Manifold classification of neuron types from microscopic images

Liu, Lijuan; Qian, Penghao

doi:10.1093/bioinformatics/btac594

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The first is the generation of connectivity associated t-type, e-type, and m-types data. A second step is building a thorough statistical model of all such data to mine the associations and distribution patterns, which could be homogeneous clusters or globally nonlinear manifold patterns (Liu and Qian, 2022).…”

Section: Discussionmentioning

confidence: 99%

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Liu

Yun²,

Manubens-Gil

et al. 2023

Preprint

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Classifications of single neurons at brain-wide scale is a powerful way to characterize the structural and functional organization of a brain. We acquired and standardized a large morphology database of 20,158 mouse neurons, and generated a whole-brain scale potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity types and subtypes (both called “c-types” for simplicity) for neurons in 31 brain regions. We found that neuronal subtypes defined by connectivity in the same regions may share statistically higher correlation in their dendritic and axonal features than neurons having contrary connectivity patterns. Subtypes defined by connectivity show distinct separation with each other, which cannot be recapitulated by morphology features, population projections, transcriptomic, and electrophysiological data produced to date. Within this paradigm, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our finding underscores the importance of connectivity in characterizing the modularity of brain anatomy, as well as the cell types and their subtypes. These results highlight that c-types supplement conventionally recognized transcriptional cell types (t-types), electrophysiological cell types (e-types), and morphological cell types (m-types) as an important determinant of cell classes and their identities.

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Section: Discussionmentioning

confidence: 99%

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Liu

Yun²,

Manubens-Gil

et al. 2023

Preprint

Self Cite

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“…These neurons extend dozens of millimeters (Figure 4). It has been often observed that different neuron classes are poorly discriminated by global morphology where features such as length and branching number are considered (Liu & Qian, 2022;Peng et al, 2021). To overcome this limitation, we registered the whole set to CCFv3 using mBrainAligner.…”

Section: Detecting Primary Distributions and Key Morphological Variab...mentioning

confidence: 99%

Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

Peng

Liu

Jiang³

et al. 2023

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We conducted a large-scale study of whole-brain morphometry, analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We spatially registered 205 mouse brains and associated data from six Brain Initiative Cell Census Network (BICCN) data sources covering three major imaging modalities from five collaborative projects to the Allen Common Coordinate Framework (CCF) atlas, annotated 3D locations of cell bodies of 227,581 neurons, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1,891 neurons along with their axonal motifs, and detected 2.58 million putative synaptic boutons. Our analysis covers six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, sub-neuronal dendritic and axonal arborization, axonal boutons, and structural motifs, along with a quantitative characterization of the diversity and stereotypy of patterns at each level. We identified 16 modules consisting of highly intercorrelated brain regions in 13 functional brain areas corresponding to 314 anatomical regions in CCF. Our analysis revealed the dendritic microenvironment as a powerful method for delineating brain regions of cell types and potential subtypes. We also found that full neuronal morphologies can be categorized into four distinct classes based on spatially tuned morphological features, with substantial cross-areal diversity in apical dendrites, basal dendrites, and axonal arbors, along with quantified stereotypy within cortical, thalamic and striatal regions. The lamination of somas was found to be more effective in differentiating neuron arbors within the cortex. Further analysis of diverging and converging projections of individual neurons in 25 regions throughout the brain reveals branching preferences in the brain-wide and local distributions of axonal boutons. Overall, our study provides a comprehensive description of key anatomical structures of neurons and their types, covering a wide range of scales and features, and contributes to our understanding of neuronal diversity and its function in the mammalian brain.

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“…Understanding how the interconnections of neurons account for brain functions has been a great pursuit over the past decades [Eichler et al, 2017, Lichtman and Denk, 2011, Liu and Qian, 2022, Dey et al, 2022, Wang et al, 2022. A brain circuit network refers to a physical map of a brain's structural connections, commonly referred to as a connectome.…”

Section: Introductionmentioning

confidence: 99%

Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

Wan

Liao

Dong

et al. 2023

Preprint

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Reconstructing neuron-level brain circuit network is a universally recognized formidable task. A significant impediment involves discerning the intricate interconnections among multitudinous neurons in a complex brain network. However, the majority of current methodologies only rely on learning local visual synapse features while neglecting the incorporation of comprehensive global topological connectivity information. In this paper, we consider the perspective of network connectivity and introduce graph neural networks to learn the topological features of brain networks. As a result, we propose Neuronal Circuit Prediction Network (NCPNet), a simple and effective model to jointly learn node structural representation and neighborhood representation, constructing neuronal connection pair feature for inferring neuron-level connections in a brain circuit network. We use a small number of connections randomly selected from a single brain circuit network as training data, expecting NCPNet to extrapolate known connections to unseen instances. We evaluated our model on \textit{Drosophila} connectome and \textit{C. elegans} worm connectome. The numerical results demonstrate that our model achieves a prediction accuracy of 91.88\% for neuronal connections in the \textit{Drosophila} connectome when utilizing only 5\% of known connections. Similarly, under the condition of 5\% known connections in \textit{C. elegans}, our model achieves an accuracy of 93.79\%. Additional qualitative analysis conducted on the learned representation vectors of Kenyon cells indicates that NCPNet successfully acquires meaningful features that enable the discrimination of neuronal sub-types. Our project is available at \url{https://github.com/mxz12119/NCPNet}

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Manifold classification of neuron types from microscopic images

Cited by 5 publications

References 8 publications

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

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