Benoit Caldairou

Multimodal MRI profiling of focal cortical dysplasia type II

Objective: To characterize in vivo MRI signatures of focal cortical dysplasia (FCD) type IIA and type IIB through combined analysis of morphology, intensity, microstructure, and function. Methods: We carried out a multimodal 3T MRI profiling of 33 histologically proven FCD type IIA (9) and IIB (24) lesions. A multisurface approach operating on manual consensus labels systematically sampled intracortical and subcortical lesional features. Geodesic distance mapping quantified the same features in the lesion perimeter. Logistic regression assessed the relationship between MRI and histology, while supervised pattern learning was used for individualized subtype prediction. Results: FCD type IIB was characterized by abnormal morphology, intensity, diffusivity, and function across all surfaces, while type IIA lesions presented only with increased fluid-attenuated inversion recovery signal and reduced diffusion anisotropy close to the gray–white matter interface. Similar to lesional patterns, perilesional anomalies were more marked in type IIB extending up to 16 mm. Structural MRI markers correlated with categorical histologic characteristics. A profile-based classifier predicted FCD subtypes with equal sensitivity of 85%, while maintaining a high specificity of 94% against healthy and disease controls. Conclusions: Image processing applied to widely available MRI contrasts has the ability to dissociate FCD subtypes at a mesoscopic level. Integrating in vivo staging of pathologic traits with automated lesion detection is likely to provide an objective definition of lesional boundary and assist emerging approaches, such as minimally invasive thermal ablation, which do not supply tissue specimen.

The spectrum of structural and functional imaging abnormalities in temporal lobe epilepsy

Although most temporal lobe epilepsy (TLE) patients show marked hippocampal sclerosis (HS) upon pathological examination, 40% present with no significant cell loss but gliotic changes only. To evaluate effects of hippocampal pathology on brain structure and functional networks, we aimed at dissociating multimodal magnetic resonance imaging (MRI) characteristics in patients with HS (TLE‐HS) and those with gliosis only (TLE‐G).

The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions

Drug-resistant temporal lobe epilepsy is increasingly recognized as a system-level disorder affecting the structure and function of large-scale grey matter networks. While diffusion magnetic resonance imaging studies have demonstrated deep fibre tract alterations, the superficial white matter immediately below the cortex has so far been neglected despite its proximity to neocortical regions and key role in maintaining cortico-cortical connectivity. Using multi-modal 3 T magnetic resonance imaging, we mapped the topography of superficial white matter diffusion alterations in 61 consecutive temporal lobe epilepsy patients relative to 38 healthy controls and studied the relationship to large-scale structural as well as functional networks. Our approach continuously sampled mean diffusivity and fractional anisotropy along surfaces running 2 mm below the cortex. Multivariate statistics mapped superficial white matter diffusion anomalies in patients relative to controls, while correlation and mediation analyses evaluated their relationship to structural (cortical thickness, mesiotemporal volumetry) and functional parameters (resting state functional magnetic resonance imaging amplitude) and clinical variables. Patients presented with overlapping anomalies in mean diffusivity and anisotropy, particularly in ipsilateral temporo-limbic regions. Diffusion anomalies did not relate to cortical thinning; conversely, they mediated large-scale functional amplitude decreases in patients relative to controls in default mode hub regions (i.e. anterior and posterior midline regions, lateral temporo-parietal cortices), and were themselves mediated by hippocampal atrophy. With respect to clinical variables, we observed more marked diffusion anomalies in patients with a history of febrile convulsions and those with longer disease duration. Similarly, more marked diffusion alterations were associated with seizure-free outcome. Bootstrap analyses indicated high reproducibility of our findings, suggesting generalizability. The temporo-limbic distribution of superficial white matter anomalies, together with the mediation-level findings, suggests that this so far neglected region serves a key link between the hippocampal atrophy and large-scale default mode network alterations in temporal lobe epilepsy.

Accurate cortical tissue classification on MRI by modeling cortical folding patterns

Accurate tissue classification is a crucial prerequisite to MRI morphometry. Automated methods based on intensity histograms constructed from the entire volume are challenged by regional intensity variations due to local radiofrequency artifacts as well as disparities in tissue composition, laminar architecture and folding patterns. Current work proposes a novel anatomy‐driven method in which parcels conforming cortical folding were regionally extracted from the brain. Each parcel is subsequently classified using nonparametric mean shift clustering. Evaluation was carried out on manually labeled images from two datasets acquired at 3.0 Tesla (n = 15) and 1.5 Tesla (n = 20). In both datasets, we observed high tissue classification accuracy of the proposed method (Dice index >97.6% at 3.0 Tesla, and >89.2% at 1.5 Tesla). Moreover, our method consistently outperformed state‐of‐the‐art classification routines available in SPM8 and FSL‐FAST, as well as a recently proposed local classifier that partitions the brain into cubes. Contour‐based analyses localized more accurate white matter–gray matter (GM) interface classification of the proposed framework compared to the other algorithms, particularly in central and occipital cortices that generally display bright GM due to their highly degree of myelination. Excellent accuracy was maintained, even in the absence of correction for intensity inhomogeneity. The presented anatomy‐driven local classification algorithm may significantly improve cortical boundary definition, with possible benefits for morphometric inference and biomarker discovery. Hum Brain Mapp 36:3563–3574, 2015.

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.

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.

Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding.

Significance Despite the progress made by postmortem cytoarchitectonic analyses and animal electrophysiology in studying the structure and function of the hippocampal circuitry, complex anatomical challenges have prevented a detailed understanding of its substructural organization in living humans. By integrating high-resolution structural and resting-state functional neuroimaging, we demonstrate two main axes of substructural organization in the human hippocampus: one that respects its long axis and a second that follows patterns of hippocampal infolding and significantly correlates with an intracortical microstructure. Given the importance of the hippocampus for cognition, affect, and disease, our results provide an integrated hippocampal coordinate system that is relevant to cognitive neuroscience, clinical neuroimaging, and network neuroscience. The hippocampus plays key roles in cognition and affect and serves as a model system for structure/function studies in animals. So far, its complex anatomy has challenged investigations targeting its substructural organization in humans. State-of-the-art MRI offers the resolution and versatility to identify hippocampal subfields, assess its microstructure, and study topographical principles of its connectivity in vivo. We developed an approach to unfold the human hippocampus and examine spatial variations of intrinsic functional connectivity in a large cohort of healthy adults. In addition to mapping common and unique connections across subfields, we identified two main axes of subregional connectivity transitions. An anterior/posterior gradient followed long-axis landmarks and metaanalytical findings from task-based functional MRI, while a medial/lateral gradient followed hippocampal infolding and correlated with proxies of cortical myelin. Findings were consistent in an independent sample and highly stable across resting-state scans. Our results provide robust evidence for long-axis specialization in the resting human hippocampus and suggest an intriguing interplay between connectivity and microstructure.

Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling

Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent multiple-template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large template library, as segmentation suffers when the boundaries of template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-template segmentation, HybridMulti could maintain accurate performance even with a 50% template library size.