Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.10.10.462694

Fetal cerebellar development: 3D morphometric analysis of fetal postmortem MRI among Chinese fetuses.

Shuwei, L.·Suluba, E. H.
View original source ↗Export metadata ↓

Abstract

The development of the cerebellum starts from early gestational period and extends postnatal. Because of its protracted development, the cerebellum is susceptible to developmental anomalies such as Dandy-Walker malformations, Blakes pouch cysts and vermin hypoplasia. Measurements of fetal cerebellar parameters of a normal growing fetus in each week of gestation is important for setting up morphometric standards and hence used as clinical reference data. Any deviation from the normal cerebellar parameters alerts the clinicians for the possibility of presence of cerebellar malformations. Study objectiveThe objective of this study was to assess the fetal cerebellar growth by quantifying the following parameters; fetal cerebellar volume, anterior-posterior diameter and superior-inferior diameter. MethodsWe used 3T and 7T MRI to scan the postmortem fetal brains at different stages of development and subsequently analyze the images using ITK-SNAP software. ResultsThe mean superior-inferior cerebellar diameter was found to be 19.12{+/-}2. 70mm.The linear(y=bo+b1t) model was the best fit (r2=0.996, F=32022.961) to describe the relationship between the gestational age and the superior-inferior diameter(y=5.89+0.49t). There was significant correlation between the superior-inferior cerebellar diameter and the gestation age, Pearson correlation coefficient of 0.999, r=0.001. The median cerebellar volume was 8607.7mm, the mean rank high among males(78.12) as compared to female(68.25).There was no statistically significant difference of the cerebellar volume between males and females (u=2193.5,p=0.16).The quadratic(y=bo+b1t+b2t2) model was the best fit regression equation (r2=0.994,F=10791.157) describing the relationship between the cerebellar volume and the gestational age. The median anterior- posterior diameter was 12.45 mm. There was significant correlation between anterior-posterior diameter and the gestational age with Spearmans rho of (0.997, p=0.01). The linear model was the best fit the best fit model (y=bo+b1t) describing the relationship between anterior-posterior diameter and the gestational age(y=3.31+0.5t) r2=0.998, F=70646.838 ConclusionSignificant correlation between the superior-inferior cerebellar diameters, the anterior-posterior cerebellar diameter and the gestational age was found. These two linear parameters follow the first-degree polynomial in relation to the gestational age. The cerebellar volume follows the second-degree polynomial as it increases with the gestational age and correlate significantly with the gestational age. This study has provided new insight to the development of the cerebellum, and setup a benchmark data of which the deviation from it will alert the clinicians for the possibility of presence of cerebellar malformations.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shuwei, L., Suluba, E. H.. 2021-10-13. Fetal cerebellar development: 3D morphometric analysis of fetal postmortem MRI among Chinese fetuses.. https://doi.org/10.1101/2021.10.10.462694

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗