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Oltmer J, Rosenblum EW, Williams EM, Roy J, Llamas-Rodriguez J, Perosa V, Champion SN, Frosch MP, Augustinack JC. Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions. Sci Rep 2023; 13:5884. [PMID: 37041300 PMCID: PMC10090178 DOI: 10.1038/s41598-023-32903-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/04/2023] [Indexed: 04/13/2023] Open
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.
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Affiliation(s)
- Jan Oltmer
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Emma W Rosenblum
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Emily M Williams
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jessica Roy
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Josué Llamas-Rodriguez
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Valentina Perosa
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, J. Philip Kistler Stroke Research Center, Cambridge Str. 175, Suite 300, Boston, MA, 02114, USA
- Department of Neurology, Otto-Von-Guericke University, Magdeburg, Germany
| | - Samantha N Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew P Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Jean C Augustinack
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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Sakamoto N, Murata T. [Analytical technologies of animal behavior using artificial intelligence]. Nihon Yakurigaku Zasshi 2022; 157:156. [PMID: 35228450 DOI: 10.1254/fpj.21111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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Maekawa T, Higashide D, Hara T, Matsumura K, Ide K, Miyatake T, Kimura KD, Takahashi S. Cross-species behavior analysis with attention-based domain-adversarial deep neural networks. Nat Commun 2021; 12:5519. [PMID: 34535659 PMCID: PMC8448872 DOI: 10.1038/s41467-021-25636-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 08/19/2021] [Indexed: 01/12/2023] Open
Abstract
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.
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Affiliation(s)
- Takuya Maekawa
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.
| | - Daiki Higashide
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Takahiro Hara
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | | | - Kaoru Ide
- Graduate School of Brain Science, Doshisha University, Kyoto, Japan
| | - Takahisa Miyatake
- Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
| | | | - Susumu Takahashi
- Graduate School of Brain Science, Doshisha University, Kyoto, Japan
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