[Computational psychiatry : Data-driven vs. mechanistic approaches]

Radiofrequency thermocoagulation of trigeminal nerve assisted by nerve bundle extraction and image fusion based on hamilton-jacobi equation

BACKGROUND AND OBJECTIVE

Radiofrequency thermocoagulation is an effective method for treating classic trigeminal neuralgia. However, the accurate positioning of thermocoagulation is difficult. The purpose of this study was to design an optimal strategy for performing adjuvant surgery.

METHODS

A total of 60 patients with trigeminal neuralgia were divided into two groups. One group received conventional computed tomography (CT) guided treatment (CT group). In the other group, neural fiber bundles were firstly extracted based on the Hamilton-Jacobi equation. Then, the MRI, CT, and fiber bundle images were fused to visualize the relationship among semilunar ganglion, trigeminal nerve, and puncture needle (fusion group).

RESULTS

Trigeminal fiber bundles were extracted quickly by the contour tracking method, and different types of image fusion were realized for radiofrequency surgery navigation. In the fusion group, 13.3% of patients could not reach semilunar ganglion, and 76.9% of the remaining cases reached the ideal damage area. In the CT group, the preoperative design shows that 26.7% of patients may have puncture difficulty, and 54.5% of remaining cases reached the ideal damage area.

CONCLUSION

The technique of neural bundle extraction and image fusion based on the Hamilton-Jacobi equation can be used to plan the personalized puncture path targeting the semilunar ganglion.

[Negative valence systems in the system of research domain criteria : Empirical results and new developments]

BACKGROUND

The research domain criteria (RDoC) domain of negative valence systems can be used to subsume long established and recently developed research approaches, which build upon theoretical knowledge and clinical practice of various psychiatric disorders.

OBJECTIVE

This article outlines how the five constructs within the RDoC domain of negative valence systems can contribute to integrating empirical studies into a coherent and differentiated biopsychosocial model.

MATERIAL AND METHODS

This is a qualitative review article that summarizes empirical results and discusses new developments on the basis of exemplary studies and selected reviews.

RESULTS AND DISCUSSION

The RDoC domain of negative valence systems differentiates in three constructs the time horizon, in which persons need to adequately react to (1) acute, (2) potential, and (3) sustained threats elicited by negative stimuli or situations. These three constructs can be outlined relatively well with specific experimental paradigms and neuronal circuits. Two further constructs focus on the negative consequences of (4) losses and (5) frustrative non-rewards. The former seems to be currently relatively diffusely defined whereas the latter is clearly circumscribed by its relation to specific forms of aggression. Behavioral, physiological, and neuronal reactions to acute and potential threats can be well compared between humans and animals and can be specified with the help of mathematical models. These models can contribute to a better understanding of how healthy and diseased persons process negative stimuli or situations.

[From the Ethics of AI to the Ethics of Consciousness: Ethical Aspects of Computational Psychiatry]

OBJECTIVE

Identifying ethical problems arising from AI research and Computational Psychiatry for psychiatric research and practice.

METHODS

Conceptual analysis and discussion of ethically relevant projects within Computational Psychiatry.

RESULTS

Computational Psychiatry promises a contribution to improving diagnostics and therapy (prediction). Ethical problems include dealing with data protection, consequences for our self-image, as well as the risk of biologization and the neglect of conscious experience.

CONCLUSION

It is necessary to consider possible applications of AI and Computational Psychiatry now in order to create the conditions for responsible use in the future. This requires a basic understanding of how AI applications work and of the associated ethical problems.

[Diagnostics and Therapy 24/7? Artificial Intelligence as a Challenge and Opportunity in Psychiatry and Psychotherapy]

OBJECTIVE

The aim of the article is to enable a fundamental understanding of the potentials and requirements of Artificial Intelligence (AI) for psychiatrists in the present and for the development of future working environments. Psychiatrists will need to understand the function of AI-systems and personalized AI-assistants in therapy systems and as part of their patients' daily life.

METHOD

The article provides an overview of basic categories and fields of application of AI and machine learning in the diagnosis, prevention and therapy of mental disorders.

RESULTS

AI-applications will shape the prevention, diagnosis and treatment as well as the basic etiological understanding of mental disorders. Notably, the treatment of mental disorders is significantly influenced by commercial product development and assistance systems outside the medical system, as the corresponding developments can exploit large data pools with significantly lower restrictions.

CONCLUSION

Psychiatrists should now seize the opportunity to actively shape the implementation of AI-systems as otherwise key competences could be transferred to a primary field outside the medical system to the detriment of the patient and the therapist.