International Review of Psychiatry, October 2013; 25(5): 619–631

Neuroimaging as a potential biomarker to optimize psychiatric research and treatment

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ESTHER WALTON1, JESSICA A. TURNER2,3 & STEFAN EHRLICH1,4,5 1Department of Child and Adolescent Psychiatry, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany, 2The Mind Research Network, Albuquerque, New Mexico, USA, 3Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, USA, 4Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA, 5MGH/MIT/HMS Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA

Abstract Complex, polygenic phenotypes in psychiatry hamper our understanding of the underlying molecular pathways and mechanisms of many diseases. The unknown aetiology, together with symptoms which often show a large variability both across individuals and over time and also tend to respond comparatively slowly to medication, can be a problem for patient treatment and drug development. We argue that neuroimaging has the potential to improve psychiatric treatment in two ways. First, by reducing phenotypic complexity, neuroimaging intermediate phenotypes can help to identify disease-related genes and can shed light into the biological mechanisms of known risk genes. Second, quantitative neuroimaging markers – reflecting the spectrum of impairment on a brain-based level – can be used as a more sensitive, reliable and immediate treatment response biomarker. In the end, enhancing both our understanding of the pathophysiology of psychiatric disorders and the prediction of treatment success could eventually optimise current therapy plans.

Introduction Many psychiatric disorders are characterized by not only a heterogeneous presentation of clinical symptoms, but also by a complex, polygenic aetiology. This considerable degree of phenotypic and genetic heterogeneity together with gene-environment interaction effects are just some of the factors hampering our understanding of the underlying molecular pathways and mechanisms. So, due to the continuing search for an aetiological explanation of many psychiatric disorders, the diagnostic criteria are predominantly symptom-based rather than pathophysiological (Sommers, 1985). However, these symptoms are only loosely connected through a largely theoretical diagnostic classification system and often lack neuropsychological or neurophysiological correlates (Craddock & Owen, 2007). Psychiatric symptoms do not only show an exceptional variability across patients, but also over time. In particular, positive symptoms as well as psychoticism and disorganization in schizophrenia have been shown to fluctuate over time (Arndt et al., 1995). Deister and Marneros (1993) observed that

schizophrenia subtypes based on different diagnostic systems, including DSM-III-R and ICD-10, were unstable, and within five years patients repeatedly changed between subtypes. This instability gives them little predictive power with respect to treatment response, potential side effects, or prognosis (Harvey et al., 1998; Velligan et al., 1997). Besides being unstable and unpredictive of an individual’s prognosis, clinically evaluated symptoms often take weeks or month to change with treatment, and changes are generally indicative of large improvements. This hampers individual treatment, spending months identifying a failure to respond to a given medication and also restricts clinical trials for new pharmaceutical treatments. In any large trial of a new medication there are responders and nonresponders. It would be helpful to find immediate markers to predict medication success both with more sensitivity and reliability, and with more speed. In summary, current clinical diagnostic and treatment efforts rely on common symptoms which relate to rather broad disease categories, neglecting individual differences and variable treatment responses.

Correspondence: Esther Walton, Department of Child and Adolescent Psychiatry, Dresden University of Technology, University Hospital Carl Gustav Carus, Translational Developmental Neuroscience Section, Fetscherstraße 74, 01307 Dresden, Germany. Tel: ⫹ 49 (0)351 458-7060. Fax: ⫹ 49 (0)351 458 -5754. E-mail: [email protected] (Received 13 May 2013; accepted 13 June 2013) ISSN 0954–0261 print/ISSN 1369–1627 online © 2013 Institute of Psychiatry DOI: 10.3109/09540261.2013.816659

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Using measures which connect well to the underlying pathophysiology could help to narrow the search for new drug targets and eventually improve treatment for a wide range of subgroups. To optimize psychiatric treatment, we need to: • advance our understanding of the pathophysiology of psychiatric disorders • take into account the variability of phenotypes • discover new drug targets which are of greater relevance to disease subgroups • find reliable biomarkers to assess treatment effect, to predict the course of illness and to identify potential non-responders and side effects

Hypotheses and methods Hypotheses In this review we argue that neuroimaging has the potential to improve psychiatric treatment through two basic approaches. First, neuroimaging metrics can serve as an intermediate phenotype. By reducing phenotypic complexity, neuroimaging phenotypes can help to (1) identify disease-related genes, which can further advance our understanding of disease aetiology (here referred to as ‘gene discovery approach’) and (2) can shed light into the biological mechanisms related to genes, which have been discovered previously in case-control studies (referred to as ‘functional approach’). Both approaches can aid in establishing new targets for drug development and treatment options. Second, neuroimaging can be used as a treatment response biomarker. Since neuroimaging – as a quantitative rather than dichotomous trait – reflects the varying degrees of impairment on a brain-based level, sensitivity to treatment effects can be enhanced. Also, considering that clinical symptoms might take a while to respond to drug therapy, neuroimaging biomarkers might be more sensitive to capture early treatment effects, predictive of future treatment responses. This would improve the assessment of treatment efficacy early during therapy, identifying non-responders more quickly and possibly predicting the development of serious side effects, which could optimize current therapy plans. So, whereas intermediate phenotypes are supposed to be ‘trait-based’ and should not change in response to factors such as medical treatment, allowing the assessment of genetic effects, response biomarkers should be more ‘state-based’ and respond to medical intervention (among others), ideally predicting a change in clinical parameters. Neuroimaging measures are not inherently one or the other, and can provide either long-term stable trait-based measures, or more dynamic changes.

Neuroimaging measures in psychiatry Neuroimaging methods include physiological measures such as EEG or MEG, as well as MRI-based measures which allow for structural imaging of grey matter, white matter, and measures of white matter tracts, functional imaging based on the blood oxygenation level dependent (BOLD) signal, either in response to carefully designed experimental conditions or in a resting state, metabolic imaging such as spectroscopy which reflects the relative amounts of various chemicals in the brain, and perfusion imaging such as arterial spin labelling, which quantifies blood flow through the brain. Each of these methods has been applied to various psychiatric populations, and elucidates different aspects of psychiatric dysfunction. Functional imaging measures can identify impaired neural activity in certain areas in response to certain cognitive demands while retaining normal activation in response to other demands, for example. With more complex analyses, functional magnetic resonance imaging (fMRI) data can be used to identify increased or decreased BOLD signal coherence across various brain regions in disease (functional connectivity). Brain volumes can be affected by many different biological processes, which spectroscopy, for example, can potentially help tease apart (such as by identifying increased glutamatergic activity supporting the idea of excitotoxicity-induced apoptosis). Our examples will draw primarily from functional and structural neuroimaging studies, as those have been most popularly used, but these only reflect a subset of the available information regarding brain function and structure. Methods Papers discussed and cited in this review represent a selection of studies identified using a combination of the following terms in PubMed: imaging, fMRI, working memory, intermediate phenotype, schizophrenia, cortical thickness, hippocampal volume, antipsychotic medication, genome-wide associations study (GWAS), gene, DNA methylation, biomarker, resting state fMRI and heritability or treatment. We also reviewed publications cited by these identified studies and review articles. Studies had to be written in English. Neuroimaging as brain-based intermediate phenotypes and treatment response biomarkers What are brain-based intermediate phenotypes? Instead of investigating clinical diagnoses, which neglects the fact that part of the spectrum of diseaserelated traits is also present in the healthy population

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Neuroimaging biomarkers (Meyer-Lindenberg, 2010a), researchers have started to focus on measurable cognitive, neurobiological or neurophysiological traits – intermediate phenotypes –, thought to be closer to the pathogenic genotype than disease status (Meyer-Lindenberg & Weinberger, 2006). However, not every continuous trait classifies as an intermediate phenotype, and certain restrictions apply, as has been put forward by authors such as Gottesman and Gould (2003), Almasy and Blangero (2001) and Meyer-Lindenberg and Weinberger (2006). Intermediate phenotypes have to be associated with the illness, have to be heritable, and should also be observed in mildly ill or unaffected relatives, albeit to a lesser degree. This can be expected given the heritability and also helps to exclude effects that are due to disease-related confounders such as medication intake. Although medication effects cannot and should not be completely excluded, these rather trait-like characteristics of intermediate phenotypes make them suitable to study above all genetic effects rather than to use them to monitor drug therapy success. In the next section we explain the advantages of neuroimaging for gene discovery and drug development using examples related to schizophrenia. However, parallels can be found for other mental disorders making this applicable to the whole field of psychiatry. DLPFC dysfunction and grey matter reduction as brain-based intermediate phenotypes for schizophrenia Dorsolateral prefrontal cortex (DLPFC) dysfunction during working memory (WkM) processing is a widely acknowledged intermediate phenotype for schizophrenia (Hall & Smoller, 2010). In contrast to matched healthy controls, patients need to recruit more neural resources (hyperfrontality) at low levels of task difficulty and may show decreased neural activity (hypofrontality) when task difficulty increases, hence exhibiting an ‘inefficiency’ of the prefrontal cortex (Callicott et al., 2003; Karlsgodt et al., 2007; Manoach et al., 1999; Potkin et al., 2009b). Many family and twin studies have shown associations between genetic risk and DLPFC inefficiency (Goghari, 2010; Karlsgodt et al., 2007; MacDonald et al., 2009), providing evidence for the heritability of DLPFC (dys)function and its relation to elevated risk for schizophrenia. Furthermore, DLPFC dysfunction can be observed in medication-naïve schizophrenia patients and in individuals showing prodromal symptoms (Fusar-Poli et al., 2010; van Veelen et al., 2010, 2011), suggesting trait-like characteristics. Manoach et al. (2001) investigated the test-retest reliability of WkM-related brain activity during the Sternberg Item Recognition Paradigm (SIRP) in healthy controls and schizophrenia patients, and found that the magnitude of brain activation in relevant

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areas was reliable in controls, but not in patients. Despite this area of concern, WkM-related brain activity as measured during the SIRP task was deemed a valid imaging biomarker for schizophrenia (Barch et al., 2012). Grey matter reductions including reduced cortical thickness or hippocampus volume is another wellaccepted intermediate phenotype for schizophrenia (Hall & Smoller, 2010), although other structural brain changes such as ventricular enlargements have also been put forward (Staal et al., 2000; Weinberger et al., 1981). Numerous structural MRI studies have reported a reduction in cortical thickness in frontal, temporal and parietal regions (Ehrlich et al., 2012; Goldman, 2009; Nesvåg et al., 2008; Schultz et al., 2010) as well as a reduction in hippocampal volume (Ehrlich et al., 2010; Heckers, 2001; Heckers et al., 1991; Velakoulis et al., 2006) in schizophrenia patients. Abnormalities in hippocampus structure and function have been associated with memory and executive impairments in schizophrenia (Goldberg et al., 1994; Gur et al., 2000; Szeszko et al., 2002), suggesting that these changes also reflect a central pathophysiological process connected to the illness. Family studies investigating structural MRI data provide evidence for the heritability of cortical thickness and various subcortical volumes. Analysing highquality T1-weighted neuroanatomic MRI images of 486 individuals from extended pedigrees, Winkler et al. (2010) found cortical thickness to be significantly influenced by genetic factors. Similar findings were reported by Goldman (2009) and Panizzon et al. (2009). For the hippocampus, sibling and family studies provide heritability estimates between 40–70% (Goldman et al., 2008; Kaymaz & van Os, 2009; Peper et al., 2007). Considering that grey matter thickness and volume are assumed to reflect the arrangement and density of neuronal and glial cells as well as passing axons, which most likely are the result of relatively slow processes and longer-term changes, it seems reasonable to conclude that these reductions are independent of short-term state factors. However, whether medication intake has confounding effects is still debated (Smieskova et al., 2009). Whereas there is some evidence that antipsychotic medication or substance dependence has an effect on brain structure (Nesvåg et al., 2007) and that typical antipsychotics especially relate to basal ganglia enlargements (Dazzan et al., 2005), there are also negative studies (Nesvåg et al., 2008) and studies reporting grey matter reductions in medication-naïve high-risk individuals, who later developed schizophrenia (Job et al., 2005). In conclusion, current research suggest that DLPFC dysfunction, cortical thickness and hippocampal volume are heritable, schizophrenia-associated

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and reflect most likely stable processes associated with risk for the disease rather than confounders such as medication intake. Thereby, they represent promising intermediate phenotypes, which can be used to study the genetic basis of schizophrenia.

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What are treatment response biomarkers? Given the wide range of neuroimaging findings in schizophrenia, another interesting question is also whether they can be used as biomarkers to monitor treatment effects. It should be clear these imaging biomarkers should relate to discrete neural systems, which also connect to disease-specific symptoms and impairments. They should be characterized by a high construct validity and test-retest reliability (Carter & Barch, 2012), but show measurable treatment responses. Compared to intermediate phenotypes, biomarkers might therefore be more state-like and informative in determining drug treatment response and symptom progression. With respect to negative symptoms and cognitive deficits in schizophrenia, advancements have been made for example by the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) consortium, supported by the US National Institute of Mental Health (Carter et al., 2008). This initiative set out to identify suitable imaging biomarkers, which relate to specific cognitive processes generally agreed to be impaired in schizophrenia (such as attention, executive control, working memory, episodic memory, perception, and emotional processing). In the domain of WkM for instance, ten cognitive tasks were reviewed as imaging biomarkers specific to the two WkM constructs ‘goal maintenance’ and ‘interference control’. Among the recommended paradigms were the AX continuous performance test/dot pattern expectancy task, the switching Stroop task, the suppress Task and the SIRP task. The aim is to use these measures to assess treatment effects on cognition in schizophrenia.

What are the advantages of using brain-based intermediate phenotypes and treatment response biomarkers? Gene discovery approach A major goal in psychiatric genetics is to identify genes underlying a given phenotype, a so-called ‘forward genetics’ approach (Schulze & McMahon, 2004). For less complex diseases such as macular degeneration, a GWAS approach has been quite successful in identifying previously unknown causal genes and their associated biological pathways (Edwards et al., 2005; Haines et al., 2005; Klein et al., 2005). In fact, for non-psychiatric diseases

such as Crohn’s disease GWAS results have already helped to identify new drugable targets. For instance, a meta-analysis of six Crohn’s disease GWAS comprising 6,333 cases and 15,056 controls in the discovery sample identified the gene TNFSF-11 (Franke et al., 2010), which can be targeted by a drug currently used to treat postmenopausal women at high risk of fracture with osteoporosis (Sanseau et al., 2012). Further studies are needed to see whether this drug can be used in Crohn’s disease and what– compared to current medication – its possible advantages are with respect to efficacy or side effects. However, the results already show the potential application of GWAS findings for the discovery of new drug targets. In psychiatry, traditional case-control GWA studies in the past have produced only few significant results, but the latest Psychiatric Genomewide Association Study Consortium study identified four independent genomic regions to be associated with five psychiatric disorders on a genome-wide significance level (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). However, the sample sizes required to achieve such discoveries are very large; in the given example 33,332 cases (including five different psychiatric disorders) and 27,888 controls were included. But especially when large sample sizes are not feasible or the aim is to investigate subclinical phenotypes or more specific subtypes, greater success might be achieved by using quantitative brain-based intermediate phenotypes instead. An intermediate phenotype approach can increase effect sizes and statistical power (Mier et al., 2010; Rose & Donohoe, 2012) and thus facilitate the search for disease susceptibility mechanisms. Indeed, several research groups have already conducted GWA studies to identify genes related to intermediate phenotypes such as DLPFC function or hippocampus volume. For DLPFC dysfunction, Potkin et al. (2008) identified in a small GWA pilot study the genes RSRC1 and ARHGAP18 , involved in prenatal brain development as well as in neural stem cell proliferation and migration. In a larger follow-up study, significant diagnosis⫻SNP interaction effects on DLPFC function were identified for chromosomal regions around the genes ROBO2-ROBO1, TNIK and CTXN3SLC12A2 (Potkin et al., 2009a). Functions associated with these genes include cortical development, particularly for forebrain and midline/callosal connections – supporting results from the pilot study – and additional functions related to the hypothalamus–pituitary–adrenal stress axis. For hippocampus volume, Stein et al. (2012) identified the gene TESC, which is associated with processes such as regulation of intracellular pH, cell volume and cytoskeletal organization, as a causal

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Neuroimaging biomarkers variant in a sample comprising a very large number of healthy controls and a small fraction of patients with varying neuropsychiatric disorders. In another GWAS on hippocampus volume – this time with a balanced proportion of controls and schizophrenia patients – Hass et al. (2013) identified the genes NR2F6, USHBP1, and BABAM1, involved in processes such as the regulation of neurodevelopment and synaptic (re)organization. However, neither study achieved genome-wide significance in the discovery sample alone and no gene variant showed differential effects in patients compared to controls. Carrying out GWAS on imaging phenotypes is a new field, and replication studies are needed to verify genetic findings. However, the hope is that consistent results can suggest concrete therapeutic targets, as has already been done for non-psychiatric diseases (Sanseau et al., 2012). Furthermore, although imaging GWAS findings point to specific cellular processes, helping to reveal the precise mechanisms for brain development, the relevance of each SNP main effect against a phenotype might be limited with respect to psychiatric research. Some have argued that the focus of intermediate phenotype studies should be put on the gene-by-diagnosis interaction effect, rather than on the main effect (Potkin et al., 2008). While the general genetic underpinnings for brain structure may be similar across patients and controls, the interest for psychiatry might lie in the gene-by-diagnosis interaction effects, which may be specific for a given disease, given the unique genetic or environmental background. Statistical interaction effects can identify gene– gene or gene–environment mechanisms. However, the detection of gene–gene interaction effects is computationally intensive and statistically difficult (Cantor et al., 2010). Independent component analyses that can identify genetic profiles which correlate with neuroimaging phenotypes can identify groups of genes which seem to have similar effects on brain structure or function, rather than single SNPs (Chen et al., 2012; Liu et al., 2012). Other methods which are being developed include assessing the joint effects of multiple SNPs simultaneously (Ge et al., 2012; Nikolova et al., 2011), hierarchical approaches to analyse combinations of both neuroimaging and genetic data (Stingo et al., 2013), as well as data reduction strategies such as expression SNPs and pathway analyses on the genotype level or network analyses on the phenotype level (Nymberg et al., 2013). While a full description of those analyses is beyond the scope of this paper, they highlight the possibility for more complex analyses of genetic effects on neuroimaging measures.

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Functional approach At least 23 schizophrenia-related GWAS with a case-control design have been carried out in the past six years to identify numerous potential risk genes, including ZNF804A, NRGN, ANK3, NOTCH4 and miR137 (Alkelai et al., 2011, 2012; Athanasiu et al., 2010; Bergen et al., 2012; Betcheva et al., 2013; Chen et al., 2011; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013; Ikeda et al., 2011; Irish Schizophrenia Genomics Consortium & Wellcome Trust Case Control Consortium 2, 2012; Kirov et al., 2009; Lencz et al., 2007; Levinson et al., 2012; Ma et al., 2011; O’Donovan et al., 2008; Purcell et al., 2009; Rietschel et al., 2011; Ripke et al., 2011; Shi et al., 2009; Shifman et al., 2008; Stefansson et al., 2009; Sullivan et al., 2008; Yamada et al., 2011; Yue et al., 2011). However, clinical and biological follow-up studies are needed to understand the precise mechanisms of these novel genetic markers in disease pathophysiology. Furthermore, even for a-priori defined candidate genes such as COMT, DISC1 or BDNF, the exact brain-based risk mechanisms and pathways are often still unknown. Investigating the impact of novel or hypothesized risk variants for psychiatric illnesses on brain function and structure, also referred to as ‘reverse phenotyping’ (Schulze & McMahon, 2004), can shed light into disease-associated modifications on a neuroscience systems level and can also – if applicable – verify GWAS results. Many groups have researched schizophrenia-risk GWAS hits and candidate gene effects on brain function (Nicodemus et al., 2010; Roffman et al., 2008; Tura et al., 2008; Walton et al., submitted). For instance, Esslinger et al. (2009) reported an effect of the schizophrenia-associated GWAS hit ZNF804A on WkM-related DLPFC connectivity and on uncoupling between this structure and the hippocampal formation in healthy controls. A meta-analysis studying the association between the COMT Val/Met genotype and prefrontal activation found consistent effect sizes of approximately d ⫽ 0.7 (Mier et al., 2010), whereas three meta-analyses on COMT genotype effect on diagnosis all failed to support an association (Fan et al., 2005; Munafò et al., 2005; Okochi et al., 2009). This provides evidence that effect sizes are likely to increase, when considering brain-based phenotypes (Rose & Donohoe, 2012). However, the polygenic nature of schizophrenia implies that there is not a single gene for schizophrenia (Purcell et al., 2009). Identifying related pathways might be facilitated if aggregates of many genes with only small individual effects are considered. Indeed, our own group (Walton et al., 2013) has derived a cumulative risk score, which was based on the additive effects of several known genetic susceptibility loci

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for schizophrenia. We found an overall positive correlation between cumulative genetic risk for schizophrenia and WkM-related activity in the DLPFC. Risk genes were related to abnormal neurodevelopment processes and dysfunctional neurotransmitter systems. Several schizophrenia-related genes, identified a priori or through GWA studies, have also been related to grey matter reductions, including, for example, DISC1, GAD or ZNF804A with respect to reduced cortical thickness (Brauns et al., 2011, 2013; Voineskos et al., 2011) and COMT, BDNF or miR137 with respect to lower hippocampal volume (Ehrlich et al., 2010; Hajek et al., 2012; Lett et al., 2013; Smith et al., 2012). For instance, Ehrlich et al. (2010) reported an association of the COMT Val108/158Met polymorphism on medial temporal lobe volumes in a linear-additive manner, hypothesizing that lower COMT activity and a subsequent increase in extracellular dopamine concentration stimulates growth of medial temporal lobe structures. These findings suggest how risk variants could act on the level of the brain and what biological risk mechanisms might associate with the disorder. Moreover, by using this somewhat reductionist approach, distinct dimensions of the disorder are described, which might correspond to certain subgroups. The results may not only advance our understanding of the aetiological causes, but could also be used to define key targets for drug development. Neuroimaging as treatment response biomarkers Next to exploring the underlying aetiology of psychiatric illnesses with the help of intermediate phenotypes, neuroimaging can also be used as a drug treatment biomarker. Antipsychotic drugs are often designed for ‘average patients’. Their efficacy and effectiveness are usually evaluated based on clinical summary measures, such as the reduction of total symptom or core positive symptoms, the time to remission of symptoms or the proportion of patients achieving remission (Lieberman et al., 2003). Sometimes the primary outcome variable is simply dichotomous, like ‘symptom improvement’ (defined as a reduction of symptoms scores by at least 20–30%) (Leucht et al., 2005; Van Veelen et al., 2011) or ‘discontinuation of treatment for any cause’ as used by the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study (Lieberman et al., 2005; Stroup et al., 2003). But because there are also non-responding subgroups for any given drug, we need to explore ways to identify these patients, ideally early on. Considering that clinical symptoms are aetiologically speaking complex, they might take time to respond to treatment. Neuroimaging markers might be more apt to detect early treatment effects.

When looking for intermediate phenotypes, imaging measures need to be ‘trait-based’, in a sense – they need to be static with relationship to risk of the disease, and ideally not changing. Response biomarkers should be more ‘state-based’ – changing with the response to the intervention, possibly preceding or predicting the change in clinical response. A response to antipsychotic treatment can depend on several factors including neurochemical, metabolic and genetic ones. Bertolino et al. (2004) found that after an 8-week olanzapine monotherapy treatment, the BOLD signal response of medication-naive patients in a WkM task differed depending on their COMT genotype. However, COMT genotype also predicted WkM performance, questioning whether neuroimaging does indeed increase sensitivity compared to behavioural or symptom-based measures to disentangle some of the factors which differentiate responders from non-responders. Still, several neuroimaging studies have identified brain patterns predictive of future treatment response in schizophrenia patients. Van Veelen et al. (2011) found that nonresponders to atypical antipsychotics (based on a reduction of less than 30% in total symptom scores on the Positive and Negative Syndrome Scale (PANSS) after 10 weeks of treatment) showed a reduced practice effect in the DLPFC during the SIRP task that was present already at baseline. Similarily, Lahti et al. (2009) – using PET data – found that responders to haloperidol treatment show increased regional cerebral blood flow in the right caudate/ventral striatum and less activation in the left hippocampus already after the first week, whereas symptoms scores did not improve until the sixth week. Many studies researching the effect of antipsychotics on WkM-related performance and associated brain activity, as well as symptom scores, have done so using the n-back paradigm (Sambataro et al., 2010; Schlagenhauf et al., 2010; Surguladze et al., 2007). For instance, Ettinger et al. (2011) investigated the impact of first and second generation antipsychotics on brain activity during the n-back task, on task performance and on PANSS scores in schizophrenia patients. Patients had to be on their present medication for at least 3 months. Interestingly, study results showed that treatment did not correlate with n-back task performance or PANSS total or subscale symptom scores, but did affect left prefrontal BOLD signal. Similarly, Friedman et al. (2008) used a longitudinal study design to test the effect of atomoxetine – a selective noradrenaline reuptake inhibitor – on PANSS symptom scores, several cognitive domains and on neural activity during the n-back task during an 8-week period. Whereas atomoxetine treatment was not related to symptom scores or cognitive improvement, it did associate with

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Neuroimaging biomarkers significantly greater post-treatment activation in the left DLPFC and the left posterior cingulate cortex. However, since neither study detected medication effects on symptoms scores or behavioural performance, they were not able to test whether the observed brain-based effects were able to differentially predict clinical outcome later on during the trial. Still, these results could suggest that neural activity may be a more sensitive marker of pharmacological treatment effects than behavioural or symptom measures, especially at an early stage. Resting state fMRI is a popular approach in clinical studies, as even patients who are severely ill can often be capable of a 5–6 min passive fMRI scan, while they would not be able to carry out a working memory or other cognitively demanding task in the scanner. Resting state fMRI (rsfMRI) measures allow for spectral analyses, which consider the frequencies of the BOLD signal over time, as well as seed-based analyses and independent component analyses, which focus on the inter-regional time course correlations across the brain. While the use of rsfMRI measures has not yet been so successful in identifying psychiatrically relevant intermediate phenotypes (though several measures are heritable, see Glahn et al. (2010), various measures of the resting state brain show response to treatment. Lui et al. (2010), for example, used both spectral and time-course correlation approaches to identify differences in the resting state brain of anti-psychotic naïve, first episode patients, both before and after treatment, and relative to healthy controls. Abbott et al. (2013) identified areas of increased signal correlations that were related to response to electroconvulsive therapy in major depression. This is a rich field of possible treatment biomarkers that deserves thorough exploration (Castellanos et al., 2013).

Remaining challenges and outlook Challenges using intermediate phenotypes and treatment response biomarkers The main challenge of using biomarkers to assess and predict treatment response lies in the selection of suitable measures. With respect to WkM, for instance, the CNTRICS initiative (Carter et al., 2008) identified the neural activity during the SIRP task as measured by fMRI to be a useful marker for goal maintenance and interference control (Barch et al., 2012). The n-back task on the other hand was disregarded, because it is less specific to precise components of WkM, but instead conflates many different aspects. However, as described above, the n-back task is predominantly used to assess antipsychotic treatment effects on WkM in neuroimaging. With respect to the improvement of individual treatment

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plans, it has to be said that using imaging biomarkers is still expensive and technically challenging, especially when done in a multi-centre setting. While being sufficient to investigate differences between groups, the moderate specificity and sensitivity of the method still limits individual applications. The question also remains of how stable neuroimaging findings should be and where the line lies between intermediate phenotypes and treatment response biomarkers. If the aim is to use neuroimaging results as a marker of response to treatment, then they should be state based rather than trait based, but then how reliable are they still as genetic biomarkers? For instance, DLPFC response to cognitive demand has been put forward both as an intermediate phenotype and a biomarker for schizophrenia, potentially confounding results, if used in both ways. However, it is possible, that imaging markers are traitlike and largely genetically determined, but still respond to some degree to treatment and medication. With respect to using neuroimaging markers as intermediate phenotypes, it has been argued that intermediate phenotypes have a greater sensitivity to detect the effects of genetic variants (Goldman & Ducci, 2007; Meyer-Lindenberg, 2010b). However, even neuroimaging studies may still require large sample sizes in order to identify specific gene effects, which may render these methods less suitable for use on an individual level, e.g. as an early detection tool. Second, gene-associated brain systems only give clues about potential risk mechanisms and can merely open up starting points for further exploration of disease causes. Real advancements will be made, when combining results from studies applying different methods, with neuroimaging being only one among others. Third, although there is a certain specificity for a given disorder, some intermediate phenotypes overlap with other disorders (Ivleva et al., 2010), drawing into question how characteristic these findings are for a given disorder. Still, this might not necessarily reflect a weakness of the intermediate phenotypes approach but rather point us to problems with the diagnostic classification systems. In fact, the dichotomy of psychiatric diagnoses was criticized repeatedly and initiatives such as the NIMH-funded Research Domain Criteria (RDoC) project have proposed using a dimensionally based classification system instead (Cuthbert & Insel, 2010). Last, the best approach can become obsolete, if it is based on the wrong assumptions. Rather than single genes, it is more likely that whole genetic profiles are associated with a brain-based network, reflecting the interaction of several biological pathways in creating the eventual dysfunction. But even more, we should not neglect the evidence for the effect of various environmental risk factors such as

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diet and smoking (Meyer-Lindenberg, 2010a). In the end, brain-based intermediate phenotypes should not only be approached from a purely genetic perspective, but also include – among others – endocrine, immune and nutritional aspects.

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Outlook: imaging epigenetics Although it is undeniable that the aetiology of psychiatric illnesses is regulated through complex gene–environment interactions, little is still known about which precise environmental factors contribute, to what extent and during which developmental period, to disease risk. While the knowledge about genetic risk factors is slowly but steadily increasing, the assessment of environmental risk factors and their impact on biological pathways seems to stagnate, possibly due to the vast amount of potentially important factors, problems of quantification and associated costs. But aetiological models have to be able to integrate genetic risk with environmental factors associated with the disorder. Epigenetics refers to the reversible regulation of various genomic functions, occurring independently of DNA sequence, mediated through changes in DNA methylation and chromatin structure (Van Winkel et al., 2010). Induced changes in gene activity can be short term, long term or even transgenerational (Abdolmaleky et al., 2004; Daxinger & Whitelaw, 2012) and have been reported to influence synaptic signalling and cognitive function (Day & Sweatt, 2011). Furthermore, epigenetic mechanisms such as DNA methylation or histone modification have been put forward as the molecular code of environmental influences (Jirtle & Skinner, 2007) and are thought to represent the combined impact of non-genetic risk (and protective) factors. These analysis methods can show how environmental conditions influence gene function or how they converge on the same molecular pathways, which are already impaired through gene defects, causing greater detrimental effects than each risk factor by itself. Investigating epigenetic processes using imaging phenotypes could shed light into the effect of complex gene– environment interactions on brain-based systems. Several studies have found differences in DNA methylation between schizophrenia patients and controls either globally (Melas et al., 2012), genomewide (Dempster et al., 2011; Liu et al., 2013; Mill et al., 2008) or for certain candidate genes such as COMT (Abdolmaleky et al., 2006; Nohesara et al., 2011) or RELN (Grayson et al., 2005). However, to our knowledge there is only one study investigating the effect of DNA methylation of a schizophreniaassociated risk gene on an imaging phenotype for schizophrenia, albeit in healthy controls. Ursini et al. (2011) investigated the interaction effect of COMT

methylation and stress on prefrontal function during a WkM task. They found that greater stress and lower COMT methylation were related to reduced cortical efficiency, showing how environmental stressors impact brain function, possibly via DNA methylation changes. Going far beyond the current knowledge, imaging epigenetics could be used to overcome the challenges of traditional imaging genetics, as mentioned above. By being able to reflect both the genetic and the non-genetic background of individuals, findings within this newly emerging field could disentangle the complex interplay between different risk factors. Epigenetics brings with it several challenges, notably whether methylation or other epigenetic markers as measured in blood or non-invasive tissue collections can predict methylation levels in specific areas in the brain and whether the very dynamic nature of methylation processes reflect disease pre-conditions or sequelae. Given that smoking, exercise, emotional stress, and anti-psychotic medication are known to have an effect on methylation levels in particular tissues (Alasaari et al., 2012; Barrès et al., 2012; Huang et al., 2011; Melas et al., 2012; Tammen et al., 2012), imaging epigenetics will require careful study designs and controls, but has the potential for unravelling the effects of such risk or protective factors on the brain as they predispose a person toward psychosis or related dysfunctions. Although still in its early phase, however, imaging epigenetics has the potential to advance our understanding of complex diseases tremendously, and it remains to be seen whether progress in this field improves existing early detection and treatment tools. Conclusion Neuroimaging has certain advantages, which can help to broaden our knowledge about psychiatric diseases and could potentially improve psychiatric treatment. As an intermediate phenotype, neuroimaging can reduce genetic and phenotypic complexities, facilitating the discovery of novel genes related to the processes underlying various psychiatric disorders. Furthermore, this approach can validate genes, previously identified in case-control studies, and shed light on their neurobiological risk mechanisms. As a treatment response biomarker, neuroimaging can also be used to identify meaningful subgroups and help to predict early treatment responses. However, despite these promising applications, neuroimaging is still largely used to compare groups and lacks the specificity and sensitivity to be used on an individual level. Since results only reflect certain aspects of psychiatric dysfunction, research conclusions and clinical decisions should be based on a range of findings, combining –among others – genetic,

Neuroimaging biomarkers epigenetic, neuropsychological, laboratory, demographical and clinical profiles. Declaration of interest: This work was supported by the US National Institutes of Health (1R01MH094524 to JT), the NARSAD Young Investigator Award (SE) and the Friedrich-Ebert Stiftung (EW). The author reports no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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