Structural analysis of fMRI data: A surface-based framework for multi-subject studies

Operto, Grégory; Rivière, Denis; Fertil, Bernard; Bulot, Rémy; Mangin, Jean‐François; Coulon, Olivier

doi:10.1016/j.media.2012.02.007

Cited by 4 publications

(5 citation statements)

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“…And because of the large number of resulting maps -one per scale, the results of the single-scale scheme might be more difficult to in-terpret. Future work could address this latter drawback by modelling more precisely the relations between clusters obtained at different scales, as was done in Operto et al (2012) for fMRI data. Finally, note that our multi-scale inference strategy could benefit to other searchlight applications, in particular for fMRI data analysis where it has grew very popular; indeed, Etzel et al (2013) suggested to use a systematic multi-scale approach to provide more robust statistical results and thus improve the interpretability, and our method directly fullfills this need.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-scale methods aim at studying phenomena for which the optimal scale to be used is unknown Koenderink (1984); Lindeberg (1994), as when studying the local organization of sulcal pits. They have been used in neuroimaging for various tasks, such as the description of activation patterns in PET (Coulon et al (2000)) and fMRI (Operto et al (2012)) data or the segmentation of subcortical regions in anatomical MRI (Wu et al (2015)). Etzel et al (2013) also suggested that multi-scale strategies could be useful to desambiguate the 3 a posteriori interpretation of regions detected by searchlight-based methods, which we implement here.…”

Section: Introductionmentioning

confidence: 99%

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Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits

Takerkart

Auzias

Brun

et al. 2017

Medical Image Analysis

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Studying the topography of the cortex has proved valuable in order to characterize populations of subjects. In particular, the recent interest towards the deepest parts of the cortical sulci - the so-called sulcal pits - has opened new avenues in that regard. In this paper, we introduce the first fully automatic brain morphometry method based on the study of the spatial organization of sulcal pits - Structural Graph-Based Morphometry (SGBM). Our framework uses attributed graphs to model local patterns of sulcal pits, and further relies on three original contributions. First, a graph kernel is defined to provide a new similarity measure between pit-graphs, with few parameters that can be efficiently estimated from the data. Secondly, we present the first searchlight scheme dedicated to brain morphometry, yielding dense information maps covering the full cortical surface. Finally, a multi-scale inference strategy is designed to jointly analyze the searchlight information maps obtained at different spatial scales. We demonstrate the effectiveness of our framework by studying gender differences and cortical asymmetries: we show that SGBM can both localize informative regions and estimate their spatial scales, while providing results which are consistent with the literature. Thanks to the modular design of our kernel and the vast array of available kernel methods, SGBM can easily be extended to include a more detailed description of the sulcal patterns and solve different statistical problems. Therefore, we suggest that our SGBM framework should be useful for both reaching a better understanding of the normal brain and defining imaging biomarkers in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits

Takerkart

Auzias

Brun

et al. 2017

Medical Image Analysis

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“…Structural atlases embed the representations of a set of subjects in the spirit of multi-subject atlases ( Aljabar et al, 2009 ; Lötjönen et al, 2010 ; Iglesias and Sabuncu, 2015 ) or the training sets of deep learning ( Coupé et al, 2020 ; Henschel et al, 2020 ). The essence of the structural strategy consists in extracting first, during a preprocessing of the subject’s images, modality-specific objects [elementary brain structures like cortical folds ( Mangin et al, 1995 ), cortical pits ( Cachia et al, 2003 ; Im et al, 2010 ; Auzias et al, 2015 ), fascicles of pseudo-fibers with similar trajectories ( Guevara et al, 2011 ), and regions homogeneous in terms of function ( Coulon et al, 2000 ; Operto et al, 2012 ), etc.]. Then, these objects can be matched across subjects using a common nomenclature.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this strategy can leverage the information embedded in the voxel-clustering operations performed during the preprocessing. These high-level representations can be used to design the automatic inference of the common nomenclature ( Coulon et al, 2000 ; Im et al, 2010 ; Guevara et al, 2012 , 2017 ; Operto et al, 2012 ). They can also be used to regularize the computer vision problem that consists in recognizing the structures corresponding to the nomenclature in new subjects ( Riviere et al, 2002 ; Perrot et al, 2011 ; Borne et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Rivière

Leprince

Labra

et al. 2022

Front. Neuroinform.

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Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this “iconic” approach has limits. We present in this study an alternative, complementary, “structural” approach, which consists in extracting structures from the individual data, and comparing them without deformation. A “structural atlas” is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

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“…Furthermore, this strategy can leverage the information embedded in the voxel-clustering operations performed during the preprocessing. These high-level representations can be used to design the automatic inference of the common nomenclature (Coulon et al, 2000;Im et al, 2010;Guevara et al, 2012Guevara et al, , 2017Operto et al, 2012). They can also be used to regularize the computer vision problem that consists in recognizing the structures corresponding to the nomenclature in new subjects (Riviere et al, 2002;Perrot et al, 2011;Borne et al, 2020).…”

Section: Introduction Brain Atlases In Brain Research and Neurosciencesmentioning

confidence: 99%

The cranial base and related internal anatomical features in Homo neanderthalensis and Homo sapiens

Balzeau

Pagano

2022

The Anatomical Record

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Structural analysis of fMRI data: A surface-based framework for multi-subject studies

Cited by 4 publications

References 65 publications

Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits

Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

The cranial base and related internal anatomical features in Homo neanderthalensis and Homo sapiens

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