Scaling up cell-counting efforts in neuroscience through semi-automated methods

Bjerke, Ingvild Elise; Yates, Sharon; Carey, Harry; Bjaalie, Jan G.; Leergaard, Trygve B.

doi:10.1016/j.isci.2023.107562

Cited by 4 publications

(2 citation statements)

References 101 publications

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“…The following step is to detect neuronal cell bodies in the entire image and quantify soma density across brain regions. The size of mesoscale data makes manual handling impractical and prone to biases; hence, automatic or semi-automatic tools are more suitable for its processing ( Bjerke et al, 2023 ). Soma detection can be made either by traditional image enhancement filters followed by intensity thresholds, such as in ClearMap ( Renier et al, 2016 ) and MIRACL ( Goubran et al, 2019 ), or by advanced machine learning techniques for pixel classification as deep learning approaches ( Tyson and Margrie, 2022 ).…”

Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Caznok Silveira,

Antunes,

Athié

et al. 2024

Front. Neurosci.

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The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

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Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Caznok Silveira,

Antunes,

Athié

et al. 2024

Front. Neurosci.

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“…This allows researchers to integrate and analyze data from different sources within a common anatomical context more easily. For example, spatial registration procedures allow image data to be directly compared and analyzed based on atlas coordinates or annotated brain structures (Puchades et al, 2019;Tappan et al, 2019;Tyson and Margrie, 2022;Kleven et al, 2023b), e.g., through use of computational analyses of features of interest in atlas-defined regions of interest (Kim et al, 2017;Bjerke et al, 2018bBjerke et al, , 2023Yates et al, 2019;Kleven et al, 2023a,b). For other data types, such as locations of electrode tracts, 3D reconstructed neurons, or other features of interest, procedures and tools have been developed to represent the data as coordinate-based points of interest allowing validation or visualization of locations (Bjerke et al, 2018b;Fiorilli et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases

Blixhavn,

Reiten,

Kleven

et al. 2024

Front. Neuroinform.

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Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

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Brain cell types and distribution

Kooijmans

2024

Reference Module in Neuroscience and Biobehavioral Psychology

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Scaling up cell-counting efforts in neuroscience through semi-automated methods

Cited by 4 publications

References 101 publications

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases

Brain cell types and distribution

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