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The extreme variability of the folding pattern of the human cortex makes the recognition of cortical sulci, both automatic and manual, particularly challenging. Reliable identification of the human cortical sulci in its entirety, is extremely difficult and is practiced by only a few experts. Moreover, these sulci correspond to more than a hundred different structures, which makes manual labeling long and fastidious and therefore limits access to large labeled databases to train machine learning. Here, we seek to improve the current model proposed in the Morphologist toolbox, a widely used sulcus recognition toolbox included in the BrainVISA package. Two novel approaches are proposed: patch-based multi-atlas segmentation (MAS) techniques and convolutional neural network (CNN)-based approaches. Both are currently applied for anatomical segmentations because they embed much better representations of inter-subject variability than approaches based on a single template atlas. However, these methods typically focus on voxel-wise labeling, disregarding certain geometrical and topological properties of interest for sulcus morphometry. Therefore, we propose to refine these approaches with domain specific bottom-up geometric constraints provided by the Morphologist toolbox. These constraints are utilized to provide a single sulcus label to each topologically elementary fold, the building blocks of the pattern recognition problem. To eliminate the shortcomings associated with the Morphologist's pre-segmentation into elementary folds, we complement this regularization scheme using a top-down perspective which triggers an additional cleavage of the elementary folds when required. All the newly proposed models outperform the current Morphologist model, the most efficient being a CNN U-Net-based approach which carries out sulcus recognition within a few seconds., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parcellate the whole human cortex. The main originality of the method is to compress for each gyrus the connectivity profiles used for the clustering without any anatomical prior information. This step takes into account the interindividual cortical and connectivity variability. To this end, we consider intersubject high density connectivity areas extracted using a surface-based watershed algorithm. A wide validation study has led to a fully automatic pipeline which is robust to variations in data preprocessing (tracking type, cortical mesh characteristics and boundaries of initial gyri), data characteristics (including number of subjects), and the main algorithmic parameters. A remarkable reproducibility is achieved in parcellation results for the whole cortex, leading to clear and stable cortical patterns. This reproducibility has been tested across non-overlapping subgroups and the validation is presented mainly on the pre- and postcentral gyri., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

We present a method for fMRI data group analysis that makes the link between two distinct frameworks: surface-based techniques, which process data in the domain defined by the surface of the cortex, and structural techniques, which use object-based representations of the data as opposed to voxel-based ones. This work is a natural surface-based extension of the volume-based structural approach presented in a previous paper. A multi-scale surface-based representation of individual activation maps is first computed for each subject. Then the inter-subject matching and the activation detection decision are performed jointly by optimization of a Markovian model. Finally, a significance measure is computed in a non-parametric way for the results, in order to assess their relevance and control the risk of type I error. The method is applied on simulated and real data and the results are compared to those produced by standard analyses. The surface-based structural analysis is shown to be particularly robust to inter-subject spatial variability and to produce relevant results with good specificity and sensitivity. We also demonstrate the advantages of the surface-based approach by comparing with the results of a 3D structural analysis., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Brain mapping techniques pair similar anatomical information across individuals. In this context, spatial normalization is mainly used to reduce inter-subject differences to improve comparisons. These techniques may benefit from anatomically identified landmarks useful to drive the registration. Automatic labeling, classification or segmentation techniques provide such labels. Most of these approaches depend strongly on normalization, as much as normalization depends on landmark accuracy. We propose in this paper a coherent Bayesian framework to automatically identify approximately 60 sulcal labels per hemisphere based on a probabilistic atlas (a mixture of spam models: Statistical Probabilistic Anatomy Map) estimating simultaneously normalization parameters. This way, the labelization method provides also with no extra computational costs a new automatically constrained registration of sulcal structures. We have limited our study to global affine and piecewise affine registration. The suggested global affine approach outperforms significantly standard affine intensity-based normalization techniques in term of sulci alignments. Further, by combining global and local joint labeling, a final mean recognition rate of 86% has been obtained with much more reliable labeling posterior probabilities. The different methods described in this paper have been integrated since the release version 3.2.1 of the BrainVISA software platform (Riviére et al., 2009)., (Copyright © 2011 Elsevier B.V. All rights reserved.)

This paper describes a complete system allowing automatic recognition of the main sulci of the human cortex. This system relies on a preprocessing of magnetic resonance images leading to abstract structural representations of the cortical folding patterns. The representation nodes are cortical folds, which are given a sulcus name by a contextual pattern recognition method. This method can be interpreted as a graph matching approach, which is driven by the minimization of a global function made up of local potentials. Each potential is a measure of the likelihood of the labelling of a restricted area. This potential is given by a multi-layer perceptron trained on a learning database. A base of 26 brains manually labelled by a neuroanatomist is used to validate our approach. The whole system developed for the right hemisphere is made up of 265 neural networks. The mean recognition rate is 86% for the learning base and 76% for a generalization base, which is very satisfying considering the current weak understanding of the variability of the cortical folding patterns.