{"title":"基于AMPA受体分布的双相情感障碍与重度抑郁症的鉴别。","authors":"Sakiko Tsugawa, Yuichi Kimura, Junichi Chikazoe, Hiroki Abe, Tetsu Arisawa, Mai Hatano, Waki Nakajima, Hiroyuki Uchida, Tomoyuki Miyazaki, Yuuki Takada, Akane Sano, Kotaro Nakano, Tsuyoshi Eiro, Akira Suda, Takeshi Asami, Akitoyo Hishimoto, Hideaki Tani, Nobuhiro Nagai, Teruki Koizumi, Shinichiro Nakajima, Shunya Kurokawa, Yohei Ohtani, Kie Takahashi, Yuhei Kikuchi, Taisuke Yatomi, Ryo Mitoma, Shunsuke Tamura, Shingo Baba, Osamu Togao, Yoji Hirano, Hirotaka Kosaka, Hidehiko Okazawa, Masaru Mimura, Takuya Takahashi","doi":"10.3389/fncir.2025.1624179","DOIUrl":null,"url":null,"abstract":"<p><p>An accurate diagnostic method using biological indicators is critically needed for bipolar disorder (BD) and major depressive disorder (MDD). The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) is a crucial regulator of synaptic function, and its dysregulation may play a central role in the pathophysiology of psychiatric disorders. Our recently developed positron emission tomography (PET) tracer, [<sup>11</sup>C]K-2, enables the quantitative visualization of AMPAR distribution and is considered useful for characterizing synaptic phenotypes in patients with psychiatric disorders. This study aimed to develop a machine learning-based method to differentiate bipolar disorder from major depressive disorder using AMPAR density. Sixteen patients with BD and 27 patients with MDD, all in depressive episodes, underwent PET scans with [<sup>11</sup>C]K-2 and structural magnetic resonance imaging. AMPAR density was estimated using the standardized uptake value ratio from 30 to 50 min after tracer injection, normalized to whole brain radioactivity. A partial least squares model was trained to predict diagnoses based on AMPAR density, and its performance was evaluated using a leave-one-pair-out cross-validation. Significant differences in AMPAR density were observed in the parietal lobe, cerebellum, and frontal lobe, notably the dorsolateral prefrontal cortex between patients with BD and patients with MDD during a depressive episode. The model achieved an area under the curve of 0.80, sensitivity of 75.0%, and specificity of 77.8%. These findings suggest that AMPAR density measured with [<sup>11</sup>C]K-2 can effectively distinguish BD from MDD and may aid diagnosis, especially in patients with ambiguous symptoms or incomplete clinical presentation.</p>","PeriodicalId":12498,"journal":{"name":"Frontiers in Neural Circuits","volume":"19 ","pages":"1624179"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12358400/pdf/","citationCount":"0","resultStr":"{\"title\":\"Differentiation between bipolar disorder and major depressive disorder based on AMPA receptor distribution.\",\"authors\":\"Sakiko Tsugawa, Yuichi Kimura, Junichi Chikazoe, Hiroki Abe, Tetsu Arisawa, Mai Hatano, Waki Nakajima, Hiroyuki Uchida, Tomoyuki Miyazaki, Yuuki Takada, Akane Sano, Kotaro Nakano, Tsuyoshi Eiro, Akira Suda, Takeshi Asami, Akitoyo Hishimoto, Hideaki Tani, Nobuhiro Nagai, Teruki Koizumi, Shinichiro Nakajima, Shunya Kurokawa, Yohei Ohtani, Kie Takahashi, Yuhei Kikuchi, Taisuke Yatomi, Ryo Mitoma, Shunsuke Tamura, Shingo Baba, Osamu Togao, Yoji Hirano, Hirotaka Kosaka, Hidehiko Okazawa, Masaru Mimura, Takuya Takahashi\",\"doi\":\"10.3389/fncir.2025.1624179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>An accurate diagnostic method using biological indicators is critically needed for bipolar disorder (BD) and major depressive disorder (MDD). The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) is a crucial regulator of synaptic function, and its dysregulation may play a central role in the pathophysiology of psychiatric disorders. Our recently developed positron emission tomography (PET) tracer, [<sup>11</sup>C]K-2, enables the quantitative visualization of AMPAR distribution and is considered useful for characterizing synaptic phenotypes in patients with psychiatric disorders. This study aimed to develop a machine learning-based method to differentiate bipolar disorder from major depressive disorder using AMPAR density. Sixteen patients with BD and 27 patients with MDD, all in depressive episodes, underwent PET scans with [<sup>11</sup>C]K-2 and structural magnetic resonance imaging. AMPAR density was estimated using the standardized uptake value ratio from 30 to 50 min after tracer injection, normalized to whole brain radioactivity. A partial least squares model was trained to predict diagnoses based on AMPAR density, and its performance was evaluated using a leave-one-pair-out cross-validation. Significant differences in AMPAR density were observed in the parietal lobe, cerebellum, and frontal lobe, notably the dorsolateral prefrontal cortex between patients with BD and patients with MDD during a depressive episode. The model achieved an area under the curve of 0.80, sensitivity of 75.0%, and specificity of 77.8%. These findings suggest that AMPAR density measured with [<sup>11</sup>C]K-2 can effectively distinguish BD from MDD and may aid diagnosis, especially in patients with ambiguous symptoms or incomplete clinical presentation.</p>\",\"PeriodicalId\":12498,\"journal\":{\"name\":\"Frontiers in Neural Circuits\",\"volume\":\"19 \",\"pages\":\"1624179\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12358400/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neural Circuits\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fncir.2025.1624179\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neural Circuits","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fncir.2025.1624179","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Differentiation between bipolar disorder and major depressive disorder based on AMPA receptor distribution.
An accurate diagnostic method using biological indicators is critically needed for bipolar disorder (BD) and major depressive disorder (MDD). The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) is a crucial regulator of synaptic function, and its dysregulation may play a central role in the pathophysiology of psychiatric disorders. Our recently developed positron emission tomography (PET) tracer, [11C]K-2, enables the quantitative visualization of AMPAR distribution and is considered useful for characterizing synaptic phenotypes in patients with psychiatric disorders. This study aimed to develop a machine learning-based method to differentiate bipolar disorder from major depressive disorder using AMPAR density. Sixteen patients with BD and 27 patients with MDD, all in depressive episodes, underwent PET scans with [11C]K-2 and structural magnetic resonance imaging. AMPAR density was estimated using the standardized uptake value ratio from 30 to 50 min after tracer injection, normalized to whole brain radioactivity. A partial least squares model was trained to predict diagnoses based on AMPAR density, and its performance was evaluated using a leave-one-pair-out cross-validation. Significant differences in AMPAR density were observed in the parietal lobe, cerebellum, and frontal lobe, notably the dorsolateral prefrontal cortex between patients with BD and patients with MDD during a depressive episode. The model achieved an area under the curve of 0.80, sensitivity of 75.0%, and specificity of 77.8%. These findings suggest that AMPAR density measured with [11C]K-2 can effectively distinguish BD from MDD and may aid diagnosis, especially in patients with ambiguous symptoms or incomplete clinical presentation.
期刊介绍:
Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.