LAUSR

World Psychiatry 22:2 June 2023 hampered the development of clinically useful diagnostic biomarkers of mental disorders. Furthermore, most identified genetic and (neuro)biological abnormalities are not spe ci fic to a single mental disorder, making them unsuitable for differential di ag nosis, which is arguably a more meaning ful objective than distinguishing people with a mental disorder from those who are heal thy. For exam ple, the abovementioned atypical energy-related symptom profile of depression linked to imm unometabolic dys reg u la tions is likely not un ique to MDD, since similar symptoms as well as immune and metabolic dis turbanc es are observed in other disorders (e.g., bipolar disorder). Therefore, incorporating transdiagnostic symp tom or behavioural dimensions as the phenotype of in terest has the potential to significantly ad vance biomarker development, albeit not necessarily facilitating a more accurate diagnosis. It is important to remind ourselves that the value of identifying biological markers of mental disorders is not restricted to whether or not they can represent useful tools for diagnostic purposes; they may also provide important clues for optimization of existing treatments or development of new interventions. For example, abnormal functional connectivity between the dorsal prefrontal cortex and subgenual anterior cingulate cortex has been suggested to be a core functional deficit in MDD. The effect size of this deficit is too small for it to serve as a diagnostic biomarker. Nonetheless, it has proven to be an important target site for repetitive transcranial magnetic stimulation (rTMS). Recent preliminary work suggests that rTMS can be optimized and individualized by targeting dorsolater al prefrontal cor tex sites that display strong er negative functional connectivity with the subgenual cingulate cortex, thereby reduc ing heterogeneity of rTMS treatment out comes. Despite suggested strategies to improve diagnostic biomarker identification, I agree with the authors that a shift from a focus on diagnostic biomarkers to prognostic or predictive biomarkers would be appropriate. Identifying prognostic biomarkers of relapse/recurrence or treatment response may be a more fruitful endeavour, as they correspond more clearly to processes involved in the outcome in question (e.g., a biological treatment). Indeed, studies have shown that therapeutic outcomes are often related to pretreatment brain differences, and that the brain changes as a result of the treatment. Moreover, grouping people based on their response to a treatment may result in more homogeneous samples than those based on a DSM diagnosis, further enhancing the likelihood of deriving clinically actionable biomarkers. Some studies are beginning to show promising candidate treatment biomarkers that have been internally and/or externally validated, including, for instance, EEG biomarkers of response to selective serotonin reuptake inhibitors in patients with MDD. Nonetheless, heterogeneity may still exist within groups of treatment responders or those who experience recurrent episodes of mental illhealth, since different underlying mechanisms may lead to the same outcome in different people (most treatments have no clear single mechanism of action). Unfortunately, most clinical trials have not been adequately powered to disentangle this withingroup heterogeneity, and future largerscale clinical trials (e.g., through clinical trial networks), data pooling initiatives of existing trial data, or more flexible trial designs are required. Another important consideration for future development of prognostic and predictive biomarkers is the extension of predictive models with timevarying predictors, facilitated by recent advancements in machine learning (e.g., recurrent neural networks). Including timevarying biomarkers could enhance predictive accuracy, given that treatment response or recurrence of mental illhealth is often not a static but a dynamic process. Very few studies to date have exploited the timevarying nature of predictor variables, although initial studies are starting to show increased prediction accu racy for treatment response when adding early changes in biomarkers to baseline predictors. In conclusion, even though a clinically actionable biomarker is yet to be discovered, sev eral developments – including largerscale studies and collaborations (en abling independent validation), advances in statistical methods, and more precise phenotyping – have the potential to accelerate progress in psychiatric biomarker research in the coming years. Harnessing heterogeneity within and across our current diagnostic classifi cations, and within groups of treatment responders, will be key to future success of bio markers in optimizing care for the next gen eration of people with men tal disorders.