Gaze Holding in Healthy Subjects

Bertolini, Gérard; Tarnutzer, Alexander A.; Olasagasti, Itsaso; Khojasteh, Elham; Weber, Konrad P.; Bockisch, Christopher J.; Straumann, Dominik; Marti, Sarah

doi:10.1371/journal.pone.0061389

Cited by 23 publications

(50 citation statements)

References 38 publications

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“…Velocity traces were obtained as the derivative of horizontal eye position traces. Saccades and blinks were removed interactively (see [19] for a detailed description). The resulting data points were assigned to one of 17 non-overlapping bins (binwidth=5deg), covering together ±40deg of gaze eccentricity.…”

Section: Resultsmentioning

confidence: 99%

“…The resulting data points were assigned to one of 17 non-overlapping bins (binwidth=5deg), covering together ±40deg of gaze eccentricity. Additionally, each participant's instantaneous eye velocity was smoothed as a function of eye eccentricity using weighted linear least-squares robust regression (smooth.m, "rloess" algorithm, MATLAB) and interpolated at every 0.1deg [19]. Values from all dependent variables were evaluated for normality using Lillierfors test.…”

Section: Resultsmentioning

confidence: 99%

“…Both eyes were recorded simultaneously. A similar "slow-moving dot" paradigm had been used for data acquisition of 20 healthy human subjects, [19] with the only difference that no eye was covered and that the flashing LED covered the whole range of 80deg tested (±40deg) in each trial. Compared to the classic approach of quantifying gaze-holding deficits by use of saccades to certain, selected angles of gaze-eccentricity, the slow-moving dot paradigm has the advantage of much higher spatial resolution (1deg of horizontal gaze eccentricity), potentially allowing the identification of more subtle changes in gaze-holding deficits and superior fitting of mathematical models.…”

Section: Methodsmentioning

confidence: 99%

“…For comparison, gaze-holding data from 20 healthy human subjects (41±11y old) previously described by [19] was used. This control group was intended to represent optimal gaze holding properties and was, therefore, not age-matched.…”

Section: Subjectsmentioning

confidence: 99%

“…Gaze shifts to moderate horizontal eccentricities evoke only very weak centripetal eye drift in healthy subjects, as cerebellar control sufficiently compensates for the inherent leakiness of the brainstem gaze-holding network [4,5,13,15]. We recently assessed horizontal gaze holding (range=±40deg) and quantified EPN in healthy human subjects, demonstrating overproportional increases in eye drift for gaze angles >20deg [19]. We hypothesized that the gaze holding system is optimized to behave linearly within a given range, but loses this behavior at larger gaze angles, resulting in a non-linear behavior even in healthy human subjects.…”

Section: Introductionmentioning

confidence: 99%

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Gaze holding deficits discriminate early from late onset cerebellar degeneration

Tarnutzer

Weber

Schuknecht³

et al. 2015

J Neurol

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The vestibulo-cerebellum calibrates the output of the inherently leaky brainstem neural velocityto-position integrator to provide stable gaze holding. In healthy humans small-amplitude centrifugal nystagmus is present at extreme gaze-angles, with a non-linear relationship between eye-drift velocity and eye eccentricity. In cerebellar degeneration this calibration is impaired, resulting in pathological gaze-evoked nystagmus (GEN). For cerebellar dysfunction, increased eye drift may be present at any gaze angle (reflecting pure scaling of eye drift found in controls) or restricted to far-lateral gaze (reflecting changes in shape of the non-linear relationship) and resulting eyed-drift patterns could be related to specific disorders. We recorded horizontal eye positions in 21 patients with cerebellar neurodegeneration (gaze-angle = ±40°) and clinically confirmed GEN. Eye-drift velocity, linearity and symmetry of drift were determined. MR-images were assessed for cerebellar atrophy. In our patients, the relation between eye-drift velocity and gaze eccentricity was non-linear, yielding (compared to controls) significant GEN at gaze-eccentricities 20°. Pure scaling was most frequently observed (n = 10/18), followed by pure shape-changing (n = 4/18) and a mixed pattern (n = 4/18). Pure shape-changing patients were significantly (p = 0.001) younger at disease-onset compared to pure scaling patients. Atrophy centered around the superior/dorsal vermis, flocculus/paraflocculus and dentate nucleus and did not correlate with the specific drift behaviors observed. Eye drift in cerebellar degeneration varies in magnitude; however, it retains its non-linear properties. With different drift patterns being linked to age at disease-onset, we propose that the gaze-holding pattern (scaling vs. shape-changing) may discriminate early-from late-onset cerebellar degeneration. Whether this allows a distinction among specific cerebellar disorders remains to be determined.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Subjectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaze holding deficits discriminate early from late onset cerebellar degeneration

Tarnutzer

Weber

Schuknecht³

et al. 2015

J Neurol

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Neural Control of Eye Movements

Skalicky

2016

Ocular and Visual Physiology

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Smartphone video nystagmography using convolutional neural networks: ConVNG

et al. 2022

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Background Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances. Methods A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation. Results ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG. Conclusions ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.

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Gaze Holding in Healthy Subjects

Cited by 23 publications

References 38 publications

Gaze holding deficits discriminate early from late onset cerebellar degeneration

Gaze holding deficits discriminate early from late onset cerebellar degeneration

Neural Control of Eye Movements

Smartphone video nystagmography using convolutional neural networks: ConVNG

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