Altered functional connectivity in adolescent anorexia nervosa is related to age and cortical thickness

Review of machine learning solutions for eating disorders

BACKGROUND

Eating Disorders (EDs) are one of the most complex psychiatric disorders, with significant impairment of psychological and physical health, and psychosocial functioning, and are associated with low rates of early detection, low recovery and high relapse rates. This underscores the need for better diagnostic and treatment methods.

OBJECTIVE

This narrative review explores current Machine Learning (ML) and Artificial Intelligence (AI) applications in the domain of EDs, with a specific emphasis on clinical management in treatment settings. The primary objective are to (i) decrease the knowledge gap between ED researchers and AI-practitioners, by presenting the current state-of-the-art AI applications (including models for causality) in different ED use-cases; (ii) identify limitations of these existing AI interventions and how to address them.

RESULTS

AI/ML methods have been applied in different ED use-cases, including ED risk factor identification and incidence prediction (including the analysis of social media content in the general population), diagnosis, monitoring patients and treatment response and prognosis in clinical populations. A comparative analysis of AI-techniques deployed in these use-cases have been performed, considering factors such as complexity, flexibility, functionality, explainability and adaptability to healthcare constraints.

CONCLUSION

Multiple restrictions have been identified in the existing methods in ML and Causality in terms of achieving actionable healthcare for ED, like lack of good quality and quantity of data for models to train on, while requiring models to be flexible, high-performing, yet being explainable and producing counterfactual explanations, for ensuring the fairness and trustworthiness of its decisions. We conclude that to overcome these limitations and for future AI research and application in clinical management of ED, (i) careful considerations are required with regards to AI-model selection, and (ii) joint efforts from ED researcher and patient community are essential in building better quality and quantity of dedicated ED datasets and secure AI-solution framework.

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m²) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

Anorexia nervosa as a disorder of the subcortical-cortical interoceptive-self

PURPOSE

Anorexia nervosa (AN) is characterized by a diminished capacity in perceiving the physiological correlates of interoceptive sensations, namely bodily self-consciousness. Given the neural division of self-processing into interoceptive-, exteroceptive- and mental-self, we hypothesize neural deficits in the interoceptive-processing regions in AN.

METHODS

To prove this, we reviewed resting state (rs), task and rest-task studies in AN literature.

RESULTS

Neuronal data demonstrate the following in AN: (i) decreased rs-functional connectivity (rsFC) of subcortical-cortical midline structures (SCMS); (ii) reduced rsFC between medial (default-mode network/DMN and salience network/SN) and lateral (executive-control network/ECN) cortical regions; (iii) decreased rsFC in mainly the regions of the interoceptive-self; (iv) altered activity with overall increased activity in response to sensory/body image stimuli, especially in the regions of the interoceptive-self; (v) lack of a clear task-related distinction between own's and others' body image.

CONCLUSION

These data may indicate that rs-hypoconnectivity between SCMS, as neural correlate of a reduced intero-exteroceptive integration resulting in self-objectification, might be linked to overall increased activity in interoceptive regions during sensory/body image stimuli in AN, engendering an "anxious bodily self."

LEVEL OF EVIDENCE

I: Systematic review.