Publications by authors named "Conor Owens-Walton"

Alterations in subcortical brain regions are linked to motor and non-motor symptoms in Parkinson's disease (PD). However, associations between clinical expression and regional morphological abnormalities of the basal ganglia, thalamus, amygdala and hippocampus are not well established. We analyzed 3D T1-weighted brain MRI and clinical data from 2525 individuals with PD and 1326 controls from 22 global sources in the ENIGMA-PD consortium.

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The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences.

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Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group.

Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions.

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Article Synopsis
  • Parkinson's disease (PD) is a neurodegenerative disorder impacting over 10 million people, and researchers are exploring the effectiveness of machine learning in identifying it from brain scans.
  • Deep learning models, specifically convolutional neural networks (CNNs), have traditionally focused on T1-weighted MRI scans, but this study investigates incorporating diffusion-weighted MRI (dMRI) for detecting PD.
  • Using data from three different institutions, the research indicates that dMRI has potential as a useful input for AI-based PD classification, suggesting it could be a valuable alternative to standard anatomical images.
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Background: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated.

Objective: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group.

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Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group.

Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions.

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Article Synopsis
  • - Parkinson's disease (PD) affects over 10 million people globally, leading to interest in using machine learning to improve diagnosis through radiological scans, particularly MRI.
  • - The study evaluates deep learning models, especially convolutional neural networks (CNNs), to determine the effectiveness of diffusion-weighted MRI (dMRI) compared to traditional T1-weighted MRI for classifying PD.
  • - Results from three different cohorts suggest that incorporating dMRI data enhances the predictive capability for PD classification, highlighting its potential for AI-based detection of the disease.
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There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch.

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Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction.

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People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts.

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Article Synopsis
  • - This study investigates the brain mechanisms behind cognitive impairment and dementia in Parkinson's disease using structural and functional MRI techniques.
  • - It finds that cognitively unimpaired patients show increased connectivity in certain brain regions, while those with mild cognitive impairment have altered connectivity patterns, particularly related to the thalamus.
  • - The research suggests that dysfunction in specific brain circuits (like the basal ganglia-thalamocortical circuit) is linked to cognitive decline, with differences in brain structure observed in dementia patients compared to those without cognitive issues.
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In Huntington's disease (HD), neurodegeneration causes progressive atrophy to the striatum, cortical areas, and white matter tracts - components of corticostriatal circuitry. Such processes may affect the thalamus, a key circuit node. We investigated whether differences in dorsal thalamic morphology were detectable in HD, and whether thalamic atrophy was associated with neurocognitive, neuropsychiatric and motor dysfunction.

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Article Synopsis
  • Parkinson's disease (PD) affects 2-3% of people over 65, leading to the loss of dopaminergic neurons in a brain area called the substantia nigra, which disrupts key brain circuits.
  • The study aimed to understand how the thalamus, which is crucial for brain function but not well understood in PD, is affected in terms of its connectivity and shape compared to healthy individuals.
  • Results showed that PD patients had increased connectivity between certain thalamic areas and motor/prefrontal regions of the brain, suggesting changes in the pathways that involve the basal ganglia, which could help explain the symptoms of PD.
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We sought to investigate morphological and resting state functional connectivity changes to the striatal nuclei in Parkinson disease (PD) and examine whether changes were associated with measures of clinical function. Striatal nuclei were manually segmented on 3T-T1 weighted MRI scans of 74 PD participants and 27 control subjects, quantitatively analysed for volume, shape and also functional connectivity using functional MRI data. Bilateral caudate nuclei and putamen volumes were significantly reduced in the PD cohort compared to controls.

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