Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells

Wang, Xiaojun; Tucciarone, Jason; Jiang, Siqi; Yin, Fang‐Fang; Wang, Bor-Shuen; Wang, Dingkang; Yao, Jia; Jia, Xueyan; Li, Yuxin; Yang, Tao; Xu, Zhengchao; Akram, Masood A.; Wang, Yusu; Zeng, Shaoqun; Ascoli, Giorgio A.; Mitra, Partha P.; Gong, Hui; Luo, Qingming; Huang, Zhi

doi:10.1016/j.celrep.2019.02.040

Cited by 59 publications

(51 citation statements)

References 50 publications

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“…3A vs C), and the accuracy is 87.22 ± 2.34% vs 83.22 ± 1.31%. Since the ChCs are located mainly in the supragranular layer 32 , their absence significantly increases the noise of the output, decreasing the accuracy to 83.06 ± 1.27% (Fig. 3B).…”

Section: Effects Of Absence Of Ll-in Selectivity On Single Column Undmentioning

confidence: 99%

Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance

Zhang

Zeng

Yang

2020

Sci Rep

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Interneurons play crucial roles in neocortex associated with high-level cognitive functions; however, the specific division of labor is still under investigation. Interneurons are exceptionally diverse in their morphological appearance and functional properties. In this study, we modify a prefrontal multicolumn circuit in which five subtypes of inhibitory interneurons play distinct roles in the maintenance of transient information. These interneurons are classified according to the extending range of axonal projections. Our work simplifies the division of labor between different types of interneurons for the maintenance of information and the principle of functional redundancy of the brain from the perspective of computational modeling. This model presents a framework to understand the cooperation between different interneurons in a recurrent cortical circuit.Most of the neocortical neurons (70-80%) are excitatory neurons, and the remaining are mostly inhibitory interneurons 1,2 . Although the proportion is limited, synaptic inhibition plays a crucial role in supporting cognitive function of the cortical cortex 3,4 . Recent years have witnessed a dramatic accumulation of our knowledge about the diverse morphological, physiological, molecular, and synaptic characteristics of interneurons 5-8 . A mechanistic understanding of these interneurons and how they support cognition remains to be further demonstrated.The computational models can integrate different levels of neuroscience data, helping us to explore the unknown. We present here, a multicolumn prefrontal cortex (PFC) circuit to elucidate distinct operations carried out by diverse interneurons. We have modified the model of a detailed data-driven single prefrontal column proposed by Durstewitz and colleagues [ModelDB (http://senselab.med.yale.edu/ModelDB/)] 9 , and used the adaptive exponential integrate-and-fire (aEIF) neuron model. The sources of anatomical structure include ferret, rodent, and primate PFC experiment 10-12 . The neuron parameters in this PFC network are derived from the experimental literature related to PFC and other areas of the neocortex 8,9,13 . Therefore, this model is a PFC network based entirely on biological experimental data.The axons of interneurons usually arborize within a cortical column and can project laterally across columns 14,15 . Thus, our network incorporates five interneuron subtypes, according to the extending range of axonal projections. We explore the division of labor between different types of interneurons for information maintenance and prove that cortex network is modular 16,17 . It contains connector hubs that have connections distributed diversely across communities 18,19 . This structure can compensate for the loss of function when partial short-range connected interneurons are damaged.Understanding the circuit mechanisms of signal transmission and information maintenance associated is expected to increase our understanding of high-level cognitive functions, such as working memory. The computational models hel...

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Section: Effects Of Absence Of Ll-in Selectivity On Single Column Undmentioning

confidence: 99%

Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance

Zhang

Zeng

Yang

2020

Sci Rep

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“…The hyperinnervation and excessive GABAergic inhibition of PyNs in Ts65Dn were rescued by normalizing the Dscam Dscam expression levels determine presynaptic terminal sizes in mammalian neurons The stereotypical morphology of ChCs axon arbors and presynaptic terminals are advantageous for quantitative assessment (50,51). Moreover, recent advances in genetic labeling of ChCs have allowed sparse labeling of single ChCs for quantifying presynaptic termi-nals at single-cell resolution (53,67). By taking advantage of this system, we show in this study that Dscam overexpression causes presynaptic overgrowth of ChCs in the neocortex.…”

Section: Discussionmentioning

confidence: 99%

Dscamgene triplication causes neocortical overinhibition in Down syndrome

Liu

Caballero‐Florán

Yang

et al. 2020

Preprint

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A growing number of molecules have been identified as regulators of inhibitory synapse development, but whether dysregulated expression of these molecules contribute to brain disorders is poorly understood. Here we show that Down syndrome cell adhesion molecule (Dscam) regulates the inhibition of neocortical pyramidal neurons (PyNs) in a level-dependent fashion. Loss of Dscam impairs inhibitory neuron development and function.In the Ts65Dn mouse model for Down syndrome, where Dscam is overexpressed, GABAergic innervation of cortical PyNs by chandelier and basket cells is increased. Genetic normalization of Dscam expression rescues the excessive GABAergic innervation and the increased inhibition of PyNs. These findings demonstrate excessive GABAergic innervation and inhibition in the neocortex of Down syndrome mouse model and identify Dscam overexpression as the cause. They also implicate dysregulated Dscam levels as a potential pathogenic driver in related neurological disorders. Dscam| Down syndrome| GABAergic| synapse| chandelier cells| basket cells| Neocortex| Ts65Dn

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“…We used two datasets for testing. The first is provided by Wang et al [11], and it consists of genetic labeling chandelier cells imaged by the fMOST system with a resolution of 0.2 × 0.2 × 1 μm 3 . The second is provided by Zhang et al [23] and Jiang et al [24], and it consists of pyramidal neurons at the same resolution.…”

Section: Dataset and Computing Environmentsmentioning

confidence: 99%

“…The morphology of a neuronal circuit is a fundamental component for neuron modeling [6]. With improvements in sparse labeling and optical microscopy techniques, scientists are able to acquire three-dimensional whole-brain images at a submicron-resolution for mammals [7][8][9][10][11]. From these images, the techniques that accurately transform them into digital descriptions of neuron morphology are very important; this transformation process is called neuron tracing.…”

Section: Introductionmentioning

confidence: 99%

Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

et al. 2020

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Background: Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, singleneuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files. Results: We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise. Conclusions: This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

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Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells

Cited by 59 publications

References 50 publications

Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance

Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance

Dscamgene triplication causes neocortical overinhibition in Down syndrome

Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

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