Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. ...Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict within-patient clinical pain intensity states (ie, lower vs higher pain) and were then applied to predict between-patient clinical pain ratings with independent training and testing data sets. Within-patient classification between lower and higher clinical pain intensity states showed best performance (accuracy = 92.45%, area under the curve = 0.97) when all 3 multimodal parameters were combined. Between-patient prediction of clinical pain intensity using independent training and testing data sets also demonstrated significant prediction across pain ratings using the combined model (Pearson's r = 0.63). Classification of increased pain was weighted by elevated cerebral blood flow in the thalamus, and prefrontal and posterior cingulate cortices, and increased primary somatosensory connectivity to frontoinsular cortex. Our machine-learning approach introduces a model with putative biomarkers for clinical pain and multiple clinical applications alongside self-report, from pain assessment in noncommunicative patients to identification of objective pain endophenotypes that can be used in future longitudinal research aimed at discovery of new approaches to combat chronic pain.
Prior studies have shown that patients suffering from chronic Low Back Pain (cLBP) have impaired somatosensory processing including reduced tactile acuity, i.e. reduced ability to resolve fine ...spatial details with the perception of touch. The central mechanism(s) underlying reduced tactile acuity are unknown but may include changes in specific brain circuitries (e.g. neuroplasticity in the primary somatosensory cortex, S1). Furthermore, little is known about the linkage between changes in tactile acuity and the amelioration of cLBP by somatically-directed therapeutic interventions, such as acupuncture. In this longitudinal neuroimaging study, we evaluated healthy control adults (HC, N = 50) and a large sample of cLBP patients (N = 102) with structural brain imaging (T1-weighted MRI for Voxel-Based Morphometry, VBM; Diffusion Tensor Imaging, DTI) and tactile acuity testing using two-point discrimination threshold (2PDT) over the lower back (site of pain) and finger (control) locations. Patients were evaluated at baseline and following a 4-week course of acupuncture, with patients randomized to either verum acupuncture, two different forms of sham acupuncture (designed with or without somatosensory afference), or no-intervention usual care control. At baseline, cLBP patients demonstrated reduced acuity (greater 2PDT, P = 0.01) over the low back, but not finger (P = 0.29) locations compared to HC, suggesting that chronic pain affects tactile acuity specifically at body regions encoding the experience of clinical pain. At baseline, Gray Matter Volume (GMV) was elevated and Fractional Anisotropy (FA) was reduced, respectively, in the S1-back region of cLBP patients compared to controls (P < 0.05). GMV in cLBP correlated with greater 2PDT-back scores (ρ = 0.27, P = 0.02). Following verum acupuncture, tactile acuity over the back was improved (reduced 2PDT) and greater improvements were associated with reduced S1-back GMV (ρ = 0.52, P = 0.03) and increased S1-back adjacent white matter FA (ρ = −0.56, P = 0.01). These associations were not seen for non-verum control interventions. Thus, S1 neuroplasticity in cLBP is linked with deficits in tactile acuity and, following acupuncture therapy, may represent early mechanistic changes in somatosensory processing that track with improved tactile acuity.
Objective
There is increasing demand for prediction of chronic pain treatment outcomes using machine‐learning models, in order to improve suboptimal pain management. In this exploratory study, we ...used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM.
Methods
FM patients participated in 2 separate double‐blind, placebo‐controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin.
Results
Connectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies.
Conclusion
Our findings indicate that brain functional connectivity patterns used in a machine‐learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.
Chronic low back pain (cLBP) is a prevalent disorder. A growing body of evidence linking the pathology of the reward network to chronic pain suggests that pain sensitization may contribute to cLBP ...chronification via disruptions of mesocortical and mesolimbic circuits in the reward system. Resting-state (RS) functional magnetic resonance imaging (fMRI) data was acquired from 90 patients with cLBP and 74 matched pain-free controls (HCs) at baseline and after a manipulation for back pain intensification. The ventral tegmental area (VTA) was chosen as a seed region to perform RS functional connectivity (FC) analysis. Baseline rsFC of both the mesocortical (between the VTA and bilateral rostral anterior cingulate cortex (rACC)/and medial prefrontal cortex (mPFC)) and mesolimbic (between the VTA and bilateral hippocampus/parahippocampus) pathways was reduced in patients with cLBP (vs. HCs). In addition, patients exhibiting higher back pain intensity (compared to the relatively lower back pain intensity condition) also showed increases in both mesocortical and mesolimbic connectivity, implicating these pathways in pain downregulation in cLBP. Mediation analysis further isolated the mesolimbic (VTA-hippocampus/parahippocampus) dysconnectivity as a neural mechanism mediating the association between mechanical pain sensitivity (indexed by P40 pressure) and cLBP severity. In sum, the current study demonstrates deficient mesocorticolimbic connectivity in cLBP, with mesolimbic dysconnectivity potentially mediating the contribution of pain sensitization to pain chronification. These reward network dysfunctions and purportedly, dopaminergic dysregulations, may help us to identify key brain targets of neuromodulation in the treatment of cLBP.
•Mesocortical and mesolimbic connectivity decreased in cLBP patients compared to controls.•Mesocortical and mesolimbic connectivity increased when back pain intensity increased.•Mesolimbic connectivity mediated the association between pain sensitivity and cLBP severity.
Neuroimaging has enhanced our understanding of the neural correlates of pain. Yet, how neural circuits interact and contribute to persistent pain remain largely unknown. Here, we investigate the ...mesoscale organization of the brain through intrinsic functional communities generated from resting state functional MRI data from two independent datasets, a discovery cohort of 43 Fibromyalgia (FM) patients and 20 healthy controls (HC) as well as a replication sample of 34 FM patients and 21 HC. Using normalized mutual information, we found that the global network architecture in chronic pain patients is less stable (more variable). Subsequent analyses of node community assignment revealed the composition of the communities differed between FM and HC. Furthermore, differences in network organization were associated with the changes in the composition of communities between patients with varying levels of clinical pain. Together, this work demonstrates that intrinsic network communities differ substantially between patients with FM and controls. These differences may represent a novel aspect of the pathophysiology of chronic nociplastic pain.
Chronic pain and mood disorders share common neuroanatomical substrates involving disruption of the reward system. Although increase in negative affect (NA) and decrease in positive affect (PA) are ...well-known factors complicating the clinical presentation of chronic pain patients, our understanding of the mechanisms underlying the interaction between pain and PA/NA remains limited. Here, we used a validated task probing behavioral and neural responses to monetary rewards and losses in conjunction with functional magnetic resonance imaging (fMRI) to test the hypothesis that dysfunction of the striatum, a key mesolimbic structure involved in the encoding of motivational salience, relates to mood alterations comorbid with chronic pain.
Twenty-eight chronic musculoskeletal pain patients (chronic low back pain, n=15; fibromyalgia, n=13) and 18 healthy controls underwent fMRI while performing the Monetary Incentive Delay (MID) task. Behavioral and neural responses were compared across groups and correlated against measures of depression (Beck Depression Inventory) and hedonic capacity (Snaith-Hamilton Pleasure Scale).
Compared to controls, patients demonstrated higher anhedonia and depression scores, and a dampening of striatal activation and incentive-related behavioral facilitation (reduction in reaction times) during reward and loss trials of the MID task (ps < 0.05). In all participants, lower activation of the right striatum during reward trials was correlated with lower incentive-related behavioral facilitation and higher anhedonia scores (ps < 0.05). Finally, among patients, lower bilateral striatal activation during loss trials was correlated with higher depression scores (ps < 0.05).
In chronic pain, PA reduction and NA increase are accompanied by striatal hypofunction as measured by the MID task.
•Striatal hypofunction accompanies mood alteration in low back pain and fibromyalgia.•Pain patients show dampened behavioral and neural response to reward and punishment.•Striatal hypofunction relates to higher depression and anhedonia scores in patients.•The monetary incentive delay task can probe striatal activity in chronic pain.
Objective
Pain catastrophizing is a common feature of chronic pain, including fibromyalgia (FM), and is strongly associated with amplified pain severity and disability. While previous neuroimaging ...studies have focused on evoked pain response modulation by catastrophizing, the brain mechanisms supporting pain catastrophizing itself are unknown. We designed a functional magnetic resonance imaging (fMRI)–based pain catastrophizing task whereby patients with chronic pain engaged in catastrophizing‐related cognitions. We undertook this study to test our hypothesis that catastrophizing about clinical pain would be associated with amplified activation in nodes of the default mode network (DMN), which encode self‐referential cognition and show altered functioning in chronic pain.
Methods
During fMRI, 31 FM patients reflected on how catastrophizing (CAT) statements (drawn from the Pain Catastrophizing Scale) impact their typical FM pain experience. Response to CAT statements was compared to response to matched neutral (NEU) statements.
Results
During statement reflection, higher fMRI signal during CAT statements than during NEU statements was found in several DMN brain areas, including the ventral (posterior) and dorsal (anterior) posterior cingulate cortex (vPCC and dPCC, respectively). Patients’ ratings of CAT statement applicability were correlated solely with activity in the vPCC, a main DMN hub supporting self‐referential cognition (r = 0.38, P < 0.05). Clinical pain severity was correlated solely with activity in the dPCC, a PCC subregion associated with cognitive control and sensorimotor processing (r = 0.38, P < 0.05).
Conclusion
These findings provide evidence that the PCC encodes pain catastrophizing in FM and suggest distinct roles for different PCC subregions. Understanding the brain circuitry encoding pain catastrophizing in FM will prove to be important in identifying and evaluating the success of interventions targeting negative affect in chronic pain management.
Patient-clinician concordance in behavior and brain activity has been proposed as a potential key mediator of mutual empathy and clinical rapport in the therapeutic encounter. However, the specific ...elements of patient-clinician communication that may support brain-to-brain concordance and therapeutic alliance are unknown. Here, we investigated how pain-related, directional facial communication between patients and clinicians is associated with brain-to-brain concordance. Patient-clinician dyads interacted in a pain-treatment context, during synchronous assessment of brain activity (fMRI hyperscanning) and online video transfer, enabling face-to-face social interaction. In-scanner videos were used for automated individual facial action unit (AU) time-series extraction. First, an interpretable machine-learning classifier of patients' facial expressions, from an independent fMRI experiment, significantly distinguished moderately painful leg pressure from innocuous pressure stimuli. Next, we estimated neural-network causality of patient-to-clinician directional information flow of facial expressions during clinician-initiated treatment of patients' evoked pain. We identified a leader-follower relationship in which patients predominantly led the facial communication while clinicians responded to patients' expressions. Finally, analyses of dynamic brain-to-brain concordance showed that patients' mid/posterior insular concordance with the clinicians' anterior insula cortex, a region identified in previously published data from this study
, was associated with therapeutic alliance, and self-reported and objective (patient-to-clinician-directed causal influence) markers of negative-affect expressivity. These results suggest a role of patient-clinician concordance of the insula, a social-mirroring and salience-processing brain node, in mediating directional dynamics of pain-directed facial communication during therapeutic encounters.
Paresthesia-dominant and pain-dominant subgroups have been noted in carpal tunnel syndrome (CTS), a peripheral neuropathic disorder characterized by altered primary somatosensory/motor (S1/M1) ...physiology. We aimed to investigate whether brain morphometry dissociates these subgroups. Subjects with CTS were evaluated with nerve conduction studies, whereas symptom severity ratings were used to allocate subjects into paresthesia-dominant (CTS-paresthesia), pain-dominant (CTS-pain), and pain/paresthesia nondominant (not included in further analysis) subgroups. Structural brain magnetic resonance imaging data were acquired at 3T using a multiecho MPRAGE T1-weighted pulse sequence, and gray matter cortical thickness was calculated across the entire brain using validated, automated methods. CTS-paresthesia subjects demonstrated reduced median sensory nerve conduction velocity (P = 0.05) compared with CTS-pain subjects. In addition, cortical thickness in precentral and postcentral gyri (S1/M1 hand area) contralateral to the more affected hand was significantly reduced in CTS-paresthesia subgroup compared with CTS-pain subgroup. Moreover, in CTS-paresthesia subjects, precentral cortical thickness was negatively correlated with paresthesia severity (r(34) = -0.40, P = 0.016) and positively correlated with median nerve sensory velocity (r(36) = 0.51, P = 0.001), but not with pain severity. Conversely, in CTS-pain subjects, contralesional S1 (r(9) = 0.62, P = 0.042) and M1 (r(9) = 0.61, P = 0.046) cortical thickness were correlated with pain severity, but not median nerve velocity or paresthesia severity. This double dissociation in somatotopically specific S1/M1 areas suggests a neuroanatomical substrate for symptom-based CTS subgroups. Such fine-grained subgrouping of CTS may lead to improved personalized therapeutic approaches, based on superior characterization of the linkage between peripheral and central neuroplasticity.
Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of ...visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.
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