Dataset Information

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

ABSTRACT: Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain using stereology. We characterize an automated high-throughput deep learning pipeline to segment hippocampal pyramidal neurons, generate pyramidal neuron estimates within the human hippocampal subfields, and relate our results to stereology neuron counts. Based on seven cases and 168 partitions, we vet deep learning parameters to segment hippocampal pyramidal neurons from the background using the open-source CellPose algorithm, and show the automated removal of false-positive segmentations. There was no difference in Dice scores between neurons segmented by the deep learning pipeline and manual segmentations (Independent Samples t-Test: t(28) = 0.33, p = 0.742). Deep-learning neuron estimates strongly correlate with manual stereological counts per subregion (Spearman's correlation (n = 9): r(7) = 0.97, p < 0.001), and for each partition individually (Spearman's correlation (n = 168): r(166) = 0.90, p <0 .001). The high-throughput deep-learning pipeline provides validation to existing standards. This deep learning approach may benefit future studies in tracking baseline and resilient healthy aging to the earliest disease progression.

SUBMITTER: Oltmer J

PROVIDER: S-EPMC10090178 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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

Oltmer Jan J Rosenblum Emma W EW Williams Emily M EM Roy Jessica J Llamas-Rodriguez Josué J Perosa Valentina V Champion Samantha N SN Frosch Matthew P MP Augustinack Jean C JC

Scientific reports 20230411 1

Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain using stereology. We characterize an automated high-throughput deep learning pipeline to segment hippocampal pyramidal neurons, generate pyramidal neuron estimates within the human hippocampal subfields, and relate our results to stereology neuron c ...[more]

PMID: 37041300

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Project description:Heterozygous loss-of-function SCN1A mutations cause Dravet syndrome, an epileptic encephalopathy of infancy that exhibits variable clinical severity. We utilized a heterozygous Scn1a knockout (Scn1a(+/-)) mouse model of Dravet syndrome to investigate the basis for phenotype variability. These animals exhibit strain-dependent seizure severity and survival. Scn1a(+/-) mice on strain 129S6/SvEvTac (129.Scn1a(+/-)) have no overt phenotype and normal survival compared with Scn1a(+/-) mice bred to C57BL/6J (F1.Scn1a(+/-)) that have severe epilepsy and premature lethality. We tested the hypothesis that strain differences in sodium current (INa) density in hippocampal neurons contribute to these divergent phenotypes. Whole-cell voltage-clamp recording was performed on acutely-dissociated hippocampal neurons from postnatal days 21-24 (P21-24) 129.Scn1a(+/-) or F1.Scn1a(+/-) mice and wild-type littermates. INa density was lower in GABAergic interneurons from F1.Scn1a(+/-) mice compared to wild-type littermates, while on the 129 strain there was no difference in GABAergic interneuron INa density between 129.Scn1a(+/-) mice and wild-type littermate controls. By contrast, INa density was elevated in pyramidal neurons from both 129.Scn1a(+/-) and F1.Scn1a(+/-) mice, and was correlated with more frequent spontaneous action potential firing in these neurons, as well as more sustained firing in F1.Scn1a(+/-) neurons. We also observed age-dependent differences in pyramidal neuron INa density between wild-type and Scn1a(+/-) animals. We conclude that preserved INa density in GABAergic interneurons contributes to the milder phenotype of 129.Scn1a(+/-) mice. Furthermore, elevated INa density in excitatory pyramidal neurons at P21-24 correlates with age-dependent onset of lethality in F1.Scn1a(+/-) mice. Our findings illustrate differences in hippocampal neurons that may underlie strain- and age-dependent phenotype severity in a Dravet syndrome mouse model, and emphasize a contribution of pyramidal neuron excitability.

Dataset Information

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

Publications

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

Similar Datasets

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