436 research outputs found

    A neuroimaging investigation of bipolar disorder and the neurocognitive effects of 5-HT7 antagonists

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    Bipolar disorder is a psychiatric disorder characterised by pathological mood states, but there is growing recognition of the role of cognitive impairment and dysfunction of emotional processes, which has a profound impact on quality of life. Many people with bipolar disorders exhibit brain volume impairment associated with cognitive dysfunction and an increased risk of dementia. In this thesis, I conducted a systematic review to understand the relationships between mood disorders and the 5-HT7 receptor. The 5-HT7 receptor is related to depression and anxiety, but the relationship between 5-HT7 and mania remains unclear; in addition, sleep and memory were also related to the 5-HT7 receptor. Followed by these findings, in the next two chapters, I examined the effects of 5-HT7 antagonists, using JNJ-18038683, on emotional and cognitive functioning, as well as their neural substrates. I then reported on neuroimaging investigations examining the effects of 5-HT7 antagonists on emotional processing and cognitive function in healthy volunteers to gain insight into their potential mode of action and utility for bipolar disorder. In fMRI analyses, the drug acted on 5-HT7 receptors potentially improving cognitive performance by modulating the function of the Cognitive Control Network in healthy controls. In the above-mentioned chapters, I gained a better understanding of the 5-HT7 antagonist, JNJ-18038683, and the putative promising effects for pharmacological treatments. However, the approach taken has some limitations, including a small sample size, potential participant bias, and a lack of systematic control of medication dose and duration of administration. In addition, in Chapter 5, I explored the brain basis of bipolar disorder and its links to cognitive and emotional dysfunction using a new ‘brain age’ approach. Individuals with bipolar disorder were found to have increased brain age compared to healthy controls. I hope that these findings can be applied to pharmacological treatment for individuals with bipolar disorder, ultimately allowing patients to benefit from the drug in the future

    Cellular and molecular mechanisms of inflammatory arthritis and fibromyalgia

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    In Study I, we examined the impact of the hR100E-NGF mutation on inflammatory pain and bone erosion in both female and male mice. Our findings indicate that the hR100E-NGF mutation did not affect the development of the peripheral sensory nervous system at the lumbar DRG, sciatic nerve, ankle joint, or glabrous skin. Moreover, hR100E-NGF mice displayed sensory thresholds similar to those of the hWT-NGF mice in response to mechanical, heat, or cold stimulation under normal conditions. The hR100E-NGF and hWT-NGF mice developed comparable mechanical and heat sensitivity impairments after the intra-articular injection of complete Freund’s adjuvant. Notably, the hR100E-NGF mice were insensitive to nociceptive stimulation in the deeper tissues assessed by weight bearing and gait analysis. Furthermore, mRNA analysis from the inflamed joint showed a differential sex-dependent gene expression profile between hR100E-NGF female and male mice. Finally, the hR100E-NGF female but not the male mice were protected against the CFA-bone erosion. These data collectively demonstrate that the R100E NGF mutation effectively protects against joint pain-like behaviors in both male and female mice while providing bone protection exclusively to female mice in a monoarthritis model. We propose that manipulating the signaling of NGF and its receptors in a manner similar to the R100E mutation could be a promising approach to treating chronic pain and maintaining bone health, particularly in women. Study II investigated the effects of injecting purified IgG from fibromyalgia (FM) patients and healthy controls (HC) in mice. We found that the injection of FM IgG but not IgG from healthy controls (HC) induces pressure, mechanical, and cold hypersensitivity in mice that were coupled to enhanced nociceptor responsiveness to mechanical and cold stimulation. The FM IgG-injected mice also developed impaired muscular strength and decreased locomotor activity. Moreover, FM IgG bound and stimulated satellite glial cells (SGCs) in vivo and in vitro. No FM or HC IgG accumulation was found in the brain or spinal cord of the injected mice. Our study also demonstrated that FM IgG can bind to satellite glial cells and neurons in the human DRG. In addition, we observed a significant reduction in the intraepidermal nerve fiber density in the mice 14 days after the FM IgG injection. Our results suggest that transferring FM IgG into mice can replicate some peripheral FM symptoms. This study can provide a valuable animal model for studying the peripheral physiology of FM. Our discovery could significantly advance the understanding and treatment of fibromyalgia and other related conditions. However, more research is needed to understand the cellular and molecular mechanisms involved in FM-IgG-mediated changes in mice. Study III aimed to investigate the frequency of anti-satellite glial cell (SGC) antibodies and the antibody association with the disease severity in FM patients. We used serum (Karolinska Institutet, Sweden; n=30/group) and plasma (McGill University, Canada; n=35/group) samples collected from FM patients and HCs. Our results showed a higher binding intensity of the FM IgG to SGC in vitro. Furthermore, the frequency of SGC bound to FM IgG was significantly higher than HC IgG-treated cells. These findings correlated with pain intensity and fibromyalgia impact questionnaire scores (FIQ, questionnaire was only assessed in the Karolinska cohort). Further cluster analysis separated the FM group into severe and mild groups. Additionally, we found that serum from FM patients contains IgG that binds in greater proportion to SGC in the human DRG, measured by higher signal intensity. There were no differences in the binding intensity to neuronal cell bodies or axons between FM and HC serum samples. Finally, the previous results were confirmed using an FM serum sample with high levels of anti-SGC antibodies in 5 more human DRGs. To summarize, our report indicates that levels of anti-human SGC and anti-mouse SGC antibodies are elevated in patients with FM, which are linked to a more severe form of the disease. Patient stratification based on their profile of anti-SGC antibodies might benefit from therapies aiming to decrease circulating IgG or prevent IgG binding. Our results point to the possible involvement of anti-SGC antibodies and SGCs in the severity of FM; however, more in-depth studies are necessary to elucidate the antigen or antigens expressed in the SGC that bind to the circulating anti-SGC antibodies. In Study IV, we aimed to explore the neuroimmune signature of the FM skin. We processed 16 FM and 16 HC sex-matched skin biopsies by immunohistochemistry. Using a pan-neuronal marker, we found lower intraepidermal nerve fiber density (IENFD) in the FM compared with HC skin. Moreover, the length and volume of dermal NF200+ nerve profiles were significantly elevated, but we found no changes in the length of dermal or epidermal Gap43+ nerve profiles in the FM group. Similarly, we found no changes in the total volume of CD31+ blood vessels between FM and HC skin. Our results showed that the density of non-nerve associated S100b+, CD68+, and CD163+ cells was significantly lower in the FM skin. Furthermore, the dermal CD117+FcERI+ mast cells in the dermis of FM patients were significantly increased compared with the HCs. Additionally, we found similar densities of CD207+, CD3+, or Neutrophil elastase+ cells between FM and HC skin biopsies. mRNA analysis of FM skin showed no changes in Cd68, Cd163, Cx3cr1, or FceR1 mRNA levels between FM and HC skin. In summary, this study reveals crucial dermal and epidermal changes in FM skin, particularly regarding nerve fibers and certain immune cell populations. These findings are highly relevant as they provide deeper insights into the complex interactions between the nervous and immune systems in FM. Understanding these changes could be key to developing more effective treatments for FM, focusing on both the neuropathic and immune components of the disease

    Cerebrovascular dysfunction in cerebral small vessel disease

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    INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    An overview of data integration in neuroscience with focus on Alzheimer's Disease

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    : This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer's Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease's knowledge particularly in the perspective of an early AD diagnosis

    A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

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    Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder

    Neonatal ECMO: be ready!:Navigating pharmacotherapy and vulnerability through training and monitoring

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    Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

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    This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

    How should we be using biomarkers in trials of disease modification in Parkinson’s disease?

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    The recent validation of the alpha synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson’s disease has formed the backbone for a proposed staging system for incorporation in Parkinson’s disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson’s disease patients into trials (as distinct from patients with non- Parkinson’s disease parkinsonism or non- Parkinson’s disease tremors). There remain however further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson’s disease, namely: optimising the distinction between different alpha synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; as sensitive means of confirming target engagement; and in the early prediction of longer-term clinical benefit. For example; levels of cerebrospinal fluid proteins such as the lysosomal enzyme ß-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer disease like pathology (detectable through cerebrospinal fluid levels of Amyloid Beta-42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline of Parkinson’s disease even in its later stages. The exploitation of additional biomarkers to the alpha synuclein seed amplification assay will therefore greatly add to our ability to plan trials and assess disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson’s disease. However, correlation with clinical progression does not necessarily equate to causation and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson’s disease
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