Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson's Disease Through Longitudinal Data-Driven Clustering.

Although primarily characterised as a motor disorder, Parkinson's Disease (PD) also presents with non-motor symptoms, including cognitive decline and affective dysfunction, which are major predictors of quality of life and mortality for individuals. However, factors associated with these non-motor symptom trajectories remain under-characterised. This study aimed to investigate predictors of cognitive and affective function over a 5-year follow-up period using data from the Progressive Parkinson's Marker Initiative.

Functional pelvic tilt frequently differs from the anterior pelvic plane in non-arthritic hip disorders : implications for 3D motion analysis.

Aims: Hip preservation surgeons are increasingly using commercially available 3D motion analysis software to investigate areas of impingement and quantify femoral head coverage. Variations in functional pelvic tilt will affect the position of the acetabular rim and projected femoral head coverage, but currently the majority of available software standardizes sagittal rotation to the anterior pelvic plane (APP). The study hypothesis was that the APP does not correlate well with patient-specific pelvic position.

Durom hip resurfacing at 15 years : predictive factors for failure.

Aims: A range of metal-on-metal hip resurfacing (MoM-HR) implants have shown good functional outcomes, but some have been associated with adverse reactions to metal debris (ARMD) and early failure, requiring regular follow-up and monitoring of the blood metal ion levels. The aim of this study was to report the minimum 15-year survival of the Durom hip resurfacing system (Zimmer Biomet, USA), the functional outcome, and factors which were predictive of failure.

Methods: A consecutive series of patients undergoing Durom MoM-HR at a single centre between January 2000 and December 2008 were included.

Deep-Diffeomorphic Networks for Conditional Brain Templates.

Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g.

A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation.

Despite its great potential for studying the living brain, magnetic resonance imaging (MRI) can be often limited by nuisance non-biological factors, such as hardware/software differences between scanners, which can interfere with biological variability. This lack of standardisation or harmonisation between scanners hinders reproducibility and quantifiability of MRI. Towards addressing this challenge, we present one of the most comprehensive MRI harmonisation resources, based on a travelling heads paradigm; healthy volunteers scanned repeatedly across different scanners.

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI.

Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning-based and hippocampal subfield segmentation methods within a single investigation.

Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI.

Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.

niimath and fslmaths: replication as a method to enhance popular neuroimaging tools.

Neuroimaging involves the acquisition of extensive 3D images and 4D time series data to gain insights into brain structure and function. The analysis of such data necessitates both spatial and temporal processing. In this context, "fslmaths" has established itself as a foundational software tool within our field, facilitating domain-specific image processing.

Multimodal nonlinear correlates of behavioural symptoms in frontotemporal dementia.

Studies exploring the brain correlates of behavioral symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioral symptoms. We analyzed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behavior Scale, and Neurobehavioral Rating Scale.

Anatomically plausible segmentations: Explicitly preserving topology through prior deformations.

Since the rise of deep learning, new medical segmentation methods have rapidly been proposed with extremely promising results, often reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection errors are often revealed, such as topological mistakes (e.g.

Predictions for functional outcome and mortality in acute ischaemic stroke following successful endovascular thrombectomy.

Background: Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population.

Methods: The study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period.

Cardiac function may be compromised in patients with elevated blood cobalt levels secondary to metal-on-metal hip implants.

Aims: Elevated blood cobalt levels secondary to metal-on-metal (MoM) hip arthroplasties are a suggested risk factor for developing cardiovascular complications including cardiomyopathy. Clinical studies assessing patients with MoM hips using left ventricular ejection fraction (LVEF) have found conflicting evidence of cobalt-induced cardiomyopathy. Global longitudinal strain (GLS) is an echocardiography measurement known to be more sensitive than LVEF when diagnosing early cardiomyopathies.

Acetabular retroversion: functional or anatomical?

Aims: The aim of this study is to evaluate whether acetabular retroversion (AR) represents a structural anatomical abnormality of the pelvis or is a functional phenomenon of pelvic positioning in the sagittal plane, and to what extent the changes that result from patient-specific functional position affect the extent of AR.

Methods: A comparative radiological study of 19 patients (38 hips) with AR were compared with a control group of 30 asymptomatic patients (60 hips). CT scans were corrected for rotation in the axial and coronal planes, and the sagittal plane was then aligned to the anterior pelvic plane.

Subthalamic nucleus shows opposite functional connectivity pattern in Huntington's and Parkinson's disease.

Huntington's and Parkinson's disease are two movement disorders representing mainly opposite states of the basal ganglia inhibitory function. Despite being an integral part of the cortico-subcortico-cortical circuitry, the subthalamic nucleus function has been studied at the level of detail required to isolate its signal only through invasive studies in Huntington's and Parkinson's disease. Here, we tested whether the subthalamic nucleus exhibited opposite functional signatures in early Huntington's and Parkinson's disease.

A resource for development and comparison of multimodal brain 3 T MRI harmonisation approaches.

Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility, and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options, can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI.

Multimodal nonlinear correlates of behavioural symptoms in frontotemporal dementia.

Background: Studies exploring the brain correlates of behavioural symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioural symptoms.

Methods: We analysed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behaviour Scale, and Neurobehavioral Rating Scale.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols.

Differential Structural Brain Changes Between Responders and Nonresponders After Physical Exercise Therapy for Chronic Nonspecific Neck Pain.

Objectives: Physical exercise therapy is effective for some people with chronic nonspecific neck pain but not for others. Differences in exercise-induced pain-modulatory responses are likely driven by brain changes. We investigated structural brain differences at baseline and changes after an exercise intervention.

A comparison of lesion mapping analyses based on CT versus MR imaging in stroke.

It is commonly asserted that MRI-derived lesion masks outperform CT-derived lesion masks in lesion-mapping analysis. However, no quantitative analysis has been conducted to support or refute this claim. This study reports an objective comparison of lesion-mapping analyses based on CT- and MRI-derived lesion masks to clarify how input imaging type may ultimately impact analysis results.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria.

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data.

Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data.

Background: Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data.