Of mice and motion: Behavioural-EEG phenotyping of Alzheimer’s disease mouse models

Barry Crouch, Jie Min Yeap, Bianca Pais, Gernot Riedel, Bettina Platt (Corresponding Author)

Research output: Contribution to journalArticle

Abstract

Background Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. EEG analysis is complicated by a number of confounds, particularly 1. The close relationship between EEG power and movement speed must be controlled for prior to further analysis. 2. The difficulty inherent in identifying EEG epochs associated with a particular behaviour.

New Method We utilized infra-red event stamping to accurately synchronize EEG recorded from superficial sites above the hippocampus and prefrontal cortex with motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model (PLB1APP) and wild-type controls (PLBWT) performing a Y-maze spontaneous alternation task. Video tracking synchronized epochs capturing specific behaviours were automatically identified and extracted prior to auto-regressive spectral analysis.

Results Despite comparable behavioural performance, PLB1APP mice demonstrated region and behavioural context specific deficits in regulation of Gamma power: In contrast to controls, hippocampal gamma response to speed as well as prefrontal activity associated with correct vs. incorrect alternations was absent in PLB1APP mice. Regulation of hippocampal Gamma power in response to direction of movement did not differ.

Comparison with existing Methods This method allows for the first time to detect behaviour-specific differences in EEG response to speed that can be quantified and actively controlled for. Analysis across multiple parameters engaging different brain regions can now be used for detailed EEG profiling of brain-region specific functions.

Conclusion Combining infrared event-stamping and auto-regressive modelling enables rapid, automated and sensitive phenotyping of AD mouse models. Subtle alterations in brain signalling can be detected prior to overt behavioural impairments.
Original languageEnglish
Pages (from-to)89-98
Number of pages10
JournalJournal of Neuroscience Methods
Volume319
Early online date30 Jun 2018
DOIs
Publication statusPublished - 1 May 2019

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Electroencephalography
Alzheimer Disease
Brain
Prefrontal Cortex
Rodentia
Hippocampus
Research
Power (Psychology)

Keywords

  • EEG
  • behaviour
  • spectral analysis
  • autoregressive modelling
  • theta
  • gamma
  • speed
  • motion
  • Y-maze
  • amyloid precursor protein
  • Motion
  • Speed
  • Behaviour
  • Autoregressive modelling
  • Amyloid precursor protein
  • Spectral analysis
  • Theta
  • Gamma
  • HIPPOCAMPAL
  • RHYTHMS
  • FREQUENCY
  • DYSFUNCTION
  • ALTERS
  • CELLS
  • SPONTANEOUS-ALTERNATION BEHAVIOR
  • RUNNING SPEED
  • THETA OSCILLATIONS

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

@article{36367caa83bf431c9d698469bbebc6fb,
title = "Of mice and motion: Behavioural-EEG phenotyping of Alzheimer’s disease mouse models",
abstract = "Background Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. EEG analysis is complicated by a number of confounds, particularly 1. The close relationship between EEG power and movement speed must be controlled for prior to further analysis. 2. The difficulty inherent in identifying EEG epochs associated with a particular behaviour. New Method We utilized infra-red event stamping to accurately synchronize EEG recorded from superficial sites above the hippocampus and prefrontal cortex with motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model (PLB1APP) and wild-type controls (PLBWT) performing a Y-maze spontaneous alternation task. Video tracking synchronized epochs capturing specific behaviours were automatically identified and extracted prior to auto-regressive spectral analysis. Results Despite comparable behavioural performance, PLB1APP mice demonstrated region and behavioural context specific deficits in regulation of Gamma power: In contrast to controls, hippocampal gamma response to speed as well as prefrontal activity associated with correct vs. incorrect alternations was absent in PLB1APP mice. Regulation of hippocampal Gamma power in response to direction of movement did not differ. Comparison with existing Methods This method allows for the first time to detect behaviour-specific differences in EEG response to speed that can be quantified and actively controlled for. Analysis across multiple parameters engaging different brain regions can now be used for detailed EEG profiling of brain-region specific functions. Conclusion Combining infrared event-stamping and auto-regressive modelling enables rapid, automated and sensitive phenotyping of AD mouse models. Subtle alterations in brain signalling can be detected prior to overt behavioural impairments.",
keywords = "EEG, behaviour, spectral analysis, autoregressive modelling, theta, gamma, speed, motion, Y-maze, amyloid precursor protein, Motion, Speed, Behaviour, Autoregressive modelling, Amyloid precursor protein, Spectral analysis, Theta, Gamma, HIPPOCAMPAL, RHYTHMS, FREQUENCY, DYSFUNCTION, ALTERS, CELLS, SPONTANEOUS-ALTERNATION BEHAVIOR, RUNNING SPEED, THETA OSCILLATIONS",
author = "Barry Crouch and Yeap, {Jie Min} and Bianca Pais and Gernot Riedel and Bettina Platt",
note = "This work was supported by the Alzheimer’s Society [project grant number AS-PG-14-039] to BP and GR.",
year = "2019",
month = "5",
day = "1",
doi = "10.1016/j.jneumeth.2018.06.028",
language = "English",
volume = "319",
pages = "89--98",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",

}

TY - JOUR

T1 - Of mice and motion

T2 - Behavioural-EEG phenotyping of Alzheimer’s disease mouse models

AU - Crouch, Barry

AU - Yeap, Jie Min

AU - Pais, Bianca

AU - Riedel, Gernot

AU - Platt, Bettina

N1 - This work was supported by the Alzheimer’s Society [project grant number AS-PG-14-039] to BP and GR.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - Background Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. EEG analysis is complicated by a number of confounds, particularly 1. The close relationship between EEG power and movement speed must be controlled for prior to further analysis. 2. The difficulty inherent in identifying EEG epochs associated with a particular behaviour. New Method We utilized infra-red event stamping to accurately synchronize EEG recorded from superficial sites above the hippocampus and prefrontal cortex with motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model (PLB1APP) and wild-type controls (PLBWT) performing a Y-maze spontaneous alternation task. Video tracking synchronized epochs capturing specific behaviours were automatically identified and extracted prior to auto-regressive spectral analysis. Results Despite comparable behavioural performance, PLB1APP mice demonstrated region and behavioural context specific deficits in regulation of Gamma power: In contrast to controls, hippocampal gamma response to speed as well as prefrontal activity associated with correct vs. incorrect alternations was absent in PLB1APP mice. Regulation of hippocampal Gamma power in response to direction of movement did not differ. Comparison with existing Methods This method allows for the first time to detect behaviour-specific differences in EEG response to speed that can be quantified and actively controlled for. Analysis across multiple parameters engaging different brain regions can now be used for detailed EEG profiling of brain-region specific functions. Conclusion Combining infrared event-stamping and auto-regressive modelling enables rapid, automated and sensitive phenotyping of AD mouse models. Subtle alterations in brain signalling can be detected prior to overt behavioural impairments.

AB - Background Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. EEG analysis is complicated by a number of confounds, particularly 1. The close relationship between EEG power and movement speed must be controlled for prior to further analysis. 2. The difficulty inherent in identifying EEG epochs associated with a particular behaviour. New Method We utilized infra-red event stamping to accurately synchronize EEG recorded from superficial sites above the hippocampus and prefrontal cortex with motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model (PLB1APP) and wild-type controls (PLBWT) performing a Y-maze spontaneous alternation task. Video tracking synchronized epochs capturing specific behaviours were automatically identified and extracted prior to auto-regressive spectral analysis. Results Despite comparable behavioural performance, PLB1APP mice demonstrated region and behavioural context specific deficits in regulation of Gamma power: In contrast to controls, hippocampal gamma response to speed as well as prefrontal activity associated with correct vs. incorrect alternations was absent in PLB1APP mice. Regulation of hippocampal Gamma power in response to direction of movement did not differ. Comparison with existing Methods This method allows for the first time to detect behaviour-specific differences in EEG response to speed that can be quantified and actively controlled for. Analysis across multiple parameters engaging different brain regions can now be used for detailed EEG profiling of brain-region specific functions. Conclusion Combining infrared event-stamping and auto-regressive modelling enables rapid, automated and sensitive phenotyping of AD mouse models. Subtle alterations in brain signalling can be detected prior to overt behavioural impairments.

KW - EEG

KW - behaviour

KW - spectral analysis

KW - autoregressive modelling

KW - theta

KW - gamma

KW - speed

KW - motion

KW - Y-maze

KW - amyloid precursor protein

KW - Motion

KW - Speed

KW - Behaviour

KW - Autoregressive modelling

KW - Amyloid precursor protein

KW - Spectral analysis

KW - Theta

KW - Gamma

KW - HIPPOCAMPAL

KW - RHYTHMS

KW - FREQUENCY

KW - DYSFUNCTION

KW - ALTERS

KW - CELLS

KW - SPONTANEOUS-ALTERNATION BEHAVIOR

KW - RUNNING SPEED

KW - THETA OSCILLATIONS

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