Head direction (HD) cells, which fire action potentials whenever an animal points its head in a particular direction, are thought to subserve the animal's sense of spatial orientation. HD cells are found prominently in several thalamo-cortical regions including anterior thalamic nuclei, postsubiculum, medial entorhinal cortex, parasubiculum, and the parietal cortex. While a number of methods in neural decoding have been developed to assess the dynamics of spatial signals within thalamo-cortical regions, studies conducting a quantitative comparison of machine learning and statistical model-based decoding methods on HD cell activity are currently lacking. Here, we compare statistical model-based and machine learning approaches by assessing decoding accuracy and evaluate variables that contribute to population coding across thalamo-cortical HD cells.

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In early Alzheimer's disease (AD) spatial navigation is one of the first impairments to emerge; however, the precise cause of this impairment is unclear. Previously, we showed that, in a mouse model of tau and amyloid beta (Aβ) aggregation, getting lost represents, at least in part, a failure to use distal cues to get oriented in space and that impaired parietal-hippocampal network level plasticity during sleep may underlie this spatial disorientation. However, the relationship between tau and amyloid beta aggregation in this brain network and impaired spatial orientation has not been assessed. Therefore, we used several approaches, including canonical correlation analysis and independent components analysis tools, to examine the relationship between pathology profile across the parietal-hippocampal brain network and spatial reorientation learning and memory performance. We found that consistent with the exclusive impairment in 3xTg-AD 6-month female mice, only 6-month female mice had an ICA identified pattern of tau pathology across the parietal-hippocampal network that were positively correlated with behavior. Specifically, a higher density of pTau positive cells predicted worse spatial learning and memory. Surprisingly, despite a lack of impairment relative to controls, 3-month female, as well as 6- and 12- month male mice all had patterns of tau pathology across the parietal-hippocampal brain network that are predictive of spatial learning and memory performance. However, the direction of the effect was opposite, a negative correlation, meaning that a higher density of pTau positive cells predicted better performance. Finally, there were not significant group or region differences in M78 density at any of the ages examined and ICA analyses were not able to identify any patterns of 6E10 staining across brain regions that were significant predictors of behavioral performance. Thus, the pattern of pTau staining across the parietal-hippocampal network is a strong predictor of spatial learning and memory performance, even for mice with low levels of tau accumulation and intact spatial re-orientation learning and memory. This suggests that AD may cause spatial disorientation as a result of early tau accumulation in the parietal-hippocampal network.

The Dynamic Time Warping (DTW) has recently been introduced to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data. In this study, we propose to adopt a more powerful method, referred to as the Fisher-Rao Registration (FRR), to study the phase variability. We systematically compare the FRR with the DTW in three aspects: 1) basic framework, 2) mathematical properties, and 3) computational efficiency.We show that the FRR has superior performance in all these aspects and the advantages are well illustrated with simulation examples. We then apply the FRR method to two real experimental recordings -one fMRI and one EEG data set. It is found the FRR method properly removes the phase variability in each set. Finally, we use the FRR framework to examine brain networks in these two data sets and the result demonstrates the effectiveness of the new method.

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