BACKGROUND AND PURPOSE In the setting of an extended time window for endovascular treatment (EVT) for acute stroke patients, computed tomography perfusion (CTP) has become a major tool in patient selection. However, there are some data suggesting that the initial ischemic core may be overestimated by CTP depending on stroke onset time. This study aims to evaluate possible predictors of overestimation of infarct core by CTP. METHODS We studied all consecutive stroke patients undergoing EVT during 1 year who underwent CTP at admission and had a successful recanalization. Admission infarct core was measured on cerebral blood volume maps generated using the Intellispace Portal (Philips Healthcare, Best, the Netherlands) and final infarct was measured on noncontrast follow‐up computed tomography at 24 hours. We defined overestimation of the infarct core as initial core minus final infarct >10 mL. RESULTS Out of 107 patients undergoing EVT in the study period, 60 were anterior circulation and had CTP done at our institute, and of them 31 were compatible with the inclusion criteria (known time of onset, no hemorrhagic conversion, and good recanalization). Median National Institute of Health Stroke Scale on admission was 13. Median time from symptoms to CTP was 148 minutes. Seventeen patients were found to have overestimation of the infarct core. Logistic regression analyses showed time from symptom onset to CTP to be inversely related to overestimation with a cutoff of 170 minutes (sensitivity 94% and specificity 43%). CONCLUSION Over estimation of the infarct core by CTP in patients undergoing EVT is time dependent and so CTP results among early arrivers should be interpreted cautiously.

Introduction and Objectives: Chronic pain is a common postcollision consequence. Wherein, a clearer understanding of acute pain can help stem the acute-to-chronic pain transition. However, the variability of acute pain is only partially explained by psychophysical pain characteristics as measured by quantitative sensory testing. The Pain Sensitivity Questionnaire (PSQ) may reflect inherent psychocognitive representations of patient's sensitivity and thus may reveal less-explored pain dimensions. In the vein of the biopsychosocial approach, this study aimed to explore whether PSQ holds additive value in explaining head and neck pain reports in very early acute-stage mild traumatic brain injury (mTBI) after collision, above the use of psychophysical assessment. Methods: Study cohort (n = 130) consisted of mTBI patients (age range 19–66, 57 F) after accident with area-of-injury pain of at least 20 on the day of testing (mean pain 58.4 ± 21.6, range 20–100 Numerical Pain Scale) who underwent clinical, psychophysical, and pain-related psychological assessment within 72-hour after injury. Results: Pain Sensitivity Questionnaire scores were significantly correlated with acute clinical, psychophysical, and pain-related psychological measures. Regression model ( R 2 = 0.241, P < 0.001) showed that, together, age, sex, high PSQ, enhanced temporal summation, and less-efficient conditioned pain modulation explained head and neck pain variance. This model demonstrated that the strongest contribution to degree of postinjury pain was independently explained by PSQ (ß = 0.32) and then pressure pain threshold-conditioned pain modulation (ß = −0.25). Conclusion: Appraisal of cognitive daily-pain representations, by way of memory and imagination, provides an additional important dispositional facet to explain the variability in the acute mTBI postcollision clinical pain experience, above assessing nociceptive responsiveness to experimentally induced pain.

Unlike other chronic pain syndromes, conditioned pain modulation is more efficient in painful than non-painful diabetic neuropathy patients, possibly due to the peripheral neuropathic sensory changes.

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Background and purpose Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data‐driven, predictive and explanatory approach is presented to discriminate painful from non‐painful diabetic polyneuropathy (DPN) patients. Methods Three minutes long, 64 electrode resting‐state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non‐painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. Results Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non‐painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non‐painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra‐band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. Conclusions Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.