Biology subjects

Griffanti, L.

Publications and source records attributed to Griffanti, L..

14 recordsLinked to original sources

bioRxiv2026

The ENIGMA-PD-WML Pipeline: A Containerized, User-Friendly Approach for Accurate, Standardized Segmentation of White Matter Lesions in Multi-Site MRI Data

Understanding vascular contributions to disease is a major unmet need. White matter lesions (WML) are an accepted imaging marker of cerebral small vessel disease, giving insights into its related pathologies. A unified approach for WML analyses in large multi-site data is lacking despite the need for pooling of data to overcome the limitations of often small heterogenous MRI studies which make subtyping and identifying patterns within disease groups difficult. Our ENIGMA-PD-WML pipeline is an open-source containerized pipeline containing all the code and packages required for pre-processing, processing and post-processing of T1-weighted and FLAIR data, outputting accurate and reproducible binary WML maps using a UNet approach. The pipeline provides a standardized image analysis approach for WML and outputs data in both native and MNI space to allow for sharing and pooling of data from multiple sites for large-data analysis. In addition to a reliable standardized approach for WML segmentation, key priorities when developing the pipeline included: usability, i.e., requiring minimal manual input and technical expertise to use, and suitability to run on various MRI scanners and acquisition parameters as is common in multi-site data. This paper describes the pipeline in detail, with rationale for each step, providing transparency and facilitating its usage to overcome reproducibility issues in large-scale WML analyses.

neuroscience↗

bioRxiv2026

Comparison of Automated White Matter Lesion Segmentation Approaches for Use in Large, Multi-Site Data Analyses in Parkinson's Disease

BackgroundParkinsons disease (PD) is the second most common neurodegenerative disorder. PD currently lacks effective disease-modifying treatments, likely due to its diverse clinical features and underlying neuropathology. The vascular role in PD is emerging, with vascular mechanisms increasingly implicated, yet the literature remains conflicted, motivating large-data analyses with greater statistical power. White matter lesions (WML) are an accepted imaging marker of small vessel disease. Accurate automated WML segmentation techniques are crucial for large-scale studies in PD due to the impracticality of manual segmentation for extensive datasets and to ensure consistency. Evaluation of the optimum approach in PD for large-scale analysis is lacking. This study aimed to evaluate various automated WML segmentation algorithms to determine the most accurate and reliable method, among those selected, for assessing WML for multi-site large data analysis in PD. MethodsWe assessed whole-brain volumetric T1-weighted and FLAIR images from 201 PD patients (mean age, 66.6 {+/-} 7.86 years) and 64 healthy controls (HC; mean age, 66.3 {+/-} 8.67) across three datasets: the Parkinsons Progression Markers Initiative (PPMI), the University of Pennsylvania (UPenn) and the Montreal Neurological Institute Biobank: Clinical Biological Imaging and Genetic Repository (C-BIG). The sample included different scanners, imaging parameters and lesion loads, as would be expected for multi-site data. WML were manually segmented to provide the gold standard, and four freely available automated algorithms were evaluated: FSLs BIANCA, FreeSurfer, SPMs LST-LPA and U-Net-pgs using the performance metrics: Dice score, Hausdorff distance, recall, precision, F1 score, log absolute volume difference (LOGAVD) and intraclass correlation coefficient (ICC). Subgroup analyses were performed based on lesion load and lobar regions. The associations of data from these automated approaches with age, and with Fazekas and Wahlund visual rating scales, were assessed through partial correlation analysis. ResultsU-Net-pgs performed best overall, with the highest Dice score (PD: 0.46 {+/-} 0.21; HC: 0.39 {+/-} 0.21), recall (PD: 0.76 {+/-} 0.25; HC: 0.62 {+/-} 0.31), precision (PD: 0.49 {+/-} 0.25; HC: 0.63 {+/-} 0.27), F1 score (PD: 0.54 {+/-} 0.22; HC: 0.56 {+/-} 0.22) and ICC (PD: 0.965; HC: 0.967) and lowest Hausdorff distance (PD: 8.89 {+/-} 3.96; HC: 6.33 {+/-} 2.91). U-Net-pgs achieved the lowest LOGAVD in the PD group (0.31 {+/-} 0.31) whereas BIANCA-LOO with a threshold of 0.9 was lowest in HC (0.27 {+/-} 0.30). U-Net also showed superior performances in all lesion loads for PD and overall across various brain regions in both PD and HC. ConclusionOverall, U-Net-pgs emerged as the best performing automated method, of those we evaluated, for WML segmentation in PD and HC within a dataset collected with various scanner and image acquisition parameters. U-Net-pgs consistently outperformed other automated approaches across lesion loads and brain regions, for most metrics. The accuracy and reliability of U-Net-pgs make it a promising tool for large-scale analyses, facilitating future research investigating WML in PD.

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bioRxiv2026

Tackling Bias in Cortical Thickness Estimation in UK Biobank Using Harmonisation Approaches

Big neuroimaging data enable researchers to study subtle structural and functional brain changes and relationships between brain characteristics and genetics, lifestyle, and disease factors. However, substantial effort is needed to minimise technical, non-biological differences between data batches to avoid incorrect inferences. In this study, we address a previously identified bias in UK Biobank FreeSurfer IDPs derived from only the T1 image compared to those using both T1 and T2-FLAIR by treating the bias as a batch effect and using harmonisation approaches. We investigate and characterise this bias through direct within-participant comparison at the image and IDP level, comparing the results with those seen in the wider UKB sample. We then assess different methods of addressing the effect of missing T2-FLAIR, starting from simple linear regression before moving to ComBat, a widely used harmonisation method, testing different approaches for applying ComBat and showing its similarity to simple linear regression. Finally, we examine how ComBat estimates vary with batch and sample size. Our results show clear benefits in using both T1 and T2-FLAIR data in FreeSurfer, as opposed to just the T1, which is more common, with the pial surface fitting being less likely to fail and showing greater biologically plausible inter-subject variability. This is particularly important for cortical thickness IDPs, where T2-FLAIR omission leads to reduced true variability and systematic underestimation, as shown through within-participant repeat testing. We demonstrate that ComBat can address this bias, with its standard use (i.e., applied separately on different IDP categories) showing the best improvement in cortical thickness measures where the bias is strongest, and we find that it is important not to pool ComBat priors across different classes of IDPs. Our proposed version of ComBat with a reference batch (i.e., estimating mean and variance only from data with T2-FLAIR available) performed best in recovering both mean and variance differences between batches across different IDP classes and offers a promising approach for cases where a reference batch is clearly identifiable. While ComBat reliably corrects mean (additive) batch effects with relatively small sample sizes ({approx}30 subjects per batch), we show that its variance (multiplicative) correction is substantially less stable, requiring much larger sample sizes and becoming unreliable when batches are small or imbalanced, or when there is a large variance difference between them.

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bioRxiv2022

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 nonlinear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1th, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for nonlinear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both, bias and variance. Furthermore, extreme caution is needed when attempting to extrapolate beyond the age range included in the source dataset. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

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bioRxiv2021

Identifying microstructural changes in diffusion MRI; How to break parameter degeneracy

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

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bioRxiv2021

Intrinsic Network Activity Reflects the Ongoing Experience of Chronic Pain

Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. However, such studies have not accounted for variable time courses of network activity and subjective pain experience. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. Using group independent component analysis and dual-regression, we extracted the time courses of 100 independent components separately for chronic back pain and chronic migraine. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the processes that matter for each patient, which are phases of relatively low pain and more straining phases of relatively high pain. The present design of ongoing assessment of the endogenous pain can be a powerful and promising tool to assess the signature of a patients endogenous pain encoding over weeks and months.

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bioRxiv2021

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.

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bioRxiv2020

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data. HIGHLIGHTSO_LIWe harmonised measures of WMHs across two studies on healthy ageing C_LIO_LISpecific pre-processing strategies can increase comparability across studies C_LIO_LIModelling of biological differences is crucial to provide calibrated measures C_LI

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bioRxiv2020

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.

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bioRxiv2020

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

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bioRxiv2019

Common genetic variation indicates separate etiologies for periventricular and deep white matter hyperintensities

We conducted a genome-wide association meta-analysis of two ischemic white matter disease subtypes in the brain, periventricular and deep white matter hyperintensities (PVWMH and DWMH). In 26,654 participants, we found 10 independent genome-wide significant loci only associated with PVWMH, four of which have not been described previously for total WMH burden (16q24.2, 17q21.31, 10q23.1, 7q36.1). Additionally, in both PVWMH and DWMH we observed the previous association of the 17q25.1 locus with total WMH. We found that both phenotypes have shared but also distinct genetic architectures, consistent with both different underlying and related pathophysiology. PVWMH had more extensive genetic overlap with small vessel ischemic stroke, and unique associations with several loci implicated in ischemic stroke. DWMH were characterized by associations with loci previously implicated in vascular as well as astrocytic and neuronal function. Our study confirms the utility of these phenotypes and identifies new candidate genes associated only with PVWMH.

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bioRxiv2018

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions.\n\nIn this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort.\n\nWe observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

neuroscience↗

bioRxiv2018

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.\n\nIn this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.\n\nWe tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.\n\nOn simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 x 10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.

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bioRxiv2017

Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

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