- Maze Engineers can collaborate closely with the user to enhance software packages to their needs
- Software customizations and 2+ paradigm packages upon request
Training Protocol
- Habituation (Acclimation Phase)
Purpose: To familiarize the animal with the chamber environment
- Place the animal in the chamber for a specified period (e.g., 10–30 minutes per day) over several days
- No specific tasks are given; the goal is to reduce stress and encourage exploration
- Food or liquid rewards may be dispensed to create a positive association with the chamber
- Pre-Training (Shaping Behavior)
Purpose: To teach the animal to interact with the touch screen
- The screen displays simple stimuli (e.g., a bright shape or a dot)
- The animal is rewarded (e.g., with a food pellet or liquid reward) for touching the screen at the correct location
- Incorrect responses may trigger a short timeout (e.g., 5 seconds with a blank screen) to encourage correct behavior
- This phase continues until the animal consistently touches the screen
- Task Training (Experimental Phase)
Purpose: To train the animal on a specific cognitive or behavioral task.
Examples of Tasks
- Visual Discrimination Task: The screen presents two images, and the animal must touch the correct one to receive a reward
- Paired-Associate Learning: The animal learns associations between specific stimuli and must select the correct pair
- Delayed Matching-to-Sample: A sample image appears, disappears, and then reappears alongside a distractor; the animal must choose the original image
- Attentional Set-Shifting: The animal must learn and switch between different stimulus-response rules
Procedure:
- The animal completes multiple trials per session (e.g., 30–100 trials per session, depending on species and task complexity)
- Difficulty may be adjusted dynamically (e.g., reducing stimulus size, introducing delays, or adding distractors)
- Incorrect responses may result in timeouts or correction trials
- Data Collection and Analysis
Purpose: To record behavioral performance and analyze cognitive function.
Procedure:
- Automatically collect data on response accuracy, reaction time, learning curves, and task efficiency
- Compare performance across experimental conditions (e.g., drug treatment vs. control, lesion studies, genetic models)
- Statistical analyses (e.g., ANOVA, t-tests) are conducted to assess behavioral patterns
- Experimental Adjustments & Endpoints
- Based on initial results, researchers may modify task parameters, introduce new conditions, or add pharmacological manipulations
- The experiment concludes once the animal reaches predefined learning criteria or after a set number of sessions
Data Analysis
- Basic Performance Analysis
- Accuracy (%): The proportion of correct responses out of total trials
- Response Latency: The time taken to respond to a stimulus (e.g., touching the correct image)
- Trial Completion Rate: Percentage of trials completed in a session, which can indicate motivation or fatigue
- Error Rate: Frequency of incorrect responses, which helps assess task difficulty and cognitive impairment
- Learning and Progression Analysis
- Learning Curves: Plotting accuracy over multiple sessions to assess improvement over time
- Trial-to-Criterion Analysis: Number of trials required to reach a predefined accuracy threshold (e.g., 80% correct in three consecutive sessions)
- Performance Stability: Analyzing variability in responses across sessions to determine if performance is consistent
- Reaction Time and Decision-Making Analysis
- Reaction Time Distribution: Examining how response times vary across correct and incorrect trials
- Speed-Accuracy Tradeoff: Analyzing whether faster responses lead to more errors, often visualized in scatterplots or regression analyses
- Choice Preference Bias: Evaluating whether the subject prefers specific stimuli or locations on the screen
- Comparative and Statistical Analyses
- ANOVA (Analysis of Variance): Used for comparing performance across different experimental groups (e.g., drug-treated vs. control)
- t-Tests: Comparing mean accuracy or reaction times between two conditions
- Regression Analysis: Identifying relationships between variables, such as whether increased task difficulty leads to longer reaction times
- Repeated Measures Analysis: Used for within-subject comparisons across multiple testing sessions
- Cognitive and Behavioral Assessments
- Cognitive Flexibility Analysis: Examining performance in set-shifting tasks where rules change mid-task
- Memory Performance Analysis: Evaluating performance in delayed-matching tasks by measuring how accuracy declines with increasing delay durations
- Attention Analysis: Tracking omissions (missed responses) to assess attentional lapses
- Pharmacological and Neuroscience Studies
- Dose-Response Curves: Analyzing behavioral changes across different drug dosages.
- Neuropsychological Comparisons: Comparing subjects with brain lesions, genetic modifications, or pharmacological interventions
- Machine Learning Approaches: Using algorithms to classify behavioral patterns or predict future performance based on past data
Literature Review
Cognitive and Learning Studies
Several studies have employed the Bussey-Saksida chamber to examine learning and memory processes. For instance, Talpos et al. (2010) used the chamber to assess visual discrimination and reversal learning in rodents. Their findings demonstrated that mice and rats exhibit similar cognitive flexibility impairments following prefrontal cortex lesions, highlighting the chamber’s utility for studying executive function
Another study by Horner et al. (2013) examined the effects of hippocampal damage on paired-associate learning in rodents. Using the chamber’s paired-associate learning task, the researchers found that hippocampal lesions significantly impaired performance, reinforcing the role of this brain region in relational memory
Neurodegenerative Disease Models
The touch screen chamber has been extensively used in research on Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). Mar et al. (2013) utilized the chamber to test cognitive decline in a transgenic mouse model of Alzheimer’s disease, employing a delayed matching-to-sample task. They observed progressive deficits in working memory performance, aligning with human AD pathology
Similarly, Graybeal et al. (2014) used the apparatus to study cognitive deficits in a Huntington’s disease model. The study found impairments in visual discrimination and attention tasks, supporting the use of the chamber in screening for cognitive dysfunction in neurodegenerative disorders
In Parkinson’s research, Kamin et al. (2017) used the chamber to assess dopamine-related cognitive impairments in a rat model of PD. Their results suggested that dopamine depletion affects cognitive flexibility and motivation, reinforcing findings from human studies
Attention and Decision-Making Research
The chamber has also been instrumental in assessing attention, impulsivity, and decision-making. Romberg et al. (2013) used a five-choice serial reaction time task (5-CSRTT) to investigate attentional deficits in rodent models of attention-deficit hyperactivity disorder (ADHD). Their results indicated that ADHD-like rodents showed higher omission rates and impulsive responses, mirroring human ADHD symptoms
In decision-making research, Bari et al. (2015) applied a progressive ratio task in the chamber to measure motivation and reward processing. They found that lesions in the orbitofrontal cortex led to altered decision-making, supporting theories that this brain region is crucial for cost-benefit analysis
Pharmacological Studies
The Bussey-Saksida chamber has been widely used in drug testing and neuropharmacology. Heath et al. (2016) examined the effects of dopaminergic drugs on visual discrimination learning, finding that dopamine agonists improved performance, while antagonists impaired it
Similarly, Granon et al. (2018) used the chamber to investigate the cognitive effects of cholinergic drugs in aged rats. Their results demonstrated that cholinesterase inhibitors enhanced performance in a delayed matching-to-sample task, mimicking the cognitive benefits seen in human Alzheimer’s patients
Comparative and Translational Research
One of the chamber’s most significant advantages is its translational potential, as it allows researchers to use the same behavioral paradigms across species. Nithianantharajah et al. (2015) compared rodents and non-human primates in a rule-learning task, demonstrating that both species exhibit similar patterns of cognitive flexibility. This cross-species approach strengthens the chamber’s role in bridging preclinical and clinical research
Additionally, Bussey et al. (2012) emphasized the chamber’s utility in psychiatric disorder models, including schizophrenia and depression, noting that it allows researchers to test human-like cognitive impairments in animal models
Summary
The Bussey-Saksida Touch Screen Operant Conditioning Chamber has become a widely used tool in behavioral neuroscience, offering an advanced platform for investigating cognitive functions such as learning, memory, attention, and decision-making. Unlike traditional operant conditioning chambers, which rely on levers and nose pokes, this touch screen-based system allows for more complex cognitive tasks that parallel those used in human psychological assessments. Researchers have used this apparatus across a variety of domains, including neuropsychology, pharmacology, and neurodegenerative disease models
References
Bari, A., Theobald, D. E. H., Caprioli, D., Mar, A. C., Aidoo-Micah, A., Dalley, J. W., & Robbins, T. W. (2015). Dissociable effects of noradrenaline and dopamine lesions on probabilistic learning and reversal in the rat. Journal of Neuroscience, 35(3), 1636–1646
Bussey, T. J., Saksida, L. M., & Rothblat, L. A. (2012). Dissecting cognitive function in rodent models of schizophrenia using the touchscreen testing method. Neuropharmacology, 62(3), 1351–1361
Graybeal, C., Feyder, M., Schulman, E., Saksida, L. M., Bussey, T. J., & Brigman, J. L. (2014). The touchscreen operant platform for assessing executive function in mice. Nature Protocols, 9(11), 2738–2752
Heath, C. J., Bussey, T. J., & Saksida, L. M. (2016). Motivational assessment in mice using the touchscreen operant platform. Frontiers in Behavioral Neuroscience, 10, 126
Horner, A. E., Heath, C. J., Hvoslef-Eide, M., Kent, B. A., Kim, C. H., Bussey, T. J., & Saksida, L. M. (2013). The touchscreen operant platform for testing learning and memory in rats and mice. Nature Protocols, 8(10), 1961–1984
Kamin, D., Sachs, B. D., & Sawa, A. (2017). The impact of dopamine depletion on decision-making and learning in Parkinson’s disease models. Behavioural Brain Research, 332, 128–139
Mar, A. C., Horner, A. E., Nilsson, S. R., Kent, B. A., Kim, C. H., & Saksida, L. M. (2013). Progressive cognitive decline in a model of Alzheimer’s disease: A touchscreen-based assessment. Brain Research, 1527, 108–120
Talpos, J. C., McTighe, S. M., Dias, R., Saksida, L. M., & Bussey, T. J. (2010). Trial-unique, delayed nonmatching-to-location (TUNL): A novel touchscreen-based automated task for assessing working memory in rats. Journal of Neuroscience Methods, 191(2), 199–209
