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Imaging genetics in depression

Introduction and historical overview

Major depressive disorder (MDD) is one of the leading causes for disability worldwide and therefore a major item on the global health agenda (Becker 2013). Though a plethora of treatment options are available ranging from pharmacological to psychotherapeutic interventions to electroconvulsive therapy and even deep brain stimulation, these measures are ineffective in a considerable part of patients and recovery remains long and tedious (Kupfer 2012).

Scaling-up treatment of depression and anxiety: a global return on investment analysis (Chisholm 2016)

The last two decades witnessed an unprecedented effort to understand the interplay between genes, neurotransmitter systems, brain circuits, and environmental effects employing an increasingly sophisticated methodological arsenal in animals and humans. Imaging genetics - the study of effects of genetic variation on the level of brain function or structure in living humans by means with neuroimaging methods - has been especially useful as a tool to draw the lines between these disparate pieces of evidence (Meyer-Lindenberg 2006). Though as yet none of these findings has made the much-needed progress from bench to bedside, an increasingly coherent picture of the neurocircuits and their modulation by genetic and environmental effects involved in depression emerged over the last years. MDD is defined in DSM-5 by the presence of at least five of the following symptoms for a minimum of two weeks: (1) depressed mood or a (2) lack of interest or pleasure in daily activities, (3) weight or appetite alterations, (4) insomnia or hypersomnia, (5) psychomotor agitation or retardation, (6) fatigue or loss of energy, (7) feelings of worthlessness or guilt, (8) diminished ability to think or concentrate, or indecisiveness, and (9) recurrent thoughts of death, suicidal ideation, suicide plans or attempts. A patient needs to exhibit at least one of the first two symptoms and has to suffer from significant distress to qualify for a diagnosis of MDD (American Psychiatric Association 2013).

Given the lack of any obligatory symptom within its definition, the clinical face of MDD is highly variable. Importantly, its operationalization is not based on any disease mechanism, which is key to future development of modern diagnostic schemes and therapies.

Since imaging can link the molecular level and the physiological level as well as the behavioral level in the living patient.

Despite an abundance of promising preclinical reports investigating genes, neurotransmitter systems, and brain circuits, none of the potential biological mechanisms has so far consistently been proven to be useful in diagnosis or treatment of MDD.

Recently, the cognitive symptoms of MDD have gained more attention from researchers, since they provide a promising new avenue given the increasing understanding of cognition and decision-making in healthy subjects.

Compared to some other psychiatric disorders such as schizophrenia, MDD is only modestly heritable (30-40 %), the genetic makeup of a patient is crucial since it moderates the relationship between stressors and clinical symptoms

Since depression is highly heritable (Wong and Licinio, 2001), there has been intense interest in candidate genes related to this behavioral phenotype. As the genetic architecture of depression is complex and genes are not directly encoding for psychiatric diagnoses or psychiatric symptoms, scientific progress was hindered by weak or contradictory results (Meyer-Lindenberg and Weinberger, 2006). The emergence of imaging genetics (Hariri and Weinberger, 2003) as a strategy for mapping neural phenotypes as a function of genotype has fostered new enthusiasm in depression research, because this approach allows for assessing the neural impact of candidate genes in vivo and thus provides for a new level of evidence. Although imaging genetics was initially proposed as a research tool ( Hariri and Weinberger, 2003), it has by now evolved into a new field of research, which is reflected in a dramatic increase of publications over the past few years. Efforts thus undertaken address several questions, such as neural effects of single genes, gene–gene (epistasis) and gene–environment interactions as well as the impact of chromosomal aberration disorders and small deletion syndromes on systems neurobiology.

in hindsight, it is obvious that and sloppy data analysis approaches rather hindered true progress in the field. In the end, it … discussion and resulted in new strategies. One of the first studies was a PET study showing the cingulate in depression (Drevets 1997). Another important field attempts to find predictors and moderators of treatment response (Phillips 2015).

After the first report of an association between genetic variation and a neuroimaging measure in 2000 42, imaging genetics has developed into a leading research strategy in neuroscience, with countless studies demonstrating the influence of risk alleles on neural intermediate phenotypes that in turn relate to different psychopathological manifestations and diagnostic entities.11, 43-45 In contrast to several candidate endophenotypes that turned out to be equally complex as behavioral phenotypes, recent meta-analyses indicate that neural intermediate phenotypes satisfy the premise of increased penetrance.16, 46, 47 For instance, a polymorphism in the promotor region (5- HTTLPR) of the serotonin transporter gene (SLC6A4) has been shown to account for up to 10% variance of amygdala activation, whereas its role in predicting behavioral phenotypes such as neuroticism, MDD or antidepressant treatment response is at least one order of magnitude lower.47-51 Accordingly, it seems as if imaging genetics can eventually provide one of the tools needed to decipher the polygenic heritability of psychiatric disorders as anticipated by Gottesman and Shields more than four decades ago.52 Despite obvious advantages, the imaging genetics approach has not been without criticism.22 Most concerns have been raised in the context of the candidate gene approach that has been almost exclusively used in imaging genetics until recently. However, the development of whole-genome techniques applicable to imaging genetics studies silenced those concerns.28 Current studies focusing on novel genome-wide supported risk variants are now shedding light on neural effects whithin pathways that have never been implicated in brain function before and will potentially establish new drug targets.53, 54

Brain circuits of depression

sgACC

Amygdala
The amygdala is a brain structure which has been researched especially in terms of memory and emotion processing . Changes in amygdala volume and activity correlate with various emotional deficits which might be involved in the pathogenesis of mental disease. With regards to Major Depressive Disorder, studies have observed amygdala hyperreactivity in response to fearful stimuli in depressed patients. Interestingly, this increase in activity has been shown to respond to antidepressant pharmacotherapy and psychotherapy. These findings were in line with a particularly compelling branch of research which positioned the amygdala as an important hub in the process of „fear conditioning“, suggesting heightened amygdala activity as an intermediate phenotype representing negatively biased emotion processing in MDD patients.

Hippocampus

DMN
Reduced default mode network suppression during a working memory task in remitted major depression (Bartova 2015)

Correlated gene expression supports synchronous activity in brain networks (Richiardi 2015).

Linking genes and brain circuits

Clinical relevance

Another problem arises from the combination of small sample sizes and the stringent statistical thresholds required in neuroimaging. In fact, many of the reported brain activation patterns that have been related to genetic or behavioral variation may actually be much less selective than assumed from the strictly thresholded images that only exhibit the largest effects (Yarkoni 2010).

Beyond these statistical problems, there are many other technical intricacies that have not yet been solved. E.g. results can differ based on … and even on operating system (Glatard 2015).

Biomarkers that will be really useful for the clinic will need to (here some discussion from bernhards paper) (Paulus 2015). Or this (Pine 2015)

Also, large-scale efforts such as the study by Schmaals et al also show that the true effect sizes of these studies are in fact much smaller and that e.g. the largest difference between MDD and healthy subjects only amounts to a Cohen’s d of 0.17, which would be a volume decrease of 1.4% (Fried 2015).

Does effect size matter in fMRI - see paper by gang chen and blog by neuroskeptic (Chen 2016)

Gene-Environment Interactions

What GWAS Can Tell Us about the Environment (Gage 2016)

RDoc Insel Cuthbert (Insel 2015)

Conclusion

In fact, when employed correctly, current measures to correct for false positives seem to be effective (Meyer-Lindenberg 2008).

Also, sophisticated meta-analysis techniques such as … may provide new insights (Yarkoni 2010)

Also, cellular based endophenotypes such as derived from pluripotent stem cells may offer new insights (Falk 2016)

Discuss ”Symptomics” approach, Eiko Fried.

Also, SLC6A4 has been characterized in marmorsets (Santangelo 2016). From this paper, also some discussion, Lukas review

Chapter requirements

The word limit for each chapter is 5000 words, including 3-5 figures or tables (with each image accounting for 250 words)

We encourage all authors to include at the beginning of their chapter a brief historical description of research on the specific topic that they focus on.

Konzept:

A) Geschichtliches
B) MDD Brain circuits
- sgACC
- Amygdala
- Hippocampus
- DMN (Hamilton JP, Farmer M, Fogelman P, Gotlib IH. (2015) Depressive Rumination, the Default-Mode Network, and the Dark Matter of Clinical Neuroscience. Biol Psychiatry 78: 224-230.)
C) Gene, die diese Circuitries regulieren
D) Clinical Relevance
- zero, weil keine clinical trials - keine causal inferrece possible
- Methods Probleme (Reproducibility, Reliability (keine öffentlicheDB, keine Standardisierung des preprocesing/statistik siehe OHMBM initiative, physiologische Artefakte (Rosenthal vene)
- Biologische Modelle: letztendlich zählt die Genexpression und die Verfügbarkeit an spezifischen neuronalen Kompartment der Zelle… Regulation (expression, packaging, trafficing) wird über komplexe Genregulationsnetzwerke umgesetzt. Bsp: SERT… Ggw. Modelle zu einfach, um klinisch relevant zu sein, weil soviel Einflussfaktoren (inkl Umwelt etc…. )… Biologische Modelle komplexer (zB Risschiardi Paper und Andere Polygenetic - Risk Scores….
E) Conclusion + outlook

we are in Section Four, Chapter Twenty Five: Imaging genetics in depression in ”Biological Processes”, maybe better Section Five: Emotional disorders

Enhancing the Informativeness and Replicability of Imaging Genomics Studies Carter CS, Bearden CE, Bullmore ET, Geschwind DH, Glahn DC, Gur RE, Meyer-Lindenberg A, Weinberger DR -¿ für die Discussion, evtl. auch Einleitung

Scharinger C, Rabl U, Pezawas L, Kasper S. (2011) The genetic blueprint of major depressive disorder: contributions of imaging genetics studies. World J Biol Psychiatry 12: 474-488.