binary behavior (active or not), the strength of interactions among them is encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by a neural message-passing algorithm, Belief Propagation (BP), working near the critical point of the human connectome. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the DMN. Moreover, we computed, using Susceptibility Propagation (SP), the matrix of correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting States Networks determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. We then show preliminary results indicating our predictions on how functional DMN maps change when the anatomical brain network suffers structural anomalies, like in Alzheimer's Disease and in lesions of the Corpus Callosum.

Background and Objectives Somatosensory function is critical for successful aging. Prior studies have shown declines in somatosensory function with age; however, this may be affected by testing site, modality, and biobehavioral factors. While somatosensory function declines are associated with peripheral nervous system degradation, little is known regarding correlates with the central nervous system and brain structure in particular. The objectives of this study were to examine age-related declines in somatosensory function using innocuous and noxious stimuli, across 2 anatomical testing sites, with considerations for affect and cognitive function, and associations between somatosensory function and brain structure in older adults. Research Design and Methods A cross-sectional analysis included 84 “younger” (n = 22, age range: 19–24 years) and “older” (n = 62, age range: 60–94 years) healthy adults who participated in the Neuromodulatory Examination of Pain and Mobility Across the Lifespan study. Participants were assessed on measures of somatosensory function (quantitative sensory testing), at 2 sites (metatarsal and thenar) using standardized procedures, and completed cognitive and psychological function measures and structural magnetic resonance imaging. Results Significant age × test site interaction effects were observed for warmth detection (p = .018, ηp2= 0.10) and heat pain thresholds (p = .014, ηp2= 0.12). Main age effects were observed for mechanical, vibratory, cold, and warmth detection thresholds (ps &lt; .05), with older adults displaying a loss of sensory function. Significant associations between somatosensory function and brain gray matter structure emerged in the right occipital region, the right temporal region, and the left pericallosum. Discussion and Implications Our findings indicate healthy older adults display alterations in sensory responses to innocuous and noxious stimuli compared to younger adults and, furthermore, these alterations are uniquely affected by anatomical site. These findings suggest a nonuniform decline in somatosensation in older adults, which may represent peripheral and central nervous system alterations part of aging processes.

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Introduction: Psychological factors have been associated with knee osteoarthritis pain severity and treatment outcomes, yet their combined contribution to phenotypic heterogeneity is poorly understood. In particular, empirically derived psychological profiles must be replicated before they can be targeted or considered for treatment studies. The objectives of this study were to (1) confirm previously identified psychological profiles using unsupervised clustering methods in persons with knee osteoarthritis pain, (2) determine the replicability of profiles using supervised machine learning in a different sample, and (3) examine associations with clinical pain, brain structure, and experimental pain. Methods: Participants included two cohorts of individuals with knee osteoarthritis pain recruited as part of the multisite UPLOAD1 ( n = 270, mean age = 56.8 ± 7.6, male = 37%) and UPLOAD2 ( n = 164, mean age = 57.73 ± 7.8, male = 36%) studies. Similar psychological constructs (e.g. optimism, coping, somatization, affect, depression, and anxiety), sociodemographic and clinical characteristics, and somatosensory function were assessed across samples. UPLOAD2 participants also completed brain magnetic resonance imaging. Unsupervised hierarchical clustering analysis was first conducted in UPLOAD1 data to derive clusters, followed by supervised linear discriminative analysis to predict group membership in UPLOAD2 data. Associations among cluster membership and clinical variables were assessed, controlling for age, sex, education, ethnicity/race, study site, and number of pain sites. Results: Four distinct profiles emerged in UPLOAD1 and were replicated in UPLOAD2. Identified psychological profiles were associated with psychological variables ( ps < 0.001), and clinical outcomes ( ps = 0.001–0.03), indicating good internal and external validation of the cluster solution. Significant associations between psychological profiles and somatosensory function and brain structure were also found. Conclusions: This study highlights the importance of considering the biopsychosocial model in knee osteoarthritis pain assessment and treatment.

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