Orienting Attention Modulates Pain Perception: An ERP Study

2011-2012 > Academic research: refereed > Publication in refereed journalpublished_fina

Correlations between the normalized pain NRS on different levels of nociceptive images and the amplitudes of the later ERP components at selected sites (included only those with p<0.05).

<p>Note: All significant level was p<0.05; Normalized Pain NRS = NRS_Imagery–NRS_Perception. Average normalized pain NRS is computed by averaging the ratings across five levels of stimulation.</p><p>No significant correlations were obtained for levels 2 and 4 normalized pain NRS.</p

Numerical rating scale (NRS) ratings of 1 to 5 level nociceptive images in Imagery and Perception conditions.

<p>Note: I – P  =  Differences in NRS ratings between the Imagery and Perception conditions. Standard deviations are in parentheses.</p

Diagrammatical representation of the experimental paradigm.

<p>(A) Perception trial Note: 1. A nociceptive stimulus #5 (S1) was delivered to the participant’s lateral malleolus coupled with a low-pitched tone which both lasted for 50 ms. 2. The participant perceived the stimulus for 3,000 ms and maintained the image. 3. A nociceptive stimulus #5 (S2) was delivered to the site for 50 ms; 4. The participant was to respond by stating whether S2 would have been at the same intensity level to that of the nociceptive image maintained during the 3000 ms (Pe1); the participant should respond “yes.” 5. The participant rated the nociceptive image from S1 on an 11-point NRS. (B) Imagery trial Note: 1. A nociceptive stimulus #5 (S1) was delivered to the participant’s lateral malleolus coupled with a high-pitched tone, which both lasted for 50 ms. 2. The participant generated a sub-nociceptive image #5 (Im1) and mentally rehearsed the sub-nociceptive image for 3,000 ms. 3. A sub-nociceptive stimulus #3 (S’1) was delivered to the site for 50 ms. 4. The participant was to respond by stating whether S’1 would have been at the same intensity level to that of Im1; the participant should respond “no.” 5. The participant rated the nociceptive image from S1 on an 11-point NRS.</p

Correlations between the normalized pain NRS on different levels of nociceptive images and the Stroop Test scores (included only those with p<0.05).

<p>Note: Normalized Pain NRS = NRS_Imagery–NRS_Perception. Average normalized pain NRS is computed by averaging the normalized pain NRS across five levels of stimulation.</p><p>Key: WR = Word reading; CN = Color Naming; INC = Incongruent color naming. Different scores are computed by subtracting the reaction time score of the earlier from the later test. Proportional scores are computed by dividing the difference scores by the total time of the earlier test.</p>*<p>p<0.05.</p>**<p>p<0.01.</p><p>No significant correlations were obtained for level 2 normalized pain NRS (mostly r <0.40). Only one significant correlation was obtained for level 5 normalized pain NRS with CN Time Error (p = 0.566, p<0.05).</p

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator

Abstract About 15–40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation