Paradigm shift in translational neuroimaging of CNS disorders

Mapping stress networks using functional magnetic resonance imaging in awake animals

The neurobiology of stress is studied through behavioral neuroscience, endocrinology, neuronal morphology and neurophysiology. There is a shift in focus toward progressive changes throughout stress paradigms and individual susceptibility to stress that requires methods that allow for longitudinal study design and study of individual differences in stress response. Functional magnetic resonance imaging (fMRI), with the advantages of noninvasiveness and a large field of view, can be used for functionally mapping brain-wide regions and circuits critical to the stress response, making it suitable for longitudinal studies and understanding individual variability of short-term and long-term consequences of stress exposure. In addition, fMRI can be applied to both animals and humans, which is highly valuable in translating findings across species and examining whether the physiology and neural circuits involved in the stress response are conserved in mammals. However, compared to human fMRI studies, there are a number of factors that are essential for the success of fMRI studies in animals. This review discussed the use of fMRI in animal studies of stress. It reviewed advantages, challenges and technical considerations of the animal fMRI methodology as well as recent literature of stress studies using fMRI in animals. It also highlighted the development of combining fMRI with other methods and the future potential of fMRI in animal studies of stress. We conclude that animal fMRI studies, with their flexibility, low cost and short time frame compared to human studies, are crucial to advancing our understanding of the neurobiology of stress.

Giving priority to preclinical pain measures resistant to existing drugs for developing innovative analgesics

Preclinical Research & Development Chronic pain is a major health and socioeconomic burden because of its high prevalence, negative influence on patients' physical and/or emotional conditions, and huge costs to society. The responses of chronic pain patients to analgesic therapies vary substantially from individual to individual, and no more than a minority of chronic pain patients with various etiologies such as neuropathy and inflammation are, in fact, successfully relieved by existing drugs including opioid analgesics, nonopioid analgesics, antiepileptics, and antidepressants. The large primary unmet medical need would therefore be the patient domain that does not respond well to existing drugs. Accordingly, the expected profile for innovative analgesics would not be efficacy in the responder patient domain, but significant efficacy in patients with existing drug-resistant chronic pain. Meanwhile, the current gold standard in preclinical pain measures for the screening of analgesic candidates is existing drug-sensitive pain measures in animal models of chronic pain. Analgesic candidates screened using such preclinical pain measures during the last decades have been far from fulfilling the expected profile for innovative analgesics. Given that it is unlikely that such existing drug-sensitive pain measures are the best approach to developing innovative analgesics, one of the other approaches would be giving priority to existing drug-resistant pain measures in preclinical research. This review introduces potentially applicable existing drug-resistant pain measures published so far and suggests that the use of them would lead to the development of innovative analgesics.

Mapping the central effects of (±)-ketamine and traxoprodil using pharmacological magnetic resonance imaging in awake rats

Major depressive disorder is a leading cause of disability globally. Improvements in the efficacy of antidepressant therapy are needed as a high proportion (>40%) of individuals with major depressive disorder fail to respond adequately to current treatments. The non-selective N-methyl-D-aspartate receptor channel blocker, (±)-ketamine, has been reported to produce a rapid and long-lasting antidepressant response in treatment-resistant major depressive disorder patients, which provides a unique opportunity for investigation of mechanisms that mediate its therapeutic effect. Efforts have also focused on the development of selective N-methyl-D-aspartate receptor subtype 2B antagonists which may retain antidepressant activity but have lower potential for dissociative/psychotomimetic effects. In the present study, we examined the central nervous system effects of acute, intravenous administration of (±)-ketamine or the N-methyl-D-aspartate receptor subtype 2B antagonist, traxoprodil, in awake rats using pharmacological magnetic resonance imaging. The study contained five treatment groups: vehicle, 3 mg/kg (±)-ketamine, and three doses of traxoprodil (0.3 mg/kg, 5 mg/kg, and 15 mg/kg). Non-linear model fitting was performed on the temporal hemodynamic pharmacological magnetic resonance imaging data to generate brain activation maps as well as regional responses based on blood oxygen level dependent signal changes for group analysis. Traxoprodil at 5 mg/kg and 15 mg/kg produced a dose-dependent pharmacological magnetic resonance imaging signal in rat forebrain regions with both doses achieving >80% N-methyl-D-aspartate receptor subtype 2B occupancy determined by ex vivo [³H]Ro 25-6981 binding. The middle dose of traxoprodil (5 mg/kg) generated region-specific activations in medial prefrontal cortex, ventral orbital cortex, and anterior cingulate cortex whereas the high dose (15 mg/kg) produced a widespread pharmacological magnetic resonance imaging response in both cortical and subcortical brain regions which was similar to that produced by (±)-ketamine (3 mg/kg, intravenous).

9.4 T small animal MRI using clinical components for direct translational studies

BACKGROUND

Magnetic resonance is a major preclinical and clinical imaging modality ideally suited for longitudinal studies, e.g. in pharmacological developments. The lack of a proven platform that maintains an identical imaging protocol between preclinical and clinical platforms is solved with the construction of an animal scanner based on clinical hard- and software.

METHODS

A small animal magnet and gradient system were connected to a clinical MR system. Several hardware components were either modified or built in-house to achieve compatibility. The clinical software was modified to account for the different field-of-view of a preclinical MR system. The established scanner was evaluated using clinical QA protocols, and platform compatibility for translational research was verified against clinical scanners of different field strength.

RESULTS

The constructed animal scanner operates with the majority of clinical imaging sequences. Translational research is greatly facilitated as protocols can be shared between preclinical and clinical platforms. Hence, when maintaining sequences parameters, maximum similarity between pulses played out on a human or an animal system is maintained.

CONCLUSION

Coupling of a small animal magnet with a clinical MR system is a flexible, easy to use way to establish and advance translational imaging capability. It provides cost and labor efficient translational capability as no tedious sequence reprogramming between moieties is required and cross-platform compatibility of sequences facilitates multi-center studies.

The need for fundamental reforms in the pain research field to develop innovative drugs

INTRODUCTION

Chronic pain is a major healthcare issue owing to its high prevalence, significant physical and emotional burden on the patients, and huge financial burden on the society. The efficacy of currently available medications is unsatisfactory owing to their limited effect size and the low responder rate (less than 50%). Thus, there is a large unmet need for innovative therapies for chronic pain. Areas covered: In this review, the author points out the need for fundamental reforms in pain research. For the last several decades, drug discovery research has extensively focused on designing new therapies using animal models of chronic pain. It has, however, made insufficient progress with respect to the launch of innovative analgesic drugs, because the translation from preclinical to clinical stages has not been satisfactory. Thus, the strategies for developing innovative analgesic drugs are discussed. Expert opinion: Points to be considered in the discovery of drugs for pain relief include: (1) the exclusion of bias incorporation and the alignment of clinical and preclinical endpoints in the assessment of analgesic efficacy; (2) the understanding of primary unmet needs; (3) the assessment of new therapies by biomarker-prioritized frameworks, and (4) the stratification of chronic pain sufferers.

Progesterone neuroprotection: The background of clinical trial failure

Since the first pioneering studies in the 1990s, a large number of experimental animal studies have demonstrated the neuroprotective efficacy of progesterone for brain disorders, including traumatic brain injury (TBI). In addition, this steroid has major assets: it easily crosses the blood-brain-barrier, rapidly diffuses throughout the brain and exerts multiple beneficial effects by acting on many molecular and cellular targets. Moreover, progesterone therapies are well tolerated. Notably, increased brain levels of progesterone are part of endogenous neuroprotective responses to injury. The hormone thus emerged as a particularly promising protective candidate for TBI and stroke patients. The positive outcomes of small Phase 2 trials aimed at testing the safety and potential protective efficacy of progesterone in TBI patients then provided support and guidance for two large, multicenter, randomized and placebo-controlled Phase 3 trials, with more than 2000 TBI patients enrolled. The negative outcomes of both trials, named ProTECT III and SyNAPSE, came as a big disappointment. If these trials were successful, progesterone would have become the first efficient neuroprotective drug for brain-injured patients. Thus, progesterone has joined the numerous neuroprotective candidates that have failed in clinical trials. The aim of this review is a reappraisal of the preclinical animal studies, which provided the proof of concept for the clinical trials, and we critically examine the design of the clinical studies. We made efforts to present a balanced view of the strengths and limitations of the translational studies and of some serious issues with the clinical trials. We place particular emphasis on the translational value of animal studies and the relevance of TBI biomarkers. The probability of failure of ProTECT III and SyNAPSE was very high, and we present them within the broader context of other unsuccessful trials.

GPU accelerated dynamic functional connectivity analysis for functional MRI data

Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses.

Preserved modular network organization in the sedated rat brain

Translation of resting-state functional connectivity (FC) magnetic resonance imaging (rs-fMRI) applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs) in rodent models under normal and various pathophysiological conditions. However, to date, little is known about the organizational architecture of FCNs in rodents in a mentally healthy state, although an understanding of the same is of paramount importance before investigating networks under compromised states. In this study, we characterized the properties of resting-state FCN in an extensive number of Sprague-Dawley rats (n = 40) under medetomidine sedation by evaluating its modular organization and centrality of brain regions and tested for reproducibility. Fully-connected large-scale complex networks of positively and negatively weighted connections were constructed based on Pearson partial correlation analysis between the time courses of 36 brain regions encompassing almost the entire brain. Applying recently proposed complex network analysis measures, we show that the rat FCN exhibits a modular architecture, comprising six modules with a high between subject reproducibility. In addition, we identified network hubs with strong connections to diverse brain regions. Overall our results obtained under a straight medetomidine protocol show for the first time that the community structure of the rat brain is preserved under pharmacologically induced sedation with a network modularity contrasting from the one reported for deep anesthesia but closely resembles the organization described for the rat in conscious state.

Neuroimaging biomarkers for early drug development in schizophrenia

Given the relative inability of currently available antipsychotic treatments to adequately provide sustained recovery and improve quality of life for patients with schizophrenia, new treatment strategies are urgently needed. One way to improve the therapeutic development process may be an increased use of biomarkers in early clinical trials. Reliable biomarkers that reflect aspects of disease pathophysiology can be used to determine if potential treatment strategies are engaging their desired biological targets. This review evaluates three potential neuroimaging biomarkers: hippocampal hyperactivity, gamma-band deficits, and default network abnormalities. These deficits have been widely replicated in the illness, correlate with measures of positive symptoms, are consistent with models of disease pathology, and have shown initial promise as biomarkers of biological response in early studies of potential treatment strategies. Two key features of these deficits, and a guiding rationale for the focus of this review, are that the deficits are not dependent upon patients' performance of specific cognitive tasks and they have analogues in animal models of schizophrenia, greatly increasing their appeal for use as biomarkers. Using neuroimaging biomarkers such as those proposed here to establish early in the therapeutic development process if treatment strategies are having their intended biological effect in humans may facilitate development of new treatments for schizophrenia.

Functional network connectivity of pain-related resting state networks in somatoform pain disorder: an exploratory fMRI study

BACKGROUND

Without stimulation, the human brain spontaneously produces highly organized, low-frequency fluctuations of neural activity in intrinsic connectivity networks (ICNs). Furthermore, without adequate explanatory nociceptive input, patients with somatoform pain disorder experience pain symptoms, thus implicating a central dysregulation of pain homeostasis. The present study aimed to test whether interactions among pain-related ICNs, such as the default mode network (DMN), cingular-insular network (CIN) and sensorimotor network (SMN), are altered in somatoform pain during resting conditions.

METHODS

Patients with somatoform pain disorder and healthy controls underwent resting functional magnetic resonance imaging that lasted 370 seconds. Using a data-driven approach, the ICNs were isolated, and the functional network connectivity (FNC) was computed.

RESULTS

Twenty-one patients and 19 controls enrolled in the study. Significant FNC (p < 0.05, corrected for false discovery rate) was detected between the CIN and SMN/anterior DMN, the anterior DMN and posterior DMN/SMN, and the posterior DMN and SMN. Interestingly, no group differences in FNC were detected.

LIMITATIONS

The most important limitation of this study was the relatively short resting state paradigm.

CONCLUSION

To our knowledge, our results demonstrated for the first time the resting FNC among pain-related ICNs. However, our results suggest that FNC signatures alone are not able to characterize the putative central dysfunction underpinning somatoform pain disorder.

The clinical value of large neuroimaging data sets in Alzheimer's disease

Rapid advances in neuroimaging and cyberinfrastructure technologies have brought explosive growth in the Web-based warehousing, availability, and accessibility of imaging data on a variety of neurodegenerative and neuropsychiatric disorders and conditions. There has been a prolific development and emergence of complex computational infrastructures that serve as repositories of databases and provide critical functionalities such as sophisticated image analysis algorithm pipelines and powerful three-dimensional visualization and statistical tools. The statistical and operational advantages of collaborative, distributed team science in the form of multisite consortia push this approach in a diverse range of population-based investigations.

Xanomeline modulation of the blood oxygenation level-dependent signal in awake rats: development of pharmacological magnetic resonance imaging as a translatable pharmacodynamic biomarker for central activity and dose selection

In vivo translational imaging techniques, such as positron emission tomography and single-photon emission-computed tomography, are the only ways to adequately determine that a drug engages its target. Unfortunately, there are far more experimental mechanisms being tested in the clinic than there are radioligands, impeding the use of this risk-mitigating approach in modern drug discovery and development. Pharmacological magnetic resonance imaging (phMRI) offers an approach for developing new biomarkers with the potential to determine central activity and dose selection in animals and humans. Using phMRI, we characterized the effects of xanomeline on ketamine-induced activation on blood oxygen level-dependent (BOLD) signal. In the present studies, xanomeline alone dose-dependently increased the BOLD signal across several regions of interest, including association and motor and sensory cortical regions. It is noteworthy that xanomeline dose-dependently attenuated ketamine-induced brain activation patterns, effects that were antagonized by atropine. In conclusion, the muscarinic 1/4-preferring receptor agonist xanomeline suppressed the effects of the N-methyl-D-aspartate channel blocker ketamine in a number of brain regions, including the association cortex, motor cortex, and primary sensory cortices. The region-specific brain activation observed in this ketamine challenge phMRI study may provide a method of confirming central activity and dose selection for novel antipsychotic drugs in early clinical trials for schizophrenia, if the data obtained in animals can be recapitulated in humans.

Can Functional Magnetic Resonance Imaging Improve Success Rates in CNS Drug Discovery?

INTRODUCTION: The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a 'language of translation' affords the opportunity to improve the objectivity of decision-making. AREAS COVERED: This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development. EXPERT OPINION: The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification.

Developing predictive CSF biomarkers-a challenge critical to success in Alzheimer's disease and neuropsychiatric translational medicine

The need to develop effective treatments for Alzheimer's disease has been confounded by repeated clinical failures where promising new chemical entities that have been extensively characterized in preclinical models of Alzheimer's disease have failed to show efficacy in the human disease state. This has been attributed to: the selection of drug targets that have yet to be shown as causal to the disease as distinct from being the result of the disease process, a lack of congruence in the animal models of Alzheimer's disease, wild-type and transgenic, to the human disease, and the enrollment of patients in proof of concept clinical trials who are at too advanced a stage of the disease to respond to any therapeutic. The development of validated biomarkers that can be used for disease diagnosis and progression is anticipated to improve patient enrollment in clinical trials, to develop new animal models and to identify new disease targets for drug discovery. The present review assesses the status of current efforts in developing CSF biomarkers for Alzheimer's disease and briefly discusses the status of CSF biomarker efforts in schizophrenia, depression, Parkinson's disease and multiple sclerosis.