Use of magnetic resonance imaging in pharmacogenomics

The fMRI global signal and its association with the signal from cranial bone

The nature of the global signal, i.e. the average signal from sequential functional imaging scans of the brain or the cortex, is not well understood, but is thought to include vascular and neural components. Using resting state data, we report on the strong association between the global signal and the average signal from the part of the volume that includes the cranial bone and subdural vessels and venous collectors, separated from each other and the subdural space by multispectral segmentation procedures. While subdural vessels carried a signal with a phase delay relative to the cortex, the association with the cortical signal was strongest in the parts of the scan corresponding to the laminae of the cranial bone, reaching 80% shared variance in some individuals. These findings suggest that in resting state data vascular components may play a prominent role in the genesis of fluctuations of the global signal. Evidence from other studies on the existence of neural sources of the global signal suggests that it may reflect the action of multiple mechanisms (including cerebrovascular reactivity and autonomic control) concurrently acting to regulate global cerebral perfusion.

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.

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Imaging in Pharmacogenetics

An increasing collection of imaging technologies makes it possible to differentiate treatment responders from nonresponders based on genetic variation. This chapter will review some of the imaging technologies currently available in nuclear medicine to visualize drug absorption, distribution, metabolism, and elimination. Some of the commonly used techniques to detect radiation-emitting compounds are the two-dimensional scintigraphy and the three-dimensional single-photon emission computed tomography (SPECT) which both detect photons using a gamma camera, and the three-dimensional positron emission tomography (PET), which detect the decay of positron-emitting radionuclides. Current examples include visualization of functional effects of genetic variants, and these provide proof of concept for imaging in pharmacogenetics as a tool to improve efficacy and safety of drugs.

White matter predicts functional connectivity in premanifest Huntington's disease

Objectives

The distribution of pathology in neurodegenerative disease can be predicted by the organizational characteristics of white matter in healthy brains. However, we have very little evidence for the impact these pathological changes have on brain function. Understanding any such link between structure and function is critical for understanding how underlying brain pathology influences the progressive behavioral changes associated with neurodegeneration. Here, we demonstrate such a link between structure and function in individuals with premanifest Huntington's.

Methods

Using diffusion tractography and resting state functional magnetic resonance imaging to characterize white matter organization and functional connectivity, we investigate whether characteristic patterns of white matter organization in the healthy human brain shape the changes in functional coupling between brain regions in premanifest Huntington's disease.

Results

We find changes in functional connectivity in premanifest Huntington's disease that link directly to underlying patterns of white matter organization in healthy brains. Specifically, brain areas with strong structural connectivity show decreases in functional connectivity in premanifest Huntington's disease relative to controls, while regions with weak structural connectivity show increases in functional connectivity. Furthermore, we identify a pattern of dissociation in the strongest functional connections between anterior and posterior brain regions such that anterior functional connectivity increases in strength in premanifest Huntington's disease, while posterior functional connectivity decreases.

Interpretation

Our findings demonstrate that organizational principles of white matter underlie changes in functional connectivity in premanifest Huntington's disease. Furthermore, we demonstrate functional antero–posterior dissociation that is in keeping with the caudo–rostral gradient of striatal pathology in HD.

Polymorphism in CYP2D6 and CYP2C19, members of the cytochrome P450 mixed-function oxidase system, in the metabolism of psychotropic drugs

Numerous studies in the field of psychopharmacological treatment have investigated the possible contribution of genetic variability between individuals to differences in drug efficacy and safety, motivated by the wide individual variation in treatment response. Genomewide analyses have been conducted in several large-scale studies on antidepressant drug response. However, no consistent findings have emerged from these studies. In a recent meta-analysis of genomewide data from the three studies capturing common variation for association with symptomatic improvement and remission revealed the absence of any strong genetic association and failed to replicate results of individual studies in the pooled data. However, there are good reasons to consider the possible importance of pharmacogenetic variants separately. These variants explain a large portion of the manifold variability in individual drug metabolism. More than 20 psychotropic drugs have now been relabelled by the FDA adding information on polymorphic drug metabolism and therapeutic recommendations. Furthermore, dose recommendations for polymorphisms in drug metabolizing enzymes, first and foremost CYP2D6 and CYP2C19, have been issued with the advice to reduce the dosage in poor metabolizers to 50% or less (in eight cases), or to choose an alternative treatment. Beside the well-described role in hepatic drug metabolism, these enzymes are also expressed in the brain and play a role in biotransformation of endogenous substrates. These polymorphisms may therefore modulate brain metabolism and affect the function of the neural substrates of cognition and emotion.

Neural correlates of emotion regulation in the ventral prefrontal cortex and the encoding of subjective value and economic utility

In many studies of the interaction between cognitive control and emotion, the orbitofrontal cortex/ventromedial prefrontal cortex (mOFC/vmPFC) has been associated with an inhibitory function on limbic areas activated by emotionally arousing stimuli, such as the amygdala. This has led to the hypothesis of an inhibitory or regulatory role of mOFC/vmPFC. In studies of cognition and executive function, however, this area is deactivated by focused effort, raising the issue of the nature of the putative regulatory process associated with mOFC/vmPFC. This issue is here revisited in light of findings in the neuroeconomics field demonstrating the importance of mOFC/vmPFC to encoding the subjective value of stimuli or their economic utility. Many studies show that mOFC/vmPFC activity may affect response by activating personal preferences, instead of resorting to effortful control mechanisms typically associated with emotion regulation. Based on these findings, I argue that a simple automatic/controlled dichotomy is insufficient to describe the data on emotion and control of response adequately. Instead, I argue that the notion of subjective value from neuroeconomics studies and the notion of attentional orienting may play key roles in integrating emotion and cognition. mOFC/vmPFC may work together with the inferior parietal lobe, the cortical region associated with attentional orienting, to convey information about motivational priorities and facilitate processing of inputs that are behaviorally relevant. I also suggest that the dominant mode of function of this ventral network may be a distinct type of process with intermediate properties between the automatic and the controlled, and which may co-operate with effortful control processes in order to steer response.