Search

Background: EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction. Methods: Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other. Results: Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity

Objective This retrospective, cross‐sectional study evaluated the feasibility and potential benefits of incorporating deep‐learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug‐resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. Methods A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross‐validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier‐predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of

OBJECTIVE Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD). METHODS SMT, NODDI, FA, and MD maps were calculated for 33 pediatric patients with suspected FCD (18 histologically confirmed). Two neuroradiologists scored lesion visibility on clinical images and diffusion maps. Signal profile changes within lesions and homologous regions were quantified using a surface‐based approach. Diffusion parameter changes at multiple cortical depths were statistically compared between FCD type IIa and type IIb. RESULTS Compared to fluid‐attenuated inversion recovery (FLAIR) or T1‐weighted imaging, lesions conspicuity on NODDI intracellular volume fraction (ICVF) maps was better/equal/worse in 5/14/14 patients, respectively, while on SMT intra‐neurite volume fraction (INVF) in 3/3/27. Compared to FA or MD, lesion conspicuity on the ICVF was better/equal/worse in 27/4/2, while on the INVF in 20/7/6. Quantitative signal profiling demonstrated significant ICVF and INVF reductions in the lesions, whereas SMT microscopic mean, radial, and axial diffusivities were significantly increased. FCD type IIb exhibited greater changes than FCD type IIa. No changes were detected on FA or MD profiles. SIGNIFICANCE FCD lesion‐specific signal changes were found in ICVF and INVF but not in FA and MD maps. ICVF and INVF showed greater contrast than FLAIR in some cases and had consistent signal changes specific to FCD, suggesting that they could improve current presurgical pediatric epilepsy imaging protocols and can provide features useful for automated lesion detection.

Objective Malformations of cortical development (MCD), including focal cortical dysplasia (FCD), are the most common cause of drug-resistant focal epilepsy in children. Histopathological lesion characterisation demonstrates abnormal cell types and lamination, alterations in myelin (typically co-localised with iron), and sometimes calcification. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility (χ) reflecting it's mineral composition. We used QSM to investigate abnormal tissue composition in a group of children with focal epilepsy with comparison to effective transverse relaxation rate (R2*) and Synchrotron radiation X-ray fluorescence (SRXRF) elemental maps. Our primary hypothesis was that reductions in χ would be found in FCD lesions, resulting from alterations in their iron and calcium content. We also evaluated deep grey matter nuclei for changes in χ with age. Methods QSM and R2* maps were calculated for 40 paediatric patients with suspected MCD (18 histologically confirmed) and 17 age-matched controls. Patients’ sub-groups were defined based on concordant electro-clinical or histopathology data. Quantitative investigation of QSM and R2* was performed within lesions, using a surface-based approach with comparison to homologous regions, and within deep brain regions using a voxel-based approach with regional values modelled with age and epilepsy as covariates. Synchrotron radiation X-ray fluorescence (SRXRF) was performed on brain tissue resected from 4 patients to map changes in iron, calcium and zinc and relate them to MRI parameters. Results Compared to fluid‐attenuated inversion recovery (FLAIR) or T1‐weighted imaging, QSM improved lesion conspicuity in 5% of patients. In patients with well-localised lesions, quantitative profiling demonstrated decreased χ, but not R2*, across cortical depth with respect to the homologous regions. Contra-lateral homologous regions additionally exhibited increased χ at 2–3 mm cortical depth that was absent in lesions. The iron decrease measured by the SRXRF in FCDIIb lesions was in agreement with myelin reduction observed by Luxol Fast Blue histochemical staining. SRXRF analysis in two FCDIIb tissue samples showed increased zinc and calcium in one patient, and decreased iron in the brain region exhibiting low χ and high R2* in both patients. QSM revealed expected age-related changes in the striatum nuclei, substantia nigra, sub-thalamic and red nucleus. Conclusion QSM non-invasively revealed cortical/sub-cortical tissue alterations in MCD lesions and in particular that χ changes in FCDIIb lesions were consistent with reduced iron, co-localised with low myelin and increased calcium and zinc content. These findings suggest that measurements of cortical χ could be used to characterise tissue properties non-invasively in epilepsy lesions.

AIM To identify clinical and radiological predictors of long-term motor outcome after childhood-onset arterial ischemic stroke (AIS) in the middle cerebral artery (MCA) territory. METHOD Medical records of 69 children (36 females, 33 males; median age at index AIS 3y 3mo, range: 1mo-16y) who presented to Great Ormond Street Hospital with first AIS in the MCA territory were reviewed retrospectively. Cases were categorized using the Childhood AIS Standardized Classification and Diagnostic Evaluation (CASCADE). Magnetic resonance imaging (MRI) and angiography were evaluated. An Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was calculated on MRI. The Recurrence and Recovery Questionnaire assessed motor outcome and was dichotomized into good/poor. RESULTS Eventual motor outcome was good in 49 children and poor in 20. There were no acute radiological predictors of eventual motor outcome. At follow-up, CASCADE 3A (i.e. moyamoya) and Wallerian degeneration were significantly associated with poor motor outcome. In the multivariate analysis, younger age and CASCADE 3A predicted poor motor outcome. INTERPRETATION In the context of recommendations regarding unproven and potentially high-risk hyperacute therapies for childhood AIS, prediction of outcome could usefully contribute to risk/benefit analysis. Unfortunately, paradigms used in adults, such as ASPECTS, are not useful in children in the acute/early subacute phase of AIS. What this paper adds Adult paradigms, such as the Alberta Stroke Program Early Computed Tomography Score system, are not useful for predicting outcome in children. Younger children tend to have a poorer long-term prognosis than older children. Moyamoya is associated with poor prognosis.

ObjectiveMalformations of cortical development (MCD), including focal cortical dysplasia (FCD), are the most common cause of drug-resistant focal epilepsy in children. Histopathological lesion characterisation demonstrates abnormal cell types and lamination, alterations in myelin (typically co-localised with iron), and sometimes calcification. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility (χ) reflecting it’s mineral composition.In a retrospective observational study, QSM was investigated abnormal tissue composition group of children with focal epilepsy with comparison to effective transverse relaxation rate (R2*) and Synchrotron radiation X-ray fluorescence (SRXRF) elemental maps. Our primary hypothesis was that reductions in χ would be found in FCD lesions, resulting from alterations in their iron and calcium content. We also evaluated deep grey matter nuclei for changes in χ with age.MethodsQSM and R2* maps were calculated for 40 paediatric patients with suspected FCD (18 histologically confirmed) and 17 age-matched controls.Patients sub-groups were defined based on concordant electro-clinical or histopathology data. Quantitative investigation of QSM and R2* were performed within lesions, using a surface-based approach with comparison to homologous regions, and globally within deep brain regions using a voxel-based approach with regional values modelled with age and epilepsy as covariates.Synchrotron radiation X-ray fluorescence (SRXRF) was performed on brain tissue resected from 4 patients to map changes in iron, calcium and zinc and relate them to MRI parameters.ResultsCompared to fluid-attenuated inversion recovery (FLAIR) or T1Lweighted imaging, QSM improved lesion conspicuity in 5% of patients.In patients with well-localised and confirmed FCDIIb lesions, quantitative profiling demonstrated decreased χ, but not R2*, across cortical depth with respect to the homologous regions. Contra-lateral homologous regions additionally exhibited increased χ at 2-3mm cortical depth that was absent in lesions. The iron decrease measured by the SRXRF in FCDIIb lesions was in agreement with myelin reduction observed by Luxol Fast Blue histochemical staining.SRXRF analysis in two FCDIIb tissue samples showed increased zinc and calcium, and decreased iron in the brain region exhibiting low χ and high R2*. QSM revealed expected age-related changes in the striatum nuclei, substantia nigra, sub-thalamic and red nucleus, but these changes were not altered in epilepsy.ConclusionQSM non-invasively revealed cortical/sub-cortical tissue alterations in MCD lesions and in particular that χ changes in FCDIIb lesions were consistent with reduced iron, co-localised with low myelin and increased calcium and zinc content. Theses findings suggests that the measurements of cortical χ measurements could be used to detect and delineate epilepsy lesions.

Objectives: To demonstrate feasibility of a 3 T multiparametric mapping (MPM) quantitative pipeline for perinatal post-mortem MR (PMMR) imaging. Methods: Whole body quantitative PMMR imaging was acquired in four cases, mean gestational age 34 weeks, range (29–38 weeks) on a 3 T Siemens Prisma scanner. A multicontrast protocol yielded proton density, T1 and magnetic transfer (MT) weighted multi-echo images obtained from variable flip angle (FA) 3D fast low angle single-shot (FLASH) acquisitions, radiofrequency transmit field map and one B0 field map alongside four MT weighted acquisitions with saturation pulses of 180, 220, 260 and 300 degrees were acquired, all at 1 mm isotropic resolution. Results: Whole body MPM was achievable in all four foetuses, with R1, R2*, PD and MT maps reconstructed from a single protocol. Multiparametric maps were of high quality and show good tissue contrast, especially the MT maps. Conclusion: MPM is a feasible technique in a perinatal post-mortem setting, which may allow quantification of post-mortem change, prior to being evaluated in a clinical setting. Advances in knowledge: We have shown that the MPM sequence is feasible in PMMR imaging and shown the potential of MT imaging in this setting.

ObjectiveA retrospective, cross-sectional study to evaluate the feasibility and potential benefits of incorporating deep-learning on structural MRI into planning stereoelectroencephalography (sEEG) implantation in paediatric patients with diagnostically complex drug-resistant epilepsy. This study aims to assess the degree of co-localisation between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG.MethodsA neural network classifier was applied to cortical features from MRI data from three cohorts. 1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). 2) Specificity was assessed in 20 paediatric healthy controls. 3) Feasibility for incorporation into sEEG implantation plans was evaluated in 38 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of ResultsIn patients with radiologically-defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity 100%). Of 34 sEEG patients, 21 patients had a focal cortical SOZ. Of these there was co-localisation between classifier output and SOZ contacts in 62%. The algorithm detected 7/8 histopathologically-confirmed FCDs (86%).ConclusionsThere was a high degree of co-localisation between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning based MRI lesion detection into sEEG implantation planning. Our findings demonstrate that automated MRI analysis could be used to plan optimal electrode trajectories.

Objective: Ischemic stroke affects language production and/or comprehension and leads to devastating long-term consequences for patients and their families. Previous studies have shown that neuroimaging can increase our knowledge of the basic mechanisms of language recovery. Currently, models for predicting patients’ outcomes have limited use in the clinic for the evaluation and optimization of rehabilitative strategies mostly because that are often based on high-resolution magnetic resonance imaging (MRI) data, which are not always possible to carry out in the clinical routine. Here, we investigate the use of Voxel-Based Morphometry (VBM), multivariate modelling and native Computed Tomography (nCT) scans routinely acquired in the acute stage of stroke for identifying biological signatures that explicate the relationships between brain anatomy and types of impairments. Methods: 80 stroke patients and 30 controls were included. nCT-scans were acquired in the acute ischemia stage and bedside clinical assessment from board-certified neurologist based on the NIH stroke scale. We use a multivariate Principal Component Analyses (PCA) to identify the brain signatures group the patients according to the presence or absence of impairment and identify the association between local Grey Matter (GM) and White Matter (WM) nCT values with the presence or absence of the impairment. Results: Individual patient’s nCT scans were compared to a group of controls’ with no radiological signs of stroke to provide an automated delineation of the lesion. Consistently across the whole group the regions that presented significant difference GM and WM values overlap with known areas that support language processing. Conclusion: In summary, the method applied to nCT scans performed in the acute stage of stroke provided robust and accurate information about brain lesions’ location and size, as well as quantitative values. We found that nCT and VBQ analyses are effective for identifying neural signatures of concomitant language impairments at the individual level, and neuroanatomical maps of aphasia at the population level. The signatures explicate the neurophysiological mechanisms underlying aetiology of the stroke. Ultimately, similar analyses with larger cohorts could lead to a more integrated multimodal model of behaviour and brain anatomy in the early stage of ischemic stroke.

Quantitative proton density (PD) maps measure the amount of free water, which is important for non-invasive tissue characterization in pathology and across lifespan. PD mapping requires the estimation and subsequent removal of factors influencing the signal intensity other than PD. These factors include the T1, T2* relaxation effects, transmit field inhomogeneities, receiver coil sensitivity profile (RP) and the spatially invariant factor that is required to scale the data. While the transmit field can be reliably measured, the RP estimation is usually based on image post-processing techniques due to limitations of its measurement at magnetic fields higher than 1.5 T. The post-processing methods are based on unified bias-field/tissue segmentation, fitting the sensitivity profile from images obtained with different coils, or on the linear relationship between T1 and PD. The scaling factor is derived from the signal within a specific tissue compartment or reference object. However, these approaches for calculating the RP and scaling factor have limitations particularly in severe pathology or over a wide age range, restricting their application. We propose a new approach for PD mapping based on a multi-contrast variable flip angle acquisition protocol and a data-driven estimation method for the RP correction and map scaling. By combining all the multi-contrast data acquired at different echo times, we are able to fully correct the MRI signal for T2* relaxation effects and to decrease the variance and the entropy of PD values within tissue class of the final map. The RP is determined from the corrected data applying a non-parametric bias estimation, and the scaling factor is based on the median intensity of an external calibration object. Finally, we compare the signal intensity and homogeneity of the multi-contrast PD map with the well-established effective PD (PD*) mapping, for which the RP is based on concurrent bias field estimation and tissue classification, and the scaling factor is estimated from the mean white matter signal. The multi-contrast PD values homogeneity and accuracy within the cerebrospinal fluid (CSF) and deep brain structures are increased beyond that obtained using PD* maps. We demonstrate that the multi-contrast RP approach is insensitive to anatomical or a priori tissue information by applying it in a patient with extensive brain abnormalities and for whole body PD mapping in post-mortem foetal imaging.

The high gray-white matter contrast and spatial resolution provided by T1-weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1-weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1-weighted images (R1 (=1/T1), R2 *, and PD) in a large cohort of healthy subjects (n = 120, aged 18-87 years). Synthetic T1-weighted images were calculated from these quantitative maps and used to extract morphometry features-gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue-myelination, iron, and water content-on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801-1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

There is an increasing interest in identifying non-invasive biomarkers of disease severityand prognosis in idiopathic Parkinson’s disease (PD). Dopamine-transporter SPECT(DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging(sMRI) provide unique information about the brain’s neurotransmitter and microstructuralproperties. In this study, we evaluate the relative and combined capability of theseimaging modalities to predict symptom severity and clinical progression inde novoPDpatients. To this end, we used MRI, SPECT, and clinical data ofde novodrug-naïvePD patients (n= 205, mean age 61±10) and age-, sex-matched healthy controls(n= 105, mean age 58±12) acquired at baseline. Moreover, we employed clinical dataacquired at 1 year follow-up for PD patients with or withoutL-Dopa treatment in orderto predict the progression symptoms severity. Voxel-based group comparisons andcovariance analyses were applied to characterize baseline disease-related alterations forDAT-SPECT, DTI, and sMRI. Cortical and subcortical alterations inde novoPD patientswere found in all evaluated imaging modalities, in line with previously reported midbrain-striato-cortical network alterations. The combination of these imaging alterations wasreliably linked to clinical severity and disease progression at 1 year follow-up in thispatient population, providing evidence for the potential use of these modalities asimaging biomarkers for disease severity and prognosis that can be integrated intoclinical trials.

The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R 1 (=1/T1), R 2*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Evidence from magnetic resonance imaging (MRI) studies shows that healthy aging is associated with profound changes in cortical and subcortical brain structures. The reliable delineation of cortex and basal ganglia using automated computational anatomy methods based on T1-weighted images remains challenging, which results in controversies in the literature. In this study we use quantitative MRI (qMRI) to gain an insight into the microstructural mechanisms underlying tissue ageing and look for potential interactions between ageing and brain tissue properties to assess their impact on automated tissue classification. To this end we acquired maps of longitudinal relaxation rate R1, effective transverse relaxation rate R2* and magnetization transfer – MT, from healthy subjects (n = 96, aged 21–88 years) using a well-established multi-parameter mapping qMRI protocol. Within the framework of voxel-based quantification we find higher grey matter volume in basal ganglia, cerebellar dentate and prefrontal cortex when tissue classification is based on MT maps compared with T1 maps. These discrepancies between grey matter volume estimates can be attributed to R2* - a surrogate marker of iron concentration, and further modulation by an interaction between R2* and age, both in cortical and subcortical areas. We interpret our findings as direct evidence for the impact of ageing-related brain tissue property changes on automated tissue classification of brain structures using SPM12. Computational anatomy studies of ageing and neurodegeneration should acknowledge these effects, particularly when inferring about underlying pathophysiology from regional cortex and basal ganglia volume changes., Highlights • We acquire quantitative bias free whole-brain maps of MT, R1 and R2* parameters. • Grey matter volume estimation based on MT maps is higher than the R1-based in specific cortical and subcortical areas. • Differences in grey matter volume estimations are correlated with iron concentration and further modulated by age. • Iron decreases locally the grey-white matter contrast in R1 but not in MT maps.