Suzana Herculano-Houzel

arXiv2026

Separating mixtures of single-fiber orientation distributions for increased sensitivity to crossing white matter fibers

Intro: Diffusion MRI allows for in vivo probing of brain tissue architecture via micron-level diffusion aggregated at the millimeter scale, but crossing white matter fibers decrease the sensitivity of downstream tasks such as microstructural modeling and tractography. Current fixel-based methods for identifying the number, orientation, and spread of fibers rely on finding the peaks of fiber orientation distributions from constrained spherical deconvolution. However, this method is biased by the spreads of nearby fibers and fails at angular separations lower than approximately 40-50 degrees. Methods: In this work, we perform a constrained nonlinear optimization called fiber segmentation by signal splitting and iterative least squares estimation (FISSILE), modeling each voxel as a mixture of single-fiber distributions. We use FISSILE to fit axially symmetric fibers in simulations and on three in vivo human subjects from the Human Connectome Project, an ex vivo raccoon brain, as well as the same human subject scanned twice at two different signal-to-noise ratios (SNRs). Results: On simulated data, FISSILE successfully identifies fibers at an angular separation of 40° and a volume fraction of 0.1 at an SNR as low as 30, while MRtrix3's fod2fixel is limited to 60° separation and 0.1 volume fraction at the same SNR. On acquired data, FISSILE identified 85.8% of white matter voxels on average in human brains and 79.4% in the raccoon with at least one fiber crossing, whereas fod2fixel identified 30.1% and 44.5%. The number of white matter fiber crossings was mildly sensitive to different noise levels in the human subject with two acquisitions. Conclusion: A method that models the crossing fiber phenomenon as an unmixing problem increases sensitivity to individual fibers for more precise characterization of anatomy.

eess.IV↗

arXiv2019

A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs.

q-bio.GN↗