Numerous genetic studies have demonstrated that schizophrenia, bipolar disorder and ADHD are highly heritable disorders. For schizophrenia, illness risk exceeds 40% in monocygotic twins and 6.5% in first degree relatives (Cardno et al. 1999; Kendler et al. 1993). Similar heritability estimates have been found for bipolar disorder (Kieseppä et al. 2004) and ADHD has been reported to have even higher heritability between 60% and 90% (Todd 2000; Faraone et al. 2005).
Despite this, studies investigating genetic markers of these illnesses at a whole-genome level have had limited success in explaining substantial parts of this heritable variance, likely in part because the genetic architecture is complex and includes pleiotropic, epistatic as well as gene-environment interaction contributions. At the individual marker level, observed genetic variation is partially consistent with changes in neurotransmitter (e.g. dopamine) systems hypothesized to be involved in the diseases’ aetiologies. Additionally, genetic association studies have implicated other biological processes in the aetiology of mental illnesses, such as neurodevelopment, neuron differentiation and cell structural processes, including synaptic scaffolding (Addington and Rapoport 2011). At the brain level, a broad spectrum of illness associated differences including structural and functional changes have been observed using imaging technologies.
Most of these studies have, however, been performed in small cohorts and have not been validated in independent datasets. In particular for schizophrenia, numerous studies have also investigated changes in peripheral molecular systems and found abnormalities in glucose metabolism, immunological changes and altered growth factor levels (Insel 2010).
Nevertheless, this evidence has so far been correlative without a clear link to potential aetiological processes affecting brain function. This is likely due to lack of multi-modal investigations where genetic, imaging and other data are combined in a single analytic framework.
Treatment of schizophrenia and bipolar disorder is hallmarked by a low response rate of approximately 50% to current treatment regimes. To compound matters, response is typically observed for only a subset of symptoms. In schizophrenia, negative and cognitive symptoms are particularly difficult to treat and frequently persist leading to chronic and often life-long impairments. Currently, there are no means to identify which of the 50% of patients do not respond to antipsychotic medication and available clinical algorithms offer little guidance for personalized treatment beyond sequential tryouts of drugs that are switched when response is insufficient, a procedure that is time-consuming, expensive, and often frustrating for client and therapist alike, reducing adherence. Further contributors to limited adherence in patients are the often serious side effect profile of many psychotropic drugs and the stigma associated with mental illness, which reduces all classes of help-seeking behavior but perhaps especially prominently drug compliance.
Second generation antipsychotics show particularly strong metabolic side-effects including weight gain, diabetes mellitus and an atherogenic lipid profile, which have been linked to increased patient morbidity and mortality, besides the resulting lack of medication compliance. To complicate matters, several lines of evidence support that mechanisms underlying the development of side-effects and treatment response are shared, since it has been observed clinically that an increase in weight often coincides with an improvement of symptoms. In the same vein, molecular studies suggest that early response of the metabolic system to antipsychotic treatment is linked to the likelihood of subsequent relapse (Schwarz et al. 2012), providing further evidence for the link between metabolic processes and clinical course. This demonstrates the potential of biological approaches to untangle a given patients response/side-effect balance for different antipsychotic medications and obtain more personalized treatment algorithms with improved clinical outcomes.
A meta-analysis found that if persistence of ADHD was defined as the maintenance of full diagnostic status, then the rate of persistence by age 25 years was ~15%. When cases were included that were consistent with the DSM-IV definition of ADHD in partial remission, the rate of persistence was much higher at ~65%. It is unclear as to why some children will show full remission whilst others will continue to present with full or partial symptoms of ADHD in adulthood (Faraone, Biederman, and Mick 2006).
Since ADHD that persists into adult age is the more severe chronic form of the disorder, neuroimaging and genetic markers of persistence would enable to develop targeted diagnostic procedures and treatment programs in adolescence.
The nosology of mental illness is currently under intense discussion (Hyman 2010). Many in the field believe that the notion of discrete, categorical mental disorders, as currently instantiated in the ICD-10 and DSM-IV, requires rethinking. These psychiatric diagnostic systems employ criteria that are derived from clinicians' observations, patient self-report, and course. Therefore, almost by definition, these diagnostic categories must be biologically heterogeneous and are unlikely to have unique sets of causal factors and neurobiological underpinnings. Even at the level of clinical symptoms and signs, dimensionality and comorbidity are pervasive (Krueger and Markon 2011). This problem is reflected in the heterogeneity of DSM categories. For example, the schizophrenia concept can be generated out of 23 different combinations of symptoms and phenomena (Schwarz, VanBeveren, et al. 2011). As a consequence, misdiagnosis is a common occurrence in the psychiatric field.
At the level of etiology, genetic complexity (i.e. pleiotropy and polygenicity) appears to be the rule, with large numbers of interacting variants combining to confer shared genetic liability to broad domains of symptomatically related disorders, such as schizophrenia and bipolar disorder (Purcell et al. 2009). Extant data converge to support a model wherein multiple sets of genetic variants predispose the development of clustered symptom domains that are common to multiple disorders, rather than to specific categorical disorders as defined by DSM-IV diagnostic criteria. While neuroimaging has undoubtedly been useful in showing functional and structural abnormalities tied to diagnoses, as well as to genetic and environmental risk factors, much of that work has been pursued within one classical diagnostic category only, in relatively small datasets, and lacks replication.
IMAGEMEND will specifically focus on schizophrenia, bipolar disorder and Attention-Deficit Hyperactivity Disorder (ADHD). These disorders are genetically related, pose severe differential diagnostic and managing problems and are among the major contributors to overall disease burden and cost, due to their early age of onset, often chronic course, absence of curative treatments and frequently life-long impairments. The predominant diagnostic system for mental illnesses are the Diagnostic and Statistical Manual (DSM) (Apa 2000) and the broadly similar ICD-10, whose fundamental structure date back to more than 100 years ago when dementia praecox and mania were first distinguished by Emil Kraepelin. Diagnostic criteria of the DSM/ICD system are defined based on course and the presence or absence of symptoms, but do not take into account neuropsychological or biological processes (Schwarz, VanBeveren, et al. 2011).
