17 research outputs found
Versatile treadmill system for measuring locomotion and neural activity in head-fixed mice
No full textHere, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. For complete details on the use and execution of this protocol, please refer to Rasmussen et al. (2019)
The elusive varicose astrocytes
No full textThe evolutionary pattern of different astrocyte types across animal species remains unresolved. In a recent study, Falcone and colleagues revealed that varicose projection astrocytes, a rare form of astrocyte characterized by long varicosities-containing processes, are exclusively found in hominoid brains while being absent from other primate brains
On the contributions of retinal direction selectivity to cortical motion processing in mice
Get PDFSynet er en fundamental sans, og sansningen af bevægelse er velsagtens en af de vigtigste opgaver for synsapparatet. Til dette formål findes der celler, som særligt reagerer på objekter, der bevæger sig i en bestemt retning og derfor siges at være retnings-selektive (RS). Siden denne type celler først blev opdaget i den primære visuelle hjernebark (V1), har man fundet RS-celler i nethinden hos en række dyrearter inklusiv mus, hvilket indikerer, at sansningen af bevægelser starter allerede her, i det første trin af synsbearbejdningen. Disse RS-celler sender forbindelser til den visuelle hjernebark via den visuelle del af thalamus, men man ved ganske lidt om, hvordan nethindens RS-celler bidrager til sansningen af visuel bevægelse i den visuelle hjernebark. Denne PhD afhandling sigter mod at belyse netop dette spørgsmål.
Det første studie i afhandlingen undersøgte, hvordan specialiseret neuronal aktivitet i højerestående områder af den visuelle hjernebark afhænger af nethindens RS-celler. For at undersøge dette anvendte vi to genetiske strategier med det formål at forstyrre nethindens retningsselektivitet, samtidig med at vi målte den cellulære aktivitet, der opstod, når synet blev stimuleret. For at udelukke uønskede effekter af generel anæstesi udførte vi målingerne i både bedøvede og vågne mus. Vi fandt, at en særlig gruppe af RS-celler i det rostrolaterale (RL) område af den visuelle hjernebark var reducerede, når nethindens retningsselektivitet var forstyrret. Denne type celler var karakteriseret ved at udvikle en iøjefaldende præference for bagudrettet bevægelse og ved at reagere kraftigere, når stimulus bevægede sig med høje hastigheder. I kontrast til dette var RS-celler i det posteromediale (PM) område upåvirkede af ændrede forhold i nethinden. Særligt interessant var det, at aktiviteten af RS-cellerne i V1 med forbindelse til RL, men ikke RS-cellerne med forbindelse til PM, også var påvirket, når nethindens retningsselektivitet var forstyrret. Dette studie kortlagde dermed et neuronalt netværk, som sikrer, at information fra nethindens RS-celler bliver dirigeret fortrinsvist til området RL via højt- specialiserede neuronale forbindelser.
Det andet studie undersøgte, hvorvidt nethindens RS-celler bidrager til sansningen af optisk flow i den visuelle hjernebark. Til dette anvendte vi en type synsstimuli, som tester hele repertoiret for horisontal bevægelse i kombination med, at vi ved hjælp af genetiske redskaber forstyrrede nethindens retningsselektivitet og efterfølgende målte den cellulære aktivitet i hjernebarken på vågne mus. I alle målte områder — V1, RL, PM, samt det anteromediale (AM) og anteriore (A) område — fandt vi celler som udviste selektivitet for enten translationel eller rotationel optisk flow. Dog fandtes disse celler især hyppigt i områderne RL og A. Særligt interessant var det, at antallet af de translations-selektive celler i V1 og af de translations-selektive og rotations-selektive
celler i RL og A var reducerede i mus, hvor nethindens retningsselektivitet var forstyrret, mens antallet af denne type celler i PM og AM var uændret. Herudover fandt vi at de visuelle hjernebarksområder i vildtype mus funktionelt kunne opdeles baseret på deres komposition af optisk flow-sensitive celler, mens denne opdeling var mindre klar i mus med forstyrret retningsselektivitet i nethinden. Dette studie påviste dermed, at nethindens RS-celler bidrager til område-specifik sansning af optisk flow i den visuelle hjernebark.
Alt i alt præsenterer denne PhD afhandling en ny fortælling om hvordan hjernen bearbejder visuel bevægelse i omgivelserne. Vores resultater viser hvordan specialiseret neuronal aktivitet i særlige områder af den visuelle hjernebark på mus afhænger af sansningen af visuel bevægelse i øjets nethinde — en del af synsapparatet som man tidligere mente bidrog langt mere beskedent til aktiviteten i højerestående områder. Således bør fundne fra disse studier få os til at revidere vores forestillinger om, hvordan hjernen skaber komplekse visuelle repræsentationer, og de understreger vigtigheden af sansebearbejdning i den perifere del af sensoriske systemer
Data_Sheet_1_EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.docx
No full textEye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.</p
Video_2_EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.AVI
No full textEye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.</p
Video_3_EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.AVI
No full textEye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.</p
Binocular integration of retinal motion information underlies optic flow processing by the cortex
No full textVideo_1_EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.AVI
No full textEye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.</p
Image_1_EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking.TIF
No full textEye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.</p
