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Microglia transform in response to changes in sensory or neural activity, such as sensory deprivation. However, little is known about how specific frequencies of neural activity, or brain rhythms, affect microglia and cytokine signaling. Using visual noninvasive flickering sensory stimulation (flicker) to induce electrical neural activity at 40 hertz, within the gamma band, and 20 hertz, within the beta band, we found that these brain rhythms differentially affect microglial morphology and cytokine expression in healthy animals. Flicker induced expression of certain cytokines independently of microglia, including interleukin-10 and macrophage colony-stimulating factor. We hypothesized that nuclear factor κB (NF-κB) plays a causal role in frequency-specific cytokine and microglial responses because this pathway is activated by synaptic activity and regulates cytokines. After flicker, phospho–NF-κB colabeled with neurons more than microglia. Inhibition of NF-κB signaling down-regulated flicker-induced cytokine expression and attenuated flicker-induced changes in microglial morphology. These results reveal a mechanism through which brain rhythms affect brain function by altering microglial morphology and cytokines via NF-κB.
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How biological attention mechanisms improve task performance in a large-scale visual system model
Imagine you have lost your cell phone. Your eyes scan the cluttered table in front of you, searching for its familiar blue case. But what is happening within the visual areas of your brain while you search? One possibility is that neurons that represent relevant features such as 'blue' and 'rectangular' increase their activity. This might help you spot your phone among all the other objects on the table. Paying attention to specific features improves our performance on visual tasks that require detecting those features. The 'feature similarity gain model' proposes that this is because attention increases the activity of neurons sensitive to specific target features, such as ‘blue’ in the example above. But is this how the brain solves such challenges in practice? Previous studies examining this issue have relied on correlations. They have shown that increases in neural activity correlate with improved performance on visual tasks. But correlation does not imply causation. Lindsay and Miller have now used a computer model of the brain’s visual pathway to examine whether changes in neural activity cause improved performance. The model was trained to use feature similarity gain to detect an object within a set of photographs. As predicted, changes in activity like those that occur in the brain did indeed improve the model’s performance. Moreover, activity changes at later stages of the model's processing pathway produced bigger improvements than activity changes earlier in the pathway. This may explain why attention affects neural activity more at later stages in the visual pathway. But feature similarity gain is not the only possible explanation for the results. Lindsay and Miller show that another pattern of activity change also enhanced the model’s performance, and propose an experiment to distinguish between the two possibilities. Overall, these findings increase our understanding of how the brain processes sensory information. Work is ongoing to teach computers to process images as efficiently as the human visual system. The computer model used in this study is similar to those used in state-of-the-art computer vision. These findings could thus help advance artificial sensory processing too.
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- PAR ID:
- 10083393
- Date Published:
- Journal Name:
- eLife
- Volume:
- 7
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.38105
