Feasibility of brain age predictions from clinical T1-weighted MRIs.

An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2-3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R2 (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.

Accelerated Brain Aging Mediates the Association Between Psychological Profiles and Clinical Pain in Knee Osteoarthritis.

Chronic pain is driven by factors across the biopsychosocial spectrum. Previously, we demonstrated that magnetic resonance images (MRI)-based brain-predicted age differences (brain-PAD: brain-predicted age minus chronological age) were significantly associated with pain severity in individuals with chronic knee pain. We also previously identified four distinct, replicable, multidimensional psychological profiles significantly associated with clinical pain. The brain aging-psychological characteristics interface in persons with chronic pain promises elucidating factors contributing to their poor health outcomes, yet this relationship is barely understood. That is why we examined the interplay between the psychological profiles in participants having chronic knee pain impacting function, brain-PAD, and clinical pain severity. Controlling for demographics and MRI scanner, we compared the brain-PAD among psychological profiles at baseline (n = 164) and over two years (n = 90). We also explored whether profile-related differences in pain severity were mediated by brain-PAD. Brain-PAD differed significantly between profiles (ANOVA's omnibus test, P = .039). Specifically, participants in the profile 3 group (high negative/low positive emotions) had an average brain-PAD ∼4 years higher than those in profile- (low somatic reactivity), with P = .047, Bonferroni-corrected, and than those in profile 2 (high coping), with P = .027, uncorrected. Repeated measures ANOVA revealed no significant change in profile-related brain-PAD differences over time, but there was a significant decrease in brain-PAD for profile 4 (high optimism/high positive affect), with P = .045. Moreover, profile-related differences in pain severity at baseline were partly explained by brain-PAD differences between profile 3 and 1, or 2; but brain-PAD did not significantly mediate the influence of variations in profiles on changes in pain severity over time. PERSPECTIVE: Accelerated brain aging could underlie the psychological-pain relationship, and psychological characteristics may predispose individuals with chronic knee pain to worse health outcomes via neuropsychological processes.

Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs.

The difference between the estimated brain age and the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age models were developed for research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from various protocols. We adopted a dual-transfer learning strategy to develop a model agnostic to modality, resolution, or slice orientation. We retrained a convolutional neural network (CNN) using 6281 clinical MRIs from 1559 patients, among 7 modalities and 8 scanner models. The CNN was trained to estimate brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a 'super-resolution' method. The model failed with T2-weighted Gradient-Echo MRIs. The mean absolute error (MAE) was 5.86-8.59 years across the other modalities, still higher than for research-grade MRIs, but comparable between actual and synthetic MPRAGEs for some modalities. We modeled the "regression bias" in brain age, for its correction is crucial for providing unbiased summary statistics of brain age or for personalized brain age-based biomarkers. The bias model was generalizable as its correction eliminated any correlation between brain-PAD and chronological age in new samples. Brain-PAD was reliable across modalities. We demonstrate the feasibility of brain age predictions from arbitrary clinical-grade MRIs, thereby contributing to personalized medicine.

Toward MR protocol-agnostic, bias-corrected brain age predicted from clinical-grade MRIs.

The predicted brain age minus the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age methods were developed to use research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from multiple protocols. To overcome this, we adopted a double transfer learning approach to develop a brain age model agnostic to modality, resolution, or slice orientation. Using 6,224 clinical MRIs among 7 modalities, scanned from 1,540 patients using 8 scanners among 15 + facilities of the University of Florida's Health System, we retrained a convolutional neural network (CNN) to predict brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a deep learning-trained 'super-resolution' method. We also modeled the "regression dilution bias", a typical overestimation of younger ages and underestimation of older ages, which correction is paramount for personalized brain age-based biomarkers. This bias was independent of modality or scanner and generalizable to new samples, allowing us to add a bias-correction layer to the CNN. The mean absolute error in test samples was 4.67-6.47 years across modalities, with similar accuracy between original MPRAGEs and their synthetic counterparts. Brain-PAD was also reliable across modalities. We demonstrate the feasibility of clinical-grade brain age predictions, contributing to personalized medicine.

Brain-predicted age difference mediates the association between PROMIS sleep impairment, and self-reported pain measure in persons with knee pain.

Knee pain, the most common cause of musculoskeletal pain (MSK), constitutes a severe public health burden. Its neurobiological causes, however, remain poorly understood. Among many possible causes, it has been proposed that sleep problems could lead to an increase in chronic pain symptomatology, which may be driven by central nervous system changes. In fact, we previously found that brain cortical thickness mediated the relationship between sleep qualities and pain severity in older adults with MSK. We also demonstrated a significant difference in a machine-learning-derived brain-aging biomarker between participants with low-and high-impact knee pain. Considering this, we examined whether brain aging was associated with self-reported sleep and pain measures, and whether brain aging mediated the relationship between sleep problems and knee pain. Exploratory Spearman and Pearson partial correlations, controlling for age, sex, race and study site, showed a significant association of brain aging with sleep related impairment and self-reported pain measures. Moreover, mediation analysis showed that brain aging significantly mediated the effect of sleep related impairment on clinical pain and physical symptoms. Our findings extend our prior work demonstrating advanced brain aging among individuals with chronic pain and the mediating role of brain-aging on the association between sleep and pain severity. Future longitudinal studies are needed to further understand whether the brain can be a therapeutic target to reverse the possible effect of sleep problems on chronic pain.

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function-a multi-institutional and multiscanner study.

ABSTRACT: Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types. However, some remaining methodological issues regarding training sample size and sex-specific effects should be tackled before settling this controversy. Here, we explored differences in brain-PAD between musculoskeletal pain types and controls using a novel convolutional neural network for predicting brain-PADs, ie, DeepBrainNet. Based on a very large, multi-institutional, and heterogeneous training sample and requiring less magnetic resonance imaging preprocessing than other methods for brain age prediction, DeepBrainNet offers robust and reproducible brain-PADs, possibly highly sensitive to neuropathology. Controlling for scanner-related variability, we used a large sample (n = 660) with different scanners, ages (19-83 years), and musculoskeletal pain types (chronic low back [CBP] and osteoarthritis [OA] pain). Irrespective of sex, brain-PAD of OA pain participants was ∼3 to 4.7 years higher than that of CBP and controls, whereas brain-PAD did not significantly differ among controls and CBP. Moreover, brain-PAD was significantly related to multiple variables underlying the multidimensional pain experience. This comprehensive work adds evidence of pain type-specific effects of chronic pain on brain age. This could help in the clarification of the debate around possible relationships between brain aging mechanisms and pain.

Protocol for a pilot and feasibility randomized-controlled trial of four weeks of oral γ-aminobutyric acid (GABA) intake and its effect on pain and sleep in middle-to-older aged adults.

Approximately 1.71 billion people globally live with musculoskeletal pain conditions, including low back pain, knee pain, and neck pain Cieza et al. (2020). In the US, an estimated 20.4% of U.S. adult had chronic pain and 8.0% of U.S. adults had high-impact chronic pain, with higher prevalence associated with advancing age Dahlhamer et al. (2018). On the other hand, between 50 and 70 million US adults have a sleep disorder (American Sleep Association). Although the link between sleep and pain is widely established, the neurobiological mechanisms underlying this relationship have yet to be fully elucidated, specifically within an aged population. As currently available sleep and chronic pain therapies are only partially effective, novel treatment approaches are urgently needed. Given the potential mechanistic role of γ-aminobutyric acid (GABA) in both conditions, and the availability of GABA supplements over the counter, the present proposal will determine the feasibility and acceptability of oral GABA administration in middle-to-older aged adults with chronic pain and sleep disorders as well as characterize the potential neurobiological mechanisms involved in both conditions. Results from the present investigation using a parallel, double-blinded, placebo-controlled study will provide novel preliminary information needed for future translational pain and sleep research.

Cross-Sectional Brain-Predicted Age Differences in Community-Dwelling Middle-Aged and Older Adults with High Impact Knee Pain.

Purpose: Knee OA-related pain varies in impact across individuals and may relate to central nervous system alterations like accelerated brain aging processes. We previously reported that older adults with chronic musculoskeletal pain had a significantly greater brain-predicted age, compared to pain-free controls, indicating an "older" appearing brain. Yet this association is not well understood. This cross-sectional study examines brain-predicted age differences associated with chronic knee osteoarthritis pain, in a larger, more demographically diverse sample with consideration for pain's impact. Patients and Methods: Participants (mean age = 57.8 ± 8.0 years) with/without knee OA-related pain were classified according to pain's impact on daily function (ie, impact): low-impact (n=111), and high-impact (n=60) pain, and pain-free controls (n=31). Participants completed demographic, pain, and psychosocial assessments, and T1-weighted magnetic resonance imaging. Brain-predicted age difference (brain-PAD) was compared across groups using analysis of covariance. Partial correlations examined associations of brain-PAD with pain and psychosocial variables. Results: Individuals with high-impact chronic knee pain had significantly "older" brains for their age compared to individuals with low-impact knee pain (p < 0.05). Brain-PAD was also significantly associated with clinical pain, negative affect, passive coping, and pain catastrophizing (p's<0.05). Conclusion: Our findings suggest that high impact chronic knee pain is associated with an older appearing brain on MRI. Future studies are needed to determine the impact of pain-related interference and pain management on somatosensory processing and brain aging biomarkers for high-risk populations and effective intervention strategies.

Experimental Pain Phenotype Profiles in Community-dwelling Older Adults.

OBJECTIVES: Pain sensitivity and the brain structure are critical in modulating pain and may contribute to the maintenance of pain in older adults. However, a paucity of evidence exists investigating the link between pain sensitivity and brain morphometry in older adults. The purpose of the study was to identify pain sensitivity profiles in healthy, community-dwelling older adults using a multimodal quantitative sensory testing protocol and to differentiate profiles based on brain morphometry. MATERIALS AND METHODS: This study was a secondary analysis of the Neuromodulatory Examination of Pain and Mobility Across the Lifespan (NEPAL) study. Participants completed demographic and psychological questionnaires, quantitative sensory testing, and a neuroimaging session. A Principal Component Analysis with Varimax rotation followed by hierarchical cluster analysis identified 4 pain sensitivity clusters (the "pain clusters"). RESULTS: Sixty-two older adults ranging from 60 to 94 years old without a specific pain condition (mean [SD] age=71.44 [6.69] y, 66.1% female) were analyzed. Four pain clusters were identified characterized by (1) thermal pain insensitivity; (2) high pinprick pain ratings and pressure pain insensitivity; (3) high thermal pain ratings and high temporal summation; and (4) thermal pain sensitivity, low thermal pain ratings, and low mechanical temporal summation. Sex differences were observed between pain clusters. Pain clusters 2 and 4 were distinguished by differences in the brain cortical volume in the parieto-occipital region. DISCUSSION: While sufficient evidence exists demonstrating pain sensitivity profiles in younger individuals and in those with chronic pain conditions, the finding that subgroups of experimental pain sensitivity also exist in healthy older adults is novel. Identifying these factors in older adults may help differentiate the underlying mechanisms contributing to pain and aging.

Chronic Musculoskeletal Pain Moderates the Association between Sleep Quality and Dorsostriatal-Sensorimotor Resting State Functional Connectivity in Community-Dwelling Older Adults.

Aging is associated with poor sleep quality and greater chronic pain prevalence, with age-related changes in brain function as potential underlying mechanisms. Objective. The following cross-sectional study aimed to determine whether self-reported chronic musculoskeletal pain in community-dwelling older adults moderates the association between sleep quality and resting state functional brain connectivity (rsFC). Methods. Community-dwelling older individuals (mean age = 73.29 years) part of the NEPAL study who completed the Pittsburg Sleep Quality Index (PSQI) and a rsFC scan were included (n = 48) in the present investigation. To that end, we tested the effect of chronic pain-by-PSQI interaction on rsFC among atlas-based brain regions-of-interest, controlling for age and sex. Results and Discussion. A significant network connecting the bilateral putamen and left caudate with bilateral precentral gyrus, postcentral gyrus, and juxtapositional lobule cortex, survived global multiple comparisons (FDR; q < 0.05) and threshold-free network-based-statistics. Greater PSQI scores were significantly associated with greater dorsostriatal-sensorimotor rsFC in the no-pain group, suggesting that a state of somatomotor hyperarousal may be associated with poorer sleep quality in this group. However, in the pain group, greater PSQI scores were associated with less dorsostriatal-sensorimotor rsFC, possibly due to a shift of striatal functions toward regulation sensorimotor aspects of the pain experience, and/or aberrant cortico-striatal loops in the presence of chronic pain. This preliminary investigation advances knowledge about the neurobiology underlying the associations between chronic pain and sleep in community-dwelling older adults that may contribute to the development of effective therapies to decrease disability in geriatric populations.

Resting-state functional connectivity patterns are associated with worst pain duration in community-dwelling older adults.

INTRODUCTION: An individual's chronic pain history is associated with brain morphometric alterations; but little is known about the association between pain history and brain function. OBJECTIVES: This cross-sectional study aimed at determining how worst musculoskeletal pain intensity (WPINT) moderated the association between worst musculoskeletal pain duration (WPDUR) and brain resting-state magnetic resonance imaging functional connectivity (RSFC) in community-dwelling older adults (60-94 years, 75% females, 97% right-handed). METHODS: Resting-state magnetic resonance imaging functional connectivity between region of interests was linearly regressed on WPDUR and WPINT. Predictions were compared with a control group's average RSFC (61-85 years, 47% females, 95% right-handed). RESULTS: Three significant patterns emerged: (1) the positive association between WPDUR and RSFC between the medial prefrontal cortex, in the anterior salience network (SN), and bilateral lateral Brodmann area 6, in the visuospatial network (VSN), in participants with more severe chronic pain, resulting in abnormally lower RSFC for shorter WPDUR; (2) the negative association between WPDUR and RSFC between right VSN occipitotemporal cortex (lateral BA37 and visual V5) and bilateral VSN lateral Brodmann area 6, independently of WPINT, resulting in abnormally higher and lower RSFC for shorter and longer WPDUR, respectively; and (3) the positive association between WPDUR and the left hemisphere's salience network-default mode network connectivity (between the hippocampus and both dorsal insula and ventral or opercular BA44), independently of WPINT, resulting in abnormally higher RSFC for longer WPDUR. CONCLUSION: Musculoskeletal effects on brain functional networks of general healthy individuals could accumulate until being observable at older ages. Results invite to examinations of these effects' impact on function and memory.

Psychological profiles in adults with knee OA-related pain: a replication study.

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.

Age Differences in Multimodal Quantitative Sensory Testing and Associations With Brain Volume.

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, η p 2 = 0.10) and heat pain thresholds (p = .014, η p 2 = 0.12). Main age effects were observed for mechanical, vibratory, cold, and warmth detection thresholds (ps < .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.

Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models.

Alongside positive blood oxygenation level-dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography-functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.

Arterial blood stealing as a mechanism of negative BOLD response: From the steady-flow with nonlinear phase separation to a windkessel-based model.

Blood Oxygen Level Dependent (BOLD) signal indirectly characterizes neuronal activity by measuring hemodynamic and metabolic changes in the nearby microvasculature. A deeper understanding of how localized changes in electrical, metabolic and hemodynamic factors translate into a BOLD signal is crucial for the interpretation of functional brain imaging techniques. While positive BOLD responses (PBR) are widely considered to be linked with neuronal activation, the origins of negative BOLD responses (NBR) have remained largely unknown. As NBRs are sometimes observed in close proximity of regions with PBR, a blood "stealing" effect, i.e., redirection of blood from a passive periphery to the area with high neuronal activity, has been postulated. In this study, we used the Hagen-Poiseuille equation to model hemodynamics in an idealized microvascular network that account for the particulate nature of blood and nonlinearities arising from the red blood cell (RBC) distribution (i.e., the Fåhraeus, Fåhraeus-Lindqvist and the phase separation effects). Using this detailed model, we evaluate determinants driving this "stealing" effect in a microvascular network with geometric parameters within physiological ranges. Model simulations predict that during localized cerebral blood flow (CBF) increases due to neuronal activation-hyperemic response, blood from surrounding vessels is reallocated towards the activated region. This stealing effect depended on the resistance of the microvasculature and the uneven distribution of RBCs at vessel bifurcations. A parsimonious model consisting of two-connected windkessel regions sharing a supplying artery was proposed to simulate the stealing effect with a minimum number of parameters. Comparison with the detailed model showed that the parsimonious model can reproduce the observed response for hematocrit values within the physiological range for different species. Our novel parsimonious model promise to be of use for statistical inference (top-down analysis) from direct blood flow measurements (e.g., arterial spin labeling and laser Doppler/Speckle flowmetry), and when combined with theoretical models for oxygen extraction/diffusion will help account for some types of NBRs.

Chronic Pain is Associated With Reduced Sympathetic Nervous System Reactivity During Simple and Complex Walking Tasks: Potential Cerebral Mechanisms.

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.

Chronic oxytocin administration in older men modulates functional connectivity during animacy perception.

While aging is associated with social-cognitive change and oxytocin plays a crucial role in social cognition, oxytocin's effects on the social brain in older age remain understudied. To date, no study has examined the effects of chronic intranasal oxytocin administration on brain mechanisms underlying animacy perception in older adults. Using a placebo-controlled, randomized, double-blinded design in generally healthy older men (mean age (SD) = 69(6); n = 17 oxytocin; n = 14 placebo), this study determined the effects of a four-week intranasal oxytocin administration (24 international units/twice a day) on functional MRI (fMRI) during the Heider-Simmel task. This passive-viewing animacy perception paradigm contains video-clips of simple shapes suggesting social interactions (SOCIAL condition) or exhibiting random trajectories (RANDOM condition). While there were no oxytocin-specific effects on brain fMRI activation during the SOCIAL compared to the RANDOM condition, pre-to-post intervention change in the SOCIAL-RANDOM difference in functional connectivity (FC) was higher in the oxytocin compared to the placebo group in a network covering occipital, temporal, and parietal areas, and the superior temporal sulcus, a key structure in animacy perception. These findings suggest oxytocin modulation of circuits involved in action observation and social perception. Follow-up analyses on this network's connections suggested a pre-to-post intervention decrease in the SOCIAL-RANDOM difference in FC among the placebo group, possibly reflecting habituation to repeated exposure to social cues. Chronic oxytocin appeared to counter this process by decreasing FC during the RANDOM and increasing it during the SOCIAL condition. This study advances knowledge about oxytocin intervention mechanisms in the social brain of older adults.

Cortical Thickness Mediates the Association Between Self-Reported Pain and Sleep Quality in Community-Dwelling Older Adults.

INTRODUCTION: Musculoskeletal pain is prevalent in older adults representing the leading cause of disability in this population. Similarly, nearly half of older adults complain of difficulty sleeping. We aimed to explore the relationship between sleep quality with self-reported musculoskeletal pain, somatosensory and pain thresholds in community-dwelling older adults and further explore brain regions that may contribute to this association. METHODS: Older adults (>60 years old, n=69) from the NEPAL study completed demographic, pain and sleep assessments followed by a quantitative sensory testing battery. A subset (n=49) also underwent a 3T high-resolution, T1-weighted anatomical scan. RESULTS: Poorer sleep quality using the Pittsburgh Sleep Quality Index was positively associated with self-reported pain measures (all p's >0.05), but not somatosensory and pain thresholds (all p's >0.05). Using a non-parametric threshold-free cluster enhancement (TFCE) approach, worse sleep quality was significantly associated with lower cortical thickness in the precentral, postcentral, precuneus, superior parietal, and lateral occipital regions (TFCE-FWE-corrected at p < 0.05). Further, only postcentral cortical thickness significantly mediated the association between sleep quality and self-reported pain intensity using bootstrapped mediation methods. CONCLUSION: Our findings in older adults are similar to previous studies in younger individuals where sleep is significantly associated with self-reported pain. Specifically, our study implicates brain structure as a significant mediator of this association in aging. Future larger studies are needed to replicate our findings and to further understand if the brain can be a therapeutic target for both improved sleep and pain relief in older individuals.

Modeling functional resting-state brain networks through neural message passing on the human connectome.

In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. 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 Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) 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. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer's disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons-Applications to Connectivity Studies.

Electrophysiological Source Imaging (ESI) is hampered by lack of "gold standards" for model validation. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) are useful for this purpose, especially primate models due to their flexibility and translational value for human research. Unfortunately, there is only one EECoG experiments in the public domain that we know of: the Multidimensional Recording (MDR) is based on a single monkey ( www.neurotycho.org ). The mining of this type of data is hindered by lack of specialized procedures to deal with: (1) Severe EECoG artifacts due to the experimental produces; (2) Sophisticated forward models that account for surgery induced skull defects and implanted ECoG electrode strips; (3) Reliable statistical procedures to estimate and compare source connectivity (partial correlation). We provide solutions to the processing issues just mentioned with EECoG-Comp: an open source platform ( https://github.com/Vincent-wq/EECoG-Comp ). EECoG lead fields calculated with FEM (Simbio) for MDR data are also provided and were used in other papers of this special issue. As a use case with the MDR, we show: (1) For real MDR data, 4 popular ESI methods (MNE, LCMV, eLORETA and SSBL) showed significant but moderate concordance with a usual standard, the ECoG Laplacian (standard partial [Formula: see text]); (2) In both monkey and human simulations, all ESI methods as well as Laplacian had a significant but poor correspondence with the true source connectivity. These preliminary results may stimulate the development of improved ESI connectivity estimators but require the availability of more EECoG data sets to obtain neurobiologically valid inferences.