Manuel Jorge Cardoso

Faciobrachial dystonic seizures: the influence of immunotherapy on seizure control and prevention of cognitive impairment in a broadening phenotype.

Voltage-gated potassium channel complex antibodies, particularly those directed against leucine-rich glioma inactivated 1, are associated with a common form of limbic encephalitis that presents with cognitive impairment and seizures. Faciobrachial dystonic seizures have recently been reported as immunotherapy-responsive, brief, frequent events that often predate the cognitive impairment associated with this limbic encephalitis. However, these observations were made from a retrospective study without serial cognitive assessments. Here, we undertook the first prospective study of faciobrachial dystonic seizures with serial assessments of seizure frequencies, cognition and antibodies in 10 cases identified over 20 months. We hypothesized that (i) faciobrachial dystonic seizures would show a differential response to anti-epileptic drugs and immunotherapy; and that (ii) effective treatment of faciobrachial dystonic seizures would accelerate recovery and prevent the development of cognitive impairment. The 10 cases expand both the known age at onset (28 to 92 years, median 68) and clinical features, with events of longer duration, simultaneously bilateral events, prominent automatisms, sensory aura, and post-ictal fear and speech arrest. Ictal epileptiform electroencephalographic changes were present in three cases. All 10 cases were positive for voltage-gated potassium channel-complex antibodies (346-4515 pM): nine showed specificity for leucine-rich glioma inactivated 1. Seven cases had normal clinical magnetic resonance imaging, and the cerebrospinal fluid examination was unremarkable in all seven tested. Faciobrachial dystonic seizures were controlled more effectively with immunotherapy than anti-epileptic drugs (P = 0.006). Strikingly, in the nine cases who remained anti-epileptic drug refractory for a median of 30 days (range 11-200), the addition of corticosteroids was associated with cessation of faciobrachial dystonic seizures within 1 week in three and within 2 months in six cases. Voltage-gated potassium channel-complex antibodies persisted in the four cases with relapses of faciobrachial dystonic seizures during corticosteroid withdrawal. Time to recovery of baseline function was positively correlated with time to immunotherapy (r = 0.74; P = 0.03) but not time to anti-epileptic drug administration (r = 0.55; P = 0.10). Of 10 cases, the eight cases who received anti-epileptic drugs (n = 3) or no treatment (n = 5) all developed cognitive impairment. By contrast, the two who did not develop cognitive impairment received immunotherapy to treat their faciobrachial dystonic seizures (P = 0.02). In eight cases without clinical magnetic resonance imaging evidence of hippocampal signal change, cross-sectional volumetric magnetic resonance imaging post-recovery, after accounting for age and head size, revealed cases (n = 8) had smaller brain volumes than healthy controls (n = 13) (P < 0.001). In conclusion, faciobrachial dystonic seizures can be prospectively identified as a form of epilepsy with an expanding phenotype. Immunotherapy is associated with excellent control of the frequently anti-epileptic drug refractory seizures, hastens time to recovery, and may prevent the subsequent development of cognitive impairment observed in this study.

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimers disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

A comparison of voxel and surface based cortical thickness estimation methods

Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.

LoAd: A locally adaptive cortical segmentation algorithm

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimers, Huntingtons, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimers Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques.

An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subjects data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimers disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test-retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression.

Attenuation Correction Synthesis for Hybrid PET-MR Scanners

The combination of functional and anatomical imaging technologies such as Positron Emission Tomography (PET) and Computed Tomography (CT) has shown its value in the preclinical and clinical fields. In PET/CT hybrid acquisition systems, CT-derived attenuation maps enable a more accurate PET reconstruction. However, CT provides only very limited soft-tissue contrast and exposes the patient to an additional radiation dose. In comparison, Magnetic Resonance Imaging (MRI) provides good soft-tissue contrast and the ability to study functional activation and tissue microstructures, but does not directly provide patient-specific electron density maps for PET reconstruction. The aim of the proposed work is to improve PET/MR reconstruction by generating synthetic CTs and attenuation-maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patients morphology to a database of pre-acquired MRI/CT pairs. Results show improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an Ultrashort-Echo-Time MRI sequence.

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups

Background: Pathological abnormalities including demyelination and neuronal loss are reported in the outer cortex in multiple sclerosis (MS). Objective: We investigated for in vivo evidence of outer cortical abnormalities by measuring the magnetisation transfer ratio (MTR) in MS patients of different subgroups. Methods: Forty-four relapsing–remitting (RR) (mean age 41.9 years, median Expanded Disability Status Scale (EDSS) 2.0), 25 secondary progressive (SP) (54.1 years, EDSS 6.5) and 19 primary progressive (PP) (53.1 years, EDSS 6.0) MS patients and 35 healthy control subjects (mean age 39.2 years) were studied. Three-dimensional (3D) 1×1×1mm3 T1-weighted images and MTR data were acquired. The cortex was segmented, then subdivided into outer and inner bands, and MTR values were calculated for each band. Results: In a pairwise analysis, mean outer cortical MTR was lower than mean inner cortical MTR in all MS groups and controls (p<0.001). Compared with controls, outer cortical MTR was decreased in SPMS (p<0.001) and RRMS (p<0.01), but not PPMS. Outer cortical MTR was lower in SPMS than PPMS (p<0.01) and RRMS (p<0.01). Conclusions: Lower outer than inner cortical MTR in healthy controls may reflect differences in myelin content. The lowest outer cortical MTR was seen in SPMS and is consistent with more extensive outer cortical (including subpial) pathology, such as demyelination and neuronal loss, as observed in post-mortem studies of SPMS patients.

Accurate Localization of Optic Radiation During Neurosurgery in an Interventional MRI Suite

Accurate localization of the optic radiation is key to improving the surgical outcome for patients undergoing anterior temporal lobe resection for the treatment of refractory focal epilepsy. Current commercial interventional magnetic resonance imaging (MRI) scanners are capable of performing anatomical and diffusion weighted imaging and are used for guidance during various neurosurgical procedures. We present an interventional imaging workflow that can accurately localize the optic radiation during surgery. The workflow is driven by a near real-time multichannel nonrigid image registration algorithm that uses both anatomical and fractional anisotropy pre- and intra-operative images. The proposed workflow is implemented on graphical processing units and we perform a warping of the pre-operatively parcellated optic radiation to the intra-operative space in under 3 min making the proposed algorithm suitable for use under the stringent time constraints of neurosurgical procedures. The method was validated using both a numerical phantom and clinical data using pre- and post-operative images from patients who had undergone surgery for treatment of refractory focal epilepsy and shows strong correlation between the observed post-operative visual field deficit and the predicted damage to the optic radiation. We also validate the algorithm using interventional MRI datasets from a small cohort of patients. This work could be of significant utility in image guided interventions and facilitate effective surgical treatments.

Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography

PURPOSE Computed tomographic (CT) colonography is a relatively new technique for detecting bowel cancer or potentially precancerous polyps. CT scanning is combined with three-dimensional (3D) image reconstruction to produce a virtual endoluminal representation similar to optical colonoscopy. Because retained fluid and stool can mimic pathology, CT data are acquired with the bowel cleansed and insufflated with gas and patient in both prone and supine positions. Radiologists then match visually endoluminal locations between the two acquisitions in order to determine whether apparent pathology is real or not. This process is hindered by the fact that the colon, essentially a long tube, can undergo considerable deformation between acquisitions. The authors present a novel approach to automatically establish spatial correspondence between prone and supine endoluminal colonic surfaces after surface parameterization, even in the case of local colon collapse. METHODS The complexity of the registration task was reduced from a 3D to a 2D problem by mapping the surfaces extracted from prone and supine CT colonography onto a cylindrical parameterization. A nonrigid cylindrical registration was then performed to align the full colonic surfaces. The curvature information from the original 3D surfaces was used to determine correspondence. The method can also be applied to cases with regions of local colonic collapse by ignoring the collapsed regions during the registration. RESULTS Using a development set, suitable parameters were found to constrain the cylindrical registration method. Then, the same registration parameters were applied to a different set of 13 validation cases, consisting of 8 fully distended cases and 5 cases exhibiting multiple colonic collapses. All polyps present were well aligned, with a mean (+/- std. dev.) registration error of 5.7 (+/- 3.4) mm. An additional set of 1175 reference points on haustral folds spread over the full endoluminal colon surfaces resulted in an error of 7.7 (+/- 7.4) mm. Here, 82% of folds were aligned correctly after registration with a further 15% misregistered by just onefold. CONCLUSIONS The proposed method reduces the 3D registration task to a cylindrical registration representing the endoluminal surface of the colon. Our algorithm uses surface curvature information as a similarity measure to drive registration to compensate for the large colorectal deformations that occur between prone and supine data acquisitions. The method has the potential to both enhance polyp detection and decrease the radiologists interpretation time.