Effects of hippocampal interictal discharge timing, duration, and spatial extent on list learning.

Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.

How sandbag-able are concussion sideline assessments? A close look at eye movements to uncover strategies.

Background: Sideline diagnostic tests for concussion are vulnerable to volitional poor performance ("sandbagging") on baseline assessments, motivated by desire to subvert concussion detection and potential removal from play. We investigated eye movements during sandbagging versus best effort on the King-Devick (KD) test, a rapid automatized naming (RAN) task.Methods: Participants performed KD testing during oculography following instructions to sandbag or give best effort.Results: Twenty healthy participants without concussion history were included (mean age 27 ± 8 years). Sandbagging resulted in longer test times (89.6 ± 39.2 s vs 48.2 ± 8.5 s, p < .001), longer inter-saccadic intervals (459.5 ± 125.4 ms vs 311.2 ± 79.1 ms, p < .001) and greater numbers of saccades (171.4 ± 47 vs 138 ± 24.2, p < .001) and reverse saccades (wrong direction for reading) (21.2% vs 11.3%, p < .001). Sandbagging was detectable using a logistic model with KD times as the only predictor, though more robustly detectable using eye movement metrics.Conclusions: KD sandbagging results in eye movement differences that are detectable by eye movement recordings and suggest an invalid test score. Objective eye movement recording during the KD test shows promise for distinguishing between best effort and post-injury performance, as well as for identifying sandbagging red flags.

Detection of normal and slow saccades using implicit piecewise polynomial approximation.

The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms.

The intrinsically restructured fovea is correlated with contrast sensitivity loss in Parkinson's disease.

Foveal structure that is specified by the thickness, depth and the overall shape of the fovea is a promising tool to qualify and quantify retinal pathology in Parkinson's disease. To determine the model variable that is best suited for discriminating Parkinson's disease eyes from those of healthy controls and to assess correlations between impaired contrast sensitivity and foveal shape we characterized the fovea in 48 Parkinson's disease patients and 45 control subjects by optical coherence tomography (OCT). The model quantifies structural changes in the fovea of Parkinson's disease patients that are correlated with a decline in contrast sensitivity. Retinal foveal remodeling may serve as a parameter for vision deficits in Parkinson's disease. Whether foveal remodeling reflects dopaminergic driven pathology or rather both dopaminergic and non-dopaminergic pathology has to be investigated in longitudinal studies.

Validity of low-resolution eye-tracking to assess eye movements during a rapid number naming task: performance of the eyetribe eye tracker.

OBJECTIVE: To evaluate the performance of the EyeTribe compared to the EyeLink for eye movement recordings during a rapid number naming test in healthy control participants. BACKGROUND: With the increasing accessibility of portable, economical, video-based eye trackers such as the EyeTribe, there is growing interest in these devices for eye movement recordings, particularly in the domain of sports-related concussion. However, prior to implementation there is a primary need to establish the validity of these devices. One current limitation of portable eye trackers is their sampling rate (30-60 samples per second, or Hz), which is typically well below the benchmarks achieved by their research-grade counterparts (e.g., the EyeLink, which samples at 500-2000 Hz). METHODS: We compared video-oculographic measurements made using the EyeTribe with those of the EyeLink during a digitized rapid number naming task (the King-Devick test) in a convenience sample of 30 controls. RESULTS: EyeTribe had loss of signal during recording, and failed to reproduce the typical shape of saccadic main sequence relationships. In addition, EyeTribe data yielded significantly fewer detectable saccades and displayed greater variance of inter-saccadic intervals than the EyeLink system. CONCLUSION: Caution is advised prior to implementation of low-resolution eye trackers for objective saccade assessment and sideline concussion screening.

The effect of linguistic background on rapid number naming: implications for native versus non-native English speakers on sideline-focused concussion assessments.

OBJECTIVE: To determine if native English speakers (NES) perform differently compared to non-native English speakers (NNES) on a sideline-focused rapid number naming task. A secondary aim was to characterize objective differences in eye movement behaviour between cohorts. BACKGROUND: The King-Devick (KD) test is a rapid number-naming task in which numbers are read from left-to-right. This performance measure adds vision-based assessment to sideline concussion testing. Reading strategies differ by language. Concussion may also impact language and attention. Both factors may affect test performance. METHODS: Twenty-seven healthy  NNES and healthy NES performed a computerized KD test under high-resolution video-oculography.  NNES also performed a Bilingual Dominance Scale (BDS) questionnaire to weight linguistic preferences (i.e., reliance on non-English language(s)). RESULTS: Inter-saccadic intervals were significantly longer in  NNES (346.3 ± 78.3 ms vs. 286.1 ± 49.7 ms, p = 0.001), as were KD test times (54.4 ± 15.1 s vs. 43.8 ± 8.6 s, p = 0.002). Higher BDS scores, reflecting higher native language dominance, were associated with longer inter-saccadic intervals in  NNES. CONCLUSION: These findings have direct implications for the assessment of athlete performance on vision-based and other verbal sideline concussion tests; these results are particularly important given the international scope of sport. Pre-season baseline scores are essential to evaluation in the event of concussion, and performance of sideline tests in the athlete's native language should be considered to optimize both baseline and post-injury test accuracy.

SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble.

We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

A novel retinal biomarker for Parkinson's disease: Quantifying the foveal pit with optical coherence tomography.

BACKGROUND: Optical coherence tomography offers a potential biomarker tool in Parkinson's disease (PD). A mathematical model quantifying symmetry, breadth, and depth of the fovea was applied. METHODS: Nintey-six subjects (72 PD and 24 healthy controls) were included in the study. Macular scans of each eye were obtained on two different optical coherence tomography devices: Cirrus and RTVue. RESULTS: The variables corresponding to the cardinal gradients of the fovea were the most sensitive indicators of PD for both devices. Principal component analysis distinguished 65% of PD patients from controls on Cirrus, 57% on RTVue. CONCLUSION: Parkinson's disease shallows the superior/inferior and to a lesser degree nasal-temporal foveal slope. The symmetry, breadth, and depth model fits optical coherence tomography data derived from two different devices, and it is proposed as a diagnostic tool in PD.

Application of an OCT data-based mathematical model of the foveal pit in Parkinson disease.

Spectral-domain Optical coherence tomography (OCT) has shown remarkable utility in the study of retinal disease and has helped to characterize the fovea in Parkinson disease (PD) patients. We developed a detailed mathematical model based on raw OCT data to allow differentiation of foveae of PD patients from healthy controls. Of the various models we tested, a difference of a Gaussian and a polynomial was found to have "the best fit". Decision was based on mathematical evaluation of the fit of the model to the data of 45 control eyes versus 50 PD eyes. We compared the model parameters in the two groups using receiver-operating characteristics (ROC). A single parameter discriminated 70 % of PD eyes from controls, while using seven of the eight parameters of the model allowed 76 % to be discriminated. The future clinical utility of mathematical modeling in study of diffuse neurodegenerative conditions that also affect the fovea is discussed.

Research Progress of Eye Movement Analyses and its Detection Algorithms in Alzheimer's Disease.

Alzheimer's disease (AD) has been considered one of the most challenging forms of dementia. The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment. Based on the previous literature summarizing the research results on the relationship between eye movement and AD before 2013, this paper reviewed 34 original eye movements research papers only closely related to AD published in the past ten years and pointed out that the prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search tasks (3 papers) are still the research objects of many researchers, Some researchers have looked at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began to use combinations of different saccade tasks to detect the relationship between eye movement and AD, which had not been done before. These reflect the diversity of eye movement tasks and the complexity and difficulty of the relationship between eye movement and AD. On this basis, the current processing and analysis methods of eye movement datasets are analyzed and discussed in detail, and we note that certain key data that may be especially important for the early diagnosis of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally, we note that the development of methods that can accurately denoise and classify and quickly process massive eye movement data is quite significant for detecting eye movements in early diagnosis of AD.

Reconstruction of Connected Digital Lines Based on Constrained Regularization.

This paper presents a new approach for reconstruction of disconnected digital lines (DDLs) based on a constrained regularization model which ensures connectivity of the digital lines (DLs) in the discrete image plane. The first step in this approach is to determine the order of given pixels of the DDL. To determine connectivity of pixels, we use the usual 8-neighbor connectivity in discrete images. For any neighboring pixels of the DDL that are not connected, we determine a number of new pixel values that need to be reconstructed between these pixels. Next, the integer-valued x - and y -coordinates of the location of the pixels of the DDLs are segregated into two 1D signal vectors. Then the x - and y -coordinates of the missing pixels of the DDLs are estimated using a new constrained regularization. While the solution of this constrained minimization problem provides real values for the x - and y -coordinates of pixels positions, the imposed constraint ensures connectivity of the resulting DLs in the image plane after transforming the computed values from [Formula: see text] to [Formula: see text]. The proposed regularization approach forces connected lines with small curvature. The experimental results demonstrate that the proposed technique improves DL intersection detection, as well. Moreover, this technique has a high potential to be used as a fast approach in binary image inpainting particularly overcoming the shortcomings of conventional methods which cause destruction of thin objects and blurring in the recovered regions.

Accuracy of clinical versus oculographic detection of pathological saccadic slowing.

Saccadic slowing as a component of supranuclear saccadic gaze palsy is an important diagnostic sign in multiple neurologic conditions, including degenerative, inflammatory, genetic, or ischemic lesions affecting brainstem structures responsible for saccadic generation. Little attention has been given to the accuracy with which clinicians correctly identify saccadic slowing. We compared clinician (n = 19) judgements of horizontal and vertical saccade speed on video recordings of saccades (from 9 patients with slow saccades, 3 healthy controls) to objective saccade peak velocity measurements from infrared oculographic recordings. Clinician groups included neurology residents, general neurologists, and fellowship-trained neuro-ophthalmologists. Saccades with normal peak velocities on infrared recordings were correctly identified as normal in 57% (91/171; 171 = 9 videos × 19 clinicians) of clinician decisions; saccades determined to be slow on infrared recordings were correctly identified as slow in 84% (224/266; 266 = 14 videos × 19 clinicians) of clinician decisions. Vertical saccades were correctly identified as slow more often than horizontal saccades (94% versus 74% of decisions). No significant differences were identified between clinician training levels. Reliable differentiation between normal and slow saccades is clinically challenging; clinical performance is most accurate for detection of vertical saccade slowing. Quantitative analysis of saccade peak velocities enhances accurate detection and is likely to be especially useful for detection of mild saccadic slowing.

King-Devick Test Performance and Cognitive Dysfunction after Concussion: A Pilot Eye Movement Study.

(1) Background: The King-Devick (KD) rapid number naming test is sensitive for concussion diagnosis, with increased test time from baseline as the outcome measure. Eye tracking during KD performance in concussed individuals shows an association between inter-saccadic interval (ISI) (the time between saccades) prolongation and prolonged testing time. This pilot study retrospectively assesses the relation between ISI prolongation during KD testing and cognitive performance in persistently-symptomatic individuals post-concussion. (2) Results: Fourteen participants (median age 34 years; 6 women) with prior neuropsychological assessment and KD testing with eye tracking were included. KD test times (72.6 ± 20.7 s) and median ISI (379.1 ± 199.1 msec) were prolonged compared to published normative values. Greater ISI prolongation was associated with lower scores for processing speed (WAIS-IV Coding, r = 0.72, p = 0.0017), attention/working memory (Trails Making A, r = -0.65, p = 0.006) (Digit Span Forward, r = 0.57, p = -0.017) (Digit Span Backward, r= -0.55, p = 0.021) (Digit Span Total, r = -0.74, p = 0.001), and executive function (Stroop Color Word Interference, r = -0.8, p = 0.0003). (3) Conclusions: This pilot study provides preliminary evidence suggesting that cognitive dysfunction may be associated with prolonged ISI and KD test times in concussion.

Reweighted generalized minimax-concave sparse regularization and application in machinery fault diagnosis.

The vibration signal of faulty rotating machinery tends to be a mixture of repetitive transients, discrete frequency components and noise. How to accurately extract the repetitive transients is a critical issue for machinery fault diagnosis. Inspired by reweighted L1 (ReL1) minimization for sparsity enhancement, a reweighted generalized minimax-concave (ReGMC) sparse regularization method is proposed to extract the repetitive transients. We utilize the generalized minimax-concave (GMC) penalty to regularize the weighted sparse representation model to overcome the underestimation deficiency of L1 norm penalty. Moreover, a new reweight strategy which is different from the reweight strategy in ReL1 for sparsity enhancement is proposed according to the statistical characteristic, i.e., squared envelope spectrum kurtosis. Then ReGMC is proposed by solving a series of weighted GMC minimization problems. ReGMC is utilized to process a simulated signal and the vibration signals of a hot-milling transmission gearbox and a run-to-failure bearing with incipient fault. The ReGMC analysis results and the comparison studies show that ReGMC can effectively extract the repetitive transients while suppressing the discrete frequency components and noise, and behaves better than GMC, improved lasso, and spectral kurtosis.

ALTIS: A new algorithm for adaptive long-term SNR estimation in multi-talker babble.

We introduce a real-time capable algorithm which estimates the long-term signal to noise ratio (SNR) of the speech in multi-talker babble noise. In real-time applications, long-term SNR is calculated over a sufficiently long moving frame of the noisy speech ending at the current time. The algorithm performs the real-time long-term SNR estimation by averaging "speech-likeness" values of multiple consecutive short-frames of the noisy speech which collectively form a long-frame with an adaptive length. The algorithm is calibrated to be insensitive to short-term fluctuations and transient changes in speech or noise level. However, it quickly responds to non-transient changes in long-term SNR by adjusting the duration of the long-frame on which the long-term SNR is measured. This ability is obtained by employing an event detector and adaptive frame duration. The event detector identifies non-transient changes of the long-term SNR and optimizes the duration of the long-frame accordingly. The algorithm was trained and tested for randomly generated speech samples corrupted with multi-talker babble. In addition to its ability to provide an adaptive long-term SNR estimation in a dynamic noisy situation, the evaluation results show that the algorithm outperforms the existing overall SNR estimation methods in multi-talker babble over a wide range of number of talkers and SNRs. The relatively low computational cost and the ability to update the estimated long-term SNR several times per second make this algorithm capable of operating in real-time speech processing applications.

Multichannel sleep spindle detection using sparse low-rank optimization.

BACKGROUND: Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert. NEW METHOD: We propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sleep EEG. Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles. RESULTS AND COMPARISON WITH OTHER METHODS: The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4min to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters. CONCLUSIONS: The proposed method attempts to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.

Capturing saccades in multiple sclerosis with a digitized test of rapid number naming.

The King-Devick (K-D) test of rapid number naming is a visual performance measure that captures saccadic eye movements. Patients with multiple sclerosis (MS) have slowed K-D test times associated with neurologic disability and reduced quality of life. We assessed eye movements during the K-D test to identify characteristics associated with slowed times. Participants performed a computerized K-D test with video-oculography. The 25-Item National Eye Institute Visual Functioning Questionnaire (NEI-VFQ-25) and its 10-Item Neuro-Ophthalmic Supplement measured vision-specific quality of life (VSQOL). Among 25 participants with MS (age 37 ± 10 years, range 20-59) and 42 controls (age 33 ± 9 years, range 19-54), MS was associated with significantly longer (worse) K-D times (58.2 ± 19.8 vs. 43.8 ± 8.6 s, P = 0.001, linear regression models, accounting for age). In MS, test times were slower among patients with higher (worse) Expanded Disability Status Scale scores (P = 0.01). Average inter-saccadic intervals (ISI) were significantly longer in MS participants compared to controls (362 ± 103 vs. 286 ± 50 ms, P = 0.001), and were highly associated with prolonged K-D times in MS (P = 0.006). MS participants generated greater numbers of saccades (P = 0.007). VSQOL scores were reduced in MS patients with longer (worse) K-D times (P = 0.04-0.001) and longer ISI (P = 0.002-0.001). Patients with MS have slowed K-D times that may be attributable to prolonged ISI and greater numbers of saccades. The K-D test and its requisite eye movements capture VSQOL and make rapid number naming a strong candidate efferent visual performance measure in MS.

Conjugate eye movements and gamma power modulation of the EEG in persistent vegetative state.

BACKGROUND: Power in the gamma band EEG increases during saccades in normal subjects. OBJECTIVE: To develop a potential method to quantify signs of cortical responsiveness in persistent vegetative state (PVS) we quantified gamma range EEG in association with conjugate slow ballistic eye movements (SBEM). METHODS: The EEG and the simultaneous electro-oculogram were recorded in 14 (8F/6M) PVS patients. Clinical scoring was based on the Glasgow Coma Scale (GCS) and Coma Rating Scale (CRS). The Wavelet Transform, followed by Hilbert transform was applied to the EEG and gamma power distribution was quantified relative to the timing of an eye movement. We correlated the clinical and the neurophysiological measures. RESULTS: Gamma activity was present in all PVS patients. Its power was modulated in association with eye movements only in less severely affected patients, with minimum power prior to, and maximum power during the eye movement. In severely affected patients there was no evidence of a temporal relationship between gamma power and the phase of the eye movement. CONCLUSIONS: Detecting changes in the time course of gamma power in relation to conjugate ballistic eye movements provides a quantitative neurophysiological method for prospective longitudinal studies to explore if the preservation of this CNS function relates to the potential for recovery in PVS patients.

Objectifying eye movements during rapid number naming: Methodology for assessment of normative data for the King-Devick test.

OBJECTIVE: Concussion is a major public health problem and considerable efforts are focused on sideline-based diagnostic testing to guide return-to-play decision-making and clinical care. The King-Devick (K-D) test, a sensitive sideline performance measure for concussion detection, reveals slowed reading times in acutely concussed subjects, as compared to healthy controls; however, the normal behavior of eye movements during the task and deficits underlying the slowing have not been defined. METHODS: Twelve healthy control subjects underwent quantitative eye tracking during digitized K-D testing. RESULTS: The total K-D reading time was 51.24 (±9.7) seconds. A total of 145 saccades (±15) per subject were generated, with average peak velocity 299.5°/s and average amplitude 8.2°. The average inter-saccadic interval was 248.4 ms. Task-specific horizontal and oblique saccades per subject numbered, respectively, 102 (±10) and 17 (±4). Subjects with the fewest saccades tended to blink more, resulting in a larger amount of missing data; whereas, subjects with the most saccades tended to make extra saccades during line transitions. CONCLUSIONS: Establishment of normal and objective ocular motor behavior during the K-D test is a critical first step towards defining the range of deficits underlying abnormal testing in concussion. Further, it sets the groundwork for exploration of K-D correlations with cognitive dysfunction and saccadic paradigms that may reflect specific neuroanatomic deficits in the concussed brain.