Neuroimaging in psychiatric pharmacogenetics research: the promise and pitfalls

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND

Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC).

AIM

To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.

METHODS

Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.

RESULTS

Compared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%.

CONCLUSION

The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Application of Research Domain Criteria to childhood and adolescent impulsive and addictive disorders: Implications for treatment

The Research Domain Criteria (RDoC) initiative provides a large-scale, dimensional framework for the integration of research findings across traditional diagnoses, with the long-term aim of improving existing psychiatric treatments. A neurodevelopmental perspective is essential to this endeavor. However, few papers synthesizing research findings across childhood and adolescent disorders exist. Here, we discuss how the RDoC framework may be applied to the study of childhood and adolescent impulsive and addictive disorders in order to improve neurodevelopmental understanding and to enhance treatment development. Given the large scope of RDoC, we focus on a single construct highly relevant to addictive and impulsive disorders - initial responsiveness to reward attainment. Findings from genetic, molecular, neuroimaging and other translational research methodologies are highlighted.

Pathways to precision medicine in smoking cessation treatments

Cigarette smoking is highly addictive and modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings to clinical practice is identifying the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. We reviewed the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that genetic risks can predict smoking cessation outcomes and moderate the effect of pharmacological treatments. Some pharmacogenetic findings have been replicated in meta-analyses or in multiple smoking cessation trials. The variation in efficacy between smokers with different genetic markers supports the notion that personalized smoking cessation intervention based upon genotype could maximize the efficiency of such treatment while minimizing side effects, thus influencing the number needed to treat (NNT) and the number needed to harm. In summary, as precision medicine is revolutionizing healthcare, smoking cessation may be one of the first areas where genetic variants may identify individuals at increased risk. Current evidence strongly suggests that genetic variants predict cessation failure and that cessation pharmacotherapy effectiveness is modulated by biomarkers such as nicotinic cholinergic receptor α5 subunit (CHRNA5) genotypes or nicotine metabolism ratio (NMR). These findings strengthen the case for the development and rigorous testing of treatments that target patients with different biological risk profiles.

Precision medicine and pharmacogenetics: what does oncology have that addiction medicine does not?

BACKGROUND AND AIMS

Precision, personalized or stratified medicine, which promises to deliver the right treatment to the right patient, is a topic of international interest in both the lay press and the scientific literature. A key aspect of precision medicine is the identification of biomarkers that predict the response to medications (i.e. pharmacogenetics). We examined why, despite the great strides that have been made in biomarker identification in many areas of medicine, only in oncology has there been substantial progress in their clinical implementation. We also considered why progress in this effort has lagged in addiction medicine.

METHODS

We compared the development of pharmacogenetic biomarkers in oncology, cardiovascular medicine (where developments are also promising) and addictive disorders.

RESULTS

The first major reason for the success of oncologic pharmacogenetics is ready access to tumor tissue, which allows in-vitro testing and insights into cancer biology. The second major reason is funding, with cancer research receiving, by far, the largest allocation by the National Institutes of Health (NIH) during the past two decades. The second largest allocation of research funding has gone to cardiovascular disease research. Addictions research received a much smaller NIH funding allocation, despite the major impact that tobacco use, alcohol consumption and illicit drug use have on the public health and healthcare costs.

CONCLUSIONS

Greater support for research on the personalized treatment of addictive disorders can be expected to yield disproportionately large benefits to the public health and substantial reductions in healthcare costs.

Individual differences in the processing of smoking-cessation video messages: An imaging genetics study

Studies testing the benefits of enriching smoking-cessation video ads with attention-grabbing sensory features have yielded variable results. Dopamine transporter gene (DAT1) has been implicated in attention deficits. We hypothesized that DAT1 polymorphism is partially responsible for this variability. Using functional magnetic resonance imaging, we examined brain responses to videos high or low in attention-grabbing features, indexed by "message sensation value" (MSV), in 53 smokers genotyped for DAT1. Compared to other smokers, 10/10 homozygotes showed greater neural response to High- vs. Low-MSV smoking-cessation videos in two a priori regions of interest: the right temporoparietal junction and the right ventrolateral prefrontal cortex. These regions are known to underlie stimulus-driven attentional processing. Exploratory analysis showed that the right temporoparietal response positively predicted follow-up smoking behavior indexed by urine cotinine. Our findings suggest that responses to attention-grabbing features in smoking-cessation messages is affected by the DAT1 genotype.

Development of a Diverse Learning Experience for Diverse Psychiatry Resident Needs: A Four-Year Biological Psychiatry Curriculum Incorporating Principles of Neurobiology, Psychopharmacology, and Evidence-Based Practice

OBJECTIVE

A clinically relevant approach to patient care grounded in neurobiological constructs and evidence based practice which emphasizes a relevant psychopharmacology is needed to optimally train psychiatry residents.

METHODS

We implemented a biological psychiatry course that now incorporates neurobiology, psychopharmacology, and evidence-based practice in conjunction with a Research Domain Criteria (RDoC) perspective. A survey launched prior to course implementation and following each class session, served as the outcome metric of residents' attitudes toward the new curriculum and followed a baseline attitudinal survey designed to evaluate the program.

RESULTS

Greater than 90% of the psychiatry residents at Duke University who took the attitudinal survey agreed or strongly agreed with needing a course that helped them develop an understanding of neurobiology, psychopharmacology, and evidence-based practice concepts. Most residents also indicated a less than adequate understanding of the neurobiology and psychopharmacology of psychiatric disorders prior to sessions.

CONCLUSION

Our biological psychiatry curriculum was associated with enthusiasm among residents regarding the incorporation of neurobiology, psychopharmacology, and evidence-based practice into course topics and discussions. A biological psychiatry curriculum with integrated neurobiology and psychopharmacology built on an evidence base approach is possible, well-received, and needed in training of future psychiatrists.

Psychiatric Pharmacogenomics: How to Integrate into Clinical Practice

Pharmacogenomic testing can be integrated into modern mental health practices to help select psychotropic drugs for individuals who have failed first-line evidence-based treatments. This can be done by the process of "equipoise"-namely, balancing the weight of all available evidence. That evidence now includes not only diagnosis-specific treatment guidelines and "personalized" patient information, such as an individual's specific symptom profile, past response to medications, side effects, family history, and patient preference, but also "precision medicine," which incorporates the ever-expanding base of pharmacogenomic evidence for how an individual's own biomarkers alter the odds for that individual's treatment response or treatment intolerance.

Toward a Global Roadmap for Precision Medicine in Psychiatry: Challenges and Opportunities

Mental disorders represent a major public health burden worldwide. This is likely to rise in the next decade, with the highest increases predicted to occur in low- and middle-income countries. Current psychotropic medication treatment guidelines focus on uniform approaches to the treatment of heterogeneous disorders and achieve only partial therapeutic success. Developing a global precision medicine approach in psychiatry appears attractive, given the value of this approach in other fields of medicine, such as oncology and infectious diseases. In this horizon scanning analysis, we review the salient opportunities and challenges for precision medicine in psychiatry over the next decade. Variants within numerous genes involved in a range of pathways have been implicated in psychotropic drug response and might ultimately be used to guide choice of pharmacotherapy. Multipronged approaches such as multi-omics (genomics, proteomics, metabolomics) analyses and systems diagnostics together with high-throughput sequencing and genotyping technologies hold promise for identifying precise and targeted treatments in mental disorders. To date, however, the vast majority of pharmacogenomics work has been undertaken in high-income countries on a relatively small proportion of the global population, and many other challenges face the field. Opportunities and challenges for establishing a global roadmap for precision medicine in psychiatry are discussed in this article.

The effects of nicotine and non-nicotine smoking factors on working memory and associated brain function

Smoking abstinence impairs executive function, which may promote continued smoking behavior and relapse. The differential influence of nicotine and non-nicotine (i.e. sensory, motor) smoking factors and related neural substrates is not known. In a fully factorial, within-subjects design, 33 smokers underwent fMRI scanning following 24 hours of wearing a nicotine or placebo patch while smoking very low nicotine content cigarettes or remaining abstinent from smoking. During scanning, blood oxygenation level-dependent (BOLD) signal was acquired while participants performed a verbal N-back task. Following 24-hour placebo (versus nicotine) administration, accuracy on the N-back task was significantly worse and task-related BOLD signal lower in dorsomedial frontal cortex. These effects were observed irrespective of smoking. Our data provide novel evidence that abstinence-induced deficits in working memory and changes in underlying brain function are due in large part to abstinence from nicotine compared with non-nicotine factors. This work has implications both for designing interventions that target abstinence-induced cognitive deficits and for nicotine-reduction policy.

Neurogenetics and gene therapy for reward deficiency syndrome: are we going to the Promised Land?

INTRODUCTION

Addiction is a substantial health issue with limited treatment options approved by the FDA and as such currently available. The advent of neuroimaging techniques that link neurochemical and neurogenetic mechanisms to the reward circuitry brain function provides a framework for potential genomic-based therapies.

AREAS COVERED

Through candidate and genome-wide association studies approaches, many gene polymorphisms and clusters have been implicated in drug, food and behavioral dependence linked by the common rubric reward deficiency syndrome (RDS). The results of selective studies that include the role of epigenetics, noncoding micro RNAs in RDS behaviors especially drug abuse involving alcohol, opioids, cocaine, nicotine, pain and feeding are reviewed in this article. New targets for addiction treatment and relapse prevention, treatment alternatives such as gene therapy in animal models, and pharmacogenomics and nutrigenomics methods to manipulate transcription and gene expression are explored.

EXPERT OPINION

The recognition of the clinical benefit of early genetic testing to determine addiction risk stratification and dopaminergic agonistic, rather than antagonistic therapies are potentially the genomic-based wave of the future. In addition, further development, especially in gene transfer work and viral vector identification, could make gene therapy for RDS a possibility in the future.

Biomarkers in the diagnosis of ADHD--promising directions

The etiology and pathogenesis of attention-deficit/hyperactivity disorder (ADHD) are unclear and a more valid diagnosis would certainly be welcomed. Starting from the literature, we built an hypothetical pyramid representing a putative set of biomarkers where, at the top, variants in DAT1 and DRD4 genes are the best candidates for their associations to neuropsychological tasks, activation in specific brain areas, methylphenidate response and gene expression levels. Interesting data come from the noradrenergic system (norepinephrine transporter, norepinephrine, 3-methoxy-4-hydroxyphenylglycol, monoamine oxidase, neuropeptide Y) for their altered peripheral levels, their association with neuropsychological tasks, symptomatology, drugs effect and brain function. Other minor putative genetic biomarkers could be dopamine beta hydroxylase and catechol-O-methyltransferase. In the bottom, we placed endophenotype biomarkers. A more deep integration of "omics" sciences along with more accurate clinical profiles and new high-throughput computational methods will allow us to identify a better list of biomarkers useful for diagnosis and therapies.

DRD2/CHRNA5 interaction on prefrontal biology and physiology during working memory

BACKGROUND

Prefrontal behavior and activity in humans are heritable. Studies in animals demonstrate an interaction between dopamine D2 receptors and nicotinic acetylcholine receptors on prefrontal behavior but evidence in humans is weak. Therefore, we hypothesize that genetic variation regulating dopamine D2 and nicotinic acetylcholine receptor signaling impact prefrontal cortex activity and related cognition. To test this hypothesis in humans, we explored the interaction between functional genetic variants in the D2 receptor gene (DRD2, rs1076560) and in the nicotinic receptor α5 gene (CHRNA5, rs16969968) on both dorsolateral prefrontal cortex mediated behavior and physiology during working memory and on prefrontal gray matter volume.

METHODS

A large sample of healthy subjects was compared for genotypic differences for DRD2 rs1076560 (G>T) and CHNRA5 rs16969968 (G>A) on prefrontal phenotypes, including cognitive performance at the N-Back task, prefrontal physiology with BOLD fMRI during performance of the 2-Back working memory task, and prefrontal morphometry with structural MRI.

RESULTS

We found that DRD2 rs1076560 and CHNRA5 rs16969968 interact to modulate cognitive function, prefrontal physiology during working memory, and prefrontal gray matter volume. More specifically, CHRNA5-AA/DRD2-GT subjects had greater behavioral performance, more efficient prefrontal cortex activity at 2Back working memory task, and greater prefrontal gray matter volume than the other genotype groups.

CONCLUSIONS

The present data extend previous studies in animals and enhance our understanding of dopamine and acetylcholine signaling in the human prefrontal cortex, demonstrating interactions elicited by working memory that are modulated by genetic variants in DRD2 and CHRNA5.

Optimizing treatments for nicotine dependence by increasing cognitive performance during withdrawal

INTRODUCTION

Current FDA-approved smoking cessation pharmacotherapies have limited efficacy and are associated with high rates of relapse. Therefore, there is a clear need to develop novel antismoking medications. Nicotine withdrawal is associated with cognitive impairments that predict smoking relapse. It has been proposed that these cognitive deficits are a hallmark of nicotine withdrawal that could be targeted in order to prevent smoking relapse. Thus, pharmacotherapies that increase cognitive performance during nicotine withdrawal may represent potential smoking cessation agents.

AREAS COVERED

The authors review the clinical literature demonstrating that nicotine withdrawal is associated with deficits in working memory, attention and response inhibition. They then briefly summarize different classes of compounds and strategies to increase cognitive performance during nicotine withdrawal. Particular emphasis has been placed on translational research in order to highlight areas for which there is strong rationale for pilot clinical trials of potential smoking cessation medications.

EXPERT OPINION

There is emerging evidence that supports deficits in cognitive function as a plausible nicotine withdrawal phenotype. The authors furthermore believe that the translational paradigms presented here may represent efficient and valid means for the evaluation of cognitive-enhancing medications as possible treatments for nicotine dependence.

Developing biomarkers in mood disorders research through the use of rapid-acting antidepressants

An impediment to progress in mood disorders research is the lack of analytically valid and qualified diagnostic and treatment biomarkers. Consistent with the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC) initiative, the lack of diagnostic biomarkers has precluded us from moving away from a purely subjective (symptom-based) toward a more objective diagnostic system. In addition, treatment response biomarkers in mood disorders would facilitate drug development and move beyond trial-and-error toward more personalized treatments. As such, biomarkers identified early in the pathophysiological process are proximal biomarkers (target engagement), while those occurring later in the disease process are distal (disease pathway components). One strategy to achieve this goal in biomarker development is to increase efforts at the initial phases of biomarker development (i.e. exploration and validation) at single sites with the capability of integrating multimodal approaches across a biological systems level. Subsequently, resultant putative biomarkers could then undergo characterization and surrogacy as these latter phases require multisite collaborative efforts. We have used multimodal approaches - genetics, proteomics/metabolomics, peripheral measures, multimodal neuroimaging, neuropsychopharmacological challenge paradigms and clinical predictors - to explore potential predictor and mediator/moderator biomarkers of the rapid-acting antidepressants ketamine and scopolamine. These exploratory biomarkers may then be used for a priori stratification in larger multisite controlled studies during the validation and characterization phases with the ultimate goal of surrogacy. In sum, the combination of target engagement and well-qualified disease-related measures are crucial to improve our pathophysiological understanding, personalize treatment selection, and expand our armamentarium of novel therapeutics.