David Germanaud

Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism.

The description of cortical folding pattern (CFP) is challenging because of geometric complexity and inter-subject variability. On a cortical surface mesh, curvature estimation provides a good scalar proxy of CFP. The oscillations of this function can be studied using a Fourier-like analysis to produce a power spectrum representative of the spatial frequency composition of CFP. First, we introduce an original method for the SPectral ANalysis of GYrication (Spangy), which performs a spectral decomposition of the mean curvature of the grey/white interface mesh based on the Laplace-Beltrami operator eigenfunctions. Spangy produces an ordered 7 bands power spectrum of curvature (B0-B6) and provides an anatomically relevant segmentation of CFP based on local spectral composition. A spatial frequency being associated with each eigenfunction, the bandwidth design assumes frequency doubling between consecutive spectral bands. Next, we observed that the last 3 spectral bands (B4, 5 and 6) accounted for 93% of the analyzed spectral power and were associated with fold-related variations of curvature, whereas the lower frequency bands were related to global brain shape. The spectral segmentation of CFP revealed 1st, 2nd and 3rd order elements associated with B4, B5 and B6 respectively. These elements could be related to developmentally-defined primary, secondary and tertiary folds. Finally, we used allometric scaling of frequency bands power and segmentation to analyze the relationship between the spectral composition of CFP and brain size in a large adult dataset. Total folding power followed a positive allometric scaling which did not divide up proportionally between the bands: B4 contribution was constant, B5 increased like total folding power and B6 much faster. Besides, apparition of new elements of pattern with increasing size only concerned the 3rd order. Hence, we demonstrate that large brains are twistier than smaller ones because of an increased number of high spatial frequency folds, ramifications and kinks that accommodate the allometric increase of cortical surface.

Are Developmental Trajectories of Cortical Folding Comparable Between Cross-sectional Datasets of Fetuses and Preterm Newborns?

Magnetic resonance imaging has proved to be suitable and efficient for in vivo investigation of the early process of brain gyrification in fetuses and preterm newborns but the question remains as to whether cortical-related measurements derived from both cases are comparable or not. Indeed, the developmental folding trajectories drawn up from both populations have not been compared so far, neither from cross-sectional nor from longitudinal datasets. The present study aimed to compare features of cortical folding between healthy fetuses and early imaged preterm newborns on a cross-sectional basis, over a developmental period critical for the folding process (21-36 weeks of gestational age [GA]). A particular attention was carried out to reduce the methodological biases between the 2 populations. To provide an accurate group comparison, several global parameters characterizing the cortical morphometry were derived. In both groups, those metrics provided good proxies for the dramatic brain growth and cortical folding over this developmental period. Except for the cortical volume and the rate of sulci appearance, they depicted different trajectories in both groups suggesting that the transition from into ex utero has a visible impact on cortical morphology that is at least dependent on the GA at birth in preterm newborns.

The Renpenning syndrome spectrum: new clinical insights supported by 13 new PQBP1-mutated males.

D Germanaud, M Rossi, G Bussy, D Gérard, L Hertz‐Pannier, P Blanchet, H Dollfus, F Giuliano, V Bennouna‐Greene, P Sarda, S Sigaudy, A Curie, MC Vincent, R Touraine, V des Portes. The Renpenning syndrome spectrum: new clinical insights supported by 13 new PQBP1‐mutated males.

Fast surface-based measurements using first eigenfunction of the Laplace-Beltrami Operator: Interest for sulcal description

In this paper we propose a fast method to compute the longitudinal extension of surfaces using the extrema of the first eigenfunction of Laplace-Beltrami Operator and the hot spots conjecture. We also propose an original definition of the surface width based on the distance to the longest geodesic. We show that the implementation of our new definition of length is consistent with the one computed from brute force and that the time complexity is considerably improved. We have tested the numerical efficiency of our approach on simple simulations and applied it to cortical surface patches from a real MRI dataset. Besides our approach enriches global descriptors of sulci shapes with a third dimension : length, depth and now width.

The dynamics of cortical folding waves and prematurity-related deviations revealed by spatial and spectral analysis of gyrification

&NA; In the human brain, the appearance of cortical sulci is a complex process that takes place mostly during the second half of pregnancy, with a relatively stable temporal sequence across individuals. Since deviant gyrification patterns have been observed in many neurodevelopmental disorders, mapping cortical development in vivo from the early stages on is an essential step to uncover new markers for diagnosis or prognosis. Recently this has been made possible by MRI combined with post‐processing tools, but the reported results are still fragmented. Here we aimed to characterize the typical folding progression ex utero from the pre‐ to the post‐term period, by considering 58 healthy preterm and full‐term newborns and infants imaged between 27 and 62 weeks of post‐menstrual age. Using a method of spectral analysis of gyrification (SPANGY), we detailed the spatial‐frequency structure of cortical patterns in a quantitative way. The modeling of developmental trajectories revealed three successive waves that might correspond to primary, secondary and tertiary folding. Some deviations were further detected in 10 premature infants without apparent neurological impairment and imaged at term equivalent age, suggesting that our approach is sensitive enough to highlight the subtle impact of preterm birth and extra‐uterine life on folding. Graphical abstract Figure. No caption available. HighlightsThe progression of cortical folding was studied from the pre‐ to post‐term period.SPANGY provided a quantitative spectral and spatial analysis of cortical patterns.We showed three successive folding waves with different characteristic age points.This approach suggested deviations in primary folding in premature infants at TEA.

Exploring the successive waves of cortical folding in the developing brain using MRI and spectral analysis of gyrification

In the developing human brain, gyrification is a complex process going through the successive appearance of primary folds (from 20 weeks of gestational age GA), secondary folds (from 32w GA) and tertiary folds (around term age). While this sequence is finely described in fetuses and preterm newborns of different ages using MRI and folding indices, there is still no fully objective assessment of the folding stage at the individual level. We examined the potential of a new method of spectral analysis of gyrification (SPANGY) that was applied to cortical surfaces of 26 preterm newborns, 9 full-term newborns and 17 infants to quantify the spatial-frequency structure of folding. Based on modelling approaches, we unraveled 4 periods along the developmental sequence from 27 to 62w GA, with relevant timepoints around 31w, 36-38w, and 44-47w GA. These periods showed specific folding features, with spatial patterns of increasing frequencies.

Spectral clustering based parcellation of FETAL brain MRI

Many neuroimaging studies are based on the idea that there are distinct brain regions that are functionally or micro-anatomically homogeneous. Obtaining such regions in an automatic way is a challenging task for fetal data due to the lack of strong and consistent anatomical features at the early stages of brain development. In this paper we propose the use of an automatic approach for parcellating fetal cerebral hemispheric surfaces into K regions via spectral clustering. Unlike previous methods, our technique has the crucial advantage of only relying on intrinsic geometrical properties of the cortical surface and thus being unsupervised. Results on a data-set of fetal brain MRI acquired in utero demonstrated a convincing parcellation reproducibility of the cortical surfaces across fetuses with varying gestational ages and folding magnitude.

SPANOL (SPectral ANalysis of Lobes): A Spectral Clustering Framework for Individual and Group Parcellation of Cortical Surfaces in Lobes

Understanding the link between structure, function and development in the brain is a key topic in neuroimaging that benefits from the tremendous progress of multi-modal MRI and its computational analysis. It implies, inter alia, to be able to parcellate the brain volume or cortical surface into biologically relevant regions. These parcellations may be inferred from existing atlases (e.g., Desikan) or sets of rules, as would do a neuroanatomist for lobes, but also directly driven from the data (e.g., functional or structural connectivity) with minimum a priori. In the present work, we aimed at using the intrinsic geometric information contained in the eigenfunctions of Laplace-Beltrami Operator to obtain parcellations of the cortical surface based only on its description by triangular meshes. We proposed a framework adapted from spectral clustering, which is general in scope and suitable for the co-parcellation of a group of subjects. We applied it to a dataset of 62 adults, optimized it and revealed a striking agreement between parcels produced by this unsupervised clustering and Freesurfer lobes (Desikan atlas), which cannot be explained by chance. Constituting the first reported attempt of spectral-based fully unsupervised segmentation of neuroanatomical regions such as lobes, spectral analysis of lobes (Spanol) could conveniently be fitted into a multimodal pipeline to ease, optimize or speed-up lobar or sub-lobar segmentation. In addition, we showed promising results of Spanol on smoother brains and notably on a dataset of 15 fetuses, with an interest for both the understanding of cortical ontogeny and the applicative field of perinatal computational neuroanatomy.

Alpha Waves as a Neuromarker of Autism Spectrum Disorder: The Challenge of Reproducibility and Heterogeneity

Background: There is no consensus in the literature concerning the presence of abnormal alpha wave profiles in patients with autism spectrum disorder (ASD). This may be due to phenotypic heterogeneity among patients as well as the limited sample sizes utilized. Here we present our results of alpha wave profile analysis based on a sample larger than most of those in the field, performed using a robust processing pipeline. Methods: We compared the alpha waves profiles at rest in children with ASD to those of age-, sex-, and IQ-matched control individuals. We used linear regression and non-parametric normative models using age as covariate forparsing the clinical heterogeneity. We explored the correlation between EEG profiles and the patient’s brain volumes, obtained from structural MRI. We automatized the detection of the alpha peak and visually quality controled our MRI measurements. We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0.05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0.05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines. Discussions: Our study did not find evidence for abnormal alpha wave profiles in ASD. We propose, however, an analysis pipeline to perform standardized and automatized EEG analyses on large cohorts. These should help the community to address the challenge of clinical heterogeneity of ASD and to tackle the problems of reproducibility.