Phylogenetic differences in the morphology and shape of the central sulcus in great apes and humans: implications for the evolution of motor functions.

The central sulcus divides the primary motor and somatosensory cortices in many anthropoid primate brains. Differences exist in the surface area and depth of the central sulcus along the dorso-ventral plane in great apes and humans compared to other primate species. Within hominid species, there are variations in the depth and aspect of their hand motor area, or knob, within the precentral gyrus. In this study, we used post-image analyses on magnetic resonance images to characterize the central sulcus shape of humans, chimpanzees (Pan troglodytes), gorillas (Gorilla gorilla), and orangutans (Pongo pygmaeus and Pongo abelii). Using these data, we examined the morphological variability of central sulcus in hominids, focusing on the hand region, a significant change in human evolution. We show that the central sulcus shape differs between great ape species, but all show similar variations in the location of their hand knob. However, the prevalence of the knob location along the dorso-ventral plane and lateralization differs between species and the presence of a second ventral motor knob seems to be unique to humans. Humans and orangutans exhibit the most similar and complex central sulcus shapes. However, their similarities may reflect divergent evolutionary processes related to selection for different positional and habitual locomotor functions.

A framework to improve the alignment of individual cytoarchitectonic maps of the Julich-Brain atlas using cortical folding landmarks.

The segregation of the cortical mantle into cytoarchitectonic areas provides a structural basis for the specialization of different brain regions. In vivo neuroimaging experiments can be linked to this postmortem cytoarchitectonic parcellation via Julich-Brain. This atlas embeds probabilistic maps that account for inter-individual variability in the localization of cytoarchitectonic areas in the reference spaces targeted by spatial normalization. We built a framework to improve the alignment of architectural areas across brains using cortical folding landmarks. This framework, initially designed for in vivo imaging, was adapted to postmortem histological data. We applied this to the first 14 brains used to establish the Julich-Brain atlas to infer a refined atlas with more focal probabilistic maps. The improvement achieved is significant in the primary regions and some of the associative areas. This framework also provides a tool for exploring the relationship between cortical folding patterns and cytoarchitectonic areas in different cortical regions to establish new landmarks in the remainder of the cortex.

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.

Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter.

In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.

PhyberSIM: a tool for the generation of ground truth to evaluate brain fiber clustering algorithms.

Diffusion Magnetic Resonance Imaging tractography is a non-invasive technique that produces a collection of streamlines representing the main white matter bundle trajectories. Methods, such as fiber clustering algorithms, are important in computational neuroscience and have been the basis of several white matter analysis methods and studies. Nevertheless, these clustering methods face the challenge of the absence of ground truth of white matter fibers, making their evaluation difficult. As an alternative solution, we present an innovative brain fiber bundle simulator that uses spline curves for fiber representation. The methodology uses a tubular model for the bundle simulation based on a bundle centroid and five radii along the bundle. The algorithm was tested by simulating 28 Deep White Matter atlas bundles, leading to low inter-bundle distances and high intersection percentages between the original and simulated bundles. To prove the utility of the simulator, we created three whole-brain datasets containing different numbers of fiber bundles to assess the quality performance of QuickBundles and Fast Fiber Clustering algorithms using five clustering metrics. Our results indicate that QuickBundles tends to split less and Fast Fiber Clustering tends to merge less, which is consistent with their expected behavior. The performance of both algorithms decreases when the number of bundles is increased due to higher bundle crossings. Additionally, the two algorithms exhibit robust behavior with input data permutation. To our knowledge, this is the first whole-brain fiber bundle simulator capable of assessing fiber clustering algorithms with realistic data.

Comparative analysis of the chimpanzee and human brain superficial structural connectivities.

Diffusion MRI tractography (dMRI) has fundamentally transformed our ability to investigate white matter pathways in the human brain. While long-range connections have extensively been studied, superficial white matter bundles (SWMBs) have remained a relatively underexplored aspect of brain connectivity. This study undertakes a comprehensive examination of SWMB connectivity in both the human and chimpanzee brains, employing a novel combination of empirical and geometric methodologies to classify SWMB morphology in an objective manner. Leveraging two anatomical atlases, the Ginkgo Chauvel chimpanzee atlas and the Ginkgo Chauvel human atlas, comprising respectively 844 and 1375 superficial bundles, this research focuses on sparse representations of the morphology of SWMBs to explore the little-understood superficial connectivity of the chimpanzee brain and facilitate a deeper understanding of the variability in shape of these bundles. While similar, already well-known in human U-shape fibers were observed in both species, other shapes with more complex geometry such as 6 and J shapes were encountered. The localisation of the different bundle morphologies, putatively reflecting the brain gyrification process, was different between humans and chimpanzees using an isomap-based shape analysis approach. Ultimately, the analysis aims to uncover both commonalities and disparities in SWMBs between chimpanzees and humans, shedding light on the evolution and organization of these crucial neural structures.

Phybers: a package for brain tractography analysis.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

Longitudinal Changes of Clinical, Imaging, and Fluid Biomarkers in Preataxic and Early Ataxic Spinocerebellar Ataxia Type 2 and 7 Carriers.

BACKGROUND AND OBJECTIVES: Brain MRI abnormalities and increases in neurofilament light chain (NfL) have mostly been observed in cross-sectional studies before ataxia onset in polyglutamine spinocerebellar ataxias. Our study aimed to identify longitudinal changes in biological, clinical, and/or imaging biomarkers in spinocerebellar ataxia (SCA) 2 and SCA7 carriers over 1 year. METHODS: We studied SCA2 and SCA7 carriers and controls (expansion-negative relatives) at the Paris Brain Institute. Inclusion criteria included Scale for the Assessment and Rating of Ataxia (SARA) scores between 0 and 15. Assessments at baseline, 6 months, and 12 months comprised neurologic, quality of life, orofacial motor, neuropsychological, and ophthalmologic examinations, along with gait and oculomotor recordings, brain MRI, CSF, and blood sampling. The primary outcome was the longitudinal change in these assessments over 1 year. RESULTS: We included 15 SCA2 carriers, 15 SCA7 carriers, and 10 controls between May 2020 and April 2021. At baseline, the ages were similar (41 [37, 46] for SCA2, 38 [28.5, 39.8] for SCA7, and 39.5 [31, 54.5] for controls, p = 0.78), as well the sex (p = 0.61); SARA scores were low but different (4 [1.25, 6.5] in SCA2, 2 [0, 11.5] in SCA7, and 0 in controls, p < 0.01). Pons and medulla volumes were smaller in SCAs (p < 0.05) and cerebellum volume only in SCA2 (p = 0.01). Plasma NfL levels were higher in SCA participants (SCA2: 14.2 pg/mL [11.52, 15.89], SCA7: 15.53 [13.27, 23.23]) than in controls (4.88 [3.56, 6.17], p < 0.001). After 1-year follow-up, in SCA2, there was significant pons (-144 ± 60 mm3) and cerebellum (-1,508 ± 580 mm3) volume loss and a worsening of gait assessment; in SCA7, SARA score significantly increased (+1.3 ± 0.4) and outer retinal nuclear layer thickness decreased (-15.4 ± 1.6 µm); for both SCA groups, the orofacial motor assessment significantly worsened. For preataxic and early ataxic carriers, the strongest longitudinal deterioration on outcome measures was orofacial motility in SCA2 and retinal thickness in SCA7. DISCUSSION: Despite the limitation of the small sample size, we detected annual changes in preataxic and early ataxic SCA individuals across brain MRI imaging, clinical scores, gait parameters, and retinal thickness. These parameters could serve as potential end points for future therapeutic trials in the preataxic phase. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT04288128.

A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients.

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.

Increased representation of the non-dominant hand in pianists demonstrated by measurement of 3D morphology of the central sulcus.

Background: Post-mortem and magnetic resonance imaging (MRI) studies of the central sulcus, as an indicator of motor cortex, have shown that in the general population there is greater representation of the dominant compared to the non-dominant hand. Studies of musicians, who are highly skilled in performing complex finger movements, have suggested this dominance is affected by musical training, but methods and findings have been mixed. Objective: In the present study, an automated image analysis pipeline using a 3D mesh approach was applied to measure central sulcus (CS) asymmetry on MR images obtained for a cohort of right-handed pianists and matched controls. Methods: The depth, length, and surface area (SA) of the CS and thickness of the cortical mantle adjacent to the CS were measured in each cerebral hemisphere by applying the BrainVISA Morphologist 2012 software pipeline to 3D T1-weighted MR images of the brain obtained for 15 right-handed pianists and 14 controls, matched with respect to age, sex, and handedness. Asymmetry indices (AIs) were calculated for each parameter and multivariate analysis of covariance (MANCOVA), and post hoc tests were performed to compare differences between the pianist and control groups. Results: A one-way MANCOVA across the four AIs, controlling for age and sex, revealed a significant main effect of group (P = 0.04), and post hoc analysis revealed that while SA was significantly greater in the left than the right cerebral hemisphere in controls (P < 0.001), there was no significant difference between left and right SA in the pianists (P = 0.634). Independent samples t-tests revealed that the SA of right CS was significantly larger in pianists compared to controls (P = 0.015), with no between-group differences in left CS. Conclusions: Application of an image analysis pipeline to 3D MR images has provided robust evidence of significantly increased representation of the non-dominant hand in the brain of pianists compared to age-, sex-, and handedness-matched controls. This finding supports prior research showing structural differences in the central sulcus in musicians and is interpreted to reflect the long-term motor training and high skill level of right-handed pianists in using their left hand.