Interrupted processing of paired somatosensory stimuli in short interstimulus intervals: role of thalamo-cortical oscillations.

BACKGROUND: Discrimination of consecutive sensory stimuli is imperative for proper sensory perception and behavioral response. We aimed to investigate the emergence of paired somatosensory responses in relation to the interstimulus interval (ISI) change. METHODS: Paired stimulus with 35 ms, 50 ms, 80 ms, 140 ms, and 500 ms ISI was applied to the median nerve and evoked responses were recorded from the primary somatosensory cortex in rats. Early and late components of both responses were analyzed in different frequency bands. RESULTS: The amplitudes were comparable for the 1st responses (S1), while the amplitude of the 2nd responses (S2), and S2/S1 sensory gating ratio were significantly lower at 35 and 50 ms ISI values. S2/S1 ratio was close to 1 at 500 ms ISI. The duration and latency of the 2nd response was also different at 35 ms ISI. In the 2nd responses, area of early high-frequency oscillations (150-400 Hz) was significantly lower at 35 ms ISI values. DISCUSSION: The shaping of 2nd somatosensory response is dependent on ISIs. Early high-frequency oscillations changes without accompanying late high-frequency oscillations alterations, may indicate that reduced thalamo-cortical drive to the cortex take a part in determining the 2nd response at short ISI. Further research is required by using neuropsychiatric disorder models where somatosensory perception is impaired.

MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels.

Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. In each iteration, before conventional training, the meta-training loop updates soft-labels so that resulting gradients updates on the base classifier would yield minimum loss on meta-data. Soft-labels are generated from extracted features of data instances, and the mapping function is learned by a single layer perceptron (SLP) network, which is called MetaLabelNet. Following, base classifier is trained by using these generated soft-labels. These iterations are repeated for each batch of training data. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases. We perform extensive experiments on benchmark datasets with both synthetic and real-world noises. Results show that our approach outperforms existing baselines. The source code of the proposed model is available at https://github.com/gorkemalgan/MetaLabelNet.

KCl-induced cortical spreading depression waves more heterogeneously propagate than optogenetically-induced waves in lissencephalic brain: an analysis with optical flow tools.

Although cortical spreading depolarizations (CSD) were originally assumed to be homogeneously and concentrically propagating waves, evidence obtained first in gyrencephalic brains and later in lissencephalic brains suggested a rather non-uniform propagation, shaped heterogeneously by factors like cortical region differences, vascular anatomy, wave recurrences and refractory periods. Understanding this heterogeneity is important to better evaluate the experimental models on the mechanistics of CSD and to make appropriate clinical estimations on neurological disorders like migraine, stroke, and traumatic brain injury. This study demonstrates the application of optical flow analysis tools for systematic and objective evaluation of spatiotemporal CSD propagation patterns in anesthetized mice and compares the propagation profile in different CSD induction models. Our findings confirm the asymmetric angular CSD propagation in lissencephalic brains and suggest a strong dependency on induction-method, such that continuous potassium chloride application leads to significantly higher angular propagation variability compared to optogenetically-induced CSDs.

Automatic 3D segmentation of individual facial muscles using unlabeled prior information.

PURPOSE: Segmentation of facial soft tissues is required for surgical planning and evaluation, but this is laborious using manual methods and has been difficult to achieve with digital segmentation methods. A new automatic 3D segmentation method for facial soft tissues in magnetic resonance imaging (MRI) images was designed, implemented, and tested. METHODS: A region growing algorithm based on local energy functions, using intensity similarities among neighboring regions as criteria, was developed. This local energy function includes the neighborhood relationships not only in the same dataset but from other training datasets. This approach differs from previous studies where the prior information was represented as manual segmented atlases. In this study, a consensus of many datasets, none of which was labeled a priori, is used to guide the segmentation. The method was tested in MRI scans for adult facial structures. MRI scans were obtained from the Alzheimer's disease neuroimaging initiative database. Comparison was made to results from expert manual segmentation and region growing techniques. RESULTS: The volumetric overlap between automated 3D segmentation results and the ground truth was 82.6% for masseter and 78.8% for temporalis tissues. CONCLUSION: A new automated method to segment various facial soft tissues was implemented and the results compared with standard region growing results. The proposed method shows 3.9% improvement in accuracy over the standard method. Reduction in segmentation errors was consistently achieved in MRI scans.

Use of the dynamic volume spline method to predict facial soft tissue changes associated with orthognathic surgery.

OBJECTIVE: The shape of the face can be estimated before the surgery by using 3-dimensional computer programs that provide tools to guide skill modifications. The aim of this study was to present the dynamic volume spline method to predict facial soft tissue changes after the modification of the skull associated with orthognathic surgery. STUDY DESIGN: Soft tissue volume is modeled by a dynamic volume spline that includes the elastic behavior of the actual tissue. The model is a hybrid of spring-mass model and finite element model, and combines their advantageous properties. It provides fast and realistic soft tissue simulations. Postsurgical shape of the patient's face is estimated by reshaping the skull and letting the soft tissue model relax over the new boundary conditions formed by the new skull shape. Postsurgical estimations were compared with the conventional method's estimations, where the soft tissue is not modeled biomechanically. Also, postsurgical estimations were compared with the actual postsurgical data for 6 orthognathic surgery patients. RESULTS: The mean of the error between the estimated shapes and the actual postsurgical shapes was ∼1.8 mm when the whole face was considered. CONCLUSION: When the facial soft tissue is modeled by the dynamic volume spline, the postsurgical shape is estimated better than by the conventional method and previous methods in the literature.

Time-frequency analysis of visual evoked potentials for interhemispheric transfer time and proportion in callosal fibers of different diameters.

This study is an extension of the experimental research of Nalçaci et al., who presented 16 subjects with a reversal of checkerboard pattern as stimuli in the right visual field or left visual field and recorded EEG at O1, O2, P3, and P4. They applied the chosen bandpass filters (4-8, 8-15, 15-20, 20-32 Hz) to the VEPs of subjects and obtained four different components for each VEP. The first aim of this study is to improve the previous report using some methods in time-frequency domain to estimate interhemispheric delays and amplitudes in a time window. Using the improved estimates of interhemispheric delays, the second aim is to estimate the proportion of callosal fibers of different diameters that are activated by visual stimuli by comparing amplitudes of VEPs in different frequency bands. If the relation between frequency components of VEP and delays for callosal fibers of different dimension were reliable, it would give us an opportunity to deal with amplitude of bandpass-filtered VEPs in order to see approximately the proportion of these fibers activated by a certain stimulus. By using frequency-dependent shifts in time and maximizing the cross correlation of direct VEP (DVEP-VEP obtained from contralateral hemisphere)-indirect VEP (IVEP-VEP obtained from ipsilateral hemisphere) pairs in the time-frequency domain, we examined the delay not only at P100 and N160 peaks but along a meaningful time interval as well. Furthermore, by shifting back the IVEP according to the delay estimated at each time window, both the amplitudes and energies of the synchronized DVEP-IVEP pairs were compared at the chosen frequency bands. The percentages of IVEPs at each band was then examined further in conjunction with the distribution of axon diameters in the posterior pole of the CC, questioning the relation between the distributions of the axon diameters and activations at each band. We established an energy definition to express the activation in the fibers. When the energy percentages of IVEPs in theta and alpha were totaled, they were found to be between 76.2% and 81.6%, which is close to the value 74-77% for fibers of 0.4-1 microm in diameter obtained from anatomical study of human CC. The sum of energy percentages in the beta1 and beta2 bands was between 20.1% and 24.2%, which probably reflects the proportion of activation of callosal fibers 1-3 microm in diameter.