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Mice with transgenic expression of human SOD1G93A are a widely used model of ALS, with a caudal–rostral progression of motor impairment. Previous studies have quantified the progression of motoneuron (MN) degeneration based on size, even though alpha (α-) and gamma (γ-) MNs overlap in size. Therefore, using molecular markers and synaptic inputs, we quantified the survival of α-MNs and γ-MNs at the lumbar and cervical spinal segments of 3- and 4-month SOD1G93A mice, to investigate whether there is a caudal–rostral progression of MN death. By 3 months, in the cervical and lumbar spinal cord, there was α-MN degeneration with complete γ-MN sparing. At 3 months, the cervical spinal cord had more α-MNs per ventral horn than the lumbar spinal cord in SOD1G93A mice. A similar spatial trend of degeneration was observed in the corticospinal tract, which remained intact in the cervical spinal cord at 3- and 4- months of age. These findings agree with the corticofugal synaptopathy model that α-MNs and CST of the lumbar spinal cord are more susceptible to degeneration in SOD1G93A mice. Hence, there is a spatial and temporal caudal–rostral progression of α-MN and CST degeneration in SOD1G93A mice.

Mice with transgenic expression of human SOD1G93A are a widely used model of ALS, with a caudal–rostral progression of motor impairment. Previous studies have quantified the progression of motoneuron (MN) degeneration based on size, even though alpha (α-) and gamma (γ-) MNs overlap in size. Therefore, using molecular markers and synaptic inputs, we quantified the survival of α-MNs and γ-MNs at the lumbar and cervical spinal segments of 3- and 4-month SOD1G93A mice, to investigate whether there is a caudal–rostral progression of MN death. By 3 months, in the cervical and lumbar spinal cord, there was α-MN degeneration with complete γ-MN sparing. At 3 months, the cervical spinal cord had more α-MNs per ventral horn than the lumbar spinal cord in SOD1G93A mice. A similar spatial trend of degeneration was observed in the corticospinal tract, which remained intact in the cervical spinal cord at 3- and 4- months of age. These findings agree with the corticofugal synaptopathy model that α-MNs and CST of the lumbar spinal cord are more susceptible to degeneration in SOD1G93A mice. Hence, there is a spatial and temporal caudal–rostral progression of α-MN and CST degeneration in SOD1G93A mice.

The weakness of the lower leg muscles due to nerve damage or muscle weakness can result in foot drop, a change in gait that manifests as an inability to lift the toes of the foot when walking. Foot drop results in a decreased quality of life, with unassisted movement becoming difficult or impossible. The increased risk of falls is particularly problematic as foot drop often affects the elderly or infirm for whom falling already presents a great danger. Current treatment options include fixed ankle–foot orthosis (AFO) aiming to provide rigid support to the foot if the impairment is mild or surgical intervention and functional electrical stimulation (FES) devices if the weakness is more severe. FES intervention is effective for providing a non-invasive treatment in even severe cases of foot drop. Limitations of current models relate to the non-naturalistic recreation of gait in the affected leg and its unsuitability for patients with extensive peripheral nerve damage. Although there are attempts to enhance integration of sensory information and mimic natural stimulation patterns, the focus on restoring a natural feedback loop is still amiss. Without such a closed loop feedback, restoration of a natural gait pattern is unlikely to occur. We here recommend, integration of motor output from multiple muscles, information about the inputs from higher-order controllers, recorded from the intact leg with a closed loop system to improve the effectiveness of FES.

This paper presents a time-frequency masking based online multi-channel speech enhancement approach that uses a convolutional recurrent neural network to estimate the mask. The magnitude and phase components of the short-time Fourier transform coefficients for multiple time frames are provided as an input such that the network is able to discriminate between the directional speech and the noise components based on the spatial characteristics of the individual signals as well as their spectro-temporal structure. The estimation of two different masks, namely, ideal ratio mask (IRM) and ideal binary mask (IBM), along with two different approaches for incorporating the mask to obtain the desired signal are discussed. In the first approach, the mask is directly applied as a real valued gain to a reference microphone signal, whereas in the second approach, the masks are used as an activity indicator for the recursive update of power spectral density (PSD) matrices to be used within a beamformer. The performance of the proposed system with the two different estimated masks utilized within the two different enhancement approaches is evaluated with both simulated as well as measured room impulse responses, where it is shown that the IBM is better suited as an indicator for the PSD updates while direct application of IRM as a real valued gain leads to a better improvement in terms of short term objective intelligibility. Analysis of the performance of the proposed system also demonstrates the robustness of the system to different angular positions of the speech source.

Background: The demand for neuromodulatory and recording tools has resulted in a surge of publications describing techniques for fabricating devices and accessories in-house suitable for neurological recordings. However, many of these fabrication protocols use equipment which are not common to biological laboratories, thus limiting researchers to the use of commercial alternatives. New method: We have developed a simple yet robust implantable stimulating surface electrode which can be fabricated in all wet-bench laboratories. Results: Female Sprague-Dawley rats received epidural implantation of the electrodes over the fore and hind limb areas of their motor cortex. Stimulation of the motor cortex successfully evoked fore- and hind limb motor outputs. The device was also able to record surface potentials of the motor cortex following epidural stimulation of the spinal cord. Comparisons with existing methods: For stimulation of the motor cortex, often stiff stainless or copper wires are roughly tucked underneath the skull, with little accuracy of localization. While, commercially available devices utilize burr holes and screw electrodes. Our new electrode design provides us stereotaxic accuracy that was not previously available. Conclusion: We developed a chronic implantable electrode capable of being fabricated in all wet-labs, are robust, versatile and electrically sensitive enough for long-term chronic use. The simple and versatile electrode design provides scientific, economical and ethical benefits.

Supervised learning-based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments. In this paper, a convolutional neural network (CNN) based supervised learning method for estimating the direction of arrival (DOA) of multiple speakers is proposed. Multi-speaker DOA estimation is formulated as a multi-class multi-label classification problem, where the assignment of each DOA label to the input feature is treated as a separate binary classification problem. The phase component of the short-time Fourier transform (STFT) coefficients of the received microphone signals are directly fed into the CNN, and the features for DOA estimation are learnt during training. Utilizing the assumption of disjoint speaker activity in the STFT domain, a novel method is proposed to train the CNN with synthesized noise signals. Through experimental evaluation with both simulated and measured acoustic impulse responses, the ability of the proposed DOA estimation approach to adapt to unseen acoustic conditions and its robustness to unseen noise type is demonstrated. Through additional empirical investigation, it is also shown that with an array of M microphone our proposed framework yields the best localization performance with M-1 convolution layers. The ability of the proposed method to accurately localize speakers in a dynamic acoustic scenario with varying number of sources is also shown.

Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance. We propose a unifying probabilistic paradigm, where deep neural network architectures are used to infer output posterior distributions. These probabilities are in turn processed to yield discrete point estimates. Designing such architectures often involves two important and complementary aspects that we investigate and discuss. First, we study how recent advances in deep architectures may be exploited for the task of speaker count estimation. In particular, we show that convolutional recurrent neural networks outperform recurrent networks used in a previous study when adequate input features are used. Even for short segments of speech mixtures, we can estimate up to five speakers, with a significantly lower error than other methods. Second, through comprehensive evaluation, we compare the best-performing method to several baselines, as well as the influence of gain variations, different data sets, and reverberation. The output of our proposed method is compared to human performance. Finally, we give insights into the strategy used by our proposed method.