Dataset Information

Estimation of self-motion duration and distance in rodents.

ABSTRACT: Spatial orientation and navigation rely on information about landmarks and self-motion cues gained from multi-sensory sources. In this study, we focused on self-motion and examined the capability of rodents to extract and make use of information about own movement, i.e. path integration. Path integration has been investigated in depth in insects and humans. Demonstrations in rodents, however, mostly stem from experiments on heading direction; less is known about distance estimation. We introduce a novel behavioural paradigm that allows for probing temporal and spatial contributions to path integration. The paradigm is a bisection task comprising movement in a virtual reality environment in combination with either timing the duration ran or estimating the distance covered. We performed experiments with Mongolian gerbils and could show that the animals can keep track of time and distance during spatial navigation.

SUBMITTER: Kautzky M

PROVIDER: S-EPMC4892454 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Estimation of self-motion duration and distance in rodents.

Kautzky Magdalena M Thurley Kay K

Royal Society open science 20160525 5

Spatial orientation and navigation rely on information about landmarks and self-motion cues gained from multi-sensory sources. In this study, we focused on self-motion and examined the capability of rodents to extract and make use of information about own movement, i.e. path integration. Path integration has been investigated in depth in insects and humans. Demonstrations in rodents, however, mostly stem from experiments on heading direction; less is known about distance estimation. We introduce ...[more]

PMID: 27293792

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Project description:BACKGROUND:Subject motion is known to produce spurious covariance among time-series in functional connectivity that has been reported to induce distance-dependent spurious correlations. PURPOSE:To present a feasibility study for applying the extended Kalman filter (EKF) framework for high temporal resolution motion correction of resting state functional MRI (rs-fMRI) series using each simultaneous multi-slice (SMS) echo planar imaging (EPI) shot as its own navigator. STUDY TYPE:Prospective feasibility study. POPULATION/SUBJECTS:Three human volunteers. FIELD STRENGTH/SEQUENCE:3T GE DISCOVERY MR750 scanner using a 32-channel head coil. Simultaneous multi-slice rs-fMRI sequence with repetition time (TR)/echo time (TE) = 800/30 ms, and SMS factor 6. ASSESSMENT:Motion estimates were computed using two techniques: a conventional rigid-body volume-wise registration; and a high-temporal resolution motion estimation rigid-body approach. The reference image was resampled using the estimates obtained from both approaches and the difference between these predicted volumes and the original moving series was summarized using the normalized mean squared error (NMSE). STATISTICAL TESTS:Direct comparison of NMSE values. RESULTS:High-temporal motion estimation was always superior to volume-wise motion estimation for the sample presented. For staged continuous rotations, the NMSE using high-temporal resolution motion estimates ranged between [0.130, 0.150] for the first volunteer (in-plane rotations), between [0.060, 0.068] for the second volunteer (in-plane rotations), and between [0.063, 0.080] for the third volunteer (through-plane rotations). These values went up to [0.384, 0.464]; [0.136, 0.179]; and [0.080, 0.096], respectively, when using volume-wise motion estimates. DATA CONCLUSION:Accurate high-temporal rigid-body motion estimates can be obtained for rs-fMRI taking advantage of simultaneous multi-slice EPI sub-TR shots. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Project description:OBJECTIVES:In this study, we present a fully automated and robust self-navigated approach to obtain 4-dimensional (4-D) motion-resolved images during free breathing. MATERIALS AND METHODS:The proposed method, Consistently Acquired Projections for Tuned and Robust Estimation (CAPTURE), is a variant of the stack-of-stars gradient-echo sequence. A 1-D navigator was consistently acquired at a fixed azimuthal angle for all stacks of spokes to reduce nonphysiological signal contamination due to system imperfections. The resulting projections were then "tuned" using complex phase rotation to adapt to scan-to-scan variations, followed by the detection of the respiratory curve. Four-dimensional motion-corrected and uncorrected images were then reconstructed via respiratory and temporal binning, respectively.This Health Insurance Portability and Accountability Act-compliant study was performed with Institutional Review Board approval. A phantom experiment was performed using a custom-made deformable motion phantom with an adjustable frequency and amplitude. For in vivo experiments, 10 healthy participants and 12 liver tumor patients provided informed consent and were imaged with the CAPTURE sequence.Two radiologists, blinded to which images were motion-corrected and which were not, independently reviewed the images and scored the image quality using a 5-point Likert scale. RESULTS:In the respiratory motion phantom experiment, CAPTURE reversed the effects of motion blurring and restored edge sharpness from 36% to 78% of that observed in the images from the static scan.Despite large intra- and intersubject variability in respiration patterns, CAPTURE successfully detected the respiratory motion signal in all participants and significantly improved the image quality according to the subjective radiological assessments of 2 raters (P < 0.05 for both raters) with a 1 to 2-point improvement in the median Likert scores across the whole set of participants. Small lesions (<1 cm in size) which might otherwise be missed on uncorrected images because of motion blurring were more clearly depicted on the CAPTURE images. CONCLUSIONS:CAPTURE provides a robust and fully automated solution for obtaining 4-D motion-resolved images in a free-breathing setting. With its unique tuning feature, CAPTURE can adapt to large intersubject and interscan variations. CAPTURE also enables better lesion delineation because of improved image sharpness, thereby increasing the visibility of small lesions.

Dataset Information

Estimation of self-motion duration and distance in rodents.

Publications

Estimation of self-motion duration and distance in rodents.

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