Do scores on the Food Craving Inventory and Three-Factor Eating Questionnaire correlate with expected brain regions of interest in people with obesity?

2018 ◽  
Vol 188 ◽  
pp. 1-10 ◽  
Author(s):  
Chanaka Nadeeshan Kahathuduwa ◽  
Tyler Davis ◽  
Michael O'Boyle ◽  
Martin Binks
Keyword(s):  
Brain Regions ◽  
Regions Of Interest ◽  
Food Craving ◽  
Three Factor Eating Questionnaire ◽  
Three Factor

Three Factor Eating Questionnaire

1985 ◽  
Author(s):  
Albert J. Stunkard ◽  
Samuel Messick
Keyword(s):  
Three Factor Eating Questionnaire ◽  
Three Factor

scholarly journals Examination of the Eating Behavior of the Hungarian Population Based on the TFEQ-R21 Model

Nutrients ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3514
Author(s):  
Zoltán Szakály ◽  
Bence Kovács ◽  
Márk Szakály ◽  
Dorka T. Nagy-Pető ◽  
Tímea Gál ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Emotional Eating ◽  
Population Based ◽  
Eating Attitudes ◽  
Multivariate Statistical ◽  
Confirmatory Factor Analyses ◽  
Reduction Techniques ◽  
Three Factor Eating Questionnaire ◽  
And Cluster Analysis ◽  
Three Factor

Several theories have emerged to study types of eating behavior leading to obesity, but most of the applied models are mainly related to food choice decisions and food consumer behavior. The purpose of this paper was to examine the eating attitudes of Hungarian consumers by applying the Three-Factor Eating Questionnaire (TFEQ-R21). The national representative questionnaire involved 1000 individuals in Hungary in 2019. Several multivariate statistical techniques were applied for the data analysis: exploratory and confirmatory factor analyses, multivariate data reduction techniques, and cluster analysis. This study successfully managed to distinguish the following factors: emotional eating, uncontrolled eating, and cognitive restraint. By using the factors, five clusters were identified: Uncontrolled Emotional Eaters; Overweight, Uncontrolled Eaters; Controlled, Conscious Eaters; the Uninterested; and the Rejecters; all of these could be addressed by public health policy with individually tailored messages. The empirical results led to rejection of the original Three-Factor Eating Questionnaire (TFEQ-R21), while the TFEQ-R16 model could be validated on a representative sample of adults, for the first time in Hungary.

scholarly journals The Three-Factor Eating Questionnaire, body mass index, and responses to sweet and salty fatty foods: a twin study of genetic and environmental associations

2008 ◽  
Vol 88 (2) ◽  
pp. 263-271 ◽  
Author(s):  
Kaisu Keskitalo ◽  
Hely Tuorila ◽  
Tim D Spector ◽  
Lynn F Cherkas ◽  
Antti Knaapila ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Twin Study ◽  
Three Factor Eating Questionnaire ◽  
Three Factor

Cognitive restraint and impulsive eating: Insights from the Three-Factor Eating Questionnaire.

1992 ◽  
Vol 6 (1) ◽  
pp. 47-53 ◽  
Author(s):  
R. Lorraine Collins ◽  
William M. Lapp ◽  
Lynn Helder ◽  
Judith A. Saltzberg
Keyword(s):  
Cognitive Restraint ◽  
Three Factor Eating Questionnaire ◽  
Three Factor

Three-factor eating questionnaire (TFEQ-18) in obese and non-obese Brazilian adults

2018 ◽  
Vol 37 ◽  
pp. S254
Author(s):  
W.R. Silva ◽  
B.G. Martins ◽  
J.A.D.B. Campos
Keyword(s):  
Three Factor Eating Questionnaire ◽  
Three Factor

scholarly journals A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

2020 ◽  
Author(s):  
Judy A. Prasad ◽  
Aishwarya H. Balwani ◽  
Erik C. Johnson ◽  
Joseph D. Miano ◽  
Vandana Sampathkumar ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Subcortical Structures ◽  
Neural Structure ◽  
Brain Areas ◽  
X Ray ◽  
Anatomical Imaging ◽  
Brain Pathways ◽  
Critical Aspects

AbstractNeural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.

scholarly journals Do Overweight People Have Worse Cognitive Flexibility? Cues-Triggered Food Craving May Have a Greater Impact

Nutrients ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 240
Author(s):  
Shiqing Song ◽  
Qingqing Li ◽  
Yan Jiang ◽  
Yong Liu ◽  
Aidi Xu ◽  
...  
Keyword(s):  
Cognitive Flexibility ◽  
Performance Test ◽  
Continuous Performance Test ◽  
Brain Regions ◽  
Normal Weight ◽  
Food Craving ◽  
Control Mechanisms ◽  
Reactive Control ◽  
Food Cravings ◽  
Overweight People

Background: Overweight people have been revealed to have poor cognitive flexibility. Cognitive flexibility reflects proactive and reactive control abilities. However, the impairment had not been explicitly positioned at the cognitive stage. Therefore, this study provides increased support for impairment of cognitive flexibility due to overweight. Method: The study included 34 overweight and 35 normal-weight participants. They were required to complete the food and flower target AX-continuous performance test (AX–CPT), including the resting-state fMRI and cue-triggered food craving subscales. We compared the performance difference between the two tasks. Furthermore, we investigated whether the cue-triggered food cravings and the corresponding brain regions mediated the effect of overweight on the two control mechanisms. Result: Significant differences were found only in the food target AX-CPT task, where overweight participants performed worse. Cue-triggered food cravings mediated this relationship. Additionally, we found that the brain regions associated with cue-triggered food cravings (bilateral SFG) can completely mediate the relationship between BMI and the z-value of the fat mass index and sensitivity to proactive control. Conclusion: In the food target task, overweight participants performed worse in both control mechanisms. Moreover, we also revealed the potential mechanism by which being overweight might affect the two control mechanisms through cue-triggered food cravings.

scholarly journals Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

2020 ◽  
Vol 63 (9) ◽  
pp. 3051-3067
Author(s):  
Amy E. Ramage ◽  
Semra Aytur ◽  
Kirrie J. Ballard
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Healthy Controls ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Language Performance

Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the “functional” dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic–phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery–Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery–Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left–right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical–semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic–phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic–phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs—particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785

scholarly journals Dissection of neuroinflammation in schizophrenia

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S274-S275
Author(s):  
Fizah Muratib ◽  
Yuya Mizuno ◽  
Ines Carreira Figueiredo ◽  
Oliver Howes ◽  
Tiago Reis Marques
Keyword(s):  
Risk Factor ◽  
Grey Matter ◽  
Psychotic Disorders ◽  
Positive Association ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Cannabis Use ◽  
Similar Response ◽  
Frequency Of Use ◽  
The Brain

AimsSchizophrenia is notoriously becoming one of the world's most debilitating mental disorders, affecting 1 in 100 people. There is increasing evidence that neuroinflammation plays a part in the pathogenesis of schizophrenia and other psychotic disorders; microglial activity acting as a marker for neuroinflammatory reactions in the brain. Furthermore, cannabis is an illicit substance that also evokes a similar response in the neuroimmune activity. This project explores how cannabis exposure influences an elevation in neuroinflammatory responses through TSPO levels, and whether this information can help us determine if cannabis use and increased TSPO levels can be associated with a risk factor for developing psychosis.Method55 participants (36 males and 19 females) were recruited from the community by the IRIS (Inflammatory Reaction in Schizophrenia) team at the IoPPN, King's College London, from which 34 patients with a diagnosis of schizophrenia and 21 healthy controls took part in the study. The eligible participants underwent clinical assessments and PET scanning, from which cannabis use history and PET data were collected. Participant neuroinflammatory levels are represented by [18F]DPA-714 volume and different regions of grey matter in the brain were analysed through multivariate analyses, the confounding variables being age and TSPO genotype.ResultA statistically significant association is shown between participants who have had exposure to cannabis and participants who have not had any exposure in their lifetime. The differences across the prioritised brain regions of interest were robust, the association appearing more apparent and statistically significant in the total (p = .00) and temporal grey matter (p = .00) regions of the brain. This may suggest that cannabis exposure influences the [18F]DPA-714 VT in the significant regions of interest. However, a negative association is seen with current use, the quantity of use, and the frequency of use.ConclusionThe initial findings for cannabis exposure show us a positive association with increased TSPO levels, however, limitations must be taken into account. Although we cannot readily establish that elevated TSPO levels in cannabis users can presently act as a risk factor marker for developing psychosis from this particular study, we can utilise this data to continue our research in disclosing a new system to predict the occurrence of psychosis.

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