TY - JOUR
T1 - Systematic data analysis and data mining in CatWalk gait analysis by heat mapping exemplified in rodent models for neurodegenerative diseases
AU - Timotius, Ivanna K.
AU - Canneva, F.
AU - Minakaki, Georgia
AU - Moceri, Sandra
AU - Plank, Anne Christine
AU - Casadei, Nicolas
AU - Riess, Olaf
AU - Winkler, Jürgen
AU - Klucken, Jochen
AU - Eskofier, Bjoern
AU - von Hörsten, Stephan
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Background: Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection. New methods: In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson's disease (PD) and Huntington disease (HD). Results: The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences. Comparison with existing method: Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported. Conclusions: Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.
AB - Background: Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection. New methods: In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson's disease (PD) and Huntington disease (HD). Results: The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences. Comparison with existing method: Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported. Conclusions: Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.
KW - CatWalk system
KW - Gait analysis
KW - Heat map
KW - Huntington disease
KW - Parkinson's disease
KW - Rodent models
UR - http://www.scopus.com/inward/record.url?scp=85069891965&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2019.108367
DO - 10.1016/j.jneumeth.2019.108367
M3 - Review article
C2 - 31351096
AN - SCOPUS:85069891965
SN - 0165-0270
VL - 326
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 108367
ER -