Healthy food and determinants of food choice on online food delivery applications.

Online food delivery applications (OFD apps) provide consumers with a wide range of options to choose from. The present study aimed to assess the usage of OFD apps and investigate the factors that affect food choices with a special emphasis on healthy food choices and hygiene. A cross-sectional study among food delivery application users in Jordan was conducted using an online questionnaire between March and May 2022. A total of 675 eligible subjects participated in the study. Consumers' demographic characteristics, data on consumers' use of OFD apps, consumers' perceptions of healthy food availability in OFD apps, and consumers' attitudes toward food safety and delivery hygiene were collected and analyzed. About 64% of the studied sample used OFD apps weekly. Fast food was the most popular option for ordering (87.1%) and lunchtime was the most preferred time to order food (67.3%) for most of the respondents. Respondents' perceptions of a "healthy meal" was associated with the presence of a variety of vegetables in the meal. Food price, food appearance, time of delivery, macronutrient content information, the availability of healthy options, and considering vegetables as part of a healthy meal were determinants of consumer food choice (p<0.05). The findings suggest that the online food environment in Jordan was perceived to be unhealthy. Nevertheless, the convenient nature and the popularity of OFD apps hold great potential to promote healthy eating among consumers

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness

Mega-analysis of association between obesity and cortical morphology in bipolar disorders:ENIGMA study in 2832 participants

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

10Kin1day: A Bottom-Up Neuroimaging Initiative.

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain

Regulation of emotion and cholinergic system connectivity in bipolar disorder

Background: The human brain comprises distributed cortico-subcortical regions that are structurally and functionally connected into a network that is known as the human connectome. Understanding how connections between regions are arranged with regards to other functionally specialised cortico-subcortical networks, and how independent functional subsystems integrate globally and relate back to anatomical networks, is key to understanding how the human brain’s architecture underpins abnormalities of mood and emotions. Structural and functional abnormalities, mostly involving prefrontal and limbic systems, alongside changes in the neuromodulatory muscarinic-cholinergic system, have been associated with disorders of emotion regulation such as bipolar disorder (BD); however, the neurobiological and molecular basis of this illness are currently poorly understood. Current pharmacological intervention of BD includes predominantly mood stabilisers and antipsychotics. However, BD treatment remains suboptimal resulting in a considerable proportion of patients remaining refractory to treatment. The search of new agents that are specific to BD may be eased by emphasising the interface between human brain organisation, preferential patterns of dysconnectivity and neuromodulatory systems influence. To date, the paucity of structural and functional graph analysis investigations in BD have yielded inconsistent findings. Thus, the present thesis avails of graph analyses to examine features of structural and functional brain network organisation and to investigate the contribution of the neuromodulatory muscarinic-cholinergic system to core emotional symptoms of BD in a predominantly euthymic sample of individuals presenting with BD relative to psychiatrically-healthy controls. Methods: Individual structural and functional connectivity matrices were constructed using a subject-specific 34-cortical and 9-subcortical bilateral nodes (Desikan-Killiany atlas). Structural edges weighted by fractional anisotropy and streamline count were derived from deterministic tractography using constrained spherical deconvolution, and functional edges were weighted by Pearson’s’ and partial correlation coefficients representing the association between averaged nodal resting-state time-series. Wholebrain connectivity measures and a permutation-based statistical approach were employed in the structural and functional connectivity analyses alongside rich-club connectivity and structure-function coupling; all to investigate topological variance in BD relative to healthy volunteers. To examine the role of the muscarinic-cholinergic system in BD, participants underwent a functional scan and performed an emotioninhibition task with intravenous physostigmine cholinergic system challenge (1 mg) or placebo between fMRI runs to assess functional activation, changes across mood (Profile of Mood States) and behavioural performance (accuracy and reaction time). Results: Subjects with BD, relative to controls, demonstrated impairments across whole-brain topological arrangements (density, degree, and efficiency) but preserved whole-brain structural and functional connectivity strength. A dysconnected structural subnetwork involving limbic and basal ganglia connections was observed in BD relative to controls. Increased betweenness centrality scores were observed generally in females and increased rich-club connectivity most evident in females with BD, with fronto-limbic and parieto-occipital nodes not members of BD rich-club. A dysconnected functional subnetwork encompassing fronto-limbic fronto-temporal and posterior-occipital connections was observed in BD relative to controls. Further, a comparable structuralfunctional relationship was observed for whole-brain and within edge-class connections. When processing emotional stimuli, under-activation of the anterior cingulate cortex and impaired behavioural performance were observed in BD relative to controls. However, cholinergic system challenge physostigmine affected behavioural performance without significantly altering mood and was associated with overactivation of the posterior cingulate cortex in BD compared to controls during the inhibition of the negative salience of the emotional stimuli. Discussion: These findings suggest BD dysconnectivity is present, it is not diffuse but rather localised to involve communication within and between structural and functional networks generally underpinning emotion, reward and salience. The structural and functional abnormalities observed in BD largely overlap with networks involved in ventromedial and ventrolateral routes to emotional control which support processes of interoception and visceromotor control. Disturbances in these neurocircuitries may modulate and thus explain maladaptive internal representations of external stimuli occurring in BD and consequent aberrant perception of emotional negative stimuli as increasingly salient. Considering that the examined subjects were predominantly euthymic at the time of scanning, the detected structural and functional abnormalities may underpin a compensatory mechanism of neural rewiring or activity that may be necessary to sustain a remitted clinical state of the illness; and provide flexibility in the ability to switch between segregated and integrated states. This thesis supports the application of graph theory to diffusion tensor and functional imaging data to identify abnormalities of subnetworks associated with BD and elucidates the underlying neurobiology of this illness highlighting the contribution of the neuromodulatory muscarinic-cholinergic system to core emotional symptoms of BD. This thesis may inform future studies aimed at incorporating neuroimaging techniques into studies of treatment mechanism and prediction of treatment response, looking at neuromodulatory systems and connectomics as a therapeutic avenue of BD.2021-10-0