Search

Study Objectives: Insufficient sleep and social stress are associated with weight gain and obesity development in adolescent girls. Functional magnetic resonance imaging (fMRI) research suggests that altered engagement of emotion-related neural networks may explain overeating when under stress. The purpose of this study is to explore the effects of acute sleep restriction on female adolescents' neural responding during social evaluative stress and their subsequent eating behavior., Methods: Forty-two adolescent females (ages 15-18 years) with overweight or obesity completed a social stress induction task in which they were told they would be rated by peers based on their photograph and profile. Participants were randomly assigned to one night of sleep deprivation or 9 h of sleep the night before undergoing fMRI while receiving positive and negative evaluations from their peers. After which, subjects participated in an ad libitum buffet., Results: Sleep deprived, relative to nondeprived girls had distinct patterns of neural engagement to positive and negative evaluation in anterior, mid, and posterior aspects of midline brain structures. Moreover, a sleep deprivation-by-evaluation valence-by-caloric intake interaction emerged in bilateral dorsal anterior cingulate. Among sleep deprived girls, greater engagement during negative, but not positive, feedback was associated with lower caloric intake. This was not observed for nonsleep deprived girls., Conclusions: Results suggest an interaction between acute sleep loss and social evaluation that predicts emotion-related neural activation and caloric intake in adolescents. This research helps to elucidate the relationship between sleep loss, social stress, and weight status using a novel health neuroscience model., (© Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Episodic memory depends on the computational process of pattern separation in order to establish distinct memory representations of similar episodes. Studies of pattern separation in humans rely on mnemonic discrimination tasks, which have been shown to tax hippocampal-dependent pattern separation. Although previous neuroimaging research has focused on hippocampal processing, little is known about how other brain regions, known to be involved in recognition memory performance, are involved in mnemonic discrimination tasks. Conversely, neuroimaging studies of pattern separation with whole-brain coverage lack spatial resolution to localize activation to hippocampal subfields. In this study, 48 healthy young adult participants underwent whole-brain high-resolution functional MRI (fMRI) scanning while completing a mnemonic discrimination task. A priori region-of-interest analyses revealed activation patterns consistent with pattern separation in distinct hippocampal subregions, particularly in the subiculum. Connectivity analyses revealed a network of cortical regions consistent with the memory retrieval network where fMRI activation was correlated with hippocampal activation. An exploratory whole-brain analysis revealed widespread activation differentially associated with performance of the mnemonic discrimination task. Taken together, these results suggest that a network of brain regions contribute to mnemonic discrimination performance, with the hippocampus and parahippocampal cortex as a hub in the network displaying clear signals consistent with pattern separation and regions such as the dorsal medial prefrontal cortex particularly important for successful lure discrimination., (© 2021 Wiley Periodicals LLC.)

The purpose of this study was to examine the effects of after-school sedentary screen time on children's brain activation in reward and cognitive control regions in response to pictures of high- and low-calorie foods. Thirty-two children participated in a randomized crossover study with counterbalanced treatment conditions. Conditions took place on separate days after school and included three hours of active or sedentary play. After each condition, neural activation was assessed using functional magnetic resonance imaging (fMRI) while participants completed a go/no-go task involving pictures of high- and low-calorie foods. General response inhibition was also measured using the Stroop task. Hunger was measured upon arrival to the testing facility and just prior to fMRI scans. Mixed effects models were used to evaluate main effects and interactions. Significant stimulus by condition interactions were found in the right superior parietal cortex, and left anterior cingulate cortex (Ps ≤ 0.05). High-calorie pictures elicited significantly more activation bilaterally in the orbitofrontal cortex compared to low-calorie pictures (Ps ≤ 0.05). Stroop task performance diminished significantly following the sedentary condition compared to the active (P ≤ 0.05). Subjective feelings of hunger were not different between conditions at any point. Sedentary screen time was associated with significantly decreased response inhibition and a reversed brain activation pattern to pictures of high- and low-calorie foods compared to active play, in areas of the brain important to the modulation of food intake. Decreased attention, and impulse control following sedentary screen time may contribute to disinhibited eating that can lead to overweight and obesity.

The ability to make predictions is thought to facilitate language processing. During language comprehension such predictions appear to occur at multiple levels of linguistic representations (i.e. semantic, syntactic and lexical). The neural mechanisms that define the network sensitive to linguistic predictability have yet to be adequately defined. The purpose of the present study was to explore the neural network underlying predictability during the normal reading of connected text. Predictability values for different linguistic information were obtained from a pre-existing text corpus. Forty-one subjects underwent simultaneous eye-tracking and fMRI scans while reading these select paragraphs. Lexical, semantic, and syntactic predictability measures were then correlated with functional activation associated with fixation onset on the individual words. Activation patterns showed both positive and negative correlations to lexical, semantic, and syntactic predictabilities. Conjunction analysis revealed regions specific to or shared between each type of predictability. The regions associated with the different predictability measures were largely separate. Results suggest that most linguistic predictions are graded in nature, activating components of the existing language system. A number of regions were also found to be uniquely associated with full lexical predictability, most notably the anterior temporal lobe and the inferior posterior temporal cortex., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Study Objectives: Sleep is an important behavior that affects appetite and eating in adolescents. Our study examined food-related neural activation in brain regions associated with food reward and inhibition in adolescents under sleep-restricted and well-rested conditions., Methods: In this within-subjects study, 52 adolescents (ages 12-18; 46% female; M age = 15.96 years, SD = 1.56) with normal weight (NW; N = 29, M body mass index % [BMI%] = 54.55, SD = 24.54) or overweight/obesity (OV/OB; N = 23, M BMI% = 93.78, SD = 4.60) spent 5 hours in bed at home each night for five consecutive nights and 9 hours in bed at home each night for 5 consecutive nights, with the first day of each condition occurring 4 weeks apart. The morning following each sleep modification period, functional magnetic resonance imaging (fMRI) data were collected while participants performed an inhibitory (go/no-go) task with food stimuli., Results: We found significantly greater activation in brain regions associated with inhibition in adolescents with NW in response to food cues when sleep restricted. No increase in inhibition-related neural activation was observed in adolescents with OV/OB when sleep restricted. We also found neural activation consistent with greater reward processing associated with food cues following sleep restriction regardless of weight status., Conclusions: These findings suggest that chronic sleep restriction may increase the likelihood of suboptimal dietary behavior for adolescents with OV/OB because they do not experience increased inhibition-related neural responding to counter possible increased reward-related neural responding following sleep restriction., (© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Effective memory representations must be specific to prevent interference between episodes that may overlap in terms of place, time, or items present. Pattern separation, a computational process performed by the hippocampus, overcomes this interference by establishing nonoverlapping memory representations. Although it is widely accepted that declarative memories are consolidated during sleep, the effects of sleep on pattern separation have yet to be elucidated. We used whole-brain, high-resolution functional neuroimaging to investigate the effects of sleep on a task that places high demands on pattern separation. Sleep had a selective effect on memory specificity and not general recognition memory. Activity in brain regions related to memory retrieval and cognitive control demonstrated an interaction between sleep and delay. Surprisingly, there was no effect of sleep on hippocampal activity using a group-level analysis. To further understand the role of the hippocampus on our task, we performed a representational similarity analysis, which showed that hippocampal activation was biased toward pattern separation relative to cortical activation and that this bias increased following a delay (regardless of sleep). Cortical activation, conversely, was biased toward pattern completion and this bias was preferentially enhanced by sleep., (© 2017 Wiley Periodicals, Inc.)

Obesity and maintaining a healthy diet have important implications for physical and mental health. One factor that may influence diet and obesity is inhibitory control. We tested how N2 and P3 amplitude, event-related potential (ERP) components that reflect inhibitory control, and functional magnetic resonance imaging (fMRI) activity in brain regions associated with inhibitory control differed toward high- and low-calorie food stimuli across BMI status. We also assessed the relationship between neural indices of food-related inhibitory control and laboratory and daily food intake. Fifty-four individuals (17 normal-weight; 18 overweight; 19 individuals with obesity) completed two food-based go/no-go tasks (one with high- and one with low-calorie foods as no-go stimuli), once during ERP data acquisition and once during fMRI data acquisition. After testing, participants were presented with an ad libitum weighed food buffet. Participants also recorded their food intake using the Automated Self-Administered 24-hour Dietary Recall (ASA24) system across four days. Individuals recruited more inhibitory control when withholding responses towards high-compared to low-calorie foods, although this effect was more consistent for N2 than P3 or fMRI assessments. BMI status did not influence food-related inhibitory control. A larger inhibitory response as measured by N2 amplitude was related to increased ASA24 food intake; P3 amplitude and fMRI region of interest activity did not predict ASA24 intake; neither method predicted food intake from the buffet. ERP and fMRI measurements show similar neural responses to food, although N2 amplitude may be somewhat more sensitive in detecting differences between food types and predicting self-reports of food intake., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing.