Cortical thickness patterns as state biomarker of anorexia nervosa

Lavagnino, Luca; Mwangi, Benson; Cao, Bo; Shott, Megan E.; Soares, Jair C.; Frank, Guido

doi:10.1002/eat.22828

Cited by 50 publications

(64 citation statements)

References 60 publications

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“…Our results of cortical thinning in different regions in acute AN patients align with several previous studies (Bär et al, ; Bernardoni et al, ; Fuglset et al, , ; King et al, ). However, Lavagnino et al () reported no significant cortical thinning, and in a later study, they found both regional cortical thinning in frontal areas and thickening in insular and orbitofrontal cortices in AN and REC cases (Lavagnino et al, ). These discrepancies might be due to different methodologies (FreeSurfer) (Lavagnino et al, , ) or shorter duration of illness (Lavagnino et al, ).…”

Section: Discussionmentioning

confidence: 97%

“…Cortical thickness, therefore, is unlikely to represent a permanent trait marker or scar (King et al, 2015). According to previous studies, cortical thinning appears widespread across the brain, focused on the frontal regions (Bär et al, 2015;Fuglset et al, 2016;Lavagnino et al, 2018). Cortical thinning in prefrontal brain areas could contribute to altered response inhibition (Oberndorfer, Kaye, Simmons, Strigo, & Matthews, 2011;Wierenga et al, 2014) and set shifting (Sato et al, 2013) in AN.…”

Section: Cortical Thicknessmentioning

confidence: 97%

“…Cortical thinning in AN was reported in the frontoparietal-cingulate network (Bär et al, 2015;Fuglset et al, 2016) and widespread areas across the brain (Bernardoni et al, 2016;Fuglset et al, 2015;King et al, 2015). Lavagnino et al (2016) reported no significant cortical thinning in AN, but a further investigation (Lavagnino et al, 2018) additionally found higher cortical thickness in the insula and orbitofrontal cortex in AN and REC. At least in young, non-chronic patients, cortical thinning in AN generally normalized after long-term weight restoration and was proposed to be a consequence of malnutrition, not premorbid trait markers (Bernardoni et al, 2016;King et al, 2015).…”

mentioning

confidence: 96%

“…To date, only a few studies have investigated cortical thickness in AN (Bär et al, 2015;Bernardoni et al, 2016;Fuglset et al, 2016;Fuglset, Endestad, Landrø, & Rø, 2015;King et al, 2015;Lavagnino et al, 2016Lavagnino et al, , 2018. Three of these previous studies also included a REC group (Bernardoni et al, 2016;King et al, 2015;Lavagnino et al, 2018), but two used less stringent definitions of recovery (Bernardoni et al, 2016;King et al, 2015). Cortical thinning in AN was reported in the frontoparietal-cingulate network (Bär et al, 2015;Fuglset et al, 2016) and widespread areas across the brain (Bernardoni et al, 2016;Fuglset et al, 2015;King et al, 2015).…”

mentioning

confidence: 99%

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Recovery of cortical volume and thickness after remission from acute anorexia nervosa

Nickel

Joos

Elst

et al. 2018

Intl J Eating Disorders

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Section: Discussionmentioning

confidence: 97%

Section: Cortical Thicknessmentioning

confidence: 97%

mentioning

confidence: 96%

mentioning

confidence: 99%

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Recovery of cortical volume and thickness after remission from acute anorexia nervosa

Nickel

Joos

Elst

et al. 2018

Intl J Eating Disorders

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“…Recent reports are in support of those findings. Studies by Nickel et al (2018) and Bang, Ro, and Endestad (2018) found normal cortical volume and thickness and indication of normal white matter after recovery from anorexia nervosa, and a study by Lavagnino et al (2018) suggests that altered cortical thickness is rather a state than trait marker in anorexia nervosa. Atypical anorexia nervosa is defined as anorexia nervosa that does not meet full diagnostic criteria, typically those individuals are not below a certain weight threshold, such as the 5th percentile for age adjusted BMI.…”

Section: Research On Brain Structurementioning

confidence: 98%

Editorial to the virtual issue highlighting neuroscience based research in eating disorders to mark the 49th Society for Neuroscience Annual Meeting

Frank

2019

Intl J Eating Disorders

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Objective This virtual issue of the International Journal of Eating Disorders highlights recently published research that is based on neuroscience concepts, to mark the 49th Society for Neuroscience Annual Meeting in Chicago, IL, in November 2019. Methods and Results The collection of articles includes research published between 2018 and 2019 that fall within the broader field of clinical neuroscience. Those articles span a broad range of themes, including food intake regulation and reward circuitry, taste perception, decision‐making, cognitive and emotional bias, and targeting altered brain circuits using novel therapeutic methods. Discussion The eating disorders field as a whole is increasingly incorporating neuroscience‐based concepts when studying those disorders and developing disease models. We hope that this virtual issue will further stimulate discussion and research that is focused on brain circuits and neurobiology to study etiology and pathophysiology of eating disorders to develop more effective treatments.

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Cortical thickness patterns as state biomarker of anorexia nervosa

Cited by 50 publications

References 60 publications

Recovery of cortical volume and thickness after remission from acute anorexia nervosa

Recovery of cortical volume and thickness after remission from acute anorexia nervosa

Editorial to the virtual issue highlighting neuroscience based research in eating disorders to mark the 49th Society for Neuroscience Annual Meeting

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

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