This work attempted to clarify the interaction of cognition and pain sensitization during a paradigm of Temporal Summation of Second Pain (TSSP). We analyzed pain ratings and electroencephalographic ...(EEG) activity obtained from 21 healthy participants during the presentation of four experimental conditions that differed in the manipulation of attention to painful stimuli or working memory load (Attention to hand & TSSP; 0‐back & TSSP (low cognitive load); 2‐back & TSSP (high cognitive load); 2‐back (without pain)). We found that the TSSP was reduced when the attention was diverted and the cognitive load increased, and this reduction was accompanied by higher midfrontal theta activity and lower posterior alpha and central beta activity. Although it is well established that TSSP is a phenomenon that occurs at the spinal level, here we show that it is also affected by supraspinal attentional mechanisms. Delivery of painful repeated stimuli did not affect the performance of the 2‐back task but was associated with smaller amplitudes of attentional event‐related potentials (ERPs) after standard stimuli (not the target). The study of brain activity during TSSP allowed to clarify the role of top‐down attentional modulation in pain sensitization processes. Results contribute to a better understanding of cognitive dysfunction in pain conditions and reinforce the use of therapeutic strategies based on distracting attention away from pain.
Background Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, ...cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. Methods We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. Discussion This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making. Keywords: Cancer pain, Chronic pain, EEG, QST, CHEPs
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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