Radiology - Research Publications
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ItemNo Preview AvailableInvestigation of Brain Iron in Niemann-Pick Type C: A 7T Quantitative Susceptibility Mapping Study(AMER SOC NEURORADIOLOGY, 2023-06-22)BACKGROUND AND PURPOSE: While brain iron dysregulation has been observed in several neurodegenerative disorders, its association with the progressive neurodegeneration in Niemann-Pick type C is unknown. Systemic iron abnormalities have been reported in patients with Niemann-Pick type C and in animal models of Niemann-Pick type C. In this study, we examined brain iron using quantitative susceptibility mapping MR imaging in individuals with Niemann-Pick type C compared with healthy controls. MATERIALS AND METHODS: A cohort of 10 patients with adolescent- and adult-onset Niemann-Pick type C and 14 age- and sex-matched healthy controls underwent 7T brain MR imaging with T1 and quantitative susceptibility mapping acquisitions. A probing whole-brain voxelwise comparison of quantitative susceptibility mapping between groups was conducted. Mean quantitative susceptibility mapping in the ROIs (thalamus, hippocampus, putamen, caudate nucleus, and globus pallidus) was further compared. The correlations between regional volume, quantitative susceptibility mapping values, and clinical features, which included disease severity on the Iturriaga scale, cognitive function, and the Social and Occupational Functioning Assessment Scale, were explored as secondary analyses. RESULTS: We observed lower volume in the thalamus and voxel clusters of higher quantitative susceptibility mapping in the pulvinar nuclei bilaterally in patients with Niemann-Pick type C compared with the control group. In patients with Niemann-Pick type C, higher quantitative susceptibility mapping in the pulvinar nucleus clusters correlated with lower volume of the thalamus on both sides. Moreover, higher quantitative susceptibility mapping in the right pulvinar cluster was associated with greater disease severity. CONCLUSIONS: Our findings suggest iron deposition in the pulvinar nucleus in Niemann-Pick type C disease, which is associated with thalamic atrophy and disease severity. This preliminary evidence supports the link between iron and neurodegeneration in Niemann-Pick type C, in line with existing literature on other neurodegenerative disorders.
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ItemNo Preview AvailableInvestigation of brain iron in anorexia nervosa, a quantitative susceptibility mapping study(BMC, 2023-08-21)BACKGROUND: Anorexia nervosa (AN) is a potentially fatal psychiatric condition, associated with structural brain changes such as gray matter volume loss. The pathophysiological mechanisms for these changes are not yet fully understood. Iron is a crucial element in the development and function of the brain. Considering the systemic alterations in iron homeostasis in AN, we hypothesized that brain iron would be altered as a possible factor associated with structural brain changes in AN. METHODS: In this study, we used quantitative susceptibility mapping (QSM) magnetic resonance imaging to investigate brain iron in current AN (c-AN) and weight-restored AN compared with healthy individuals. Whole-brain voxel wise comparison was used to probe areas with possible group differences. Further, the thalamus, caudate nucleus, putamen, nucleus accumbens, hippocampus, and amygdala were selected as the regions of interest (ROIs) for ROI-based comparison of mean QSM values. RESULTS: Whole-brain voxel-wise and ROI-based comparison of QSM did not reveal any differences between groups. Exploratory analyses revealed a correlation between higher regional QSM (higher iron) and lower body mass index, higher illness severity, longer illness duration, and younger age at onset in the c-AN group. CONCLUSIONS: This study did not find evidence of altered brain iron in AN compared to healthy individuals. However, the correlations between clinical variables and QSM suggest a link between brain iron and weight status or biological processes in AN, which warrants further investigation.
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ItemInterpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study(OXFORD UNIV PRESS, 2022-11-21)One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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ItemEvent-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data(WILEY, 2022-08)OBJECTIVE: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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ItemNo Preview AvailablePrevalence and Significance of Impaired Microvascular Tissue Reperfusion Despite Macrovascular Angiographic Reperfusion (No-Reflow)(LIPPINCOTT WILLIAMS & WILKINS, 2022-02-22)BACKGROUND AND OBJECTIVES: The relevance of impaired microvascular tissue-level reperfusion despite complete upstream macrovascular angiographic reperfusion (no-reflow) in human stroke remains controversial. We investigated the prevalence and clinical-radiologic features of this phenomenon and its associations with outcomes in 3 international randomized controlled thrombectomy trials with prespecified follow-up perfusion imaging. METHODS: In a pooled analysis of the Extending the Time for Thrombolysis in Emergency Neurological Deficits-Intra-Arterial (EXTEND-IA; ClinicalTrials.gov NCT01492725), Tenecteplase Versus Alteplase Before Endovascular Therapy for Ischemic Stroke (EXTEND-IA TNK; NCT02388061), and Determining the Optimal Dose of Tenecteplase Before Endovascular Therapy for Ischaemic Stroke (EXTEND-IA TNK Part 2; NCT03340493) trials, patients undergoing thrombectomy with final angiographic expanded Treatment in Cerebral Infarction score of 2c to 3 score for anterior circulation large vessel occlusion and 24-hour follow-up CT or MRI perfusion imaging were included. No-reflow was defined as regions of visually demonstrable persistent hypoperfusion on relative cerebral blood volume or flow maps within the infarct and verified quantitatively by >15% asymmetry compared to a mirror homolog in the absence of carotid stenosis or reocclusion. RESULTS: Regions of no-reflow were identified in 33 of 130 patients (25.3%), encompassed a median of 60.2% (interquartile range 47.8%-70.7%) of the infarct volume, and involved both subcortical (n = 26 of 33, 78.8%) and cortical (n = 10 of 33, 30.3%) regions. Patients with no-reflow had a median 25.2% (interquartile range 16.4%-32.2%, p < 0.00001) relative cerebral blood volume interside reduction and 19.1% (interquartile range 3.9%-28.3%, p = 0.00011) relative cerebral blood flow reduction but similar mean transit time (median -3.3%, interquartile range -11.9% to 24.4%, p = 0.24) within the infarcted region. Baseline characteristics were similar between patients with and those without no-reflow. The presence of no-reflow was associated with hemorrhagic transformation (adjusted odds ratio [aOR] 1.79, 95% confidence interval [CI] 2.32-15.57, p = 0.0002), greater infarct growth (β = 11.00, 95% CI 5.22-16.78, p = 0.00027), reduced NIH Stroke Scale score improvement at 24 hours (β = -4.06, 95% CI 6.78-1.34, p = 0.004) and being dependent or dead at 90 days as assessed by the modified Rankin Scale (aOR 3.72, 95% CI 1.35-10.20, p = 0.011) in multivariable analysis. DISCUSSION: Cerebral no-reflow in humans is common, can be detected by its characteristic perfusion imaging profile using readily available sequences in the clinical setting, and is associated with posttreatment complications and being dependent or dead. Further studies evaluating the role of no-reflow in secondary injury after angiographic reperfusion are warranted. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that cerebral no-reflow on CT/MRI perfusion imaging at 24 hours is associated with posttreatment complications and poor 3-month functional outcome.
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ItemIn Vivo 7-Tesla MRI Investigation of Brain Iron and Its Metabolic Correlates in Chronic Schizophrenia(NATURE PORTFOLIO, 2022-10-26)Brain iron is central to dopaminergic neurotransmission, a key component in schizophrenia pathology. Iron can also generate oxidative stress, which is one proposed mechanism for gray matter volume reduction in schizophrenia. The role of brain iron in schizophrenia and its potential link to oxidative stress has not been previously examined. In this study, we used 7-Tesla MRI quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy (MRS), and structural T1 imaging in 12 individuals with chronic schizophrenia and 14 healthy age-matched controls. In schizophrenia, there were higher QSM values in bilateral putamen and higher concentrations of phosphocreatine and lactate in caudal anterior cingulate cortex (caCC). Network-based correlation analysis of QSM across corticostriatal pathways as well as the correlation between QSM, MRS, and volume, showed distinct patterns between groups. This study introduces increased iron in the putamen in schizophrenia in addition to network-wide disturbances of iron and metabolic status.
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ItemStructural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression(NATURE PORTFOLIO, 2022-07-27)Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
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ItemTopographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy(OXFORD UNIV PRESS, 2022-03-25)Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.
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ItemMachine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy(WILEY, 2022-05)OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.
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ItemStroke population-specific neuroanatomical CT-MRI brain atlas(SPRINGER, 2022-08)PURPOSE: Development of a freely available stroke population-specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT. METHODS: By reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients. RESULTS: The resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1-80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5). CONCLUSION: The newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs ).