Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions

Oltmer, Jan; Rosenblum, Emma W; Williams, Emily; Roy, Jessica; Llamas‐Rodríguez, Josue; Perosa, Valentina; Champion, Samantha N; Frosch, Matthew P.; Augustinack, Jean C.

doi:10.1038/s41598-023-32903-y

Cited by 9 publications

(7 citation statements)

References 66 publications

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“…Third, improving anatomical accuracy will impact potential consequences in neuroinformatics. Recently, deep learning approaches have been applied to histologic analyses (Niazi et al., 2019; Perosa et al., 2022; Waisman et al., 2021; Oltmer et al., 2023). Deep learning methods for segmenting pyramidal neurons may be applied to a high throughput experimental paradigm in the future, which may detect subtle neuronal loss and ultimately benefit disease treatment.…”

Section: Discussionmentioning

confidence: 99%

The prosubiculum in the human hippocampus: A rostrocaudal, feature‐driven, and systematic approach

Rosenblum,

Williams,

Champion

et al. 2024

J of Comparative Neurology

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The hippocampal subfield prosubiculum (ProS), is a conserved neuroanatomic region in mouse, monkey, and human. This area lies between CA1 and subiculum (Sub) and particularly lacks consensus on its boundaries; reports have varied on the description of its features and location. In this report, we review, refine, and evaluate four cytoarchitectural features that differentiate ProS from its neighboring subfields: (1) small neurons, (2) lightly stained neurons, (3) superficial clustered neurons, and (4) a cell sparse zone. ProS was delineated in all cases (n = 10). ProS was examined for its cytoarchitectonic features and location rostrocaudally, from the anterior head through the body in the hippocampus. The most common feature was small pyramidal neurons, which were intermingled with larger pyramidal neurons in ProS. We quantitatively measured ProS pyramidal neurons, which showed (average, width at pyramidal base = 14.31 µm, n = 400 per subfield). CA1 neurons averaged 15.57 µm and Sub neurons averaged 15.63 µm, both were significantly different than ProS (Kruskal–Wallis test, p < .0001). The other three features observed were lightly stained neurons, clustered neurons, and a cell sparse zone. Taken together, these findings suggest that ProS is an independent subfield, likely with distinct functional contributions to the broader interconnected hippocampal network. Our results suggest that ProS is a cytoarchitecturally varied subfield, both for features and among individuals. This diverse architecture in features and individuals for ProS could explain the long‐standing complexity regarding the identification of this subfield.

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Section: Discussionmentioning

confidence: 99%

The prosubiculum in the human hippocampus: A rostrocaudal, feature‐driven, and systematic approach

Rosenblum,

Williams,

Champion

et al. 2024

J of Comparative Neurology

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“…Experimental results confirmed the excellent performance of the method and its capacity to provide valuable information for neuron reconstruction ( Wei et al, 2023 ). The user-friendly software CellPose ( Stringer et al, 2021 ) also has a DeepLearning module that has been used to count pyramidal neurons in histopathological images ( Oltmer et al, 2023 ). An alternative option is to employ Suite2p software ( Pachitariu et al, 2017 ), which offers AutoROI cell segmentation designed for simultaneous analysis of functional and morphological two-photon calcium images.…”

Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%

“…MIRACL is based on a multimodal approach that integrates CLARITY data at the microscopic level with macroscopic in vivo and ex vivo imaging data, including structural, diffusion, and quantitative MRI, all aligned to the Allen atlas reference frame "ARA." This integration facilitates various analyses, including the CellPose (Stringer et al, 2021;Oltmer et al, 2023) Light microscopy, HE stained histopathological images A simulated diffusion process generates spatial gradients pointing toward the center of a cell, and a neural network trained on these gradients, along with pixel categorization, forms a gradient vector field used to predict masks by constructing a dynamical system with fixed points.…”

Section: Processing At the Mesoscale Level: Insights Into Neurite And...mentioning

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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“…Supervised segmentation algorithms based on deep learning (DL) can learn from relatively small volumes manually annotated with somata coordinates and predict the somata locations in new (unseen) volumes. 2D deep learning techniques have been employed in conjunction with unbiased stereology 7 and have been reported to produce count estimates that highly correlate with classic stereological approaches 8 . A related AI-based approach 9 has been shown to reduce the error rate of unbiased stereology estimates on novel test images; it also operates in 2D and requires a human-in-the-loop procedure.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference

Checcucci,

Wicinski,

Mazzamuto

et al. 2024

Sci Rep

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Abstract3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a $$4~\text {-cm}^3$$ 4 -cm 3 portion of the Broca’s area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design.

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Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions

Cited by 9 publications

References 66 publications

The prosubiculum in the human hippocampus: A rostrocaudal, feature‐driven, and systematic approach

The prosubiculum in the human hippocampus: A rostrocaudal, feature‐driven, and systematic approach

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

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference

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