serial-two-photon-tomography-whole-marmoset-brain-for-neuroanatomical

Serial Two-Photon Tomography of the Whole Marmoset Brain for Neuroanatomical Analyses

Serial Two-photon tomography (STPT) is a technique to image a mass of tissue in its three-dimensional shape by combining two-photon imaging with automatic ...

RIKEN Center for Brain Science

In an early stage of the postnatal development of cats, orientation maps mature and spatial frequency selectivity is consolidated. To investigate the time course of orientation map maturation associated with the consolidation of spatial frequency selectivity, we performed optical imaging of intrinsic signals in areas 17 and 18 of cats under the stimulation of drifting square-wave gratings with different orientations and spatial frequencies. First, orientation maps for lower spatial frequencies emerged in the entire part of the lateral gyrus, which includes areas 17 and 18, and then these orientation maps in the posterior part of the lateral gyrus disappeared as orientation maps for higher spatial frequencies matured. Independent of age, an anteroposterior gradient of response strengths from lower to higher spatial frequencies was observed. This indicates that the regional distribution of spatial frequencies is innately determined. The size of iso-orientation domains tended to decrease as the stimulus spatial frequency increased at every age examined. In contrast, orientation representation bias changed with age. In cats younger than 3 months, the cardinal (vertical and horizontal) orientations were represented predominantly over the oblique orientations. However, in young adult cats from 3 to 9 months old, the representation bias switched to predominantly oblique orientations. These age-dependent changes in the orientation representation bias imply that orientation maps continue to elaborate within postnatal 1 year with the consolidation of spatial frequency selectivity. We conclude that both intrinsic and mutual factors lead to the development of orientation maps and spatial frequency selectivity.

Parallel development of orientation maps and spatial frequency selectivity in cat visual cortex.

To date, numerous mathematical models have been proposed on the basis of some types of Hebbian synaptic learning to account for the activity-dependent development of orientation maps as well as neuronal orientation selectivity. These models successfully reproduced orientation map-like spatial patterns. Nevertheless, we still have questions: (1) How does synaptic rewiring occur in the visual cortex during the formation of orderly orientation maps in early life? (2) How does visual experience contribute to the maturation of orientation selectivity of visual cortical neurons and reorganize orientation maps? (3) How does the sensitive period for orientation plasticity end? In this study, we performed animal experiments and mathematical modeling to understand the mechanisms underlying synaptic rewiring for experience-dependent formation and reorganization of orientation maps. At first, we visualized orientation maps from the intrinsic signal optical imaging in area 17 of kittens reared under single-orientation exposure through cylindrical-lens-fitted goggles. The experiments revealed that the degree of expansion of cortical domains representing the experienced orientation depends on the age at which the single-orientation exposure starts. As a result, we obtained the sensitive period profile for orientation plasticity. Next, we refined our previously proposed mathematical model for the activity-dependent self-organization of thalamo-cortical inputs on the assumption that rewiring is caused by the competitive interactions among transient synaptic contacts on the same dendritic spine. Although various kinds of molecules have been reported to be involved in such interactions, we attempt to build a mathematical model to describe synaptic rewiring focusing on brain-derived neurotrophic factor (BDNF) and its related molecules. Performing computer simulations based on the refined model, we successfully reproduced orientation maps reorganized in kittens reared under single-orientation exposure as well as normal visual experience. We also reproduced the experimentally obtained sensitive period profile for orientation plasticity. The excellent agreement between experimental observations and theoretical reproductions suggests that the BDNF-induced competitive interaction among synaptic contacts from different axons on the same spine is an important factor for the experience-dependent formation and reorganization of orientation selectivity and orientation maps.

Development and Reorganization of Orientation Representation in the Cat Visual Cortex: Experience-Dependent Synaptic Rewiring in Early Life.

Magnetic resonance imaging (MRI) is a non-invasive neuroimaging technique that is useful for identifying normal developmental and aging processes and for data sharing. Marmosets have a relatively shorter life expectancy than other primates, including humans, because they grow and age faster. Therefore, the common marmoset model is effective in aging research. The current study investigated the aging process of the marmoset brain and provided an open MRI database of marmosets across a wide age range. The Brain/MINDS Marmoset Brain MRI Dataset contains brain MRI information from 216 marmosets ranging in age from 1 and 10 years. At the time of its release, it is the largest public dataset in the world. It also includes multi-contrast MRI images. In addition, 91 of 216 animals have corresponding high-resolution ex vivo MRI datasets. Our MRI database, available at the Brain/MINDS Data Portal, might help to understand the effects of various factors, such as age, sex, body size, and fixation, on the brain. It can also contribute to and accelerate brain science studies worldwide.

Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of   for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness (  ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean  percentile Hausdorff distance (95HD) of   . Whereas a mean 95HD of   was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality,  Jaccard Index acquired from our pipeline, while  was stated in their paper.

Article	Year
Parallel development of orientation maps and spatial frequency selectivity in cat visual cortex. The European journal of neuroscience\| PubMed ID: 22211742	2012
Development and Reorganization of Orientation Representation in the Cat Visual Cortex: Experience-Dependent Synaptic Rewiring in Early Life. Frontiers in neuroinformatics\| PubMed ID: 32973480	2020
Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range. Scientific data\| PubMed ID: 37105968	2023
A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. Neuroinformatics\| PubMed ID: 39417954	2024

Toshiki Tani

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