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An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected.

ABSTRACT: The tracts between cortical areas are conceived as playing a central role in cortical information processing, but their actual numbers have never been determined in humans. Here, we estimate the absolute number of axons linking cortical areas from a whole-cortex diffusion MRI (dMRI) connectome, calibrated using the histologically measured callosal fiber density. Median connectivity is estimated as approximately 6,200 axons between cortical areas within hemisphere and approximately 1,300 axons interhemispherically, with axons connecting functionally related areas surprisingly sparse. For example, we estimate that <5% of the axons in the trunk of the arcuate and superior longitudinal fasciculi connect Wernicke's and Broca's areas. These results suggest that detailed information is transmitted between cortical areas either via linkage of the dense local connections or via rare, extraordinarily privileged long-range connections.

SUBMITTER: Rosen BQ

PROVIDER: S-EPMC8947121 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected.

Rosen Burke Q BQ Halgren Eric E

PLoS biology 20220314 3

The tracts between cortical areas are conceived as playing a central role in cortical information processing, but their actual numbers have never been determined in humans. Here, we estimate the absolute number of axons linking cortical areas from a whole-cortex diffusion MRI (dMRI) connectome, calibrated using the histologically measured callosal fiber density. Median connectivity is estimated as approximately 6,200 axons between cortical areas within hemisphere and approximately 1,300 axons in ...[more]

PMID: 35286306

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Project description:Cortical microinfarcts (CMIs) are microscopically identified wedge-shaped ischemic lesions that occur at or near the cortical surface and result from occlusion of penetrating arterioles. These microscopic lesions can be observed with high-resolution magnetic resonance imaging in aging brains and in patients with cerebrovascular disease. Recent studies have suggested that strategically located microinfarcts strongly correlate with cognitive deficits, which can contribute to Alzheimer's disease as well as other forms of dementia. We have recently shown that the molecular organization of axons into functional microdomains is altered in areas adjacent to white matter lacunar and microinfarcts, creating a peri-infarct penumbral injury in surviving axons. Whether similar changes in nodal, adjacent paranodal, and proximal axon initial segment molecular organization occur in the cortex adjacent to human CMIs is not known. Paraffin-embedded sections of autopsy brain tissue from five patients with CMIs were immunofluorescently labeled for nodal and paranodal markers including beta-IV spectrin, ankyrin-G, and contactin-associated protein. High magnification images from the peri-infarct cortical tissue were generated using confocal microscopy. In surviving cortical tissue adjacent to microinfarcts, we observed a dramatic loss of axon initial segments, suggesting that neuronal firing capacity in adjacent cortical tissue is likely compromised. The number of identifiable nodal/paranodal complexes in surviving cortical tissue is reduced adjacent to microinfarcts, while the average paranodal length is increased indicating a breakdown of axoglial contact. This axonal microdomain disorganization occurs in the relative absence of changes in the structural integrity of myelinated axons as measured by myelin basic protein and neurofilament staining. These findings indicate that the molecular organization of surviving axons adjacent to human CMIs is abnormal, reflecting lost axoglial contact and the functional elements necessary for neural transmission. This study provides support for the concept of a microinfarct penumbral injury that may account for the cumulative cognitive effect of these tiny strokes.

Project description:BackgroundThere is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study.MethodsBoth the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered.ResultsThe simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised [Formula: see text] models performed similarly and better than the independent and localised [Formula: see text] models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice.ConclusionDetecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.

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An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected.

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An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected.

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