Static.cambridge.org
Supplemental materialSM 1 MethodsSM 1.1 ParticipantAN patients were recruited from specialized eating disorder programs of a university child and adolescent psychiatry and psychosomatic medicine department and diagnosed according to DSM-V criteria using semi-structured clinical interviews. All AN patients were admitted to eating disorder programs of a university child and adolescent psychiatry and psychosomatic medicine department and were assessed within 96 hours after the beginning of a behaviorally-oriented nutritional rehabilitation program. Comorbid psychiatric diagnoses were made by an expert clinician and included examination of the participant and careful archival chart review (including medical and psychiatric history, physical examination and several psychiatric screening instruments). The patients were amenorrheic with two exceptions: One patient took oral contraceptives, thus the natural menstrual cycle could not be evaluated and the other continued to maintain a menstrual cycle.Exclusion criteria and possible confounding variables, e.g. the use of psychotropic medications and medical comorbidities, were obtained using the SIAB-EX and our own semi-structured interview. HC participants were excluded if they had any history of psychiatric illness, a lifetime BMI below the 10th age percentile (if younger than 18 years) or BMI below 18.5kg/m2 (if older than 18 years), or were currently obese (BMI not over 97th age percentile if younger than 18 years; BMI not over 30kg/m2 if older than 18 years). Participants of all study groups were excluded if they had a lifetime history of any of the following clinical diagnoses: organic brain syndrome, schizophrenia, substance dependence, psychosis NOS, bipolar disorder, bulimia nervosa or binge-eating disorder (or “regular” binge eating - defined as bingeing at least once weekly for three or more consecutive months). Further exclusion criteria for all participants were IQ lower than 85; psychotropic medication within four weeks prior to the study; current substance abuse; current inflammatory, neurologic or metabolic illness; chronic medical or neurological illness that could affect appetite, eating behavior, or body weight (e.g., diabetes); clinical relevant anemia; pregnancy; breast feeding.Pairwise case-control age-matching was carried out using the Munkres algorithm ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"j2elqbuqi","properties":{"formattedCitation":"(Munkres 1957)","plainCitation":"(Munkres 1957)"},"citationItems":[{"id":768,"uris":[""],"uri":[""],"itemData":{"id":768,"type":"article-journal","title":"Algorithms for the assignment and transportation problems","container-title":"Journal of the Society for Industrial & Applied Mathematics","page":"32-38","volume":"5","issue":"1","ISSN":"0368-4245","shortTitle":"Algorithms for the assignment and transportation problems","author":[{"family":"Munkres","given":"James"}],"issued":{"date-parts":[["1957"]]}}}],"schema":""} (Munkres 1957) resulting in a maximum difference of 1.6 years between the individuals within one pair. Study data were collected between May 2014 and September 2015 and managed using secure, web-based electronic data capture tools REDCap ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"JooEAUhL","properties":{"formattedCitation":"{\\rtf (Research Electronic Data Capture; Harris {\\i{}et al.} 2009)}","plainCitation":"(Research Electronic Data Capture; Harris et al. 2009)"},"citationItems":[{"id":497,"uris":[""],"uri":[""],"itemData":{"id":497,"type":"article-journal","title":"Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support","container-title":"Journal of biomedical informatics","page":"377-381","volume":"42","issue":"2","ISSN":"1532-0464","shortTitle":"Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support","author":[{"family":"Harris","given":"Paul A"},{"family":"Taylor","given":"Robert"},{"family":"Thielke","given":"Robert"},{"family":"Payne","given":"Jonathon"},{"family":"Gonzalez","given":"Nathaniel"},{"family":"Conde","given":"Jose G"}],"issued":{"date-parts":[["2009"]]}},"prefix":"Research Electronic Data Capture; "}],"schema":""} (Research Electronic Data Capture; Harris et al. 2009). SM 1.2 Clinical measuresFor all participants, current diagnoses of eating disorders were evaluated by the expert form of the SIAB-EX ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2np3rpnt1j","properties":{"formattedCitation":"(Fichter & Quadflieg 1999)","plainCitation":"(Fichter & Quadflieg 1999)"},"citationItems":[{"id":370,"uris":[""],"uri":[""],"itemData":{"id":370,"type":"book","title":"SIAB. Struckturiertes Inventar fuer anorektische und Bulimische Essstoerungen nach DSM-IV und ICD-10","publisher":"Huber","publisher-place":"Bern","event-place":"Bern","shortTitle":"SIAB. Struckturiertes Inventar fuer anorektische und Bulimische Essstoerungen nach DSM-IV und ICD-10","author":[{"family":"Fichter","given":"M."},{"family":"Quadflieg","given":"N."}],"issued":{"date-parts":[["1999"]]}}}],"schema":""} (Fichter & Quadflieg 1999), a well-validated 87-item semi-standardized interview that assesses the prevalence and severity of specific eating-related psychopathology over the past three months. The interview provides diagnoses according to the ICD-10 and DSM-IV. Interviews were conducted by clinically experienced and trained research assistants under the supervision of the attending child and adolescent psychiatrist. Intelligence quotient (IQ) was assessed with a short version of the German adaption of the Wechsler Adult Intelligence Scale ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"28gmdgh5n5","properties":{"formattedCitation":"{\\rtf (Von Aster {\\i{}et al.} 2006)}","plainCitation":"(Von Aster et al. 2006)"},"citationItems":[{"id":1113,"uris":[""],"uri":[""],"itemData":{"id":1113,"type":"article-journal","title":"Wechsler Intelligenztest für Erwachsene (WIE). Deutschsprachige Bearbeitung und Adaptation des WAIS-III von David Wechsler","container-title":"Frankfurt/Main, Germany: Harcourt Test Services","shortTitle":"Wechsler Intelligenztest für Erwachsene (WIE). Deutschsprachige Bearbeitung und Adaptation des WAIS-III von David Wechsler","author":[{"family":"Von Aster","given":"M"},{"family":"Neubauer","given":"A"},{"family":"Horn","given":"R"}],"issued":{"date-parts":[["2006"]]}}}],"schema":""} (Von Aster et al. 2006) for participants aged 16 years and older or a short version of the German adaption of the Wechsler Intelligence Scale for Children ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1th8uedvh0","properties":{"formattedCitation":"{\\rtf (Daseking {\\i{}et al.} 2007)}","plainCitation":"(Daseking et al. 2007)"},"citationItems":[{"id":264,"uris":[""],"uri":[""],"itemData":{"id":264,"type":"article-journal","title":"Intelligenzdiagnostik mit dem HAWIK-IV","container-title":"Kindheit und Entwicklung","page":"250-259","volume":"16","issue":"4","ISSN":"0942-5403","shortTitle":"Intelligenzdiagnostik mit dem HAWIK-IV","author":[{"family":"Daseking","given":"Monika"},{"family":"Petermann","given":"Ulrike"},{"family":"Petermann","given":"Franz"}],"issued":{"date-parts":[["2007"]]}}}],"schema":""} (Daseking et al. 2007) for participants aged 15 years or younger. Self-reported hunger (“How hungry are you?”) was assessed with the use of a visual analogue scale ranging from “not at all” (value of 0) to “extremely” ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"YeYyYt3e","properties":{"formattedCitation":"{\\rtf (value of 10; Blundell {\\i{}et al.} 2010)}","plainCitation":"(value of 10; Blundell et al. 2010)"},"citationItems":[{"id":1205,"uris":[""],"uri":[""],"itemData":{"id":1205,"type":"article-journal","title":"Appetite control: methodological aspects of the evaluation of foods","container-title":"Obesity Reviews","page":"251-270","volume":"11","issue":"3","source":"Wiley Online Library","abstract":"This report describes a set of scientific procedures used to assess the impact of foods and food ingredients on the expression of appetite (psychological and behavioural). An overarching priority has been to enable potential evaluators of health claims about foods to identify justified claims and to exclude claims that are not supported by scientific evidence for the effect cited. This priority follows precisely from the principles set down in the PASSCLAIM report. The report allows the evaluation of the strength of health claims, about the effects of foods on appetite, which can be sustained on the basis of the commonly used scientific designs and experimental procedures. The report includes different designs for assessing effects on satiation as opposed to satiety, detailed coverage of the extent to which a change in hunger can stand alone as a measure of appetite control and an extensive discussion of the statistical procedures appropriate for handling data in this field of research. Because research in this area is continually evolving, new improved methodologies may emerge over time and will need to be incorporated into the framework. One main objective of the report has been to produce guidance on good practice in carrying out appetite research, and not to set down a series of commandments that must be followed.","DOI":"10.1111/j.1467-789X.2010.00714.x","ISSN":"1467-789X","shortTitle":"Appetite control","language":"en","author":[{"family":"Blundell","given":"J."},{"family":"De Graaf","given":"C."},{"family":"Hulshof","given":"T."},{"family":"Jebb","given":"S."},{"family":"Livingstone","given":"B."},{"family":"Lluch","given":"A."},{"family":"Mela","given":"D."},{"family":"Salah","given":"S."},{"family":"Schuring","given":"E."},{"family":"Van Der Knaap","given":"H."},{"family":"Westerterp","given":"M."}],"issued":{"date-parts":[["2010"]],"season":"M?rz"}},"prefix":"value of 10; "}],"schema":""} (value of 10; Blundell et al. 2010) after the scanning session. SM 1.3 Task and StimuliTo ensure the attention and wakefulness of the participant, an attention capture task was introduced between each mini-block (10 trials of one stimulus category) of the task. Specifically, a crosshair was presented centrally for 1309ms and either the left or the right part of the horizontal line turned red, while the rest of the crosshair remained white. The participant was asked to indicate the position of the red part of the horizontal line with a button-press. Immediately before and after the attention capture task, a completely white crosshair was presented for 4-6s (jitter). Neutral (arousal=2.55(0.47)) and social stimuli (arousal=4.5(0.9)) presented in the task differed significantly regarding their arousal (T(44.24)=10.64; p<0.001).SM 1.4 Functional ConnectivityOn the first level, the physiological activity was obtained by calculating the first eigen-variate across the voxel within the source region and adjusting for the effects of interest. For every participant a whole-brain GLM analysis was performed using the following regressors: the deconvolved physiological activity of the seed region, the three psychological factors (food supraliminal, neutral supraliminal and social supraliminal), and the three products of the physiological activity and each psychological factor (referred as “PPI regressors”). On the second level, the contrast images foodsupra>neutralsupra and socialsupra>neutralsupra were subjected to a two-sample t-test using SPM8. For the hypothesis-based approach findings were small volume corrected by using the limbic target-regions (ventral striatum, amygdala, orbitofrontal cortex and insula). The masks of the amygdala, insula and orbitofrontal cortex were created by merging the left and right frontal label from the Automated Anatomical Labelling (AAL) atlas provided in the Wake Forest University (WFU) PickAtlas for SPM (Figure SM 1.4). The mask of the ventral striatum was specified by binarizing a probabilistic map with a threshold value of 0.4. Figure SM 1.4: Mask of the ventral striatum, insula, amygdala, orbitofrontal cortex used in the hypothesis-based approach of the functional connectivity analysis.SM 2 ResultsSM 2.1 Stimulus-specific activation pattern during subliminal and supraliminal stimulation conditionWe assume that our task works as intended as we found that in both, the subliminal as well as the supraliminal stimulation condition, stimulus-specific activation patterns were apparent in HC. In detail, the contrast socialsublim>neutralsublim in HC showed increased activation in brain regions including the ventral striatum (T=7.23; pFWE=0.05; Figure 2A); a region known to play a central role in reward processing ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"jfjtlba28","properties":{"formattedCitation":"{\\rtf (Wang {\\i{}et al.} 2016)}","plainCitation":"(Wang et al. 2016)"},"citationItems":[{"id":1661,"uris":[""],"uri":[""],"itemData":{"id":1661,"type":"article-journal","title":"Using fMRI to study reward processing in humans: past, present, and future","container-title":"Journal of Neurophysiology","page":"1664-1678","volume":"115","issue":"3","source":"jn.","abstract":"Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for 1) the corroboration of significant animal findings in the human brain, and 2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.","DOI":"10.1152/jn.00333.2015","ISSN":"0022-3077, 1522-1598","note":"PMID: 26740530","shortTitle":"Using fMRI to study reward processing in humans","language":"en","author":[{"family":"Wang","given":"Kainan S."},{"family":"Smith","given":"David V."},{"family":"Delgado","given":"Mauricio R."}],"issued":{"date-parts":[["2016",3,1]]}}}],"schema":""} (Wang et al. 2016), while no cluster associated with reward processing was found for the contrast foodsublim>neutralsublim (Figure 2B). Also the contrast socialsupra>neutralsupra showed increased activity in regions associated with reward processing including the ventral striatum, the anterior cingulate cortex and the insula (T=9.74; pFWE<0.05; Figure 2C). When investigating the contrast foodsupra>neutralsupra we found increased activity in clusters known to be involved in food processing ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"244thmmq2u","properties":{"formattedCitation":"{\\rtf (van der Laan {\\i{}et al.} 2011)}","plainCitation":"(van der Laan et al. 2011)"},"citationItems":[{"id":1583,"uris":[""],"uri":[""],"itemData":{"id":1583,"type":"article-journal","title":"The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues","container-title":"NeuroImage","page":"296-303","volume":"55","issue":"1","source":"ScienceDirect","abstract":"Food selection is primarily guided by the visual system. Multiple functional neuro-imaging studies have examined the brain responses to visual food stimuli. However, the results of these studies are heterogeneous and there still is uncertainty about the core brain regions involved in the neural processing of viewing food pictures. The aims of the present study were to determine the concurrence in the brain regions activated in response to viewing pictures of food and to assess the modulating effects of hunger state and the food's energy content.\n\nWe performed three Activation Likelihood Estimation (ALE) meta-analyses on data from healthy normal weight subjects in which we examined: 1) the contrast between viewing food and nonfood pictures (17 studies, 189 foci), 2) the modulation by hunger state (five studies, 48 foci) and 3) the modulation by energy content (seven studies, 86 foci).\n\nThe most concurrent brain regions activated in response to viewing food pictures, both in terms of ALE values and the number of contributing experiments, were the bilateral posterior fusiform gyrus, the left lateral orbitofrontal cortex (OFC) and the left middle insula. Hunger modulated the response to food pictures in the right amygdala and left lateral OFC, and energy content modulated the response in the hypothalamus/ventral striatum.\n\nOverall, the concurrence between studies was moderate: at best 41% of the experiments contributed to the clusters for the contrast between food and nonfood. Therefore, future research should further elucidate the separate effects of methodological and physiological factors on between-study variations.","DOI":"10.1016/j.neuroimage.2010.11.055","ISSN":"1053-8119","shortTitle":"The first taste is always with the eyes","journalAbbreviation":"NeuroImage","author":[{"family":"Laan","given":"L. N.","non-dropping-particle":"van der"},{"family":"Ridder","given":"D. T. D.","non-dropping-particle":"de"},{"family":"Viergever","given":"M. A."},{"family":"Smeets","given":"P. A. M."}],"issued":{"date-parts":[["2011"]],"season":"M?rz"}}}],"schema":""} (van der Laan et al. 2011) e.g. the ventral striatum, ventral anterior cingulate cortex, posterior fusiform gyrus and superior parietal lobule (T=3.34; punc.<0.001; Figure 2D) Figure SM 2.1: Test of stimulus type specific activation patterns under subliminal and supraliminal stimulation condition. Brain maps are displayed at p=0.001 for visualization purpose only; Coordinates in Montreal Neurological Institute (MNI) space [x, y, z]; color bars represent t-values.SM 2.2 Control analyses: Effect of ageIFJF (p)SOGF (p)FFG/PHGF (p)Age 0.29 (0.595)0.145 (0.705)1.113 (0.295)Group5.00 (0.029)0.10 (0.756)0.30 (0.588)Stimulation condition 85.89 (<0.001)558.00 (<0.001)974.241 (<0.001)Stimulus type12.40 (<0.001)8.07 (<0.001)4.543 (0.011)Group*stimulation condition12.07 (0.001)0.73 (0.394)0.55 (0.459)Group*stimulus type8.85 (<0.001)3.45 (0.033)3.36 (0.036)Stimulation condition*stimulus type2.18 (0.115)6.35 (0.002)2.80 (0.062)Group*stimulation condition*stimulus type11.722 (<0.001)6.073 (0.003)10.907 (<0.001)To test whether our main findings were influenced by age we reanalyzed the data including age as a covariate in the linear mixed model. Results were essentially identical to those obtained using the linear mixed model without age as covariate and age itself did not explain variance. The statistics of the analyses are reported in the table below.Table SM 2.2: Main effects and interaction effect of the linear mixed model (based on extracted ?-values for each relevant cluster) with age as covariate; IFJ=inferior frontal junction; SOG=superior occipital gyrus; FFG/PHG=fusiform gyrus/parahipocampal gyrus. SM 2.3 Control analyses: Effect of comorbiditiesTo investigate whether our main findings were influenced by psychiatric comorbidities, we rerun the linear mixed model after excluding all subjects who had a comorbid diagnosis (n=3). The results matched the findings obtained from the original sample. The statistics of the analyses are reported in the table below.IFJF (p)SOGF (p)FFG/PHGF (p)Group4.35 (0.041)0.29 (0.593)0.837 (0.364)Stimulation condition 92.31 (<0.001)590.32 (<0.001)1038.308 (<0.001)Stimulus type10.95 (<0.001)9.22 (<0.001)4.608 (0.011)Group*stimulation condition15.22 (0.001)2.801 (0.095)2.446 (0.119)Group*stimulus type9.49 (<0.001)4.721 (0.010)4.222 (0.015)Stimulation condition*stimulus type2.016 (0.135)6.222 (0.002)2.940 (0.054)Group*stimulation condition*stimulus type11.42 (<0.001)5.952 (0.003)11.232 (<0.001)Table SM 2.3: Main effects and interaction effect of the linear mixed model (based on extracted ?-values for each relevant cluster) excluding patients with comorbid diagnosis; IFJ=inferior frontal junction; SOG=superior occipital gyrus; FFG/PHG=fusiform gyrus/parahipocampal gyrus.SM 2.4 Control analyses: Effect of AN subtypeTo test whether the AN subtype (binge/purge) has influenced the result we excluded the AN patients diagnosed with binge/purge subtype (n=2) and reanalyzed the data using the linear mixed model. On the whole, we found that excluding those patients has not relevantly influenced our findings. The statistics of the analyses are reported in the table below.IFJF (p)SOGF (p)FFG/PHGF (p)Group3.71 (0.059)0.22 (0.884).266 (0.608)Stimulation condition 90.102 (<0.001)535.350 (<0.001)925.626 (<0.001)Stimulus type11.221 (<0.001)7.975 (<0.001)4.633 (0.01)Group*stimulation condition13.807 (<0.001)0.756 (0.385).349 (0.555)Group*stimulus type10.005 (<0.001)3.241 (0.040)3.354 (0.036)Stimulation condition*stimulus type2.645 (0.073)5.888 (0.003)2.796 (0.063)Group*stimulation condition*stimulus type11.681 (<0.001)5.785 (0.003)10.644 (<0.001)Table SM 2.4: Main effects and interaction effect of the linear mixed model (based on extracted ?-values for each relevant cluster) excluding patients diagnosed with the binge/purge subtype of AN; IFJ=inferior frontal junction; SOG=superior occipital gyrus; FFG/PHG=fusiform gyrus/parahipocampal gyrus.References ADDIN ZOTERO_BIBL {"custom":[]} CSL_BIBLIOGRAPHY Blundell J, De Graaf C, Hulshof T, Jebb S, Livingstone B, Lluch A, Mela D, Salah S, Schuring E, Van Der Knaap H, Westerterp M (2010). Appetite control: methodological aspects of the evaluation of foods. Obesity Reviews 11, 251–270.Daseking M, Petermann U, Petermann F (2007). Intelligenzdiagnostik mit dem HAWIK-IV. Kindheit und Entwicklung 16, 250–259.Fichter M, Quadflieg N (1999). SIAB. Struckturiertes Inventar fuer anorektische und Bulimische Essstoerungen nach DSM-IV und ICD-10. Huber: Bern.Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG (2009). Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of biomedical informatics 42, 377–381.van der Laan LN, de Ridder DTD, Viergever MA, Smeets PAM (2011). The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues. NeuroImage 55, 296–303.Munkres J (1957). Algorithms for the assignment and transportation problems. Journal of the Society for Industrial & Applied Mathematics 5, 32–38.Von Aster M, Neubauer A, Horn R (2006). Wechsler Intelligenztest für Erwachsene (WIE). Deutschsprachige Bearbeitung und Adaptation des WAIS-III von David Wechsler. Frankfurt/Main, Germany: Harcourt Test ServicesWang KS, Smith DV, Delgado MR (2016). Using fMRI to study reward processing in humans: past, present, and future. Journal of Neurophysiology 115, 1664–1678. ................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related searches
- static equilibrium equation
- static equilibrium calculations formula
- static equilibrium physics
- static equilibrium physics problems
- static equilibrium equations lever
- static equilibrium examples
- static equilibrium lab report
- static equilibrium calculator
- static equilibrium physics lab
- moment static equilibrium equation
- static equilibrium equations
- dynamic and static equilibrium physics