The digitalisation of mental health care is expected to improve the accessibility and quality of specialised treatment services and introduce innovative methods to study, assess, and monitor mental health disorders. In this narrative review and practical recommendation of the European Psychiatric Association (EPA), we aim to help healthcare providers and policymakers to navigate this rapidly evolving field. We provide an overview of the current scientific and implementation status across two major domains of digitalisation: i) digital mental health interventions and ii) digital phenotyping, discuss the potential of each domain to improve the accessibility and outcomes of mental health services, and highlight current challenges faced by researchers, clinicians, and service users. Furthermore, we make several recommendations meant to foster the widespread adoption of evidence-based digital solutions for mental health care in the member states of the EPA. To realise the vision of a digitalised, patient-centred, and data-driven mental health ecosystem, a number of implementation challenges must be considered and addressed, spanning from human, technical, ethical–legal, and economic barriers. The list of priority areas and action points our expert panel has identified could serve as a playbook for this process.

Depressive disorders represent the largest proportion of mental illnesses, and by 2030, they are expected to be the first cause of disability-adjusted life years [1]. The COVID-19 pandemic exacerbated prevalence and burden of depression and increased the occurrence of depressive symptoms in general population [2]. The urgency of implementing mental health services to address new barriers to care persuaded clinicians to use telemedicine to follow patients and stay in touch with them, and to explore digital therapeutics (DTx) as potential tools for clinical intervention [2]. The combination of antidepressants and psychotherapy is widely recommended for depression by international guidelines [3] but is less frequently applied in real-world practice. Commonly used treatments are pharmacological, but while being effective, some aspects such as adherence to the drug regimen, residual symptoms, resistance, lack of information, and stigma may hinder successful treatment. In case of less severe depression, standalone psychological therapies should be the first-line treatment option [3], but access to trained psychotherapists remains inequitable. DTx are evidence-based therapies driven by software programs to treat or complement treatment of a specific disease. DTx are classified as Medical Devices, and given their therapeutic purpose, they need to be validated through randomized controlled clinical trials, as for drug-based therapies. In the last 10 years, studies of digital interventions have proliferated; these studies demonstrate that digital interventions increase remission rates and lower the severity of depressive symptoms compared with waitlist, treatment as usual, and attention control conditions [4]. Despite the efficacy demonstrated in clinical trials, many of these tools never reach real-life patients; thus, it might be necessary to implement DTx in the public health system to expand access to valid treatment options. In this framework, DTx represent a good opportunity to help people with depression receive optimal psychotherapeutic care [5].

The ongoing developments of psychiatric classification systems have largely improved reliability of diagnosis, including that of schizophrenia. However, with an unknown pathophysiology and lacking biomarkers, its validity still remains low, requiring further advancements. Research has helped establish multiple sclerosis (MS) as the central nervous system (CNS) disorder with an established pathophysiology, defined biomarkers and therefore good validity and significantly improved treatment options. Before proposing next steps in research that aim to improve the diagnostic process of schizophrenia, it is imperative to recognize its clinical heterogeneity. Indeed, individuals with schizophrenia show high interindividual variability in terms of symptomatic manifestation, response to treatment, course of illness and functional outcomes. There is also a multiplicity of risk factors that contribute to the development of schizophrenia. Moreover, accumulating evidence indicates that several dimensions of psychopathology and risk factors cross current diagnostic categorizations. Schizophrenia shares a number of similarities with MS, which is a demyelinating disease of the CNS. These similarities appear in the context of age of onset, geographical distribution, involvement of immune-inflammatory processes, neurocognitive impairment and various trajectories of illness course. This article provides a critical appraisal of diagnostic process in schizophrenia, taking into consideration advancements that have been made in the diagnosis and management of MS. Based on the comparison between the two disorders, key directions for studies that aim to improve diagnostic process in schizophrenia are formulated. All of them converge on the necessity to deconstruct the psychosis spectrum and adopt dimensional approaches with deep phenotyping to refine current diagnostic boundaries.

Electroencephalography has probably represented the first modern and scientifically sound attempt to functionally explore the in vivo activity of the human brain and it has, since ever, attracted attention of psychiatrists, from both the clinical and the research viewpoint.

Probably due to the limitations implied by their traditional low spatial resolution, the use of psychophysiological techniques in psychiatry has been not continuous over the last century; however, the availability of newer EEG-based brain imaging techniques has recently renovated some interest (1)). Furthermore, recent theories proposed that psychopathology may result from the failure to integrate the activity of different areas involved in cognitive processes, rather than from the impairment of one or more brain areas (2)); within this view, a reliable brain imaging tool should be able to explore the dynamics of complex interactions among brain regions, with high sensitivity to the subtle deviation in complex processes that last fractions of seconds; psychophysiological techniques, indeed, offer the possibility to explore the functional correlates of major psychiatric illnesses, as well as to understand of the effects of psychotropic drugs on the central nervous system, with incomparable time resolution. Finally, the recent technical possibility to combine different brain imaging approaches has further fostered a renovated enthusiasm to ward the use of EEG-based techniques in psychiatry.

This contribution will provide an historical overview of the EEG-based brain imaging techniques and an update on some recent advances concerning the use of such techniques within the psychiatric field. Finally, some examples of psychophysiological and ''multimodal'' imaging investigations in subjects with different psychiatric conditions will be provided.

Aims — The present review is aimed to evaluate the recent contribution of brain imaging techniques to the definition of neuroanatomofunctional models of panic disorder (PD). Methods — Structural and functional brain imaging studies of PD, conducted from January 1993 to October 2003 and selected through a comprehensive Medline search (key-words: panic disorder, emotions, brain imaging, EEG, Event-Related Potentials, MRI, fMRI, PET, SPECT, TC) were included in the review. The Medline search has been complemented by bibliographic cross-referencing. Results — The majority of the reviewed studies suggests that a dysfunction of a neural circuit encompassing prefrontal and temporo-Iimbic cortices is present in PD. A right hemisphere preferential involvement in PD has been shown by several studies. Conclusions — Reviewed neuroimaging studies suggest a dysfunction of frontal and temporo-Iimbic circuitries in PD. However, those studies cannot be considered conclusive because of several methodological limitations. Longitudinal and multi-modal studies involving larger patient samples, possibly integrated with population-based and genetic studies, would provide more insight into pathophysiological mechanisms of PD.

Declaration of Interest: Authors declare that none of them had any known real, potential, or apparent conflict of interest and that there was no business or personal interest that might be relevant to the topic of this article.