751 research outputs found

    Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

    Get PDF
    Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

    Development and validation of novel and quantitative MRI methods for cancer evaluation

    Get PDF
    Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)

    Cerebrovascular dysfunction in cerebral small vessel disease

    Get PDF
    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

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

    Get PDF
    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

    Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles

    Get PDF
    The 3Rs principles—reduction, refinement, replacement—are at the core of preclinical research within drug discovery, which still relies to a great extent on the availability of models of disease in animals. Minimizing their distress, reducing their number as well as searching for means to replace them in experimental studies are constant objectives in this area. Due to its non-invasive character in vivo imaging supports these efforts by enabling repeated longitudinal assessments in each animal which serves as its own control, thereby enabling to reduce considerably the animal utilization in the experiments. The repetitive monitoring of pathology progression and the effects of therapy becomes feasible by assessment of quantitative biomarkers. Moreover, imaging has translational prospects by facilitating the comparison of studies performed in small rodents and humans. Also, learnings from the clinic may be potentially back-translated to preclinical settings and therefore contribute to refining animal investigations. By concentrating on activities around the application of magnetic resonance imaging (MRI) and ultrasound elastography to small rodent models of disease, we aim to illustrate how in vivo imaging contributes primarily to reduction and refinement in the context of pharmacological research

    On harmonisation of brain MRI data across scanners and sites

    Get PDF
    Magnetic resonance imaging (MRI) of the brain has revolutionised neuroscience by opening unique opportunities for studying unknown aspects of brain organisation, function and pathology-induced dysfunction. Despite the huge potential, MRI measures can be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance non-biological factors, such as hardware, software, calibration differences between scanners and post-processing options can contribute or drive trends in neuroimaging features to an extent that interferes with biological variability and obstructs scientific explorations and clinical applications. Such lack of consistency, or harmonisation across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. This thesis contributes to better understanding and addressing it. We specifically build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3.0T scanners from all the 3 major vendors and using 5 imaging modalities. We use this dataset to explore the between-scanner variability of hundreds of imaging-extracted features and compare these to within-scanner (within-subject) variability and biological (between-subject) variability. We identify subsets of features that are/are not reliable across scanners and use our resource as a testbed to enable new investigations which until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline gold standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent and reproducible and also set references for future studies in this field

    Neural Correlates of Formal Thought Disorder Dimensions in Psychosis.

    Get PDF
    BACKGROUND AND HYPOTHESIS Formal thought disorder (FTD) is a core symptom of psychosis, but its neural correlates remain poorly understood. This study tested whether four FTD dimensions differ in their association with brain perfusion and brain structure. STUDY DESIGN This cross-sectional study investigated 110 patients with schizophrenia spectrum disorders using 3T magnetic resonance imaging (MRI). The Thought and Language Disorder scale (TALD) was utilized, which comprises four subscales: Objective Positive (OP), Objective Negative (ON), Subjective Positive (SP), and Subjective Negative (SN). Resting-state cerebral blood flow (rsCBF), cortical thickness (CortTh), gray matter volume (GMV), and diffusion MRI tractography were tested for associations with TALD subscales controlling for age, medication, total intracranial volume, and for variance of the 3 other TALD subscales. STUDY RESULTS Following Bonferroni correction, the FTD dimensions presented distinct neural correlates. OP scores were associated with increased rsCBF and increased GMV in the right cerebellum lingual gyrus. Higher SP scores were linked to increased GMV in bilateral prefrontal cortex. In contrast, ON was associated with increased GMV in the right premotor cortex. At more liberal statistical thresholds, higher SP was associated with increased CortTh in the right inferior frontal gyrus, whereas SN scores were linked to decreased GMV in the right prefrontal lobe, the left inferior temporal gyrus, and the left supplementary motor area. Unadjusted analyses mostly corroborated these findings. CONCLUSION These findings stress the heterogeneity in FTD, suggesting distinct neural patterns for specific FTD experiences. In sum, FTD in psychosis may require distinct treatment strategies and further mechanistic investigations on single-item levels

    Interrogating autism from a multidimensional perspective: an integrative framework.

    Get PDF
    Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully elucidated by scientists. At present, the diagnosis of ASD typically involves a set of gold-standard diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS), the Autism Diagnostic Interview-Revised (ADI-R), and the more cost-effective Social Responsive Scale (SRS). Administering these diagnostic tests, which involve assessing communication and behavioral patterns, along with obtaining a clinical history, requires the expertise of a team of qualified clinicians. This process is time-consuming, effortful, and involves a degree of subjectivity due to the reliance on clinical judgment. Aside from conventional observational assessments, recent developments in neuroimaging and machine learning offer a fast and objective alternative for diagnosing ASD using brain imaging. This comprehensive work explores the use of different imaging modalvities, namely structural MRI (sMRI) and resting-state functional MRI (rs-fMRI), to investigate their potential for autism diagnosis. The proposed study aims to offer a new approach and perspective in comprehending ASD as a multidimensional problem, within a behavioral space that is defined by one of the available ASD diagnostic tools. This dissertation introduces a thorough investigation of the utilization of feature engineering tools to extract distinctive insights from various brain imaging modalities, including the application of novel feature representations. Additionally, the use of a machine learning framework to aid in the precise classification of individuals with autism is also explored in detail. This extensive research, which draws upon large publicly available datasets, sheds light on the influence of various decisions made throughout the pipeline on diagnostic accuracy. Furthermore, it identifies brain regions that may be impacted and contribute to an autism diagnosis. The attainment of high global state-of-the-art cross-validated, and hold-out set accuracy validates the advantages of feature representation and engineering in extracting valuable information, as well as the potential benefits of employing neuroimaging for autism diagnosis. Furthermore, a suggested diagnostic report has been put forth to assist physicians in mapping diagnoses to underlying neuroimaging markers. This approach could enable an earlier, automated, and more objective personalized diagnosis
    • …
    corecore