Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals. J Neural Eng 2024;21:026014. [PMID: 38525843 DOI: 10.1088/1741-2552/ad3498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective.Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet.Approach.Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution.Main results.While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand.Significance.We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

Parallel encoding of CO2 in attractive and aversive glomeruli by selective lateral signaling between olfactory afferents

We describe a novel form of selective crosstalk between specific classes of primary olfactory receptor neurons (ORNs) in the Drosophila antennal lobe. Neurotransmitter release from ORNs is driven by two distinct sources of excitation: direct activity derived from the odorant receptor and stimulus-selective lateral signals originating from stereotypic subsets of other ORNs. Consequently, the level of presynaptic neurotransmitter release from an ORN can be significantly dissociated from its firing rate. Stimulus-selective lateral signaling results in the distributed representation of CO2-a behaviorally important environmental cue that directly excites a single ORN class-in multiple olfactory glomeruli, each with distinct response dynamics. CO2-sensitive glomeruli coupled to behavioral attraction respond preferentially to fast changes in CO2 concentration, whereas those coupled to behavioral aversion more closely follow absolute levels of CO2. Behavioral responses to CO2 also depend on the temporal structure of the stimulus: flies walk upwind to fluctuating, but not sustained, pulses of CO2. Stimulus-selective lateral signaling generalizes to additional odors and glomeruli, revealing a subnetwork of lateral interactions between ORNs that reshapes the spatial and temporal structure of odor representations in a stimulus-specific manner.

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.

Spatiotemporal Antialiasing in Photoacoustic Computed Tomography

Photoacoustic computed tomography (PACT) based on a full-ring ultrasonic transducer array is widely used for small animal wholebody and human organ imaging, thanks to its high in-plane resolution and full-view fidelity. However, spatial aliasing in full-ring geometry PACT has not been studied in detail. If the spatial Nyquist criterion is not met, aliasing in spatial sampling causes artifacts in reconstructed images, even when the temporal Nyquist criterion has been satisfied. In this work, we clarified the source of spatial aliasing through spatiotemporal analysis. We demonstrated that the combination of spatial interpolation and temporal filtering can effectively mitigate artifacts caused by aliasing in either image reconstruction or spatial sampling, and we validated this method by both numerical simulations and in vivo experiments.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.

Temporal and spatial tuning of dorsal lateral geniculate nucleus neurons in unanesthetized rats

Visual response properties of neurons in the dorsolateral geniculate nucleus (dLGN) have been well described in several species, but not in rats. Analysis of responses from the unanesthetized rat dLGN will be needed to develop quantitative models that account for visual behavior of rats. We recorded visual responses from 130 single units in the dLGN of 7 unanesthetized rats. We report the response amplitudes, temporal frequency, and spatial frequency sensitivities in this population of cells. In response to 2-Hz visual stimulation, dLGN cells fired 15.9 ± 11.4 spikes/s (mean ± SD) modulated by 10.7 ± 8.4 spikes/s about the mean. The optimal temporal frequency for full-field stimulation ranged from 5.8 to 19.6 Hz across cells. The temporal high-frequency cutoff ranged from 11.7 to 33.6 Hz. Some cells responded best to low temporal frequency stimulation (low pass), and others were strictly bandpass; most cells fell between these extremes. At 2- to 4-Hz temporal modulation, the spatial frequency of drifting grating that drove cells best ranged from 0.008 to 0.18 cycles per degree (cpd) across cells. The high-frequency cutoff ranged from 0.01 to 1.07 cpd across cells. The majority of cells were driven best by the lowest spatial frequency tested, but many were partially or strictly bandpass. We conclude that single units in the rat dLGN can respond vigorously to temporal modulation up to at least 30 Hz and spatial detail up to 1 cpd. Tuning properties were heterogeneous, but each fell along a continuum; we found no obvious clustering into discrete cell types along these dimensions.

A g-factor metric for k-t SENSE and k-t PCA based parallel imaging

PURPOSE

To propose and validate a g-factor formalism for k-t SENSE, k-t PCA and related k-t methods for assessing SNR and temporal fidelity.

METHODS

An analytical gxf -factor formulation in the spatiotemporal frequency domain is derived, enabling assessment of noise and depiction fidelity in both the spatial and frequency domain. Using pseudoreplica analysis of cardiac cine data the gxf -factor description is validated and example data are used to analyze the performance of k-t methods for various parameter settings.

RESULTS

Analytical gxf -factor maps were found to agree well with pseudoreplica analysis for 3x, 5x, and 7x k-t SENSE and k-t PCA. While k-t SENSE resulted in lower average gxf values (gx (avg) ) in static regions when compared with k-t PCA, k-t PCA yielded lower gx (avg) values in dynamic regions. Temporal transfer was better preserved with k-t PCA for increasing undersampling factors.

CONCLUSION

The proposed gxf -factor and temporal transfer formalism allows assessing noise performance and temporal depiction fidelity of k-t methods including k-t SENSE and k-t PCA. The framework enables quantitative comparison of different k-t methods relative to frame-by-frame parallel imaging reconstruction.

Impact of methodological variables on functional connectivity findings in autism spectrum disorders

Growing evidence suggests that Autism Spectrum Disorder (ASD) involves abnormalities of multiple functional networks. Neuroimaging studies of ASD have therefore increasingly focused on connectivity. Many functional connectivity (fcMRI) studies have reported network underconnectivity in children and adults with ASD. However, there are notable inconsistencies, with some studies reporting overconnectivity. A previous literature survey suggested that a few methodological factors play a crucial role in differential fcMRI outcomes. Using three ASD data sets (two task-related, one resting state) from 54 ASD and 51 typically developing (TD) participants (ages 9-18 years), we examined the impact of four methodological factors: type of pipeline (co-activation vs. intrinsic analysis, related to temporal filtering and removal of task-related effects), seed selection, field of view (whole brain vs. limited ROIs), and dataset. Significant effects were found for type of pipeline, field of view, and dataset. Notably, for each dataset results ranging from robust underconnectivity to robust overconnectivity were detected, depending on the type of pipeline, with intrinsic fcMRI analyses (low bandpass filter and task regressor) predominantly yielding overconnectivity in ASD, but co-activation analyses (no low bandpass filter or task removal) mostly generating underconnectivity findings. These results suggest that methodological variables have dramatic impact on group differences reported in fcMRI studies. Improved awareness of their implications appears indispensible in fcMRI studies when inferences about "underconnectivity" or "overconnectivity" in ASD are made. In the absence of a gold standard for functional connectivity, the combination of different methodological approaches promises a more comprehensive understanding of connectivity in ASD.

Differential depression at excitatory and inhibitory synapses in visual cortex

The function of cortical circuits depends critically on the balance between excitation and inhibition. This balance reflects not only the relative numbers of excitatory and inhibitory synapses but also their relative strengths. Recent studies of excitatory synapses in visual and somatosensory cortices have emphasized that synaptic strength is not a fixed quantity but is a dynamic variable that reflects recent presynaptic activity. Here, we compare the dynamics of synaptic transmission at excitatory and inhibitory synapses onto visual cortical pyramidal neurons. We find that inhibitory synapses show less overall depression than excitatory synapses and that the kinetics of recovery from depression also differ between the two classes of synapse. When excitatory and inhibitory synapses are stimulated concurrently, this differential depression produces a time- and frequency-dependent shift in the reversal potential of the composite postsynaptic current. These results indicate that the balance between excitation and inhibition can change dynamically as a function of activity.

Postexcitatory inhibition of the crayfish lateral giant neuron: a mechanism for sensory temporal filtering

Crayfish escape from threats by either giant neuron-mediated "reflex" tail flexions that occur with very little delay but do not allow for much sensory guidance of trajectory or by "nongiant" tail flexion responses that allow for sensory guidance but occur much less promptly. Thus, when a stimulus occurs, the nervous system must make a rapid assessment of whether to use the faster reflex system or the slower nongiant one. It does this on the basis of the abruptness of stimulus onset; only stimuli of very abrupt onset trigger giant-mediated responses. We report here that stimuli which excite the lateral giant (LG) command neurons for one form of reflex escape also produce a slightly delayed postexcitatory inhibition (PEI) of the command neurons. As a result, only stimuli that become strong enough to excite the command neurons to firing threshold before the onset of PEI, within a few milliseconds of stimulus onset, can cause giant-mediated responses. This inhibition is directed to distal dendrites of the LG neurons, which allows for some location specificity of PEI within the sensory field of a single hemisegment.

A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex

Cortical synapses exhibit several forms of short-term plasticity, but the contribution of this plasticity to visual response dynamics is unknown. In part, this is because the simple patterns of stimulation used to probe plasticity in vitro do not correspond to patterns of activity that occur in vivo. We have developed a method of quantitatively characterizing short-term plasticity at cortical synapses that permits prediction of responses to arbitrary patterns of stimulation. Synaptic responses were recorded intracellularly as EPSCs and extracellularly as local field potentials in layer 2/3 of rat primary visual cortical slices during stimulation of layer 4 with trains of electrical stimuli containing random mixtures of frequencies. Responses exhibited complex dynamics that were well described by a simple three-component model consisting of facilitation and two forms of depression, a stronger form that decayed exponentially with a time constant of several hundred milliseconds and a weaker, but more persistent, form that decayed with a time constant of several seconds. Parameters obtained from fits to one train were used to predict accurately responses to other random and constant frequency trains. Control experiments revealed that depression was not caused by a decrease in the effectiveness of extracellular stimulation or by a buildup of inhibition. Pharmacological manipulations of transmitter release and postsynaptic sensitivity suggested that both forms of depression are mediated presynaptically. These results indicate that firing evoked by visual stimuli is likely to cause significant depression at cortical synapses. Hence synaptic depression may be an important determinant of the temporal features of visual cortical responses.