In image training, we propose two contextual regularization strategies for dealing with unannotated regions: multi-view Conditional Random Field (mCRF) loss and Variance Minimization (VM) loss. The mCRF loss strengthens consistency in pixel labeling for similar feature groups, and the VM loss reduces intensity variation within the segmented foreground and background We use, as pseudo-labels in the second phase, the outputs predicted by the pre-trained model from the initial stage. Employing a Self and Cross Monitoring (SCM) strategy, we address noise in pseudo-labels by combining self-training with Cross Knowledge Distillation (CKD) between a primary model and an auxiliary model, which learn from each other's soft labels. OPN expression inhibitor 1 The public Vestibular Schwannoma (VS) and Brain Tumor Segmentation (BraTS) datasets were used to assess our model. Its initial training phase yielded substantial improvements over existing weakly supervised approaches, and further training with SCM brought its performance on the BraTS task near that of its fully supervised counterpart.
The identification of the surgical phase is a critical component within computer-assisted surgical systems. Full annotation, which is an expensive and time-consuming process, is a prerequisite for most existing works, causing surgeons to have to repeatedly watch video footage to mark the exact start and end times of each surgical stage. This study introduces timestamp supervision to train models for surgical phase recognition, requiring surgeons to pinpoint a single timestamp falling within each phase's temporal span. immune stress Compared to the complete annotation process, this annotation type significantly diminishes the cost of manual annotation. To maximize the benefit of timestamp supervision, we introduce a novel method named uncertainty-aware temporal diffusion (UATD) to create reliable pseudo-labels for training. Due to the characteristic of surgical videos, where phases are extended events composed of successive frames, our UATD was developed. The single labeled timestamp is disseminated by UATD in an iterative manner to its neighboring frames exhibiting high confidence (i.e., low uncertainty). Our study using timestamp supervision in surgical phase recognition uncovers key insights. Surgeons' code and annotations, documented and available, can be accessed through the link https//github.com/xmed-lab/TimeStamp-Surgical.
By synergistically integrating complementary data, multimodal methods prove highly promising for neuroscience studies. There has been an inadequate amount of multimodal work examining the alterations in brain development.
We present a method for learning a shared dictionary and modality-specific sparse representations from multimodal data and its sparse deep autoencoder encodings, developing an explainable multimodal deep dictionary learning approach, to reveal the shared and unique characteristics of diverse modalities.
Considering three fMRI paradigms, gathered during two tasks and resting state, as modalities, our proposed approach analyzes multimodal data to reveal developmental differences in the brain. The results highlight the proposed model's ability to achieve superior reconstruction performance, and simultaneously demonstrate the presence of age-associated variation in recurrent patterns. Both children and young adults favor switching between tasks during active engagement, while resting within a single task, yet children show a more broadly distributed functional connectivity, in contrast to the more focused patterns observed in young adults.
To elucidate the shared and distinct characteristics of three fMRI paradigms across developmental stages, multimodal data and their encodings are leveraged to train a shared dictionary and modality-specific sparse representations. Examining variations in brain networks provides insight into the development and maturation of neural circuits and brain systems throughout the lifespan.
Developmental differences in response to three fMRI paradigms are investigated by training a shared dictionary and modality-specific sparse representations using multimodal data and their encodings. Distinguishing features of brain networks helps to unravel the mechanisms of how neural circuits and brain networks form and mature as individuals age.
Characterizing the interplay between ion concentrations and ion pump activity in causing conduction blockage of myelinated axons from prolonged direct current (DC) exposure.
A novel axonal conduction model for myelinated axons, drawing upon the classic Frankenhaeuser-Huxley (FH) equations, is presented. This model incorporates ion pump activity and accounts for intracellular and extracellular sodium concentrations.
and K
Axonal activity serves as a catalyst for fluctuations in concentrations.
The novel model effectively replicates the generation, propagation, and acute DC block of action potentials, within a millisecond span, mimicking the precision of the classical FH model in maintaining stable ion concentrations and ion pump inactivity. Unlike the established model, the new model faithfully reproduces the post-stimulation block, representing the interruption of axonal conduction after a 30-second application of direct current, as documented recently in animal studies. A substantial K value is revealed by the model's results.
Ion pump activity in the post-stimulation period is hypothesized to reverse the post-DC block, which could be due to substances accumulating outside the axonal node.
Changes in ion pump activity and ion concentrations are responsible for the post-stimulation block occurring after prolonged direct current stimulation.
Neuromodulation therapies, often relying on long-duration stimulation, exhibit effects on axonal conduction and block that are not yet completely understood. The underlying mechanisms of long-lasting stimulation, including the changes in ion concentrations and the subsequent activation of ion pumps, will be better understood using this new model.
In the realm of neuromodulation therapies, long-duration stimulation is widely employed, yet the ramifications for axonal conduction and potential blocking are insufficiently understood. This model, through its application, will effectively reveal the mechanisms of long-duration stimulation and how it impacts ion concentrations, setting off ion pump activity.
Understanding brain states and how to manipulate them is essential for advancing the application of brain-computer interfaces (BCIs). This paper presents an exploration of transcranial direct current stimulation (tDCS) as a neuromodulation technique, specifically focusing on its capacity to enhance the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. The effects of pre-stimulation, sham-tDCS, and anodal-tDCS are scrutinized by analyzing variations in EEG oscillation and fractal component characteristics. Furthermore, this study presents a novel brain state estimation approach for evaluating neuromodulation's impact on brain arousal levels, specifically for SSVEP-BCIs. Results show that tDCS, particularly the anodal variety, can augment SSVEP amplitude, thus potentially boosting the efficiency of systems employing SSVEP-based brain-computer interfaces. Indeed, the existence of fractal features strongly suggests that tDCS-based neuromodulation produces an increased level of neural arousal. Based on personal state interventions, this study's findings illuminate ways to improve BCI performance, offering an objective method for quantitative brain state monitoring, which can be utilized in EEG modeling of SSVEP-BCIs.
The gait of healthy adults shows long-range autocorrelations, meaning the interval of each stride is statistically affected by preceding gait cycles, this dependency continuing for hundreds of strides. Earlier investigations revealed alterations to this property in Parkinson's patients, leading to their gait exhibiting a more unpredictable pattern. To computationally analyze the decrease in LRA seen in patients, we adapted a gait control model. Maintaining a constant velocity in gait was tackled using a Linear-Quadratic-Gaussian control model, which hinges on the coordinated regulation of stride length and stride duration. This objective's redundant approach to velocity control by the controller leads to the development of LRA. The model's analysis, within this framework, indicated that patients displayed a reduced reliance on task redundancy, possibly to counteract increased variability in their stride-to-stride movements. snail medick Consequently, we applied this model to assess the prospective advantage of an active orthosis on the walking patterns of the patients. The model incorporated the orthosis as a low-pass filter applied to the stride parameter series. Computer simulations indicate that a well-designed assistive device, such as the orthosis, can enable patients to recover a gait pattern exhibiting LRA similar to healthy control participants. Our investigation, using LRA's presence in a series of strides as a marker of healthy gait, supports the conceptualization of gait assistance devices to decrease the chance of falling, a common issue with Parkinson's disease.
MRI-compatible robots offer a method for investigating brain function during complex sensorimotor learning, including adaptation. To accurately interpret the neural correlates of behavior, as determined by MRI-compatible robots, the measurements of motor performance obtained through these devices must be validated. In prior studies, the MR-SoftWrist, an MRI-compatible robot, was used to analyze wrist adaptation to applied force fields. While examining arm-reaching tasks, we observed a diminished level of adaptation, accompanied by trajectory error reductions that exceeded the explained range of adaptation. Therefore, we proposed two hypotheses: that the disparities we noted were attributable to measurement errors of the MR-SoftWrist, or that impedance control substantially affected wrist movement management during dynamic disruptions.