5 research outputs found
Leveraging Public Knowledge Project\u27s Open Conference Systems for Digital Scholarship
No full textThe Media History Exchange (MHX) is an archive, social network, conference management tool, and collaborative workspace for the international, interdisciplinary community of researchers studying the history of journalism and communication. It opens a new scholarly space between the academic conference and the peer-reviewed journal by archiving “born digital” conference papers and abstracts that frequently have not been saved previously. In the spring of 2017, MHX migrated to the Public Knowledge Project’s Open Conference Systems. If your library is interested in expanding its digital scholarship offerings to include conference support, or offers its own library-focused conference, this technology might be exactly what you need. Co-author: Elliot King, Ph.D. (Loyola University Maryland
English Provincial Society from the Reformation to the Revolution: Religion, Politics and Society in Kent 1500-1640
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Tudor Royal Proclamations Vol. II: The Later Tudors, 1553-1587; Vol. III: The Later Tudors, 1588-1603
No full textIn the trenches of real-world self-control: Neural correlates of breaking the link between craving and smoking
No full textAbstract Successful goal pursuit involves repeatedly engaging self-control against temptations or distractions that arise along the way. Laboratory studies have identified the brain systems recruited during isolated instances of self-control, and ecological studies have linked self-control capacity to goal outcomes. However, no study has identified the neural systems of everyday self-control during long-term goal pursuit. The present study integrated neuroimaging and experiencesampling methods to investigate the brain systems of successful self-control among smokers attempting to quit. A sample of 27 cigarette smokers completed a go/no-go task during functional magnetic resonance imaging before they attempted to quit smoking and then reported everyday self-control using experience sampling eight times daily for 3 weeks while they attempted to quit. Increased activation in right inferior frontal gyrus, pre-supplementary motor area, and basal ganglia regions of interest during response inhibition at baseline was associated with an attenuated association between cravings and subsequent smoking. These findings support the ecological validity of neurocognitive tasks as indices of everyday response inhibition. Keywords self-control; smoking cessation; brain-as-predictor; right inferior frontal gyrus; response inhibition; text messaging Ridding oneself of an unwanted habit or tendency is a war that consists of a series of momentary self-control skirmishes. A longtime smoker may decide to quit, but success in reaching that goal will depend on the individual outcomes of a series of battles with cigarette cravings. Understanding the neural processes involved in these brief repeated struggles, in smoking and in other domains, is essential to understanding how self-control works in the trenches of real-world goal pursuit. The investigation reported here focused on response inhibition as one key factor that influences the ultimate success or failure of goal pursuit, and overcoming addiction in particular. Behavioral studies have examined how response inhibition during lab-based tasks relates to general real-world success at overriding an © The Author(s) 2011 Corresponding Author: Elliot T. Berkman, Department of Psychology, University of Oregon, 1227 University of Oregon, Eugene, OR 97403-1227 [email protected]. Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. Supplemental Material Additional supporting information may be found at http://pss.sagepub.com/content/by/supplemental-data NIH Public Access unwanted habitual behavior in favor of a desired novel one Behavioral performance on simple laboratory response-inhibition tasks (e.g., go/no-go) has been consistently linked to success in reaching a variety of real-world goals that involve self-regulation. For instance, the capacity to engage response inhibition has been linked to success at dieting Neuroscience studies have converged in identifying a consistent network of brain regions that are active during brief, laboratory-based manipulations of response inhibition. A number of functional neuroimaging Thus, on one hand, behavioral measures of response inhibition have been associated with a broad array of real-world outcomes, such as prevention of addiction relapse. On the other hand, the brain systems recruited for inhibiting responses during brief laboratory tasks are being mapped with increasing precision. Juxtaposing the behavioral and neuroscience literatures on response inhibition highlights why the neural processes underlying real-world instances of response inhibition have remained unexplored. There is almost no overlap between these literatures beyond similarity in the tasks used to assess response inhibition. Consequently, it is unknown whether the neural systems involved in laboratory assessments of response inhibition are the same ones recruited in the brief and repeated everyday battles between habit and self-control. For example, it is possible that the neural systems recruited during the stop-signal task are different from those associated with increasing exercise. Linking these disparate levels of analysis (i.e., neural and social/behavioral) and time scales (i.e., seconds/minutes and days/weeks) requires a paradigm for examining response inhibition during real-life situations and also during neuroim-aging tasks in the same sample of individuals. Accordingly, we made this link by measuring the neural mechanisms and everyday implementation of response inhibition within a single study. We recruited a sample of individuals just before they were to engage in the long-term, real-life response-inhibition task of quitting cigarette smoking and used functional MRI (fMRI) to examine their neural activation during a laboratory response-inhibition task. Next, we used experience sampling to track their progress throughout each day for the first 3 weeks of their smoking-cessation attempt Method Participants Thirty-one participants (15 female, 16 male) were recruited from smoking-cessation programs in Los Angeles via in-person announcements at orientation sessions. All participants were heavy smokers (> 10 cigarettes/day, 7 days/week, for at least 1 year and urinary cotinine > 1,000 ng/mL) enrolled in a professionally led cessation program (e.g., Freedom From Smoking). To be included in the study, participants also were required to have a score of 9 or 10 (out of 10) on the Contemplation Ladder, a single-item measure of intentions to quit Of the original 31 participants, all completed the scanning session, but 1 withdrew from participation in the experience-sampling phase, and 3 were excluded for insufficient data; thus, 27 participants were included in the analyses reported here. Participants were compensated 1 for each experience-sampling response returned, for a possible total of 248. All participants provided written informed consent approved by the UCLA Institutional Review Board. Procedure Phone screening-Following recruitment, participants were contacted by telephone to assess their intentions to quit (with the Contemplation Ladder and Readiness to Change Questionnaire) and their targeted quit date (TQD), as well as whether they met any of the Baseline (scanning) session-Participants came to the UCLA Ahmanson-Lovelace Brainmapping Center for a baseline session at least 1 day prior to their quit date We used a go/no-go task to examine the neural activation associated with response inhibition After completing this task, participants were removed from the scanner and brought into a quiet testing room for the duration of the session. Participants completed measures of demographics, smoking history, waking hours, nicotine dependence (Fagerström Test of Nicotine Dependence; Heatherton, Experience sampling-Following the scanning session, and beginning 1 day prior to their quit date, participants received prompts via text message eight times per day for 21 consecutive days. The first text prompt on each day was sent 15 min after morning rise, the last prompt was sent 15 min before bedtime, and the other six were evenly distributed throughout the day. Rise times and bedtimes were adjusted for each participant for weekdays and weekends. The interprompt interval varied across subjects between 1 hr 50 m and 2 hr 25 m. At each prompt, participants responded to three questions: "How many cigarettes have you smoked since the previous signal?" (numerical response), "How much are you craving a cigarette right now?" (0 = not at all, 1 = a little, 2 = somewhat, 3 = a lot, 4 = extremely), and "Overall, how is your mood right now?" (0 = extremely negative, 1 = somewhat negative, NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript End-point session-An end-point session was scheduled within 7 days of the end of the 21-day experience-sampling period. Exhaled CO was reassessed along with nicotine dependence (Fagerström Test of Nicotine Dependence) and smoking urges (Questionnaire on Smoking Urges). Participants were compensated 1 for each text-message response (M = 38). fMRI data acquisition and analysis Brain-imaging data were acquired on a 3-T Siemens Trio scanner at the UCLA AhmansonLovelace Brainmapping Center using standard data-acquisition and preprocessing steps (see the Supplemental Text in the Supplemental Material). The main effect of response inhibition was defined using a linear contrast for each participant (i.e., no-go > go). Contrast images were averaged across runs for each participant and then entered into a random-effects analysis at the group level. We constructed regions of interest (ROIs; Experience-sampling data-analysis strategy Multilevel linear modeling was used to address the nested nature of the experience-sampling data (HLM 6; Scientific Software International, Lincolnwood, IL; Raudenbush, Integration of fMRI and experience-sampling data To assess everyday response inhibition, we estimated the prospective relationship between craving for a cigarette at one time point and smoking at the subsequent time point. The magnitude of the relationship between these measures provided an ecological measure of response inhibition because cravings are among the primary impulses that must be regulated in successful smoking cessation Results Behavioral responses to the go/no-go task Participants completed 108 no-go and 492 go trials across 12 go/no-go blocks. The error rate on no-go trials was 4.6%. Error trials were included in the model but not examined because of insufficient N. The mean response time on go trials was 547.9 ms (SD = 160.5). Experience-sampling response rates Participants responded to 84% of the prompts during the experience-sampling phase of the study (~6.7 responses out of 8 prompts daily). Most responses were sent within 23 min of the signal (SD = 44 min). For a given participant, a day was excluded if it contained fewer than four responses. In total, 90 days were excluded (M = 3.33 per participant). Robustness analyses suggest that the missing data did not affect the results (see Supplemental Text in the Supplemental Material). There were a total of 3,811 Level 1 observations (time points within days), 477 Level 2 observations (days within participants), and 27 Level 3 observations (participants) in our multilevel model. Smoking and craving during experience sampling Participants smoked 20.2 cigarettes per day (SD = 9.4) at baseline and 5.2 cigarettes per day (SD = 5.4) at the end point (mean change = 15.0), t(26) = 7.62, p < .01. Nicotine dependence and urges also decreased significantly There was a positive within-day relationship between craving at one time point and smoking at the next when craving was entered alone into the model (i.e., without neural activations; log-expectation γ = .19, SE = .08), t(476) = 2.14, p < .05. Reductions in the number of cigarettes smoked per day were inversely related to the daily craving-smoking link (see Supplemental Text in the Supplemental Material for analysis of the relationship between the craving-smoking slope and smoking reductions). Predicting everyday response inhibition from neuroimaging data To examine the association between neural activation at baseline and longitudinal outcomes, we extracted activations from the no-go > go contrast for anatomically defined ROIs in the IFG, basal ganglia, and pre-SMA There was an overall positive relationship between craving at time i and smoking at time i + 1. The IFG, basal ganglia, and pre-SMA ROIs each significantly and negatively moderated that slope We also examined the relationship between activity in each ROI and long-term cessation success (i.e., across 4 weeks). Only activity in basal ganglia, and not in the other two ROIs, predicted long-term reductions in smoking as measured by change in exhaled CO (Montreal Neurological Institute coordinates: x = 30, y = 5, z = 4; 86-voxel extent, t = 5.02, falsedetection-rat
