Surface electromyography for testing motor dysfunction in amyotrophic lateral sclerosis

OBJECTIVES

To investigate the use of a set of dynamical features, extracted from surface electromyography, to study upper motor neuron (UMN) degeneration in amyotrophic lateral sclerosis (ALS).

METHODS

We acquired surface EMG signals from the upper limb muscles of 13 ALS patients and 20 control subjects and classified them according to a novel set of muscle activity features, describing the temporal and frequency dynamic behavior of the signals, as well as measures of its complexity. Using a battery of classification approaches, we searched for the most discriminating combination of those features, as well as a suitable strategy to identify ALS.

RESULTS

We observed significant differences between ALS patients and controls, in particular when considering features highlighting differences between forearm and hand recordings, for which classification accuracies of up to 94% were achieved. The most robust discriminations were achieved using features based on detrended fluctuation analysis and peak frequency, and classifiers such as decision trees, random forest and Adaboost.

CONCLUSION

The current work shows that it is possible to achieve good identification of UMN changes in ALS by taking into consideration the dynamical behavior of surface electromyographic (sEMG) data.

Electrophysiological effects of mindfulness meditation in a concentration test

In this paper, we evaluate the effects of mindfulness meditation training in electrophysiological signals, recorded during a concentration task. Longitudinal experiments have been limited to the analysis of psychological scores through depression, anxiety, and stress state (DASS) surveys. Here, we present a longitudinal study, confronting DASS survey data with electrocardiography (ECG), electroencephalography (EEG), and electrodermal activity (EDA) signals. Twenty-five university student volunteers (mean age = 26, SD = 7, 9 male) attended a 25-h mindfulness-based stress reduction (MBSR) course, over a period of 8 weeks. There were four evaluation periods: pre/peri/post-course and a fourth follow-up, after 2 months. All three recorded biosignals presented congruent results, in line with the expected benefits of regular meditation practice. In average, EDA activity decreased throughout the course, -64.5%, whereas the mean heart rate displayed a small reduction, -5.8%, possibly as a result of an increase in parasympathetic nervous system activity. Prefrontal (AF3) cortical alpha activity, often associated with calm conditions, saw a very significant increase, 148.1%. Also, the number of stressed and anxious subjects showed a significant decrease, -92.9% and -85.7%, respectively. Easy to practice and within everyone's reach, this mindfulness meditation can be used proactively to prevent or enhance better quality of life. 25 volunteers attended a Mindfulness-Based Stress Reduction (MBSR) course in 4 evaluation periods: Pre/Peri/Post-course and a fourth follow-up after two months. A Depression, Anxiety and Stress State (DASS) survey is completed in each period. Electrodermal Activity (EDA), Electrocardiography (ECG) and Electroencephalography (EEG) are also recorded and processed. By integrating self-reported surveys and electrophysiological recordings there is strong evidence of evolution in wellbeing. Mindfulness meditation can be used proactively to prevent or enhance better quality of life.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

Early-Stage White Matter Lesions Detected by Multispectral MRI Segmentation Predict Progressive Cognitive Decline

White matter lesions (WML) are the main brain imaging surrogate of cerebral small-vessel disease. A new MRI tissue segmentation method, based on a discriminative clustering approach without explicit model-based added prior, detects partial WML volumes, likely representing very early-stage changes in normal-appearing brain tissue. This study investigated how the different stages of WML, from a “pre-visible” stage to fully developed lesions, predict future cognitive decline. MRI scans of 78 subjects, aged 65–84 years, from the Leukoaraiosis and Disability (LADIS) study were analyzed using a self-supervised multispectral segmentation algorithm to identify tissue types and partial WML volumes. Each lesion voxel was classified as having a small (33%), intermediate (66%), or high (100%) proportion of lesion tissue. The subjects were evaluated with detailed clinical and neuropsychological assessments at baseline and at three annual follow-up visits. We found that voxels with small partial WML predicted lower executive function compound scores at baseline, and steeper decline of executive scores in follow-up, independently of the demographics and the conventionally estimated hyperintensity volume on fluid-attenuated inversion recovery images. The intermediate and fully developed lesions were related to impairments in multiple cognitive domains including executive functions, processing speed, memory, and global cognitive function. In conclusion, early-stage partial WML, still too faint to be clearly detectable on conventional MRI, already predict executive dysfunction and progressive cognitive decline regardless of the conventionally evaluated WML load. These findings advance early recognition of small vessel disease and incipient vascular cognitive impairment.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

Separation of synchronous sources through phase locked matrix factorization

In this paper, we study the separation of synchronous sources (SSS) problem, which deals with the separation of sources whose phases are synchronous. This problem cannot be addressed through independent component analysis methods because synchronous sources are statistically dependent. We present a two-step algorithm, called phase locked matrix factorization (PLMF), to perform SSS. We also show that SSS is identifiable under some assumptions and that any global minimum of PLMFs cost function is a desirable solution for SSS. We extensively study the algorithm on simulated data and conclude that it can perform SSS with various numbers of sources and sensors and with various phase lags between the sources, both in the ideal (i.e., perfectly synchronous and nonnoisy) case, and with various levels of additive noise in the observed signals and of phase jitter in the sources.

The effect of automatic blink correction on auditory evoked potentials

The effects of blink correction on auditory event-related potential (ERP) waveforms is assessed. Two blink correction strategies are compared. ICA-SSP combines independent component analysis (ICA) with signal space projection (SSP) and ICA-EMD uses empirical mode decomposition (EMD) to improve the performance of the standard ICA method. Five voluntary subjects performed an auditory oddball task. The resulting ERPs are used to compare the two blink correction methods to each other and against blink rejection. The results suggest that both methods qualitatively preserve the ERP waveform but that they underestimate some of the peak amplitudes. ICA-EMD performs slightly better than ICA-SSP. In conclusion, the use of blink correction is justified, especially if blink rejection leads to severe data loss.

Dependencies between stimuli and spatially independent fMRI sources: towards brain correlates of natural stimuli

Natural stimuli are increasingly used in functional magnetic resonance imaging (fMRI) studies to imitate real-life situations. Consequently, challenges are created for novel analysis methods, including new machine-learning tools. With natural stimuli it is no longer feasible to assume single features of the experimental design alone to account for the brain activity. Instead, relevant combinations of rich enough stimulus features could explain the more complex activation patterns. We propose a novel two-step approach, where independent component analysis is first used to identify spatially independent brain processes, which we refer to as functional patterns. As the second step, temporal dependencies between stimuli and functional patterns are detected using canonical correlation analysis. Our proposed method looks for combinations of stimulus features and the corresponding combinations of functional patterns. This two-step approach was used to analyze measurements from an fMRI study during multi-modal stimulation. The detected complex activation patterns were explained as resulting from interactions of multiple brain processes. Our approach seems promising for analysis of data from studies with natural stimuli.

Analyzing consistency of independent components: an fMRI illustration

Independent component analysis (ICA) is a powerful data-driven signal processing technique. It has proved to be helpful in, e.g., biomedicine, telecommunication, finance and machine vision. Yet, some problems persist in its wider use. One concern is the reliability of solutions found with ICA algorithms, resulting from the stochastic changes each time the analysis is performed. The consistency of the solutions can be analyzed by clustering solutions from multiple runs of bootstrapped ICA. Related methods have been recently published either for analyzing algorithmic stability or reducing the variability. The presented approach targets the extraction of additional information related to the independent components, by focusing on the nature of the variability. Practical implications are illustrated through a functional magnetic resonance imaging (fMRI) experiment.

Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?

The impressive increase in the understanding of some basic processing in the human brain has recently led to the formulation of efficient computational methods, which when applied in the design of better signal processing tools, provides a deeper and clearer view to study the functioning of the human brain. The recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic and magnetoencephalographic recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. Extensions of the basic ICA methodology have also been employed to reveal otherwise hidden information. This paper reviews our recent results in this field.

Independent component approach to the analysis of EEG and MEG recordings

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.

Extraction of event-related signals from multichannel bioelectrical measurements

Independent component analysis (ICA) is a powerful tool for separating signals from their mixtures. In this field, many algorithms were proposed, but they poorly use a priori information in order to find the desired signal. Here, we propose a fixed point algorithm which uses a priori information to find the signal of interest out of a number of sensors. We particularly applied the algorithm to cancel cardiac artifacts from a magnetoencephalogram.