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@ARTICLE{Schnemann:277731,
      author       = {Schünemann, Kerstin D and Hattingh, Roxanne M and Verhoog,
                      Matthijs B and Yang, Danqing and Bak, Aniella V and Peter,
                      Sabrina and van Loo, Karen M J and Wolking, Stefan and
                      Kronenberg-Versteeg, Deborah and Weber, Yvonne and Schwarz,
                      Niklas and Raimondo, Joseph V and Melvill, Roger and Tromp,
                      Sean A and Butler, James T and Höllig, Anke and Delev,
                      Daniel and Wuttke, Thomas V and Kampa, Björn M and Koch,
                      Henner},
      title        = {{C}omprehensive analysis of human dendritic spine
                      morphology and density.},
      journal      = {Journal of neurophysiology},
      volume       = {133},
      number       = {4},
      issn         = {0022-3077},
      address      = {Bethesda, Md.},
      publisher    = {Soc.},
      reportid     = {DZNE-2025-00452},
      pages        = {1086 - 1102},
      year         = {2025},
      abstract     = {Dendritic spines, small protrusions on neuronal dendrites,
                      play a crucial role in brain function by changing shape and
                      size in response to neural activity. So far, in-depth
                      analysis of dendritic spines in human brain tissue is
                      lacking. This study presents a comprehensive analysis of
                      human dendritic spine morphology and density using a unique
                      dataset from human brain tissue from 27 patients (8 females,
                      19 males, aged 18-71 yr) undergoing tumor or epilepsy
                      surgery at three neurosurgery sites. We used acute slices
                      and organotypic brain slice cultures to examine dendritic
                      spines, classifying them into the three main morphological
                      subtypes: mushroom, thin, and stubby, via three-dimensional
                      (3-D) reconstruction using ZEISS arivis Pro software. A deep
                      learning model, trained on 39 diverse datasets, automated
                      spine segmentation and 3-D reconstruction, achieving a
                      $74\%$ F1-score and reducing processing time by over $50\%.$
                      We show significant differences in spine density by sex,
                      dendrite type, and tissue condition. Females had higher
                      spine densities than males, and apical dendrites were denser
                      in spines than basal ones. Acute tissue showed higher spine
                      densities compared with cultured human brain tissue. With
                      time in culture, mushroom spines decreased, whereas stubby
                      and thin spine percentages increased, particularly from 7-9
                      to 14 days in vitro, reflecting potential synaptic
                      plasticity changes. Our study underscores the importance of
                      using human brain tissue to understand unique synaptic
                      properties and shows that integrating deep learning with
                      traditional methods enables efficient large-scale analysis,
                      revealing key insights into sex- and tissue-specific
                      dendritic spine dynamics relevant to neurological
                      diseases.NEW $\&$ NOTEWORTHY This study presents a dataset
                      of nearly 4,000 morphologically reconstructed human
                      dendritic spines across different ages, gender, and tissue
                      conditions. The dataset was further used to evaluate a deep
                      learning algorithm for three-dimensional spine
                      reconstruction, offering a scalable method for semiautomated
                      spine analysis across various tissues and microscopy setups.
                      The findings enhance understanding of human neurology,
                      indicating potential connections between spine morphology,
                      brain function, and the mechanisms of neurological and
                      psychiatric diseases.},
      keywords     = {Humans / Male / Female / Middle Aged / Adult / Dendritic
                      Spines: physiology / Aged / Adolescent / Young Adult / Deep
                      Learning / Imaging, Three-Dimensional: methods / deep
                      learning (Other) / dendritic spines (Other) / human tissue
                      (Other) / morphology (Other)},
      cin          = {AG Kronenberg-Versteeg},
      ddc          = {000},
      cid          = {I:(DE-2719)1210015},
      pnm          = {351 - Brain Function (POF4-351)},
      pid          = {G:(DE-HGF)POF4-351},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40013734},
      doi          = {10.1152/jn.00622.2024},
      url          = {https://pub.dzne.de/record/277731},
}