Abstract
Most mental disorders have a typical onset between 12 and 25 years of age, highlighting the importance of this period for the pathogenesis, diagnosis, and treatment of mental ill-health. This perspective addresses interactions between risk and protective factors and brain development as key pillars accounting for the emergence of psychopathology in youth. Moreover, we propose that novel approaches towards early diagnosis and interventions are required that reflect the evolution of emerging psychopathology, the importance of novel service models, and knowledge exchange between science and practitioners. Taken together, we propose a transformative early intervention paradigm for research and clinical care that could significantly enhance mental health in young people and initiate a shift towards the prevention of severe mental disorders.
Similar content being viewed by others
Rethinking Mental Health as “Youth Mental Health”
“Mental disorders are chronic diseases of the young.” [1]
Mental disorders constitute a major challenge to both society and science. Syndromes such as schizophrenia, depression, anxiety, and personality disorders comprise some of the largest disease burdens worldwide [2]. Despite the promises of genetics and translational neuroscience, insights into the causal mechanisms of major syndromes remain rudimentary [3], and the search for biomarkers to improve diagnosis and stratification has largely been unsuccessful [4]. Moreover, effect sizes for current pharmacological and psychological treatments are overall modest [5] and a significant number of patients will not respond to treatments [6].
A cardinal feature of the existing paradigm in mental health has been its emphasis on fully established disorders in adulthood while early intervention and prevention have been relatively neglected [7, 8]. However, there is now consistent epidemiological evidence that has highlighted that all major syndromes constituting approximately 75% of mental disorders begin before the age of 25 years [9, 10] (Fig. 1).
In this perspective, we will make the case for a transformative paradigm in mental health that emphasizes early intervention and prevention of emerging mental disorders during youth, the period between 12 and 25 years of ageFootnote 1, with wide ranging implications for diagnosis, research, and interventions. Our approach is critically informed by the early intervention paradigm in psychosis [11]. Its scope has now been broadened to target emerging mental disorders during youth more generally given that young people with clinical high-risk criteria for psychosis (CHR-P) rarely present solely with signs of psychosis [12] and evidence that early identification and intervention is also potentially effective in personality disorders [13], eating disorders [14], and bipolar disorder [15].
The early intervention paradigm is furthermore motivated by the finding that young people face many barriers to accessing mental health care [16] during a developmental period, which is critical for social and occupational adjustment [17]. Importantly, the COVID pandemic has accelerated this trend with youth reporting a disproportionate increase in mental ill-health [18]. As a result, emerging mental disorders during youth frequently lead to sustained mental health problems during adulthood and lower overall functioning [17, 19].
To address these fundamental challenges, we set out core “pillars” for a youth mental health paradigm. Specifically, we propose that ongoing modifications in behavioral functions and underlying neural circuits suggest the presence of “sensitive periods” for the symptomatic expression of mental ill-health and corresponding “windows of opportunity” for early intervention. Secondly, risk factors interact with these sensitive periods on multiple levels that can be conceptualized as “developmental cascades”. Thirdly, novel diagnostic approaches are needed to facilitate interventions for sub-threshold symptomatic expressions of emerging psychopathology. Finally, the implications of these findings are discussed with respect to novel clinical and policy approaches and models for youth mental health.
Brain development, sensitive periods, and emerging psychopathology
An important principle in brain development is the notion of “sensitive periods” [20, 21]. Sensitive periods can be described as time-limited developmental windows during which environmental exposures have a pronounced effect on the functionality and organization of neural circuits and behavior as a result of heightened plasticity. Originally first described in the visual system [22], there is mounting data that several neural and cognitive systems that are relevant for emerging psychopathology, such as fear and stress regulation, higher cognitive processes, social cognition, and reward-processing, undergo time-limited modifications in circuit properties during youth (Fig. 2).
A core function that is impaired across many mental disorders is the ability to respond to threats and stress [23]. Importantly, anxiety disorders emerge during the first decade, peak around 15 years of age [9], and often persist into adulthood with significant effects on functioning and quality of life [24]. Recent work has identified unique changes in the neural circuits underlying threat regulation [25]. Specifically, fear extinction during adolescence is reduced in both humans and mice [26] and probing neural circuitry in mice has revealed altered synaptic plasticity and connectivity in prefrontal cortical-hippocampal-amygdala [26]. Interestingly in adolescent mice, if extinction training takes place in the threat-conditioning context that engages hippocampal-based circuits, extinction retention is significantly greater than that in the same-age counterparts that underwent extinction training in a novel context [27]. However, once outside this ‘sensitive period’, this form of context-based extinction has minimal additional effects.
The hypothalamic-pituitary-adrenal (HPA)-axis is the primary site for regulating the body’s stress response through releasing glucocorticoid hormones and HPA-axis dysregulation is involved in several mental disorders and contributes to emerging psychopathology [28]. Evidence from animal studies suggests that a variety of stressors are associated with elevated and prolonged HPA responses in youth compared with adulthood [29] and that differences in the psychopathological phenotypes observed may depend on the timing of the stress exposure during youth [30]. Conversely, environmental enrichment in juvenile animals can reverse the effects of prenatal stress and maternal separation [31, 32]. Together, these findings suggest that both risk and protective factors interact with HPA-functioning during youth [33].
In addition, ongoing modifications in higher-order cognitive functions, such as working memory (WM), response inhibition, and performance monitoring, as well as cognitive control are a central aspect of late brain development [34]. Anatomically, these functions are closely related to the integrity of prefrontal cortical (PFC) circuits, a region that is impaired in a range of mental disorders, including schizophrenia, depression, and bipolar disorder [35].
There is substantial evidence that the composition and interaction of the dopaminergic, glutamatergic, and GABAergic receptors in the PFC undergo profound changes during youth [34]. PV+ interneurons are of particular interest as they contribute towards the opening and closing of sensitive periods [21] and recent evidence suggests that PV+ interneuron expression continues to increase during youth while time-limited downregulation leads to permanent changes in E/I-balance during adulthood [36], a process which may be involved in emerging psychopathology [37].
Modifications of local PFC changes are accompanied by an extensive integration with cortical and subcortical areas through the maturation of long-range connections [38,39,40,41,42]. A core hypothesis underlying brain development is the view that ongoing changes in brain maturation lead to a period when limbic structures, such as the amygdala and striatum, predominate over prefrontal areas during youth [43, 44]. The change to predominance of executive prefrontal regions in adulthood reflect a potential neurobiological basis for improvements in emotion regulation which is critically impaired in affective disorders [45] but also in borderline personality disorders [46].
One important manifestation of the predominance of limbic processing over executive function is risk-taking behavior during youth [47], such as substance abuse. Evidence suggests that the likelihood of developing substance dependence is highest if substance abuse is initiated before age 14 [48], suggesting a sensitive period that is closely related to ongoing changes in incentive salience as well as changes in dopaminergic neurotransmission in the striatum and PFC [49] which is permanently altered by drug abuse [48]. In addition, tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, may lead to a permanent disruption of E/I-balance and cognition during youth but not during adulthood [50].
Finally, converging findings have highlighted the possibility of a sensitive period for the development of the social-cognitive processes [51, 52]. Social cognition includes several domains, including theory of mind, emotion processing, and social cue identification, that have been linked to specific brain regions, such as the medial prefrontal cortex (mPFC) and tempo-parietal junction (TPJ) [53]. Recent evidence suggests that self-oriented thinking overlaps with regions required for understanding others, suggesting that the development of identity, a core task for youths, is an overlapping and intertwined process [52].
Reward circuits are particularly sensitive to peer influence during this age group, which can be related to higher levels of risk-taking behavior in social settings [54], and peer evaluations affect the self-image more than during other developmental periods [55]. Social interactions are also a necessary environmental exposure for establishing adult social behavior [56], a particularly pertinent issue given the widespread reduction of social interactions during the COVID-pandemic [57].
Developmental cascades, the environment, and social-cultural context
Youth is a unique period during human development characterized by the onset of puberty, followed by psychosocial milestones, such as the separation from family, developing romantic attachments as well as discovering one’s sexual orientation and identity. The wide-ranging manifestations of youth across and within cultures suggest that this phase is highly shaped by the environmental context. Accordingly, understanding the relationship between sensitive periods, environmental factors, and the emergence of psychopathology necessitates the application of appropriate theoretical and conceptual frameworks that address the highly dynamic and context-dependent nature of this developmental period that in turn can be harnessed to identify risk and resilience factors for emerging mental ill-health.
The population neuroscience approach is well suited to applying a life-course epidemiology paradigm to mental disorders that acknowledges the complexity in time and space of environmental and genomic factors [58, 59]. In this context, it is important to understand and apply the concept of developmental cascades, which is integral to population neuroscience approaches in appreciating how transactions at different timescales (e.g., perinatal, infancy, adolescence, early adulthood), constructs (cognition, mood, behavior), and levels (molecular, physiology, individual, and social) have a domino effect on subsequent development [60]. These developmental cascades refer to the cumulative consequences of the many interactions and transactions occurring during development that result in spreading after-effects across levels, among domains at the same level, and across different systems [61].
Developmental cascades are potentially a useful approach to conceptualizing emerging psychopathology as many mental disorders in adulthood frequently have precursors in non-specific symptomatic manifestations during youth [62] but also in childhood [63, 64]. Recent evidence suggests that the risk of mental disorders is associated with elevated risk for other disorders and that younger age of onset is a predictor of longer duration of symptoms, comorbidity, and worse outcomes [19], highlighting the importance of interventions to target the earliest signs of mental ill-health.
Effects of specific environmental exposures on the outcomes of mental disorders and their behavioral precursors can be potentially mediated or moderated by a broad range of contextual, cultural, and biological factors. Mediators and moderators can, therefore, reveal time-sensitive windows for therapeutic interventions that target remediation of the exposure, mediating, or moderating factors. Thus, social media use has differential effects depending on the developmental timing [65], suggesting that sensitive periods during youth may be particularly malleable by environmental exposures [66].
Until recently, conceptualizing the multitude of environmental exposures and their contribution towards emerging psychopathology has been challenging. The exposome represents the totality of environmental exposures that an individual experiences from conception throughout the lifespan as well as the interaction among these exposures [67]. Choi et al. [68] examined genomic and exposome influences on internalizing and externalizing symptoms in youth, highlighting that additive and interactive influences of the genome and exposome explained over 30% and 60% of the variance in internalizing and externalizing symptoms while a single environmental risk factor accounted for only 1% of the variance.
Emerging psychopathology and diagnostic framework(s)
The onset of the majority of mental disorders during youth [9] as well as the cascading and dynamic nature of psychopathology during development [19] have important implications for diagnostic frameworks. However, current diagnostic systems (DSM-5, ICD-11) have several shortcomings that require novel approaches to enable early detection and intervention. While several alternatives have been suggested, such as the Research Domain Criteria [69] and the Hierarchical Taxonomy of Psychopathology (HiTOP) [70], a developmental perspective on emerging psychopathology is rarely explicitly addressed (but see [71, 72]). In addition, both DSM-5 and ICD-11 do not allow a diagnosis to be made if symptom expression is below a certain cut-off value, even though there is established evidence for prodromal periods for psychosis [73] and bipolar disorder [74] as well as possibly for eating disorders [75], depression [76], and obsessive-compulsive disorders [77] (Table 1).
CHR-P criteria were first developed for psychosis over 20 years ago [78] and comprise Attenuated Psychotic Symptoms (APS), Brief (and Limited) Intermittent Psychotic Symptoms (BLIPS or BIPS), and Genetic Risk and Deterioration Syndrome (GRD) (for details see [73]). CHR-P criteria are associated with high prognostic accuracy that is comparable to other paradigms of preventative medicine [79]. Thus, approximately 20% of individuals meeting CHR-P will develop a first episode of psychosis (FEP) in the initial 2 years [80]. There is evidence that clinical [81], cognitive [82], and neuroimaging measures [83] constitute possible biomarkers that significantly increase accuracy for predicting clinical outcomes in CHR-P participants.
Following the CHR-P paradigm, high-risk criteria were developed for bipolar disorders (CHR-BP) that comprise subthreshold mania and depressive symptoms [84]. Cross-sectional studies have indicated good internal reliability and consistency of these instruments [74]. First evidence suggests that CHR-BP criteria are associated with a conversion rate to bipolar disorder of 14.3% within 12 months [85].
Building on the high-risk paradigm that is oriented along established diagnostic categories, alternative approaches have been advocated that reflect psychopathological dimensions [19, 86]. The clinical staging framework positions individuals along a multidimensional gradient of health to illness, capturing elements of risk, onset, course, and trajectory of illness [87, 88] (Fig. 3). Similar to clinical staging models in other areas of healthcare, staging frameworks hold the promise of guiding treatment selection, with less intensive interventions preferred at earlier stages and interventions with a higher risk/benefit ratio reserved for later stages [89].
Clinical staging models in youth mental health have been described in increasing levels of detail with some organized around specific diagnostic categories while others are transdiagnostic in nature [88]. The latter often aims to bridge the nonspecific, pluripotent nature of early-stage phenomena with more delineated presentations, extension to other dimensions of illness, and added layers of comorbidity seen in later stages. In integrating severity, multidimensionality, and pluripotentiality into a single model, clinical staging should be guided to develop a closer fit with youth-onset clinical syndromes with the ultimate goal of clinical utility [88].
Preventive approaches in the broader population may, however, require alternative approaches that are informed by data about normative development as well as vulnerability and risk exposures. Pediatric growth charts are a clinically valuable tool rooted in normative development and reflecting both physiology and external influences [90, 91]. Importantly, growth charts also allow the prediction of specific traits, such as height and weight, and if a deviation occurs then interventions to correct a possible developmental anomaly can be implemented during sensitive periods (Fig. 3).
A prerequisite for the application of growth charts in youth mental health is the availability of normative developmental data that comprise important domains of emerging psychopathology, such as cognition, emotion regulation, and sleep [92]. These can also be complemented by neuroimaging data, peripheral, digital, and genetic information as well as information about known risk factors and knowledge about sensitive periods. These multidimensional mental health growth charts would determine where an individual is located within normative development that in turn then lead to appropriate stage- and risk-adapted interventions.
Developing and implementing tools for prevention and early intervention
The data on sensitive periods [23, 25, 33, 48, 49] as well as the peak incidence of mental disorders during youth [9, 10] have implications for service provision and treatments. Current approaches and service models, however, remain primarily organized around established diagnostic categories of mental disorders in adulthood [88] that impose further barriers for early intervention by the artificial distinction between child and adolescent as well as adult mental health services [93]. Accordingly, novel clinical approaches for youth mental health are required that emphasize early intervention, low-threshold service delivery, population-based prevention, novel technologies as well as translational and interactional research models.
Sensitive periods and interventions for youth mental health
One important implication of sensitive periods during youth is that the timing of interventions is potentially critical for illness course and prognosis, suggesting that the plastic potential of neural circuits can be harnessed to modify ongoing developmental processes. Currently, however, substantial treatment delays for the majority of syndromes with an onset during youth, such as psychosis, bipolar disorders, and eating disorders, are pervasive [74, 94, 95] (Table 1), ranging from 2-5 years.
Duration of untreated psychosis (DUP) is an important determinant for symptomatic and functional treatment responses in FEP patients [94]. Similar data are available for BP [96], obsessive-compulsive [77] as well as eating disorders [95]. Moreover, there is evidence from indicated prevention in FEP-populations [97], suggesting that specialized psycho-social and pharmacological interventions can improve clinical outcomes compared to standard care [97]. In addition, there is evidence that CBT can reduce transition rates in CHR-P participants [98]. However, a more recent study suggested that clinical gains in FEP may not be sustained beyond the first two years of treatment [99].
Recent studies have extended the early intervention approach to personality disorders [100], substance abuse [101], and eating disorders [102]. Specialized psycho-social interventions for youth with borderline personality disorder (BPD) are effective in improving functioning and access to care [13] and early detection of eating disorders can improve prognosis, and decrease morbidity and mortality [14].
In order to maximize the benefits of the early intervention, we furthermore propose that behavioral and pharmacological interventions may benefit from incorporating data on sensitive periods [103]. Data from animal models of schizophrenia, for example, have shown that different behavioral and neurobiological interventions during the adolescent period but not during earlier or later developmental windows can completely rescue cognitive deficits and associated neural circuit dysfunctions [104,105,106].
The large majority of pharmacological and psycho-social interventions applied to youths have been developed for adult populations, potentially neglecting important differences in the properties of neural circuits as well as psychological variables. For example, a high proportion of youths with anxiety disorders do not respond to cognitive-behavioral therapy (CBT) [107] which may be due to inherent differences in fronto-amygdala circuitry [108]. Similarly, pharmacological interventions may also need to be adjusted given the ongoing modifications in neurotransmitter systems [34, 36].
A critical consideration in early pharmacologic intervention is minimizing side-effects. For many young people, early adverse experiences with psychotropic medication can reduce long-term adherence with all psychiatric treatments. As a result, the early intervention and the clinical staging models have encouraged investigations into more benign pharmacological treatments including “neutraceuticals”, that is, food or food products with health and medical benefits, such as fish oil [109], N-acetylcysteine [110], and cannabidiol [111], that may be suitable for targeting emerging psychopathology.
Novel service models
The developmental timing of mental disorders and the unique social-cultural embedding of youth highlight the need for novel service models that also address the fact that young people have the most limited access to mental health services across the lifespan [16]. The first, and now most extensive example is the Australian headspace model, a national youth mental health service stream designed to provide highly accessible, youth-friendly centers that promote and support early intervention for mental and substance use disorders in young people [112, 113]. In addition to these face-to-face services, headspace also runs a 24/7 nationwide online support service (eheadspace; www.eheadspace.org.au). This service model has now been implemented in several countries [93].
A recent study investigated outcome data in 58.000 clients examining self-reported psychological distress, quality of life, and clinician-reported social and occupational functioning. The results showed that approximately 70% of young people who attended headspace centers in Australia significantly improved on at least one outcome measure [114]. However, functional improvements were observed in only 1/3 of cases which may reflect the fact that interventions were too short and not very intensive [115]. Headspace is a prominent example but not the only available model; further evaluations of youth mental health services are currently being conducted globally [116,117,118,119].
Digital technologies
Early intervention approaches may also critically benefit from incorporating digital mental health technologies with unique opportunities and challenges [120]. However, Lettie et al. [121] highlighted that few studies using digital approaches and technologies targeting youth mental health have been replicated or assessed in real-world clinical settings. Many studies remain challenging to interpret given high rates of bias and few using causal methods to assess impact [122]. Furthermore, currently available commercial apps for youth mental health lack scientific evidence [123].
In contrast, there is emerging evidence for hybrid-interventions for youth mental health, involving both technology and some degree of human support. For example, the Moderated Online Social Therapy (MOST) offers intervention for youth diagnosed with early-stage psychosis, depression, help-seeking young people, and carers in a coherent platform [124,125,126]. New clinical models offering telehealth visits combined with a smartphone app, mindLAMP, involving both digital phenotyping for personalizing care and digital interventions for practicing skills, also show promise for rapidly reducing anxiety and depression-related symptoms [127]. Furthermore, a new generation of apps, such as EMIcompass, capture digital signals related to daily life (eg sleep patterns, mood) and use that data to respond with personalized and just-in-time support, thereby offering scalable and customized support for youth [128].
Digital technologies can also be used to detect emerging mental disorders outside established clinical pathways, an important prerequisite for population-based preventive approaches. In a recent study, a web-based screening platform allowed the identification of youth with CHR-P status as well as individuals with fully manifested psychosis with good sensitivity and specificity [129]. Digital phenotyping methods can also aid in relapse prediction by detecting changes in symptoms and behavioral patterns unique to each youth that may be associated with clinical deterioration [130]. Moreover, analysis of text messages on social media, in combination with machine and deep-learning techniques, may provide novel ways of identifying emerging mental disorders [131].
Knowledge exchange between science and clinical care
The development of novel interventions for youth mental health requires consideration of the unique possibilities and requirements needed to enable the targeted search for preventive interventions and treatments. Specifically, we propose a bidirectional knowledge exchange that is reframed as a three-way interaction of democratizing research across researchers, clinicians, and youth with lived experience, including patients and carers [132], targeting the discovery of risk factors, mechanisms, and clinical responses to existing interventions in youth mental health [133]. Limiting youth and their families from the knowledge exchange network would be a critical oversight. Their participation in all aspects of youth mental health is thus vital to ensure that care provided is accessible, appropriate and effective. As such, co-design of interventions and services is an important aspect of youth mental health.
Towards a paradigm for youth mental health
The converging findings from epidemiology, and basic and clinical research provide a powerful and complementary imperative for a “youth mental health paradigm” to guide science, practice, and policies. These are motivated by the highly plastic properties of neural circuits and associated cognitive and behavioral processes during youth which coincide with the peak incidence of major mental disorders between 12-25 years of age.
A critical implication of the timing of mental ill-health during youth and from the developmental cascade model is a broader focus on early manifestations of mental disorders as a primary focus for targeted interventions to prevent the occurrence of long-standing and chronic mental health conditions in adulthood [2]. In our view, this will require a shift towards early intervention models for a broad range of syndromes to enable selective and indicative prevention, low-threshold services for youth mental health [93], and population-based preventive approaches [8]. Here, prognostic algorithms that utilize knowledge of risk factors in combination with sensitive periods as well as biomarkers may be important for guiding clinical decision-making (Table 1).
The available evidence that early intervention is effective in psychosis [97, 134] as well as in other syndromes [8] together with the large unmet need in young people that has recently dramatically accelerated [9] provides additional impetus for such an endeavor. Several avenues towards interventions and implementation for youth mental health may follow from the framework outlined here that can be tested in large-scale studies. Firstly, prevention may focus on specific syndromes, such as the CHR-P paradigm vs. broader transdiagnostic phenotypes in youth mental health [88]. Secondly, population-based approaches that target the earliest manifestation of ill-mental health based on normative, developmental data vs. secondary prevention in clinical settings. Finally, the utility of knowledge derived from sensitive periods to guide the development and implementation of interventions in youth mental health remains to be demonstrated but could potentially be more effective for changing developmental trajectories vs. the application of established psycho-social and pharmacological therapies that were developed for adult populations.
While ambitious in scope, the benefits of such a youth mental health paradigm could be substantial and address the urgent need to improve the treatment of the most vulnerable age group for mental ill-health.
Notes
We chose “youth” over other concepts, such as adolescence, as youth encompasses a broader definition of developmental phenomena and age-ranges.
References
Insel TR, Fenton WS. Psychiatric epidemiology: it’s not just about counting anymore. Arch Gen Psychiatry. 2005;62:590–2.
Collaborators, GBDMD. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9:137–50.
Borsboom D, Cramer AOJ, Kalis A. Brain disorders? Not really: Why network structures block reductionism in psychopathology research. Behav Brain Sci. 2018;42:e2.
Carvalho AF, Solmi M, Sanches M, Machado MO, Stubbs B, Ajnakina O, et al. Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders. Transl Psychiatry. 2020;10:152.
Leichsenring F, Steinert C, Rabung S, Ioannidis J. The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: an umbrella review and meta-analytic evaluation of recent meta-analyses. World Psychiatry. 2022;21:133–45.
Howes OD, Thase ME, Pillinger T. Treatment resistance in psychiatry: state of the art and new directions. Mol Psychiatry. 2022;27:58–72.
Uhlhaas PJ, Wood, SJ, Youth Mental Health: A Paradigm for Prevention and Early Intervention. Strüngmann Forum Reports. Vol. 28. 2020: MIT Press.
Fusar-Poli P, Correll CU, Arango C, Berk M, Patel V, Ioannidis J. Preventive psychiatry: a blueprint for improving the mental health of young people. World Psychiatry. 2021;20:200–21.
Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2022;27:281–95.
Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:593–602.
McGorry PD, Killackey E, Yung A. Early intervention in psychosis: concepts, evidence and future directions. World Psychiatry. 2008;7:148–56.
Lin A, Wood SJ, Nelson B, Beavan A, McGorry P, Yung AR. Outcomes of nontransitioned cases in a sample at ultra-high risk for psychosis. Am J Psychiatry. 2015;172:249–58.
Chanen AM, Betts JK, Jackson H, Cotton SM, Gleeson J, Davey CG, et al. A comparison of adolescent versus young adult outpatients with first-presentation borderline personality disorder: findings from the MOBY randomized controlled trial. Can J Psychiatry. 2022;67:26–38.
Kalindjian N, Hirot F, Stona AC, Huas C, Godart N. Early detection of eating disorders: a scoping review. Eat Weight Disord. 2022;27:21–68.
Vieta E, Salagre E, Grande I, Carvalho AF, Fernandes BS, Berk M, et al. Early Intervention in Bipolar Disorder. Am J Psychiatry. 2018;175:411–26.
Radez J, Reardon T, Creswell C, Lawrence PJ, Evdoka-Burton G, Waite P. Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies. Eur Child Adolesc Psychiatry. 2021;30:183–211.
Thompson, EJ, et al. Changes in the adult consequences of adolescent mental ill-health: findings from the 1958 and 1970 British birth cohorts. Psychol Med, 2021: 1-10.
Hafstad GS, Augusti EM. A lost generation? COVID-19 and adolescent mental health. Lancet Psychiatry. 2021;8:640–1.
Caspi A, Houts RM, Ambler A, Danese A, Elliott ML, Hariri A, et al. Longitudinal assessment of mental health disorders and comorbidities across 4 decades among participants in the dunedin birth cohort study. JAMA Netw Open. 2020;3:e203221.
Knudsen EI. Sensitive periods in the development of the brain and behavior. J Cogn Neurosci. 2004;16:1412–25.
Reh RK, Dias BG, Nelson CA, Kaufer D, Werker JF, Kolb B, et al. Critical period regulation across multiple timescales. Proc Natl Acad Sci USA. 2020;117:23242–51.
Hooks BM, Chen C. Critical periods in the visual system: changing views for a model of experience-dependent plasticity. Neuron. 2007;56:312–26.
Fuhrmann D, Knoll LJ, Blakemore SJ. Adolescence as a sensitive period of brain development. Trends Cogn Sci. 2015;19:558–66.
Essau CA, Lewinsohn PM, Olaya B, Seeley JR. Anxiety disorders in adolescents and psychosocial outcomes at age 30. J Affect Disord. 2014;163:125–32.
Gerhard DM, Meyer HC, Lee FS. An adolescent sensitive period for threat responding: impacts of stress and sex. Biol Psychiatry. 2021;89:651–8.
Pattwell SS, Duhoux S, Hartley CA, Johnson DC, Jing D, Elliott MD, et al. Altered fear learning across development in both mouse and human. Proc Natl Acad Sci USA. 2012;109:16318–23.
Pattwell SS, Liston C, Jing D, Ninan I, Yang RR, Witztum J, et al. Dynamic changes in neural circuitry during adolescence are associated with persistent attenuation of fear memories. Nat Commun. 2016;7:11475.
Nederhof E, van Oort FV, Bouma EM, Laceulle OM, Oldehinkel AJ, Ormel J. Predicting mental disorders from hypothalamic-pituitary-adrenal axis functioning: a 3-year follow-up in the TRAILS study. Psychol Med. 2015;45:2403–12.
Romeo RD. The metamorphosis of adolescent hormonal stress reactivity: a focus on animal models. Front Neuroendocrinol. 2018;49:43–51.
Gomes FV, Zhu X, Grace AA. The pathophysiological impact of stress on the dopamine system is dependent on the state of the critical period of vulnerability. Mol Psychiatry. 2020;25:3278–91.
Gunnar MR, DePasquale CE, Reid BM, Donzella B, Miller BS. Pubertal stress recalibration reverses the effects of early life stress in postinstitutionalized children. Proc Natl Acad Sci USA. 2019;116:23984–8.
DePasquale CE, Herzberg MP, Gunnar MR. The pubertal stress recalibration hypothesis: potential neural and behavioral consequences. Child Dev Perspect. 2021;15:249–56.
Sisk LM, Gee DG. Stress and adolescence: vulnerability and opportunity during a sensitive window of development. Curr Opin Psychol. 2022;44:286–92.
Larsen B, Luna B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev. 2018;94:179–95.
Chini M, Hanganu-Opatz IL. Prefrontal cortex development in health and disease: lessons from rodents and humans. Trends Neurosci. 2021;44:227–40.
Caballero A, Flores-Barrera E, Cass DK, Tseng KY. Differential regulation of parvalbumin and calretinin interneurons in the prefrontal cortex during adolescence. Brain Struct Funct. 2014;219:395–406.
Larsen B, Cui Z, Adebimpe A, Pines A, Alexander-Bloch A, Bertolero M, et al. A developmental reduction of the excitation:inhibition ratio in association cortex during adolescence. Sci Adv. 2022;8:eabj8750.
Park BY, Paquola C, Bethlehem R, Benkarim O, Neuroscience in Psychiatry Network (NSPN) C, Mišić B, et al. Adolescent development of multiscale structural wiring and functional interactions in the human connectome. Proc Natl Acad Sci USA. 2022;119:e2116673119.
Jalbrzikowski M, Larsen B, Hallquist MN, Foran W, Calabro F, Luna B. Development of white matter microstructure and intrinsic functional connectivity between the amygdala and ventromedial prefrontal cortex: associations with anxiety and depression. Biol Psychiatry. 2017;82:511–21.
Calabro FJ, Murty VP, Jalbrzikowski M, Tervo-Clemmens B, Luna B. Development of hippocampal-prefrontal cortex interactions through adolescence. Cereb Cortex. 2020;30:1548–58.
Nabel EM, Garkun Y, Koike H, Sadahiro M, Liang A, Norman KJ, et al. Adolescent frontal top-down neurons receive heightened local drive to establish adult attentional behavior in mice. Nat Commun. 2020;11:3983.
Benoit LJ, Holt ES, Posani L, Fusi S, Harris AZ, Canetta S, et al. Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function. Nat Neurosci. 2022;25:714–25.
Casey BJ, Heller AS, Gee DG, Cohen AO. Development of the emotional brain. Neurosci Lett. 2019;693:29–34.
Silvers JA. Adolescence as a pivotal period for emotion regulation development. Curr Opin Psychol. 2022;44:258–63.
Sheppes G, Suri G, Gross JJ. Emotion regulation and psychopathology. Annu Rev Clin Psychol. 2015;11:379–405.
Chapman AL. Borderline personality disorder and emotion dysregulation. Dev Psychopathol. 2019;31:1143–56.
Duell N, Steinberg L, Icenogle G, Chein J, Chaudhary N, Di Giunta L, et al. Age patterns in risk taking across the world. J Youth Adolesc. 2018;47:1052–72.
Jordan CJ, Andersen SL. Sensitive periods of substance abuse: early risk for the transition to dependence. Dev Cogn Neurosci. 2017;25:29–44.
Luciana M, Collins PF. Is adolescence a sensitive period for the development of incentive-reward motivation? Curr Top Behav Neurosci. 2022;53:79–99.
Verrico CD, Gu H, Peterson ML, Sampson AR, Lewis DA. Repeated Delta9-tetrahydrocannabinol exposure in adolescent monkeys: persistent effects selective for spatial working memory. Am J Psychiatry. 2014;171:416–25.
Andrews JL, Ahmed SP, Blakemore SJ. Navigating the social environment in adolescence: the role of social brain development. Biol Psychiatry. 2021;89:109–18.
Crone EA, Fuligni AJ. Self and others in adolescence. Annu Rev Psychol. 2020;71:447–69.
Blakemore SJ, Mills KL. Is adolescence a sensitive period for sociocultural processing? Annu Rev Psychol. 2014;65:187–207.
Smith AR, Steinberg L, Strang N, Chein J. Age differences in the impact of peers on adolescents’ and adults’ neural response to reward. Dev Cogn Neurosci. 2015;11:75–82.
Gruenenfelder-Steiger AE, Harris MA, Fend HA. Subjective and objective peer approval evaluations and self-esteem development: a test of reciprocal, prospective, and long-term effects. Dev Psychol. 2016;52:1563–77.
Bicks LK, Yamamuro K, Flanigan ME, Kim JM, Kato D, Lucas EK, et al. Prefrontal parvalbumin interneurons require juvenile social experience to establish adult social behavior. Nat Commun. 2020;11:1003.
Foulkes L, Blakemore SJ. Individual differences in adolescent mental health during COVID-19: The importance of peer relationship quality. Neuron. 2021;109:3203–5.
Paus T. Population neuroscience. Handb Clin Neurol. 2016;138:17–37.
Paus T. Population neuroscience: why and how. Hum Brain Mapp. 2010;31:891–903.
Paus, T, Population Neuroscience. 2013, Berlin/Heidelberg: Springer.
Masten AS, Cicchetti D. Developmental cascades. Dev Psychopathol. 2010;22:491–5.
Shah JL, Jones N, van Os J, McGorry PD, Gülöksüz S. Early intervention service systems for youth mental health: integrating pluripotentiality, clinical staging, and transdiagnostic lessons from early psychosis. Lancet Psychiatry. 2022;9:413–22.
Lahey BB, Lee SS, Sibley MH, Applegate B, Molina B, Pelham WE. Predictors of adolescent outcomes among 4-6-year-old children with attention-deficit/hyperactivity disorder. J Abnorm Psychol. 2016;125:168–81.
Arslan IB, Lucassen N, van Lier P, de Haan AD, Prinzie P. Early childhood internalizing problems, externalizing problems and their co-occurrence and (mal)adaptive functioning in emerging adulthood: a 16-year follow-up study. Soc Psychiatry Psychiatr Epidemiol. 2021;56:193–206.
Orben A, Przybylski AK, Blakemore SJ, Kievit RA. Windows of developmental sensitivity to social media. Nat Commun. 2022;13:1649.
Gee DG. When do sensitive periods emerge later in development? Trends Cogn Sci. 2022;26:97–98.
Guloksuz S, van Os J, Rutten BPF. The exposome paradigm and the complexities of environmental research in psychiatry. JAMA Psychiatry. 2018;75:985–6.
Choi KW, Wilson M, Ge T, Kandola A, Patel CJ, Lee SH, et al. Integrative analysis of genomic and exposomic influences on youth mental health. J Child Psychol Psychiatry. 2022;63:1196–205.
Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.
Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. J Abnorm Psychol. 2017;126:454–77.
Durbin CE, Wilson S, MacDonald AW. Integrating development into the Research Domain Criteria (RDoC) framework: Introduction to the special section. J Psychopathol Clin Sci. 2022;131:535–41.
Casey BJ, Oliveri ME, Insel T. A neurodevelopmental perspective on the research domain criteria (RDoC) framework. Biol Psychiatry. 2014;76:350–3.
Fusar-Poli P, Salazar de Pablo G, Correll CU, Meyer-Lindenberg A, Millan MJ, Borgwardt S, et al. Prevention of psychosis: advances in detection, prognosis, and intervention. JAMA Psychiatry. 2020;77:755–65.
Van Meter AR, Burke C, Youngstrom EA, Faedda GL, Correll CU. The bipolar prodrome: meta-analysis of symptom prevalence prior to initial or recurrent mood episodes. J Am Acad Child Adolesc Psychiatry. 2016;55:543–55.
Stice E, Desjardins CD, Rohde P, Shaw H. Sequencing of symptom emergence in anorexia nervosa, bulimia nervosa, binge eating disorder, and purging disorder and relations of prodromal symptoms to future onset of these disorders. J Abnorm Psychol. 2021;130:377–87.
Benasi G, Fava GA, Guidi J. Prodromal symptoms in depression: a systematic review. Psychother Psychosom. 2021;90:365–72.
Fineberg NA, Dell’Osso B, Albert U, Maina G, Geller D, Carmi L, et al. Early intervention for obsessive compulsive disorder: An expert consensus statement. Eur Neuropsychopharmacol. 2019;29:549–65.
Yung AR, Yuen HP, McGorry PD, Phillips LJ, Kelly D, Dell’Olio M, et al. Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States. Aust NZJ Psychiatry. 2005;39:964–71.
Fusar-Poli P, Cappucciati M, Rutigliano G, Schultze-Lutter F, Bonoldi I, Borgwardt S, et al. At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction. World Psychiatry. 2015;14:322–32.
Salazar de Pablo G, Radua J, Pereira J, Bonoldi I, Arienti V, Besana F, et al. Probability of Transition to Psychosis in Individuals at Clinical High Risk: An Updated Meta-analysis. JAMA Psychiatry. 2021;78:970–8.
Cannon TD, Yu C, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, et al. An Individualized Risk Calculator for Research in Prodromal Psychosis. Am J Psychiatry. 2016;173:980–8.
Catalan A, Salazar de Pablo G, Aymerich C, Damiani S, Sordi V, Radua J, et al. Neurocognitive Functioning in Individuals at Clinical High Risk for Psychosis: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2021;78:859–67.
Group, E.C.H.R.F.P.W, Jalbrzikowski M, Hayes RA, Wood SJ, Nordholm D, Zhou JH, et al. Association of structural magnetic resonance imaging measures with psychosis onset in individuals at clinical high risk for developing psychosis: an ENIGMA working group mega-analysis. JAMA Psychiatry. 2021;78:753–66.
Correll CU, Olvet DM, Auther AM, Hauser M, Kishimoto T, Carrión RE, et al. The Bipolar Prodrome Symptom Interview and Scale-Prospective (BPSS-P): description and validation in a psychiatric sample and healthy controls. Bipolar Disord. 2014;16:505–22.
Bechdolf A, Ratheesh A, Cotton SM, Nelson B, Chanen AM, Betts J, et al. The predictive validity of bipolar at-risk (prodromal) criteria in help-seeking adolescents and young adults: a prospective study. Bipolar Disord. 2014;16:493–504.
Waszczuk MA, Zavos HM, Gregory AM, Eley TC. The phenotypic and genetic structure of depression and anxiety disorder symptoms in childhood, adolescence, and young adulthood. JAMA Psychiatry. 2014;71:905–16.
Hickie IB, Scott EM, Hermens DF, Naismith SL, Guastella AJ, Kaur M, et al. Applying clinical staging to young people who present for mental health care. Early Inter Psychiatry. 2013;7:31–43.
Shah JL, Scott J, McGorry PD, Cross S, Keshavan MS, Nelson B, et al. Transdiagnostic clinical staging in youth mental health: a first international consensus statement. World Psychiatry. 2020;19:233–42.
McGorry PD, Hickie IB, Yung AR, Pantelis C, Jackson HJ. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust N. Z J Psychiatry. 2006;40:616–22.
Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature. 2022;604:525–33.
Rutherford S, Fraza C, Dinga R, Kia SM, Wolfers T, Zabihi M, et al. Charting brain growth and aging at high spatial precision. Elife. 2022;11:e72904.
Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann CF. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry. 2019;24:1415–24.
McGorry PD, Mei C, Chanen A, Hodges C, Alvarez-Jimenez M, Killackey E. Designing and scaling up integrated youth mental health care. World Psychiatry. 2022;21:61–76.
Howes OD, Whitehurst T, Shatalina E, Townsend L, Onwordi EC, Mak T, et al. The clinical significance of duration of untreated psychosis: an umbrella review and random-effects meta-analysis. World Psychiatry. 2021;20:75–95.
Austin A, Flynn M, Richards K, Hodsoll J, Duarte TA, Robinson P, et al. Duration of untreated eating disorder and relationship to outcomes: A systematic review of the literature. Eur Eat Disord Rev. 2021;29:329–45.
Drancourt N, Etain B, Lajnef M, Henry C, Raust A, Cochet B, et al. Duration of untreated bipolar disorder: missed opportunities on the long road to optimal treatment. Acta Psychiatr Scand. 2013;127:136–44.
Correll CU, Galling B, Pawar A, Krivko A, Bonetto C, Ruggeri M, et al. Comparison of early intervention services vs treatment as usual for early-phase psychosis: a systematic review, meta-analysis, and meta-regression. JAMA Psychiatry. 2018;75:555–65.
Mei C, van der Gaag M, Nelson B, Smit F, Yuen HP, Berger M, et al. Preventive interventions for individuals at ultra high risk for psychosis: An updated and extended meta-analysis. Clin Psychol Rev. 2021;86:102005.
Hansen HG, Starzer M, Nilsson SF, Hjorthøj C, Albert N, Nordentoft M. Clinical recovery and long-term association of specialized early intervention services vs treatment as usual among individuals with first-episode schizophrenia spectrum disorder: 20-year follow-up of the OPUS trial. JAMA Psychiatry. 2023;80:371–9. p.
Chanen AM, Thompson KN. Early intervention for personality disorder. Curr Opin Psychol. 2018;21:132–5.
Stockings E, Hall WD, Lynskey M, Morley KI, Reavley N, Strang J, et al. Prevention, early intervention, harm reduction, and treatment of substance use in young people. Lancet Psychiatry. 2016;3:280–96.
Austin A, Flynn M, Shearer J, Long M, Allen K, Mountford VA, et al. The first episode rapid early intervention for eating disorders - upscaled study: clinical outcomes. Early Interv Psychiatry. 2022;16:97–105.
Casey BJ, Glatt CE, Lee FS. Treating the developing versus developed brain: translating preclinical mouse and human studies. Neuron. 2015;86:1358–68.
Lee H, Dvorak D, Kao HY, Duffy ÁM, Scharfman HE, Fenton AA. Early cognitive experience prevents adult deficits in a neurodevelopmental schizophrenia model. Neuron. 2012;75:714–24.
Cabungcal JH, Counotte DS, Lewis E, Tejeda HA, Piantadosi P, Pollock C, et al. Juvenile antioxidant treatment prevents adult deficits in a developmental model of schizophrenia. Neuron. 2014;83:1073–84.
Mukherjee A, Carvalho F, Eliez S, Caroni P. Long-lasting rescue of network and cognitive dysfunction in a genetic schizophrenia model. Cell. 2019;178:1387–1402.e14.
Ginsburg GS, Becker-Haimes EM, Keeton C, Kendall PC, Iyengar S, Sakolsky D, et al. Results from the child/adolescent anxiety multimodal extended long-term study (CAMELS): primary anxiety outcomes. J Am Acad Child Adolesc Psychiatry. 2018;57:471–80.
Odriozola P, Gee DG. Learning about safety: conditioned inhibition as a novel approach to fear reduction targeting the developing brain. Am J Psychiatry. 2021;178:136–55.
Susai SR, Sabherwal S, Mongan D, Föcking M, Cotter DR. Omega-3 fatty acid in ultra-high-risk psychosis: a systematic review based on functional outcome. Early Inter Psychiatry. 2022;16:3–16.
Bradlow RCJ, Berk M, Kalivas PW, Back SE, Kanaan RA. The potential of N-Acetyl-L-cysteine (NAC) in the treatment of psychiatric disorders. CNS Drugs. 2022;36:451–82.
Chesney E, Oliver D, McGuire P. Cannabidiol (CBD) as a novel treatment in the early phases of psychosis. Psychopharmacol (Berl). 2022;239:1179–90.
McGorry PD, Tanti C, Stokes R, Hickie IB, Carnell K, Littlefield LK, et al. Headspace: Australia’s National Youth Mental Health Foundation–where young minds come first. Med J Aust. 2007;187:S68–70.
McGorry PD, Goldstone SD, Parker AG, Rickwood DJ, Hickie IB. Cultures for mental health care of young people: an Australian blueprint for reform. Lancet Psychiatry. 2014;1:559–68.
Rickwood, D, McEachran, J, Saw, A, Telford, N, Trethowan, J, McGorry, P, Sixteen years of innovation in youth mental healthcare in Australia: Outcomes for young people attending headspace centre services. medRxiv 2022. 2022.08.24.22279102.
Iorfino F, Scott EM, Hickie IB. Social and occupational outcomes for young people who attend early intervention mental health services: a longitudinal study. Med J Aust. 2022;217:218.
Iyer SN, Shah J, Boksa P, Lal S, Joober R, Andersson N, et al. A minimum evaluation protocol and stepped-wedge cluster randomized trial of ACCESS Open Minds, a large Canadian youth mental health services transformation project. BMC Psychiatry. 2019;19:273.
Abba-Aji A, Hay K, Kelland J, Mummery C, Urichuk L, Gerdes C, et al. Transforming youth mental health services in a large urban centre: ACCESS Open Minds Edmonton. Early Inter Psychiatry. 2019;13:14–19.
Boonstra, A, et al., @ease peer-to-peer youth walk-in centres in The Netherlands: A protocol for evaluating longitudinal outcomes, follow-up results and cost-of-illness. Early Interv Psychiatry, 2023.
Shah JL, Moinfar Z, Anderson KK, Gould H, Hutt-Macleod D, Jacobs P, et al. Return on investment from service transformation for young people experiencing mental health problems: Approach to economic evaluations in ACCESS Open Minds (Esprits ouverts), a multi-site pan-Canadian youth mental health project. Front Psychiatry. 2023;14:1030407.
Uhlhaas P, Torous J. Digital tools for youth mental health. NPJ Digit Med. 2019;2:104.
Lattie EG, Stiles-Shields C, Graham AK. An overview of and recommendations for more accessible digital mental health services. Nat Rev Psychol. 2022;1:87–100.
Domhardt M, Engler S, Nowak H, Lutsch A, Baumel A, Baumeister H. Mechanisms of Change in Digital Health Interventions for Mental Disorders in Youth: Systematic Review. J Med Internet Res. 2021;23:e29742.
Domhardt M, Messner EM, Eder AS, Engler S, Sander LB, Baumeister H, et al. Mobile-based interventions for common mental disorders in youth: a systematic evaluation of pediatric health apps. Child Adolesc Psychiatry Ment Health. 2021;15:49.
Gleeson J, Lederman R, Koval P, Wadley G, Bendall S, Cotton S, et al. Moderated Online Social Therapy: A Model for Reducing Stress in Carers of Young People Diagnosed with Mental Health Disorders. Front Psychol. 2017;8:485. p.
Alvarez-Jimenez M, Koval P, Schmaal L, Bendall S, O’Sullivan S, Cagliarini D, et al. The Horyzons project: a randomized controlled trial of a novel online social therapy to maintain treatment effects from specialist first-episode psychosis services. World Psychiatry. 2021;20:233–43.
Engel, L, Alvarez-Jimenez M, Cagliarini D, D’Alfonso S, Faller J, Valentine L, et al., The Cost-Effectiveness of a Novel Online Social Therapy to Maintain Treatment Effects From First-Episode Psychosis Services: Results From the Horyzons Randomized Controlled Trial. Schizophr Bull, 2023.
Chang S, Gray L, Torous J. Smartphone app engagement and clinical outcomes in a hybrid clinic. Psychiatry Res. 2023;319:115015.
Reininghaus U, Paetzold I, Rauschenberg C, Hirjak D, Banaschewski T, Meyer-Lindenberg A, et al. Effects of a novel, transdiagnostic ecological momentary intervention for prevention, and early intervention of severe mental disorder in youth (EMIcompass): findings from an exploratory randomized controlled trial. Schizophr Bull. 2023;49:592–604.
Fusar-Poli P, Sullivan SA, Shah JL, Uhlhaas PJ. Improving the detection of individuals at clinical risk for psychosis in the community, primary and secondary care: an integrated evidence-based approach. Front Psychiatry. 2019;10:774.
Cohen A, Naslund JA, Chang S, Nagendra S, Bhan A, Rozatkar A, et al. Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study. Schizophrenia (Heidelb). 2023;9:6.
Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-Jepsen M, Whelan P, et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021;20:318–35.
Lloyd K, White J. Democratizing clinical research. Nature. 2011;474:277–8.
Baumann PS, Crespi S, Marion-Veyron R, Solida A, Thonney J, Favrod J, et al. Treatment and early intervention in psychosis program (TIPP-Lausanne): Implementation of an early intervention programme for psychosis in Switzerland. Early Inter Psychiatry. 2013;7:322–8.
Anderson KK, Norman R, MacDougall A, Edwards J, Palaniyappan L, Lau C, et al. Effectiveness of early psychosis intervention: comparison of service users and nonusers in population-based health administrative data. Am J Psychiatry. 2018;175:443–52.
Stockings EA, Degenhardt L, Dobbins T, Lee YY, Erskine HE, Whiteford HA, et al. Preventing depression and anxiety in young people: a review of the joint efficacy of universal, selective and indicated prevention. Psychol Med. 2016;46:11–26.
Breedvelt JJF, Kandola A, Kousoulis AA, Brouwer ME, Karyotaki E, Bockting C, et al. What are the effects of preventative interventions on major depressive disorder (MDD) in young adults? A systematic review and meta-analysis of randomized controlled trials. J Affect Disord. 2018;239:18–29.
Lawrence PJ, Rooke SM, Creswell C. Review: Prevention of anxiety among at-risk children and adolescents - a systematic review and meta-analysis. Child Adolesc Ment Health. 2017;22:118–30.
Carney T, Myers B. Effectiveness of early interventions for substance-using adolescents: findings from a systematic review and meta-analysis. Subst Abus Treat Prev Policy. 2012;7:25.
Addington J, Stowkowy J, Weiser M. Screening tools for clinical high risk for psychosis. Early Inter Psychiatry. 2015;9:345–56.
Powers AR, Addington J, Perkins DO, Bearden CE, Cadenhead KS, Cannon TD, et al. Duration of the psychosis prodrome. Schizophr Res. 2020;216:443–9.
Merritt K, McGuire PK, Egerton A, -MRS in Schizophrenia I, Aleman A, Block W, et al. Association of age, antipsychotic medication, and symptom severity in schizophrenia with proton magnetic resonance spectroscopy brain glutamate level: a mega-analysis of individual participant-level data. JAMA Psychiatry. 2021;78:667–81.
Van Meter A, Guinart D, Bashir A, Sareen A, Cornblatt BA, Auther A, et al. Bipolar prodrome symptom scale - abbreviated screen for patients: description and validation. J Affect Disord. 2019;249:357–65.
Sekhar DL, Schaefer EW, Waxmonsky JG, Walker-Harding LR, Pattison KL, Molinari A, et al. Screening in high schools to identify, evaluate, and lower depression among adolescents: a randomized clinical trial. JAMA Netw Open. 2021;4:e2131836.
Scott J, Graham A, Yung A, Morgan C, Bellivier F, Etain B. A systematic review and meta-analysis of delayed help-seeking, delayed diagnosis and duration of untreated illness in bipolar disorders. Acta Psychiatr Scand. 2022;146:389–405.
Ghio L, Gotelli S, Marcenaro M, Amore M, Natta W. Duration of untreated illness and outcomes in unipolar depression: a systematic review and meta-analysis. J Affect Disord. 2014;152-154:45–51.
Chanen AM, Jovev M, Djaja D, McDougall E, Yuen HP, Rawlings D, et al. Screening for borderline personality disorder in outpatient youth. J Pers Disord. 2008;22:353–64.
Dierker LC, Albano AM, Clarke GN, Heimberg RG, Kendall PC, Merikangas KR, et al. Screening for anxiety and depression in early adolescence. J Am Acad Child Adolesc Psychiatry. 2001;40:929–36.
Wang PS, Berglund P, Olfson M, Pincus HA, Wells KB, Kessler RC. Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:603–13.
Pilowsky DJ, Wu LT. Screening instruments for substance use and brief interventions targeting adolescents in primary care: a literature review. Addict Behav. 2013;38:2146–53.
Piqueras JA, Rodríguez-Jiménez T, Ortiz AG, Moreno E, Lázaro L, Godoy A. Validation of the Short Obsessive-Compulsive Disorder Screener (SOCS) in children and adolescents. BJPsych Open. 2015;1:21–26.
Morgan JF, Reid F, Lacey JH. The SCOFF questionnaire: assessment of a new screening tool for eating disorders. BMJ. 1999;319:1467–8.
Bryant E, Miskovic-Wheatley J, Touyz SW, Crosby RD, Koreshe E, Maguire S. Identification of high risk and early stage eating disorders: first validation of a digital screening tool. J Eat Disord. 2021;9:109.
Andrés-Pepiñá S, Plana MT, Flamarique I, Romero S, Borràs R, Julià L, et al. Long-term outcome and psychiatric comorbidity of adolescent-onset anorexia nervosa. Clin Child Psychol Psychiatry. 2020;25:33–44. p.
Oliver, D, et al. Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis. Mol Psychiatry, 2022.
Erickson MA, Ruffle A, Gold JM. A meta-analysis of mismatch negativity in schizophrenia: from clinical risk to disease specificity and progression. Biol Psychiatry. 2016;79:980–7.
Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry. 2021;78:195–209.
Fusar-Poli P, De Micheli A, Rocchetti M, Cappucciati M, Ramella-Cravaro V, Rutigliano G, et al. Semistructured Interview for Bipolar At Risk States (SIBARS). Psychiatry Res. 2018;264:302–9.
Birmaher B, Merranko JA, Goldstein TR, Gill MK, Goldstein BI, Hower H, et al. A risk calculator to predict the individual risk of conversion from subthreshold bipolar symptoms to bipolar disorder I or II in youth. J Am Acad Child Adolesc Psychiatry. 2018;57:755–763.e4.
Saraf G, Moazen-Zadeh E, Pinto JV, Ziafat K, Torres IJ, Kesavan M, et al. Early intervention for people at high risk of developing bipolar disorder: a systematic review of clinical trials. Lancet Psychiatry. 2021;8:64–75.
Werner-Seidler A, Perry Y, Calear AL, Newby JM, Christensen H. School-based depression and anxiety prevention programs for young people: A systematic review and meta-analysis. Clin Psychol Rev. 2017;51:30–47.
Hugh-Jones S, Beckett S, Tumelty E, Mallikarjun P. Indicated prevention interventions for anxiety in children and adolescents: a review and meta-analysis of school-based programs. Eur Child Adolesc Psychiatry. 2021;30:849–60.
Moreno-Peral P, Conejo-Cerón S, Rubio-Valera M, Fernández A, Navas-Campaña D, Rodríguez-Morejón A, et al. Effectiveness of psychological and/or educational interventions in the prevention of anxiety: a systematic review, meta-analysis, and meta-regression. JAMA Psychiatry. 2017;74:1021–9.
Norberg MM, Kezelman S, Lim-Howe N. Primary prevention of cannabis use: a systematic review of randomized controlled trials. PLoS One. 2013;8:e53187.
Jensen CD, Cushing CC, Aylward BS, Craig JT, Sorell DM, Steele RG. Effectiveness of motivational interviewing interventions for adolescent substance use behavior change: a meta-analytic review. J Consult Clin Psychol. 2011;79:433–40.
Shah JL. Bringing clinical staging to youth mental health: from concept to operationalization (and back again). JAMA Psychiatry. 2019;76:1121–3.
Acknowledgements
This perspective is based on an Ernst Strüngmann Forum on Youth Mental Health, which occurred between July 29 and August 3, 2018, in Frankfurt am Main, Germany. A book on this topic was published: Uhlhaas PJ, Wood SJ, eds. Youth Mental Health: A Framework for Early Intervention and Detection. MIT Press: 2020. We wish to dedicate this paper to the memory of Prof. George Patton who passed during the preparation of this manuscript. George was a pioneer in youth mental health and an eminent psychiatrist and psychiatric epidemiologist whose career focussed on improving the health of young people in Australia and across the globe.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Contributions
PJU, CGD, UMM, JS and JT drafted the manuscript. NBA, SA, TBA, AC, EYHC, CUC, KQD, HLF, SF, IBH, MSK, KK, FSL, CHL, BL, PDM, AML, MD, DÖ, GCP, TP, UR, AS, MS, GS; VHS, ES. SKV, TWW, LWY, ARY and SJW approved the work.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Uhlhaas, P.J., Davey, C.G., Mehta, U.M. et al. Towards a youth mental health paradigm: a perspective and roadmap. Mol Psychiatry 28, 3171–3181 (2023). https://doi.org/10.1038/s41380-023-02202-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41380-023-02202-z
This article is cited by
-
A transdiagnostic prodrome for severe mental disorders: an electronic health record study
Molecular Psychiatry (2024)
-
The need for youth psychiatry fellowship: a physician-scientist trainee perspective
Molecular Psychiatry (2024)
-
Developmental changes of bodily self-consciousness in adolescent girls
Scientific Reports (2024)
-
Mechanisms linking social media use to adolescent mental health vulnerability
Nature Reviews Psychology (2024)
-
Social Media and Youth Mental Health: Assessing the Impact Through Current and Novel Digital Phenotyping Methods
Current Treatment Options in Psychiatry (2024)