"Domain gauges": A reference system for multivariate profiling of brain fMRI activation patterns induced by psychoactive drugs in rats

Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases

Translational magnetic resonance imaging of the rodent brain provides invaluable information for preclinical drug development. However, the automated segmentation of such images for quantitative analyses is limited compared to human brain imaging mainly due to the inferior anatomical contrast and the resulting less advanced registration and atlasing tools. Here, we investigated the potential of deep learning models for the segmentation of magnetic resonance images of rat brains into an entire set of multiple regions of interest (rather than individual loci), focusing on the development of a robust method that accommodates changes in the input based on differences in animal strain (genotype) and size. Manually generated labels are expensive, so we tested the ability of neural networks to learn brain structures from noisy but inexpensive registration-based labels, allowing very large datasets to be leveraged for training. We compared three distinct model architectures (U-Net, Attention-U-Net and DeepLab) by training them on a dataset of >10,000 magnetic resonance images of rat brains and found that each model was able to segment the entire brain into predefined sets of 29 and 58 regions, respectively, with the Attention U-Net achieving the best performance. The models canceled out unstructured label noise in the imperfect training data to provide smoother and more symmetric segmentations than registration-based labeling, and were more robust when presented with input variations, thus outperforming the noisy ground truth. Our pipeline also includes uncertainty estimation and an explainability mechanism, hence providing features essential for anomaly detection and quality assurance. In summary, our study shows that deep learning models do achieve accurate brain segmentation in high-throughput quantitative preclinical imaging without the need for expensive expert-generated labels.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Imaging of the dopamine system with focus on pharmacological MRI and neuromelanin imaging

The dopamine system in the brain is involved in a variety of neurologic and psychiatric disorders, such as Parkinson's disease, attention-deficit/hyperactivity disorder and psychosis. Different aspects of the dopamine system can be visualized and measured with positron emission tomography (PET) and single photon emission computed tomography (SPECT), including dopamine receptors, dopamine transporters, and dopamine release. New developments in MR imaging also provide proxy measures of the dopamine system in the brain, offering alternatives with the advantages MR imaging, i.e. no radiation, lower costs, usually less invasive and time consuming. This review will give an overview of these developments with a focus on the most developed techniques: pharmacological MRI (phMRI) and neuromelanin sensitive MRI (NM-MRI). PhMRI is a collective term for functional MRI techniques that administer a pharmacological challenge to assess its effects on brain hemodynamics. By doing so, it indirectly assesses brain neurotransmitter function such as dopamine function. NM-MRI is an upcoming MRI technique that enables in vivo visualization and semi-quantification of neuromelanin in the substantia nigra. Neuromelanin is located in the cell bodies of dopaminergic neurons of the nigrostriatal pathway and can be used as a proxy measure for long term dopamine function or degeneration of dopaminergic neurons. Both techniques are still primarily used in clinical research, but there is promise for clinical application, in particular for NM-MRI in dopaminergic neurodegenerative diseases like Parkinson's disease.

Recent advances in basic science methodology to evaluate opioid safety profiles and to understand opioid activities

Opioids are powerful drugs used by humans for centuries to relieve pain and are still frequently used as pain treatment in current clinical practice. Medicinal opioids primarily target the mu opioid receptor (MOR), and MOR activation produces unmatched pain-alleviating properties, as well as side effects such as strong rewarding effects, and thus abuse potential, and respiratory depression contributing to death during overdose. Therefore, the ultimate goal is to create opioid pain-relievers with reduced respiratory depression and thus fewer chances of lethality. Efforts are also underway to reduce the euphoric effects of opioids and avoid abuse liability. In this review, recent advances in basic science methodology used to understand MOR pharmacology and activities will be summarized. The focus of the review will be to describe current technological advances that enable the study of opioid analgesics from subcellular mechanisms to mesoscale network responses. These advances in understanding MOR physiological responses will help to improve knowledge and future design of opioid analgesics.

Discovery of Balovaptan, a Vasopressin 1a Receptor Antagonist for the Treatment of Autism Spectrum Disorder

We recently reported the discovery of a potent, selective, and brain-penetrant V1a receptor antagonist, which was not suitable for full development. Nevertheless, this compound was found to improve surrogates of social behavior in adults with autism spectrum disorder in an exploratory proof-of-mechanism study. Here we describe scaffold hopping that gave rise to triazolobenzodiazepines with improved pharmacokinetic properties. The key to balancing potency and selectivity while minimizing P-gp mediated efflux was fine-tuning of hydrogen bond acceptor basicity. Ascertaining a V1a antagonist specific brain activity pattern by pharmacological magnetic resonance imaging in the rat played a seminal role in guiding optimization efforts, culminating in the discovery of balovaptan (RG7314, RO5285119) 1. In a 12-week clinical phase 2 study in adults with autism spectrum disorder balovaptan demonstrated improvements in Vineland-II Adaptive Behavior Scales, a secondary end point comprising communication, socialization, and daily living skills. Balovaptan entered phase 3 clinical development in August 2018.

Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures

Despite their wide-spread use, only limited information is available on the comparative test-retest reliability of task-based functional and resting state magnetic resonance imaging measures of blood oxygen level dependence (tb-fMRI and rs-fMRI) and cerebral blood flow (CBF) using arterial spin labeling. This information is critical to designing properly powered longitudinal studies. Here we comprehensively quantified and compared the test-retest reliability and reproducibility performance of 8 commonly applied fMRI tasks, 6 rs-fMRI metrics and CBF in 30 healthy volunteers. We find large variability in test-retest reliability performance across the different tb-fMRI paradigms and rs-fMRI metrics, ranging from poor to excellent. A larger extent of activation in tb-fMRI is linked to higher between-subject reliability of the respective task suggesting that differences in the amount of activation may be used as a first reliability estimate of novel tb-fMRI paradigms. For rs-fMRI, a good reliability of local activity estimates is paralleled by poor performance of global connectivity metrics. Evaluated CBF measures provide in general a good to excellent test-reliability matching or surpassing the best performing tb-fMRI and rs-fMRI metrics. This comprehensive effort allows for direct comparisons of test-retest reliability between the evaluated MRI domains and measures to aid the design of future tb-fMRI, rs-fMRI and CBF studies.

The Relationship Between Dopamine Neurotransmitter Dynamics and the Blood-Oxygen-Level-Dependent (BOLD) Signal: A Review of Pharmacological Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) is widely used in investigations of normal cognition and brain disease and in various clinical applications. Pharmacological fMRI (pharma-fMRI) is a relatively new application, which is being used to elucidate the effects and mechanisms of pharmacological modulation of brain activity. Characterizing the effects of neuropharmacological agents on regional brain activity using fMRI is challenging because drugs modulate neuronal function in a wide variety of ways, including through receptor agonist, antagonist, and neurotransmitter reuptake blocker events. Here we review current knowledge on neurotransmitter-mediated blood-oxygen-level dependent (BOLD) fMRI mechanisms as well as recently updated methodologies aimed at more fully describing the effects of neuropharmacologic agents on the BOLD signal. We limit our discussion to dopaminergic signaling as a useful lens through which to analyze and interpret neurochemical-mediated changes in the hemodynamic BOLD response. We also discuss the need for future studies that use multi-modal approaches to expand the understanding and application of pharma-fMRI.

Cerebral blood flow predicts differential neurotransmitter activity

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans.

The role of fMRI in drug development

Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.

Recent Advances in Translational Magnetic Resonance Imaging in Animal Models of Stress and Depression

Magnetic resonance imaging (MRI) is a valuable translational tool that can be used to investigate alterations in brain structure and function in both patients and animal models of disease. Regional changes in brain structure, functional connectivity, and metabolite concentrations have been reported in depressed patients, giving insight into the networks and brain regions involved, however preclinical models are less well characterized. The development of more effective treatments depends upon animal models that best translate to the human condition and animal models may be exploited to assess the molecular and cellular alterations that accompany neuroimaging changes. Recent advances in preclinical imaging have facilitated significant developments within the field, particularly relating to high resolution structural imaging and resting-state functional imaging which are emerging techniques in clinical research. This review aims to bring together the current literature on preclinical neuroimaging in animal models of stress and depression, highlighting promising avenues of research toward understanding the pathological basis of this hugely prevalent disorder.

The role of beta-arrestin2 in shaping fMRI BOLD responses to dopaminergic stimulation

RATIONALE

The dopamine D₂ receptor (D₂R) couples to inhibitory G_i/o proteins and is targeted by antipsychotic and antiparkinsonian drugs. Beta-arrestin2 binds to the intracellular regions of the agonist-occupied D₂R to terminate G protein activation and promote internalization, but also to initiate downstream signaling cascades which have been implicated in psychosis. Functional magnetic resonance imaging (fMRI) has proven valuable for measuring dopamine receptor-mediated changes in neuronal activity, and might enable beta-arrestin2 function to be studied in vivo.

OBJECTIVES

The present study examined fMRI blood oxygenation level dependent (BOLD) signal changes elicited by a dopamine agonist in wild-type (WT) and beta-arrestin2 knockout (KO) mice, to investigate whether genetic deletion of beta-arrestin2 prolongs or otherwise modifies D₂R-dependent responses.

METHODS

fMRI BOLD data were acquired on a 9.4 T system. During scans, animals received 0.2 mg/kg apomorphine, i.v. In a subset of experiments, animals were pretreated with 2 mg/kg of the D₂R antagonist, eticlopride.

RESULTS

Following apomorphine administration, BOLD signal decreases were observed in caudate/putamen of WT and KO animals. The time course of response decay in caudate/putamen was significantly slower in KO vs. WT animals. In cingulate cortex, an initial BOLD signal decrease was followed by a positive response component in WT but not in KO animals. Eticlopride pretreatment significantly reduced apomorphine-induced BOLD signal changes.

CONCLUSIONS

The prolonged striatal response decay rates in KO animals might reflect impaired D₂R desensitization, consistent with the known function of beta-arrestin2. Furthermore, the apomorphine-induced positive response component in cingulate cortex may depend on beta-arrestin2 signaling downstream of D₂R.

A novel anesthesia regime enables neurofunctional studies and imaging genetics across mouse strains

Functional magnetic resonance imaging (fMRI) has revolutionized neuroscience by opening a unique window that allows neurocircuitry function and pathological alterations to be probed non-invasively across brain disorders. Here we report a novel sustainable anesthesia procedure for small animal neuroimaging that overcomes shortcomings of anesthetics commonly used in rodent fMRI. The significantly improved preservation of cerebrovascular dynamics enhances sensitivity to neural activity changes for which it serves as a proxy in fMRI readouts. Excellent cross-species/strain applicability provides coherence among preclinical findings and is expected to improve translation to clinical fMRI investigations. The novel anesthesia procedure based on the GABAergic anesthetic etomidate was extensively validated in fMRI studies conducted in a range of genetically engineered rodent models of autism and strains commonly used for transgenic manipulations. Etomidate proved effective, yielded long-term stable physiology with basal cerebral blood flow of ~0.5 ml/g/min and full recovery. Cerebrovascular responsiveness of up to 180% was maintained as demonstrated with perfusion- and BOLD-based fMRI upon hypercapnic, pharmacological and sensory stimulation. Hence, etomidate lends itself as an anesthetic-of-choice for translational neuroimaging studies across rodent models of brain disorders.

The power of using functional fMRI on small rodents to study brain pharmacology and disease

Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal’s physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.