ネオコルテックス の 量 の 飽和 した 再構成
Narayanan Kasthuri1, Kenneth Jeffrey Hayworth1, Daniel Raimund Berger2
1Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Cell
|August 2, 2015
まとめ
研究者はナノメートルの解像度でマウスの新皮質の構造をマッピングする 自動化された技術を開発しました この詳細な脳地図は 近接がシナプス接続を 予測するという考えを否定し 新しいデータに基づいた洞察を 提供しています
科学分野:
- 神経科学
- コンピュータ生物学
- バイオ物理学
背景:
- 神経組織の構造を理解することは 脳の機能に不可欠です
- 以前の方法では セルラーとサブセルラーを 詳細に捉える解像度が不足していました
研究 の 目的:
- 高解像度の神経組織再構築のための自動化された技術を開発する.
- マウスの新皮質のサブボリュームの 総合的なデータベースを作成します
- 脳組織とシナプス結合の 物理的性質を調査する
主な方法:
- 神経組織のナノメートルの解像度のイメージングのための自動化された技術を利用した.
- ネズミの新皮質部分の 飽和した再構成を生成した.
- セルラーとサブセルラー構成要素を 検索可能なデータベースに
主要な成果:
- すべての細胞のオブジェクト (軸索,デンドライト,グリア) とサブ細胞のコンポーネント (シナプス,膀,脊髄,ミトコンドリア) を成功裏にレンダリングし,細分化しました.
- 軸索の軌跡と 樹状脊髄の並列の分析は,物理的近接とシナプス結合に関する"ピーターズ法則"を否定した.
- 新皮質の複雑性に アクセスできる オンラインのデータベースを作りました
結論:
- ナノメートルの解像度で 自動化された再構築は 神経組織の複雑さに 前例のない洞察力を提供します
- 物理的な接近はシナプス接続性を予測するのに不十分で 長い間持っていた仮定に挑戦します
- 作成されたデータベースは,脳の構造と機能に関するさらなるデータ主導の研究を容易にする.
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