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Abstract
The Morris water maze (MWM) is widely used to evaluate rodent spatial learning and memory. However, current evaluation measures are not comprehensive because there is a wide distribution in the measured response. Utilizing the graph cognition hypothesis, we proposed four new deviation indices to evaluate cognitive function in the MWM that compared the optimal swim track to the actual track taken. These include the sum of the lateral deviation vectors, the sum of the offset angles, the sum of the correction vectors, and the sum of the lateral deviation vectors to the initial optimal route. We compared the four new deviation indices to the classically used escape latency measures in a vascular dementia model and demonstrated a higher consistency in the normal distribution between the vascular dementia group and the control rats. Further, the new measures displayed higher sensitivity and specificity compared to what escape latency displayed in the Monte Carlo simulation. From the receiver operating characteristic curve, the diagnostic values of the new deviation indices are higher than those of escape latency. Therefore, including these new evaluation indices in MWM experiments provided a more effective analysis of cognitive function compared to using escape latency.
Highlights
Such contradictions, Maei et al, suggested that using the P measure may reveal more efficient detection of spatial learning[9]
The aim of the present study is to explore dynamic evaluation indicators based on behavioral processes of animal learning and memory, and to make up for the lack of summative evaluation that only pays attention to the final results in a limited time
The area under the curve (AUC) of each new deviation index was larger than that of escape latency, especially when the false positive rate was lower than 20%
Summary
Such contradictions, Maei et al, suggested that using the P measure may reveal more efficient detection of spatial learning[9]. The new indices reported here are based upon sufficient trajectory information, and they use the accumulation of real-time deviations to quantitatively describe the dynamic processes of animal learning behavior. The aim of the present study is to explore dynamic evaluation indicators based on behavioral processes of animal learning and memory, and to make up for the lack of summative evaluation that only pays attention to the final results in a limited time.
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