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

We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain.

Humans demonstrate behavioural advantages (biases) towards particular dimensions (colour or shape of visual objects), but such biases are significantly altered in neuropsychological disorders. Recent studies have shown that lesions in the prefrontal cortex do not abolish dimensional biases, and therefore suggest that such biases might not depend on top-down prefrontal-mediated attention and instead emerge as bottom-up processing advantages. We hypothesised that if dimensional biases merely emerge from an enhancement of object features, the presence of visual objects would be necessary for the manifestation of dimensional biases. In a specifically-designed working memory task, in which macaque monkeys and humans performed matching based on the object memory rather than the actual object, we found significant dimensional biases in both species, which appeared as a shorter response time and higher accuracy in the preferred dimension (colour and shape dimension in humans and monkeys, respectively). Moreover, the mnemonic demands of the task influenced the magnitude of dimensional bias. Our findings in two primate species indicate that the dichotomy of top-down and bottom-up processing does not fully explain the emergence of dimensional biases. Instead, dimensional biases may emerge when processed information regarding visual object features interact with mnemonic and executive functions to guide goal-directed behaviour.

Dimension of visual information interacts with working memory in monkeys and humans.

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.

The prefrontal cortex (PFC) has dramatically expanded in primates, but its organization and interactions with other brain regions are only partially understood. We performed high-resolution connectomic mapping of the marmoset PFC and found two contrasting corticocortical and corticostriatal projection patterns: "patchy" projections that formed many columns of submillimeter scale in nearby and distant regions and "diffuse" projections that spread widely across the cortex and striatum. Parcellation-free analyses revealed representations of PFC gradients in these projections' local and global distribution patterns. We also demonstrated column-scale precision of reciprocal corticocortical connectivity, suggesting that PFC contains a mosaic of discrete columns. Diffuse projections showed considerable diversity in the laminar patterns of axonal spread. Altogether, these fine-grained analyses reveal important principles of local and long-distance PFC circuits in marmosets and provide insights into the functional organization of the primate brain.

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain.

Transcranial direct current stimulation (tDCS) has garnered significant interest for its potential to enhance cognitive functions and as a therapeutic intervention in various cognitive disorders. However, the clinical application of tDCS has been hampered by significant variability in its cognitive outcomes. Furthermore, the widespread use of tDCS has raised concerns regarding its safety and efficacy, particularly in light of our limited understanding of its underlying neural mechanisms at the cellular level. We still do not know 'where', 'when' and 'how' tDCS modulates information encoding by neurons, in order to lead to the observed changes in cognitive functions. Without elucidating these fundamental unknowns, the root causes of its outcome variability and long-term safety remain elusive, challenging the effective application of tDCS in clinical settings. Addressing this gap, our study investigates the effects of tDCS, applied over the dorsolateral prefrontal cortex, on cognitive abilities and individual neuron activity in macaque monkeys performing cognitive tasks. Like humans performing a delayed match-to-sample task, monkeys exhibited practice-related slowing in their responses (within-session behavioural adaptation). Concurrently, there were practice-related changes in simultaneously recorded activity of prefrontal neurons (within-session neuronal adaptation). Anodal tDCS attenuated both these behavioural and neuronal adaptations when compared with sham stimulation. Furthermore, tDCS abolished the correlation between response time of monkeys and neuronal firing rate. At a single-cell level, we also found that following tDCS, neuronal firing rate was more likely to exhibit task-specific modulation than after sham stimulation. These tDCS-induced changes in both behaviour and neuronal activity persisted even after the end of tDCS stimulation. Importantly, multiple applications of tDCS did not alter burst-like firing rates of individual neurons when compared with sham stimulation. This suggests that tDCS modulates neural activity without enhancing susceptibility to epileptiform activity, confirming a potential for safe use in clinical settings. Our research contributes unprecedented insights into the 'where', 'when' and 'how' of tDCS effects on neuronal activity and cognitive functions by showing that modulation of the behaviour of monkeys by the tDCS of the prefrontal cortex is accompanied by alterations in prefrontal cortical cell activity ('where') during distinct trial phases ('when'). Importantly, tDCS led to task-specific and state-dependent alterations in prefrontal cell activities ('how'). Our findings suggest a significant shift from the view that the effects of tDCS are merely attributable to polarity-specific shifts in cortical excitability and instead propose a more complex mechanism of action for tDCS that encompasses various aspects of cortical neuronal activity without increasing burst-like epileptiform susceptibility.

Direct current stimulation modulates prefrontal cell activity and behaviour without inducing seizure-like firing.

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
The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain. Brain structure & function\| PubMed ID: 32367264	2020
Dimension of visual information interacts with working memory in monkeys and humans. Scientific reports\| PubMed ID: 35351948	2022
Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range. Scientific data\| PubMed ID: 37105968	2023
Local and long-distance organization of prefrontal cortex circuits in the marmoset brain. Neuron\| PubMed ID: 37196659	2023
Direct current stimulation modulates prefrontal cell activity and behaviour without inducing seizure-like firing. Brain : a journal of neurology\| PubMed ID: 39166526	2024
A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. Neuroinformatics\| PubMed ID: 39417954	2024

Hiroshi Abe

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