A subset of ipRGCs regulates both maturation of the circadian clock and segregation of retinogeniculate projections in mice
- Johns Hopkins University, United States
- University of Akron, United States
- National Institute of Mental Health, National Institute of Health, United States
- Brown University, United States
- Washington University, United States
- Insistut de Recherches Cliniques de Montréal, Canada
Abstract
The visual system consists of two major subsystems, image-forming circuits that drive conscious vision and non-image-forming circuits for behaviors such as circadian photoentrainment. While historically considered non-overlapping, recent evidence has uncovered crosstalk between these subsystems. Here we investigated shared developmental mechanisms. We revealed an unprecedented role for light in the maturation of the circadian clock and discovered that intrinsically photosensitive retinal ganglion cells (ipRGCs) are critical for this refinement process. In addition, ipRGCs regulate retinal waves independent of light, and developmental ablation of a subset of ipRGCs disrupts eye-specific segregation of retinogeniculate projections. Specifically, a subset of ipRGCs, comprising ~200 cells and which project intraretinally and to circadian centers in the brain, are sufficient to mediate both of these developmental processes. Thus, this subset of ipRGCs constitute a shared node in the neural networks that mediate light-dependent maturation of the circadian clock and light-independent refinement of retinogeniculate projections.
Article and author information
Author details
William Thomas Keenan
Funding
National Institute of General Medical Sciences (GM076430)
- Samer Hattar
National Eye Institute (F32-EY20108)
- Jordan M Renna
National Eye Institute (R15EY026255)
- Jordan M Renna
Canadian Institutes of Health Research (MOP-77570)
- Michel Cayouette
National Eye Institute (R01-EY019053)
- Samer Hattar
David and Lucile Packard Foundation
- Samer Hattar
Alfred P. Sloan Foundation
- Samer Hattar
Johns Hopkins University
- Samer Hattar
National Eye Institute (R01-EY017137)
- David M Berson
National Institute on Deafness and Other Communication Disorders (DC007395)
- Haiqing Zhao
National Institute of General Medical Sciences (R01-GM104991)
- Erik D Herzog
National Heart, Lung, and Blood Institute (R01-HL089742)
- Paul A Gray
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Animals were housed and treated in accordance with NIH and IACUC guidelines, and used protocols approved by the Johns Hopkins University and Brown University Animal Care and Use Committees (Protocol numbers MO16A212 and 1010040).
Copyright
© 2017, Chew et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,720
- views
-
- 758
- downloads
-
- 73
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
A subset of ipRGCs regulates both maturation of the circadian clock and segregation of retinogeniculate projections in mice
eLife 6:e22861.
https://doi.org/10.7554/eLife.22861
Further reading
-
- Neuroscience
During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.
-
- Neuroscience
Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey–Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation, and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multihead convolutional neural network. The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably, and time-efficiently the performance in the ROCF test from hand-drawn images.
