ORCID Profile
0000-0002-4819-548X
Current Organisation
KU Leuven
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Publisher: Wiley
Date: 21-08-2019
DOI: 10.1002/HBM.24753
Publisher: Springer Science and Business Media LLC
Date: 05-04-2021
Publisher: Cold Spring Harbor Laboratory
Date: 19-10-2023
Publisher: Cold Spring Harbor Laboratory
Date: 04-03-2022
DOI: 10.1101/2022.03.02.22271654
Abstract: Graph theoretical analysis of the structural connectome has been employed successfully to characterise brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalised connectomics approach that examines structural brain alterations in six chronic patients with moderate-to-severe TBI who underwent anatomical and diffusion magnetic resonance imaging (MRI). We generated in idualised profiles of lesion characteristics and network measures (including personalised graph metric ‘GraphMe’ plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N=12) to assess brain damage qualitatively and quantitatively at the in idual level. Our findings revealed clinically significant alterations of brain networks with high variability between patients. Our profiling can be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalised rehabilitation protocols based on their unique lesion load and connectome.
Publisher: Cold Spring Harbor Laboratory
Date: 14-10-2021
DOI: 10.1101/2021.10.13.464139
Abstract: Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an ex le, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals. (Top) shows the FWT pipeline for both CSTs, AF, and motor CC bundles. (Left to right) show the required input structural parcellation maps and a priori atlases for FWT and the resulting virtual dissection include/exclude VOIs. FWT provides two approaches to virtual dissection: (1) is a bundle-specific approach where streamlines are only seeded for the bundle of interest, (2) is a whole brain tractography followed by streamlines segmentation, (top right) shows output tractograms. (Middle) Group-averaged T1 and fODF images are generated from the HCP test-retest data, and FWT is applied to generate the HCP-atlas using the bundle-specific approach (1*). FWT’s whole brain tracking and segmentation approach (2*) was applied to the HCP and MASSIVE dataset (right and left) and conducted model-based, and pair-wise similarity analyses and generated voxel-wise cumulative maps per bundle. FWT= Fun With Tracts, FS= FreeSurfer, MSBP= MultiScaleBrainParcellator, PD25= NIST Parkinson’s histological, JHU= John’s Hopkins university, Juelich= Juelich university histological atlas, AC/PC= anterior commissure osterior commissure) UKBB= UK Biobank, SUIT (spatially unbiased cerebellar atlas template), dMRI= diffusion magnetic resonance imaging, CSD= constrained spherical deconvolution, fODF= fiber orientation distribution function, CST= corticospinal tract, AF= arcuate fasciculus, CC= corpus callosum, HCP= human connectome project, MASSIVE= Multiple acquisitions for standardization of structural imaging validation and evaluation.
Publisher: MIT Press
Date: 2023
DOI: 10.1162/NETN_A_00277
Abstract: Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated in idualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the in idual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.
Publisher: Cold Spring Harbor Laboratory
Date: 02-10-2020
DOI: 10.1101/2020.09.30.20204701
Abstract: Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate MR images in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted input image which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n=100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P .010, synthetic patients U(48,48) = 2076, z = 7.336, P .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labelling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations, for ex le by providing input data for automated solutions for fiber tractography or resting-state fMRI analyses that could also be used in the clinic. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license ( github.com/KUL-Radneuron/KUL_VBG ). (A) shows T1 images from two patients with gliomatous lesions. VBG is a lesion replacement/filling workflow with one approach for unilateral lesions (uVBG) and another for bilateral lesions (bVBG). (B) shows the recon-all approach selected, (C) & (D) show the output, tissue segmentations (C) and whole brain parcellations (D). If VBG is not used (non-VBG) recon-all may finish with some errors in the parcellations (left) or fail to generate a parcellation entirely (right). However, using either VBG method allows recon-all to complete where it had previously failed and also improves parcellation quality.
Publisher: Elsevier BV
Date: 04-2021
Publisher: Wiley
Date: 13-08-2023
DOI: 10.1002/MDS.29570
Abstract: To investigate whether mild motor signs (MMS) in old age correlate with synaptic density in the brain. Normal aging is associated with a decline in movement quality and quantity, commonly termed “mild parkinsonian signs” or more recently MMS. Whether MMS stem from global brain aging or pathology within motor circuits remains unresolved. The synaptic vesicle glycoprotein 2A positron emission tomography (PET) ligand 11 C‐UCB‐J allows the investigation of brain‐motor associations at the synaptic level in vivo. Fifty‐eight healthy older adults (≥50 years) were included from two monocentric control cohorts. Brain magnetic resonance imaging and 11 C‐UCB‐J PET data were available in 54 participants. 11 C‐UCB‐J PET binding was quantified by standardized uptake value ratio (SUVR) values in grey matter (GM) volumes of interest (VOIs): caudate, putamen, globus pallidus, substantia nigra, thalamus, cerebellum, and the frontal, parietal, temporal, and occipital cortex. Multiple linear regression analyses were performed with Movement Disorder Society‐Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) part III score measuring MMS as the dependent variable and mean SUVR values in each VOI as the independent variable with age, Fazekas score (white matter lesion [WML] load), VOI and cohort as covariates. Participants (68 ± 7.5 years 52% female) had an average MDS‐UPDRS part III score of 3.3 ± 2.8. The MDS‐UPDRS part III score was inversely associated with synaptic density, independently of WML load or GM volume, in the caudate, substantia nigra, thalamus, cerebellum, and parietal, occipital, temporal cortex. Cohen's f 2 showed moderate effect sizes for subcortical (range, 0.30–0.35), cortical (0.28–0.35) and cerebellar VOIs (0.31). MMS in healthy aging are associated with lower synaptic density throughout the brain. © 2023 International Parkinson and Movement Disorder Society.
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119029
Abstract: Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an ex le, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals.
Publisher: Cold Spring Harbor Laboratory
Date: 20-06-2023
DOI: 10.1101/2023.06.13.23290806
Abstract: Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This work investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth. Five different tractography methods were compared (3 DTI-based and 2 CSD-based) in 22 preoperative neurosurgical patients. The corticospinal tract (CST, N=20) and arcuate fasciculus (AF, N=7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes. Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE 0.05). CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice. CSD-based tractograms of the CST are more sensitive than DTI-based tractograms when validated against sensory-motor DES mapping. This also demonstrated the feasibility of fully-automated CSD-based tractography for presurgical planning of the CST. Presurgical white matter mapping using probabilistic CSD tractography is more accurate and sensitive than manual DTI FACT or automated probabilistic DTI tractography. This study included 22 patients with DES data, which was used as the ground truth. Distance in mm between tractograms and DES data resulted in 860 datapoints, 685 of which belonged to the CST and were used for linear modeling, DTI = diffusion tensor imaging, CSD = constrained spherical deconvolution, TCK = tractogram/tractography, FWE = family-wise error rate, AUC = area under the curve
No related grants have been discovered for Ahmed Radwan.