by McGill University

Population recordings in the mouse ADN. a, Schematic of the recording environment within a 360° LED screen. Scale bar, 20 cm. b, GCaMP6f expression in the ADN. In total, 12 mice were injected and implanted for this study, and only 3 (Extended Data Fig. 1a–c) provided enough simultaneously recorded HD cells for continued experimentation. Scale bar, 2 mm. c, Example tuning curves of ADN cells with high directional tuning in polar coordinates. The red lines and numbers show the mean resultant vectors and PFD, respectively. R, correlation coefficient. d, Field of view (FOV) of the ADN showing the PFD of each cell. Scale bar, 0.125 mm. e, The distribution of ADN cells recorded across mice (n = 3) and sessions (n = 99). The red line indicates the median (minimum, maximum, median, 25th percentile and 75th percentile, respectively, are as follows: mouse 1 (all): 38, 188, 105, 70 and 131; mouse 1 (HD): 35, 154, 96, 66 and 128; mouse 2 (all): 102, 168, 138, 126.5 and 147; mouse 2 (HD): 97, 154, 129, 114.75 and 139.75; mouse 3 (all): 90, 255, 174, 137 and 204.5; mouse 3 (HD): 88, 239, 162.5, 133 and 195.5). The values above the box plots indicate the percentage of HD cells (green) among all recorded ADN cells (blue) shown as mean ± s.d. f, The distribution of correlation coefficients of ADN cells. The dashed yellow line represents the HD neuron detection threshold (shuffled control: P < 0.05). Data from three 10 min baseline recording sessions (one per mouse). g, HD population coverage of the azimuthal plane from one session. h, Projection of high-dimensional neural data onto a 2D polar plane using a feedforward neural network during a baseline recording. i, HD decoding. Top, log-likelihood distribution across time. Bottom, measured HD (blue) and decoded HD (red) using maximum likelihood. j, The distribution of the absolute residual error across baseline recordings from the first experiment (n = 42 sessions). Credit: Nature (2023). DOI: 10.1038/s41586-023-05813-2

Scientists have gained new insights into the part of the brain that gives us a sense of direction, by tracking neural activity with the latest advances in brain imaging techniques. The findings shed light on how the brain orients itself in changing environments—and even the processes that can go wrong with degenerative diseases like dementia, that leave people feeling lost and confused.

"Neuroscience research has witnessed a technology revolution in the last decade, allowing us to ask and answer questions that could only be dreamed of just years ago," says Mark Brandon, an Associate Professor of psychiatry at McGill University and researcher at the Douglas Research Centre, who co-led the research with Zaki Ajabi, a former student at McGill University and now a postdoctoral research fellow at Harvard University.

Reading the brain's internal compass

To understand how visual information impacts the brain's internal compass, the researchers exposed mice to a disorienting virtual world while recording the brain's neural activity. The team recorded the brain's internal compass with unprecedented precision using the latest advances in neuronal recording technology.

This ability to accurately decode the animal's internal head direction allowed the researchers to explore how the Head-Direction cells, which make up the brain's internal compass, support the brain's ability to re-orient itself in changing surroundings. Specifically, the research team identified a phenomenon they term "network gain" that allowed the brain's internal compass to reorient after the mice were disoriented.

"It's as if the brain has a mechanism to implement a 'reset button' allowing for rapid reorientation of its internal compass in confusing situations," says Ajabi.

Although the animals in this study were exposed to unnatural visual experiences, the authors argue that such scenarios are already relevant to the modern human experience, especially with the rapid spread of virtual reality technology. These findings "may eventually explain how virtual reality systems can easily take control over our sense of orientation," adds Ajabi.

The results inspired the research team to develop new models to better understand the underlying mechanisms. "This work is a beautiful example of how experimental and computational approaches together can advance our understanding of brain activity that drives behavior," says co-author Xue-Xin Wei, a computational neuroscientist and an assistant professor at The University of Texas at Austin.

Degenerative diseases

The research is published in the journal Nature. The findings also have significant implications for Alzheimer's disease. "One of the first self-reported cognitive symptoms of Alzheimer's is that people become disoriented and lost, even in familiar settings," says Brandon.

The researchers expect that a better understanding of how the brain's internal compass and navigation system works will lead to earlier detection and better assessment of treatments for Alzheimer's disease.

More information: Zaki Ajabi et al, Population dynamics of head-direction neurons during drift and reorientation, Nature (2023). DOI: 10.1038/s41586-023-05813-2

Journal information: Nature 

Provided by McGill University