Neda Bernasconi

How do we decide what to do? : Resting-state connectivity patterns and components of self-generated thought linked to the development of more concrete personal goals

Human cognition is not limited to the available environmental input but can consider realities that are different to the here and now. We describe the cognitive states and neural processes linked to the refinement of descriptions of personal goals. When personal goals became concrete, participants reported greater thoughts about the self and the future during mind-wandering. This pattern was not observed for descriptions of TV programmes. Connectivity analysis of participants who underwent a resting-state functional magnetic resonance imaging scan revealed neural traits associated with this pattern. Strong hippocampal connectivity with ventromedial pre-frontal cortex was common to better-specified descriptions of goals and TV programmes, while connectivity between hippocampus and the pre-supplementary motor area was associated with individuals whose goals were initially abstract but became more concrete over the course of the experiment. We conclude that self-generated cognition that arises during the mind-wandering state can allow goals to be refined, and this depends on neural systems anchored in the hippocampus.

A Surface Patch-Based Segmentation Method for Hippocampal Subfields

Several neurological disorders are associated with hippocampal pathology. As changes may be localized to specific subfields or spanning across different subfields, accurate subfield segmentation may improve non-invasive diagnostics. We propose an automated subfield segmentation procedure, which combines surface-based processing with a patch-based template library and feature matching. Validation experiments in 25 healthy individuals showed high segmentation accuracy (Dice >82 % across all subfields) and robustness to variations in the template library size. Applying the algorithm to a cohort of patients with temporal lobe epilepsy and hippocampal sclerosis, we correctly lateralized the seizure focus in >90 %. This advantageously compares to classifiers relying on volumes retrieved from other state-of-the-art algorithms.

On the classification of temporal lobe epilepsy using MR image appearance

Classification of neurological diseases based on image characteristics often requires extensive modeling and user intervention. While other techniques concentrate on specific structures, the novelty of the method presented here resides in its analysis of the grey-level appearance of large, non-specific Volumes of Interest (VOI) from T1 MRI data. No manual intervention is required other than the selection of the VOI. This work presents the methodological framework and preliminary results towards our aim of classifying normal subjects and patients with Temporal Lobe Epilepsy (TLE) within the Medial Temporal Lobe. For this purpose, principal component analysis is performed on a set of normal subjects for the creation of a multi-dimensional space representative of a normal population. New data for normal and TLE subjects are projected in this space, under the assumption that the distributions of the projections are not identical and can be used for classification. It is shown that linear discriminant analysis of the eigencoordinates of the projected data can be used to classify normals vs TLE with a 70% accuracy based on only 10 eigenvectors. This results can go up to 100% if all eigenvectors defining the grey-level space are used.

Multivariate Hippocampal Subfield Analysis of Local MRI Intensity and Volume: Application to Temporal Lobe Epilepsy

We propose a multispectral MRI-based clinical decision support approach to carry out automated seizure focus lateralization in patients with temporal lobe epilepsy (TLE). Based on high-resolution T1- and T2-weighted MRI with hippocampal subfield segmentations, our approach samples MRI features along the medial sheet of each subfield to minimize partial volume effects. To establish correspondence of sampling points across subjects, we propagate a spherical harmonic parameterization derived from the hippocampal boundary along a Laplacian gradient field towards the medial sheet. Volume and intensity data sampled on the medial sheet are finally fed into a supervised classifier. Testing our approach in TLE patients in whom the seizure focus could not be lateralized using conventional MR volumetry, the proposed approach correctly lateralized all patients and outperformed classification performance based on global subfield volumes or mean T2-intensity (100% vs. 68%). Moreover, statistical group-level comparisons revealed patterns of subfield abnormalities that were not evident in the global measurements and that largely agree with known histopathological changes.

MRI-Based Lesion Profiling of Epileptogenic Cortical Malformations

Focal cortical dysplasia (FCD), a malformation of cortical development, is a frequent cause of drug-resistant epilepsy. This lesion is histologically classified into Type-IIA (dyslamination, dysmorphic neurons) and Type-IIB (dyslamination, dysmorphic neurons, and balloon cells). Reliable in-vivo identification of lesional subtypes is important for preoperative decision-making and surgical prognostics. We propose a novel multi-modal MRI lesion profiling based on multiple surfaces that systematically sample intra- and subcortical tissue. We applied this framework to histologically-verified FCD. We aggregated features describing morphology, intensity, microstructure, and function from T1-weighted, FLAIR, diffusion, and resting-state functional MRI. We observed alterations across multiple features in FCD Type-IIB, while anomalies in IIA were subtle and mainly restricted to FLAIR intensity and regional functional homogeneity. Anomalies in Type-IIB were seen across all intra- and sub-cortical levels, whereas those in Type-IIA clustered at the cortico-subcortical interface. A supervised classifier predicted the FCD subtype with 91% accuracy, validating our profiling framework at the individual level.

Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI

Focal cortical dysplasia (FCD), a malformation of cortical development, is a frequent cause of drug-resistant epilepsy. This surgically-amenable lesion is histologically characterized by cortical dyslamination, dysmorphic neurons, and balloon cells, which may extend into the immediate subcortical white matter. On MRI, FCD is typically associated with cortical thickening, blurring of the cortical boundary, and intensity anomalies. Notably, even histologically-verified FCD may not be clearly visible on preoperative MRI. We propose a novel FCD detection algorithm, which aggregates surface-based descriptors of morphology and intensity derived from T1-weighted (T1w) MRI, T2-weighted fluid attenuation inversion recovery (FLAIR) MRI, and FLAIR/T1w ratio images. Features were systematically sampled at multiple intracortical/subcortical levels and fed into a two-stage classifier for automated lesion detection based on ensemble learning. Using 5-fold cross-validation, we evaluated the approach in 41 patients with histologically-verified FCD and 38 age-and sex-matched healthy controls. Our approach showed excellent sensitivity (83%, 34/41 lesions detected) and specificity (92%, no findings in 35/38 controls), suggesting benefits for presurgical diagnostics.

Temporal Lobe Epilepsy Lateralization Based on MR Image Intensity and Registration Features

In the context of MR imaging, explicit segmentation followed by stereologic volumetry of the hippocampus (HC) has been the standard approach toward temporal lobe epilepsy (TLE) lateralization of the seizure focus. The novelty of the method presented here resides in its analysis of characteristics of large, non-specific Volumes of Interest from T1 MRI data aiming to lateralize the seizure focus in patients with TLE without segmentation. For this purpose, Principal Components Analysis (PCA) of two image features are united to create a multi-dimensional space representative of a training set population composed of 150 normal subjects. The feature instances consist of grey-level intensity and an approximation of the Jacobian matrix of non-linear registration-derived dense deformation fields. New data for TLE subjects are projected in this space, under the assumption that the distributions of the projections of normal and patients are not identical and can be used for lateralization. Results are presented following PCA modeling of the left medial temporal lobe only for all subjects. It is shown that linear discriminant analysis of the eigencoordinates can be used to lateralize the seizure focus in TLE patients with a 75% accuracy. It is expected that adding a right temporal lobe model will improve lateralization results beyond those of HC volumetry.

Connectome-Based Pattern Learning Predicts Histology and Surgical Outcome of Epileptogenic Malformations of Cortical Development

Focal cortical dysplasia (FCD) type II, a surgically amenable epileptogenic malformation, is characterized by intracortical dyslamination and dysmorphic neurons, either in isolation (IIA) or together with balloon cells (IIB). While evidence suggests diverging local function between these two histological grades, patterns of connectivity to the rest of the brain remain unknown. We present a novel MRI framework that subdivides a given FCD lesion into a set of smaller cortical patches using hierarchical clustering of resting-state functional connectivity profiles. We assessed the yield of this connectome-based subtyping to predict histological grade and response to surgery in individual patients. As the human functional connectome consists of multiple large-scale communities (e.g., the default mode and fronto-parietal networks), we dichotomized connectivity profiles of lesional patches into connectivity to the cortices belonging to the same functional community (intra-community) and to other communities (inter-community). Clustering these community-based patch profiles in 27 patients with histologically-proven FCD objectively identified three distinct lesional classes with (1) decreased intra- and inter-community connectivity, (2) decreased intra-community but normal inter-community connectivity, and (3) increased intra- as well as inter-community connectivity, relative to 34 healthy controls. Ensemble classifiers informed by these classes predicted histological grading (i.e., IIA vs. IIB) and post-surgical outcome (i.e., seizure-free vs. non-free) with high accuracy (≥84%, above-chance significance based on permutation tests, p < 0.01), suggesting benefits of MRI-based connectome stratification for individualized presurgical prognostics.

Temporal Lobe Epilepsy Surgical Outcome Prediction

We wished to study pre-operative T1-weighted MRI of intractable temporal lobe epilepsy (TLE) patients who had undergone selective amygdala-hippocampectomy as part of their surgical treatment. We performed a voxel-based morphometry study of gray and white matter (GM,WM) concentration changes by comparing TLE patients with positive and negative surgical outcome. GM concentration changes were primarily located in the left lateral temporal neocortical region, while more extensive changes were found in left lateral temporal and occipital WM. Using those areas to define a region of interest, we showed that mean GM and WM concentration for all voxels within that region can be used to predict surgical outcome with 97% accuracy.