Name: a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis
Uploaded: 2020-02-06T15:00:00
Description: Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

元の論文2で使用された2マウスの社会的相互作用テストの生データを提供してくれたPingping ZhaoとPeyman Golshaniに感謝したいと思います。この研究は、NIH NS109315およびNVIDIA GPU助成金(AA)によってサポートされました。

1. GPU と Python のセットアップ
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GPUソフトウェア コンピューターがディープラーニングアプリケーション用に最初にセットアップされるとき、GPUの対応するソフトウェアとドライバをインストールする必要があります。(この研究で使用されるもののための材料の表を参照してください)。</li>
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Python 2.7 のインストール コンピュータでコマン?...

<ol>
	<li>Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroscience+Needs+Behavior:+Correcting+a+Reductionist+Bias.">Neuroscience Needs Behavior: Correcting a Reductionist Bias.</a> Neuron. 93 (3), 480-490 (2017).</li><li>Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., Golshani, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepBehavior:+A+Deep+Learning+Toolbox+for+Automated+Analysis+of+Animal+and+Human+Behavior+Imaging+Data.">DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.</a> Front Syst Neurosci. 13, 20 (2019).</li><li>Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S., Murthy, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nat Methods. 16 (1), 117-125 (2019).</li><li>Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li><li>Stern, U., He, R., Yang, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+animal+behavior+via+classifying+each+video+frame+using+convolutional+neural+networks.">Analyzing animal behavior via classifying each video frame using convolutional neural networks.</a> Sci Rep. 5, 14351 (2015).</li><li>Tinbergen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+aims+and+methods+of+ethology.">On aims and methods of ethology.</a> Zeitschrift fu&#776;r Tierpsychologie. 20, 410-433 (1963).</li><li>LeCun, Y., Bengio, Y., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Learning.">Deep Learning.</a> Nature. 521 (7553), 436-444 (2015).</li><li>Zhao, Z., Zheng, P., Xu, S., Wu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Object+Detection+With+Deep+Learning:+A+Review.">Object Detection With Deep Learning: A Review.</a> IEEE Transactions on Neural Networks and Learning Systems. , 1-21 (2019).</li><li>He, K., Zhang, X., Ren, S., Deep Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Residual+Learning+for+Image+Recognition.">Residual Learning for Image Recognition.</a> arXiv. , (2015).</li><li>Krizhevsky, A., Sutskever, I., Hinton, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImageNet+classification+with+deep+convolutional+neural+networks.">ImageNet classification with deep convolutional neural networks.</a> Proceedings of the 25th International Conference on Neural Information Processing Systems. 1, 1097-1105 (2012).</li><li>Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Going+deeper+with+convolutions.">Going deeper with convolutions.</a> 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 7-12 (2015).</li><li>Stewart, R., Andriluka, M., Ng, A. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=End-to-End+People+Detection+in+Crowded+Scenes.">End-to-End People Detection in Crowded Scenes.</a> 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 27-30 (2016).</li><li>Redmon, J., Farhadi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=YOLOv3:+An+Incremental+Improvement.">YOLOv3: An Incremental Improvement.</a> arXiv. , (2018).</li><li>Cao, Z., Simon, T., Wei, S. E., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Realtime+Multi-Person+2D+Pose+Estimation+using+Part+Affinity+Fields.">Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.</a> arXiv. , (2017).</li><li>Simon, T., Joo, H., Matthews, I., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hand+Keypoint+Detection+in+Single+Images+using+Multiview+Bootstrapping.">Hand Keypoint Detection in Single Images using Multiview Bootstrapping.</a> arXiv. , (2017).</li><li>Wei, S. E., Ramakrishna, V., Kanade, T., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convolutional+Pose+Machines.">Convolutional Pose Machines.</a> arXiv. , (2016).</li></ol>

ここでは、最近開発されたディープラーニングベースのディープラーニングベースの動物および人間の行動イメージングデータ分析2の実装に関するステップバイステップガイドを提供します。各ネットワークアーキテクチャのフレームワークをインストールするための各ステップの詳細な説明を提供し、これらのフレームワークを実行するためのオープンソース要件のイン...

行動の詳細な分析は、脳と行動の関係を理解するための鍵です。神経集団を高い時間分解能で記録・操作する方法論は多くの刺激的な進歩を遂げてきましたが、行動解析法は同じ速度で発達しておらず、間接的な測定と還元論的アプローチ1に限定されています。近年、ディープラーニングベースの方法は、自動および詳細な行動分析2、3、4、5を実行するために開発されています。このプロトコルは、DeepBehavior ツールボックスの実装ガイドを段階的に提供します。
従来の行動分析方法には、複数のエバリュエーターによるデータの手動によるラベル付けが含まれるので、実験者が行動を定義する方法が異なることがあります。データの手動ラベル付けには、収集されるデータ量に対して不均衡に増加する時間とリソースが必要です。さらに、手動でラベル付けされたデータは、行動の豊かさを捉えないカテゴリ測定値に行動結果を減らし、より主観的になります。したがって、現在の伝統的な方法は、自然な行動の細部をキャプチャする際に制限され得る。
DeepBehaviorツールボックスは、行動分析のためにディープラーニングを使用して、正確で詳細で、非常に時間的で自動化されたソリューションを提供します。ディープラーニングは、オープンソースのツールとパッケージを使用して、すべての人がすばやくアクセスできるようになります。畳み込みニューラルネットワーク(CNN)は、オブジェクト認識およびトラッキングタスク7、8において非常に効果的であることが証明されています。現代のCnnsと高性能グラフィックス処理ユニット(GPU)を使用すると、大きな画像およびビデオデータセットを高精度7,9,10,11で素早く処理できます。DeepBehaviorには、3つの異なる畳み込みニューラルネットアーキテクチャ、テンソルボックス、YOLOv3、およびOpenPose2があります。
最初のフレームワーク、Tensorboxは、オブジェクト検出12のための多くの異なるCNNアーキテクチャを組み込んだ汎用性の高いフレームワークです。テンソルボックスは、画像ごとに1つのオブジェクトクラスのみを検出するのに最適です。結果として得られる出力は、対象オブジェクトの境界ボックス (図 1) と境界ボックスのデカルト座標です。
2番目のCNNフレームワークはYOLOv3で、「あなたは一度だけ見る」13の略です。YOLOv3は、個別に追跡しなければならない複数の対象オブジェクトがある場合に有利です。このネットワークの出力には、関連付けられたオブジェクト ラベル クラスを持つ境界ボックスと、ビデオ フレーム内のオブジェクトの境界ボックスデカルト座標が含まれます (図 2)。
前の2つのフレームワークは、動物被験者の標準的な実験室実験から収集された一般化された行動データに有利である。最後のCNNフレームワークは、人間の関節ポーズ推定に使用されるOpenPose14、15、16です。OpenPose は、画像上の人体、手、顔、足のキーポイントを検出します。フレームワークの出力には、人間の被写体の画像と、身体の25個のキーポイントと各手の21のキーポイントの座標がラベル付けされています(図3)。
最近開発したオープンソースのDeepBehaviorツールボックスの実装に関するこの詳細なステップバイステップガイドは、動物の行動(例えば足の動き)または人間の行動(例えばタスクに到達する)を追跡するために最先端の畳み込みニューラルネットを採用しています。行動を追跡することで、位置、速度、加速度などの行動から有用なキネマティクスを導き出すことができます。プロトコルでは、各 CNN アーキテクチャのインストール、トレーニング データセットの作成方法、ネットワークのトレーニング方法、トレーニング済みネットワーク上で新しいビデオを処理する方法、新しいビデオでネットワークからデータを抽出する方法、および方法を説明します。出力データを後処理して、さらなる分析に役立ちます。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CUDA v8.0.61</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Software</td>
		</tr>
		<tr>
			<td>MATLAB R2016b</td>
			<td>Mathworks</td>
			<td>n/a</td>
			<td>Matlab</td>
		</tr>
		<tr>
			<td>Python 2.7</td>
			<td>Python</td>
			<td>n/a</td>
			<td>Python Version</td>
		</tr>
		<tr>
			<td>Quadro P6000</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Processor</td>
		</tr>
		<tr>
			<td>Ubuntu v16.04</td>
			<td>Ubuntu</td>
			<td>n/a</td>
			<td>Operating System</td>
		</tr>
	</tbody>
</table>

a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis

行動を理解することは、それを駆動する脳内の神経メカニズムを真に理解するための最初のステップです。従来の行動分析法では、自然な行動に固有の豊かさを捉えないことがよくあります。ここでは、最近の方法論 DeepBehavior の視覚化を使用して、詳細な手順を説明します。DeepBehaviorツールボックスは、畳み込みニューラルネットワークで構築されたディープラーニングフレームワークを使用して、行動ビデオを迅速に処理および分析します。このプロトコルは、単一オブジェクト検出、複数オブジェクト検出、および 3 次元 (3D) の人間のジョイント ポーズトラッキングのための 3 つの異なるフレームワークを示します。これらのフレームワークは、動作ビデオの各フレームについて、対象となるオブジェクトのデカルト座標を返します。DeepBehaviorツールボックスから収集されたデータは、従来の動作分析方法よりもはるかに詳細に含まれ、行動のダイナミクスに関する詳細な洞察を提供します。DeepBehaviorは、堅牢で自動化された正確な方法で行動タスクを定量化します。動作の識別に続いて、行動ビデオから情報と視覚化を抽出する後処理コードが提供されます。

プロトコルに従う場合、各ネットワーク アーキテクチャのデータは次のようになります。TensorBox の場合、対象のオブジェクトの周りに境界ボックスが出力されます。この例では、食品ペレットのビデオをタスクに到達させ、右足にラベルを付けて動きを追跡しました。図 1に示すように、正面図と側面図カメラの両方で、右足を異なる位置で検...

Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

このプロトコルの目的は、事前に構築された畳み込みニューラルネットを利用して行動追跡を自動化し、詳細な行動分析を行うことである。行動追跡は、任意のビデオデータまたは画像のシーケンスに適用することができ、任意のユーザー定義オブジェクトを追跡するために一般化可能です。

自動行動分析のためのDeepBehavior、ディープラーニングツールボックスのステップバイステップ実装

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods ...

脳の行動関係を理解するには、詳細な行動分析を行う必要があります。行動を評価する最良の方法の1つは、注意深い観察を通じてです。ただし、観察された動作を定量化するのは時間がかかり、困難です。古典的な行動分析方法は容易に定量化が容易ではなく、本質的に主観的である。機械学習と人工知能分野の分野であるディープラーニングの最近の発展は、画像やビデオの自動化された客観的な定量化の機会を提供します。ここでは、深いニューラルネットワークを利用してげっ歯類やヒトの詳細な行動解析を行う方法を紹介します。この手法の主な利点は、動作分析のための任意のイメージング データに対する柔軟性と適用性です。DeepBehavior ツールボックスは、単一のオブジェクトの識別、マルチオブジェクト検出、および人間のポーズトラッキングをサポートしています。さらに、MATLAB では、より詳細なキネマティック解析方法のポスト処理コードも提供しています。テンソルボックスを設定して開始します。環境をアクティブにし、GitHub を使用してテンソル ボックスを複製し、マシンと追加の依存関係にインストールします。次に、ラベル付けグラフィカルユーザーインターフェイスを起動し、動作フレームの広い分布から少なくとも600枚の画像にラベルを付けます。画像にラベルを付けるには、対象オブジェクトの左上隅をクリックし、次に右下隅をクリックします。次に、バウンディング ボックスがオブジェクト全体をキャプチャすることを確認します。次のフレームに移動するには、次のフレームをクリックします。トレーニング イメージをネットワーク ハイパー パラメータ ファイルにリンクするには、overfeat_rezoom開きます。テキストエディター内の json を使用し、labels.json にtrain_idl下のファイルパスを置き換えます。次に、test-idl の下に同じファイル パスを追加し、変更を保存します。600,000 回の反復のトレーニングを開始し、結果として得られた畳み込みニューラル ネットワークの重みを出力フォルダに生成するトレーニング スクリプトを開始します。次に、新しい画像に対して予測を実行し、ネットワークの出力をラベル付きイメージとして、境界ボックス座標として表示します。YOLOv3 をインストールします。次に、Yolo_mark-data-obga フォルダに画像を配置し、グラフィカル ユーザー インターフェイスで 1 つずつラベルを付けて、トレーニング データにYolo_markラベルを付けます。約200枚の画像にラベルを付けます。次に、構成ファイルをセットアップします。構成ファイルを変更するには、YOLO-obj を開きます。cfg フォルダ。バッチ、サブディビジョン、およびクラスの行を変更します。次に、YOLO レイヤーの前の各畳み込みレイヤーのフィルターを変更します。ネットワークウェイトをダウンロードし、ダークネットビルドに配置します。x64 フォルダ。トレーニング アルゴリズムを実行し、完了したらイテレーションを表示します。人間の主題の複数の身体部分を追跡するには、OpenPoseをインストールし、それを使用して目的のビデオを処理します。DeepBehaviorツールボックスの機能は、食餌ペレットがタスクに達するマウスのビデオで実証されました。彼らの右足はラベル付けされ、動きはフロントビューカメラとサイドビューカメラで追跡されました。カメラキャリブレーションで後処理した後、リーチの3D軌道が得られた。YOLOv3 の出力は、複数のオブジェクトを追跡できるため、複数のバウンディング ボックスです。境界ボックスは、身体の一部となり得る対象のオブジェクトの周囲にあります。OpenPoseでは、ネットワークがジョイント位置を検出し、カメラキャリブレーションで後処理した後、被写体の3Dモデルが作成されました。このプロトコルでカバーされていない重要なステップの 1 つは、デバイスが適切な Python バージョンと依存関係と、GPU で構成されたデバイスを使用してから開始する前に持っていることを確認することです。ネットワークからトラックの動作を正常に取得した後、さらに動作のキネマティクスとパターンを分析するために追加の後処理を行うことができます。なぜDeepBehaviorツールボックスはげっ歯類およびヒトの被験者の疾患モデルの診断アプローチに適用可能であるが、直接的な治療上の利益ではない。これらの技術を診断用または予後診断ツールとして使用することは、当研究室で活発な研究を行っています。この技術は、げっ歯類における熟練した運動行動の神経メカニズムを調べるとともに、神経疾患患者の運動回復を評価する臨床研究で使用されている。

a-step-step-implementation-deepbehavior-deep-learning-toolbox-for

Research

JoVE Journal

Behavior

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice are placed into a conditioning chamber and are given parings of a conditioned stimulus (an auditory cue) and an aversive unconditioned stimulus (an electric footshock). After a delay time, the mice are exposed to the same conditioning chamber and a differently shaped chamber with presentation of the auditory cue. Freezing behavior during the test is measured as an index of fear memory. To analyze the behavior automatically, we have developed a video analyzing system using the ImageFZ application software program, which is available as a free download at http://www.mouse-phenotype.org/. Here, to show the details of our protocol, we demonstrate our procedure for the contextual and cued fear conditioning test in C57BL/6J mice using the ImageFZ system. In addition, we validated our protocol and the video analyzing system performance by comparing freezing time measured by the ImageFZ system or a photobeam-based computer measurement system with that scored by a human observer. As shown in our representative results, the data obtained by ImageFZ were similar to those analyzed by a human observer, indicating that the behavioral analysis using the ImageFZ system is highly reliable. The present movie article provides detailed information regarding the test procedures and will promote understanding of the experimental situation.

This article presents a protocol for a contextual and cued fear conditioning test using a video analyzing system to assess fear learning and memory in mice.

マウスにおけるビデオ解析システムを用いた文脈と手掛り恐怖条件付け試験

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association ...

行動学

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

コンピューターの自動学習装置（ALDM）テストシステムのネットワークを用いた非ヒト霊長類における社会的認知の評価

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

乳児で学習を定量化：追跡脚アクションを発見学習タスクの実行中に

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different forms of learning. Specifically, viral-mediated delivery is used to express a genetically modified inhibitory G-protein coupled receptor (the Designer Receptor) into a discrete brain region in the rodent. Three weeks later, when designer receptor expression levels are high, a pharmacological agent (the Designer Drug) is administered systemically 30 min prior to a specific behavioral session. The drug has affinity for the designer receptor and thus results in inhibition of neurons that express the designer receptor, but is otherwise biologically inert. The brain region remains silenced for 2-5 hr (depending on the dose and route of administration). Upon completion of the behavioral paradigm, brain tissue is assessed for correct placement and receptor expression. This approach is particularly useful for determining the contribution of individual brain regions to specific components of behavior and can be used across any number of behavioral paradigms.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.

学習の離散段階中げっ歯類におけるリモートサイレン神経活動のための方法

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and ...

A General Method for Evaluating Deep Brain Stimulation Effects on Intravenous Methamphetamine Self-Administration

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people. There is a need for novel, effective and practical treatment strategies that are validated in animal models. Neuromodulation, including deep brain stimulation (DBS) therapy, refers to the use of electricity to influence pathological neuronal activity and has shown promise for psychiatric disorders, including drug dependence. DBS in clinical practice involves the continuous delivery of stimulation into brain structures using an implantable pacemaker-like system that is programmed externally by a physician to alleviate symptoms. This treatment will be limited in methamphetamine users due to challenging psychosocial situations. Electrical treatments that can be delivered intermittently, non-invasively and remotely from the drug-use setting will be more realistic. This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence. Methamphetamine dependence is rapidly developed in rodents using an operant paradigm of intravenous (IV) self-administration that incorporates a period of extended access to drug and demonstrates both escalation of use and high motivation to obtain drug.

This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence.

静脈内メタンフェタミン自己管理に脳深部刺激効果を評価するための一般的な方法

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people.  There is a need ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

官能評価のための自動化された表情解析技術と時間的分析へのデータの収集と分析応用のためのプロトコル

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

分割トレッドミルを使用した運動適応の一般化の評価

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

Automated Analysis of a Nematode Population-based Chemosensory Preference Assay

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection of the neural circuits underlying these behaviors, the development of robust and reproducible behavioral assays is necessary. Previous C. elegans behavioral studies have used variations of a "drop test", a "chemotaxis assay", and a "retention assay" to investigate the response of C. elegans to soluble compounds. The method described in this article seeks to combine the complementary strengths of the three aforementioned assays. Briefly, a small circle in the middle of each assay plate is divided into four quadrants with the control and experimental solutions alternately placed. After the addition of the worms, the assay plates are loaded into a behavior chamber where microscope cameras record the worms' encounters with the treated regions. Automated video analysis is then performed and a preference index (PI) value for each video is generated. The video acquisition and automated analysis features of this method minimizes the experimenter's involvement and any associated errors. Furthermore, minute amounts of the experimental compound are used per assay and the behavior chamber's multi-camera setup increases experimental throughput. This method is particularly useful for conducting behavioral screens of genetic mutants and novel chemical compounds. However, this method is not appropriate for studying stimulus gradient navigation due to the close proximity of the control and experimental solution regions. It should also not be used when only a small population of worms is available. While suitable for assaying responses only to soluble compounds in its current form, this method can be easily modified to accommodate multimodal sensory interaction and optogenetic studies. This method can also be adapted to assay the chemosensory responses of other nematode species.

We present a behavior recording setup and protocol that enables automated analysis of the nematode, Caenorhabditis elegans' preference for soluble compounds in a population-based assay. This article describes the construction of a behavior chamber, the behavioral assay protocol, and video analysis software usage.

線虫集団ベースの化学感覚嗜好アッセイの自動解析

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection ...

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat&#8217;s paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson&#8217;s disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

We describe a novel gait analysis system, Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER), an open-access automated system for the quantification of gait characteristics in rats that utilizes a novel semitransparent floor to automatically quantify gait. This system was validated using the haloperidol model of Parkinson&#8217;s Disease.

コントラスト強化録音(PrAnCER)の足プリント解析:運動不足を評価するための低コストでオープンアクセスの自動歩行解析システム

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

モバイルアイトラッカーを用いた関節視覚注意を捉える方法論

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...