Ishtiaq Mawla

The imagined itch: brain circuitry supporting nocebo-induced itch in atopic dermatitis patients.

Psychological factors are known to significantly modulate itch in patients suffering from chronic itch. Itch is also highly susceptible to both placebo and nocebo (negative placebo) effects. Brain activity likely supports nocebo‐induced itch, but is currently unknown.

Primary somatosensory/motor cortical thickness distinguishes paresthesia-dominant from pain-dominant carpal tunnel syndrome.

Abstract 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.

Reduced insula habituation associated with amplification of trigeminal brainstem input in migraine.

Background Impaired sensory processing in migraine can reflect diminished habituation, increased activation, or even increased gain or amplification of activity from the primary synapse in the brainstem to higher cortical/subcortical brain regions. Methods We scanned 16 episodic migraine (interictal) and 16 healthy controls (cross-sectional study), and evaluated brain response to innocuous air-puff stimulation over the right forehead in the ophthalmic nerve (V1) trigeminal territory. We further evaluated habituation, and cortical/subcortical amplification relative to spinal trigeminal nucleus (Sp5) activation. Results Migraine subjects showed greater amplification from Sp5 to the posterior insula and hypothalamus. In addition, while controls showed habituation to repetitive sensory stimulation in all activated cortical regions (e.g. the bilateral posterior insula and secondary somatosensory cortices), for migraine subjects, habituation was not found in the posterior insula. Moreover, in migraine, the habituation slope was correlated with the amplification ratio in the posterior insula and secondary somatosensory cortex, i.e. greater amplification was associated with reduced habituation in these regions. Conclusions These findings suggest that in episodic migraine, amplified information processing from spinal trigeminal relay nuclei is linked to an impaired habituation response. This phenomenon was localized in the posterior insula, highlighting the important role of this structure in mechanisms supporting altered sensory processing in episodic migraine.

Encoding of Self-Referential Pain Catastrophizing in the Posterior Cingulate Cortex in Fibromyalgia

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.

Brain Mechanisms of Anticipated Painful Movements and Their Modulation by Manual Therapy in Chronic Low Back Pain

Heightened anticipation and fear of movement-related pain has been linked to detrimental fear-avoidance behavior in chronic low back pain (cLBP). Spinal manipulative therapy (SMT) has been proposed to work partly by exposing patients to nonharmful but forceful mobilization of the painful joint, thereby disrupting the relationship among pain anticipation, fear, and movement. Here, we investigated the brain processes underpinning pain anticipation and fear of movement in cLBP, and their modulation by SMT, using functional magnetic resonance imaging. Fifteen cLBP patients and 16 healthy control (HC) subjects were scanned while observing and rating video clips depicting back-straining or neutral physical exercises, which they knew they would have to perform at the end of the visit. This task was repeated after a single session of spinal manipulation (cLBP and HC group) or mobilization (cLBP group only), in separate visits. Compared with HC subjects, cLBP patients reported higher expected pain and fear of performing the observed exercises. These ratings, along with clinical pain, were reduced by SMT. Moreover, cLBP, relative to HC subjects, demonstrated higher blood oxygen level-dependent signal in brain circuitry that has previously been implicated in salience, social cognition, and mentalizing, while observing back straining compared with neutral exercises. The engagement of this circuitry was reduced after SMT, and especially the spinal manipulation session, proportionally to the magnitude of SMT-induced reduction in anticipated pain and fear. This study sheds light on the brain processing of anticipated pain and fear of back-straining movement in cLBP, and suggests that SMT may reduce cognitive and affective-motivational aspects of fear-avoidance behavior, along with corresponding brain processes. PERSPECTIVE: This study of cLBP patients investigated how SMT affects clinical pain, expected pain, and fear of physical exercises. The results indicate that one of the mechanisms of SMT may be to reduce pain expectancy, fear of movement, and associated brain responses.

Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics

Abstract 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 (Pearsons 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.

Brain Correlates of Continuous Pain in Rheumatoid Arthritis as Measured by Pulsed Arterial Spin Labeling

Central nervous system pathways involving pain modulation shape the pain experience in patients with chronic pain. The aims of this study were to understand the mechanisms underlying pain in patients with rheumatoid arthritis (RA) and to identify brain signals that may serve as imaging markers for developing targeted treatments for RA‐related pain.