Reproducibility assessment of brain responses to visual food stimuli in adults with overweight and obesity

Objective The brain’s reward system influences ingestive behavior and subsequently, obesity risk. Functional magnetic resonance imaging (fMRI) is a common method for investigating brain reward function. We sought to assess the reproducibility of fasting-state brain responses to visual food stimuli using BOLD fMRI. Methods A priori brain regions of interest included bilateral insula, amygdala, orbitofrontal cortex, caudate, and putamen. Fasting-state fMRI and appetite assessments were completed by 28 women (n=16) and men (n=12) with overweight or obesity on 2 days. Reproducibility was assessed by comparing mean fasting-state brain responses and measuring test-retest reliability of these responses on the 2 testing days. Results Mean fasting-state brain responses on Day 2 were reduced compared to Day 1 in the left insula and right amygdala, but mean Day 1 and Day 2 responses were not different in the other regions of interest. With the exception of the left orbitofrontal cortex response (fair reliability), test-retest reliabilities of brain responses were poor or unreliable. Conclusion fMRI-measured responses to visual food cues in adults with overweight or obesity show relatively good mean-level reproducibility, but considerable within-subject variability. Poor test-retest reliability reduces the likelihood of observing true correlations and increases the necessary sample sizes for studies

Understanding the contribution of neural and physiological signal variation to the low repeatability of emotion-induced BOLD responses

Previous studies have reported low repeatability of BOLD activation measures during emotion processing tasks. It is not clear, however, whether low repeatability is a result of changes in the underlying neural signal over time, or due to insufficient reliability of the acquired BOLD signal caused by noise contamination. The aim of this study was to investigate the influence of “cleaning” the BOLD signal, by correcting for physiological noise and for differences in BOLD responsiveness, on measures of repeatability. Fifteen healthy volunteers were scanned on two different occasions, performing an emotion provocation task with faces (neutral, 50% fearful, 100% fearful) followed by a breath-hold paradigm to provide a marker of BOLD responsiveness. Repeatability of signal distribution (spatial repeatability) and repeatability of signal amplitude within two regions of interest (amygdala and fusiform gyrus) were estimated by calculating the intraclass correlation coefficient (ICC). Significant repeatability of signal amplitude was only found within the right amygdala during the perception of 50% fearful faces, but disappeared when physiological noise correction was performed. Spatial repeatability was higher within the fusiform gyrus than within the amygdala, and better at the group level than at the participant level. Neither physiological noise correction, nor consideration of BOLD responsiveness, assessed through the breath-holding, increased repeatability. The findings lead to the conclusion that low repeatability of BOLD response amplitude to emotional faces is more likely to be explained by the lack of stability in the underlying neural signal than by physiological noise contamination. Furthermore, reported repeatability might be a result of repeatability of task-correlated physiological variation rather than neural activity. This means that the emotion paradigm used in this study might not be useful for studies that require the BOLD response to be a stable measure of emotional processing, for example in the context of biomarkers

How to Enhance the Power to Detect Brain–Behavior Correlations With Limited Resources

Neuroscience has been diagnosed with a pervasive lack of statistical power and, in turn, reliability. One remedy proposed is a massive increase of typical sample sizes. Parts of the neuroimaging community have embraced this recommendation and actively push for a reallocation of resources toward fewer but larger studies. This is especially true for neuroimaging studies focusing on individual differences to test brain–behavior correlations. Here, I argue for a more efficient solution. Ad hoc simulations show that statistical power crucially depends on the choice of behavioral and neural measures, as well as on sampling strategy. Specifically, behavioral prescreening and the selection of extreme groups can ascertain a high degree of robust in-sample variance. Due to the low cost of behavioral testing compared to neuroimaging, this is a more efficient way of increasing power. For example, prescreening can achieve the power boost afforded by an increase of sample sizes from n = 30 to n = 100 at ∼5% of the cost. This perspective article briefly presents simulations yielding these results, discusses the strengths and limitations of prescreening and addresses some potential counter-arguments. Researchers can use the accompanying online code to simulate the expected power boost of prescreening for their own studies

Test‐retest reliability of amygdala response to emotional faces

In the current study, we evaluated the test‐retest reliability of amygdala response using an emotional face‐matching task that has been widely used to examine pathophysiology and treatment mechanisms in psychiatric populations. Activation within the fusiform face area ( FFA ) was also examined. Twenty‐seven healthy volunteers completed a variation of the face‐matching paradigm developed by Hariri et al. (2000) at two time points approximately 90 days apart. Estimates of test‐retest reliability of amygdala response to fearful faces were moderate, whereas angry and happy faces showed poor reliability. Test‐retest reliability of the FFA was moderate to strong, regardless of facial affect. Collectively, these findings indicate that the reliability of the BOLD MR signal in the amygdala varies substantially by facial affect. Efforts to improve measurement precision, enlarge sample sizes, or increase the number of assessment occasions seem warranted.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100342/1/psyp12129.pd

The importance of high quality real-life social interactions during the COVID-19 pandemic

The coronavirus pandemic has brought about dramatic restrictions to real-life social interactions and a shift towards more online social encounters. Positive social interactions have been highlighted as an important protective factor, with previous studies suggesting an involvement of the amygdala in the relationship between social embeddedness and well-being. The present study investigated the effect of the quality of real-life and online social interactions on mood, and explored whether this association is affected by an individual’s amygdala activity. Sixty-two participants of a longitudinal study took part in a one-week ecological momentary assessment (EMA) during the first lockdown, reporting their momentary well-being and their engagement in real-life and online social interactions eight times per day (N ~ 3000 observations). Amygdala activity was assessed before the pandemic during an emotion-processing task. Mixed models were calculated to estimate the association between social interactions and well-being, including two-way interactions to test for the moderating effect of amygdala activity. We found a positive relationship between real-life interactions and momentary well-being. In contrast, online interactions had no effect on well-being. Moreover, positive real-life social interactions augmented this social affective benefit, especially in individuals with higher amygdala being more sensitive to the interaction quality. Our findings demonstrate a mood-lifting effect of positive real-life social interactions during the pandemic, which was dependent on amygdala activity before the pandemic. As no corresponding effect was found between online social interactions and well-being, it can be concluded that increased online social interactions may not compensate for the absence of real-life social interactions

Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

Abstract: Purpose of Review: Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient’s likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative ‘biomarkers’. Recent Findings: Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention. Summary: A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brainwide switching between activity states. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Schizophrenia patients show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations

ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS AND WORKING MEMORY FMRI BRAIN ACTIVITY

Working memory (WM) and associated brain areas show deficits with increasing age. However, higher cardiorespiratory fitness (CRF) has been associated with better WM across the lifespan. The mechanisms by which CRF impacts WM are poorly understood. One possible mechanism is that CRF influences the integrity and function of brain regions that are involved in supporting WM function, and that this, in turn, influences accuracy rates on tasks that measure WM. Very few studies have tested whether the association between CRF and WM is statistically mediated by measures of brain function. This study addressed this gap in knowledge by examining the relationship between CRF, brain activation, and WM. We tested whether brain activation during a WM task statistically mediated the relationship between CRF and WM. Baseline data of 125 adults (M=44.34 ± 8.60 years) were included in this study. CRF was assessed via a submaximal graded exercise test. Magnetic resonance images were collected during the n-back task to examine neural responses to WM. FMRIB’s Software Library was used for fMRI data preprocessing and analysis. Regions-of-interest were defined by conducting a conjunction analysis to identify brain regions sensitive to both CRF and performance on the n-back task. Linear regression models examined the association of CRF with WM and brain activation in the left anterior cingulate cortex, left insula, and right insula. After controlling for age, gender, race, and years of education, CRF was not significantly related to accuracy on the WM task (all p>.15). However, consistent with our hypotheses, higher CRF was significantly related to greater brain activation in the left insula (p<.028) during the 2-back WM condition. Heightened brain activation in the left insula was not associated with WM accuracy (p=.12) after correction for variation due to age, gender, race, and education. Thus, statistical mediation could not be tested. Although higher CRF was associated with greater brain activation in the left insula, neither CRF nor heightened left insula activation were associated with variations in WM performance after adjusting for several confounding variables. These results suggest that there are other mediators that explain the relationship between CRF and WM performance in midlife

Aerobic exercise modulates anticipatory reward processing via the mu-opioid receptor system

Physical exercise modulates food reward and helps control body weight. The endogenous mu-opioid receptor (MOR) system is involved in rewarding aspects of both food and physical exercise, yet interaction between endogenous opioid release following exercise and anticipatory food reward remains unresolved. Here we tested whether exercise-induced opioid release correlates with increased anticipatory reward processing in humans. We scanned 24 healthy lean men after rest and after a 1 h session of aerobic exercise with positron emission tomography (PET) using MOR-selective radioligand [C-11]carfentanil. After both PET scans, the subjects underwent a functional magnetic resonance imaging (fMRI) experiment where they viewed pictures of palatable versus nonpalatable foods to trigger anticipatory food reward responses. Exercise-induced changes in MOR binding in key regions of reward circuit (amygdala, thalamus, ventral and dorsal striatum, and orbitofrontal and cingulate cortices) were used to predict the changes in anticipatory reward responses in fMRI. Exercise-induced changes in MOR binding correlated negatively with the exercise-induced changes in neural anticipatory food reward responses in orbitofrontal and cingulate cortices, insula, ventral striatum, amygdala, and thalamus: higher exercise-induced opioid release predicted higher brain responses to palatable versus nonpalatable foods. We conclude that MOR activation following exercise may contribute to the considerable interindividual variation in food craving and consumption after exercise, which might promote compensatory eating and compromise weight control

Variance decomposition for single-subject task-based fMRI activity estimates across many sessions

AbstractHere we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results