Deep transfer learning compared to subject-specific models for sEMG decoders.

. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g.

Brain network dynamics codify heterogeneity in seizure evolution.

Dynamic functional brain connectivity facilitates adaptive cognition and behaviour. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures.

Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics.

Purpose: This study examined the relationship between voice quality and glottal geometry dynamics in patients with adductor spasmodic dysphonia (ADSD).

Method: An objective computer vision and machine learning system was developed to extract glottal geometry dynamics from nasolaryngoscopic video recordings for 78 patients with ADSD. General regression models were used to examine the relationship between overall voice quality and 15 variables that capture glottal geometry dynamics derived from the computer vision system.

Estimating the time structure of descending activation that generates movements at different speeds.

In targeted movements of the hand, descending activation patterns must not only generate muscle activation but also adjust spinal reflexes from stabilizing the initial to stabilizing the final postural state. We estimate descending activation patterns that change minimally while generating a targeted movement within a given movement time based on a model of the biomechanics, the muscle dynamics, and the stretch reflex. The estimated descending activation patterns predict human movement trajectories quite well.

Sources of independent mu components reveal different brain areas involved in motor imagery, motor execution, and movement observation.

To answer the question of whether the same brain circuit(s) facilitates motor imagery (MI), motor execution (ME), and movement observation (MO), we conducted electroencephalography (EEG) experiment combining the three motor conditions in the same experimental runs. The EEG data were analyzed using two different independent component analysis (ICA) decomposition approaches: a single ICA decomposition on all EEG data combined and separate ICA decomposition on the EEG data obtained from the separate conditions. The results indicated that the separate ICA approach may provide a better fit to the EEG data obtained from the separate conditions to deliver specific independent right mu components with distinct topographies for each of the motor conditions.

IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal.

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals.

Storage in Visual Working Memory Recruits a Content-Independent Pointer System.

Past work has shown that storage in working memory elicits stimulus-specific neural activity that tracks the stored content. Here, we present evidence for a distinct class of load-sensitive neural activity that indexes items without representing their contents per se. We recorded electroencephalogram (EEG) activity while adult human subjects stored varying numbers of items in visual working memory.

Learning shifts the preferred theta phase of gamma oscillations in CA1.

Hippocampal neuronal oscillations reflect different cognitive processes and can therefore be used to dissect the role of hippocampal subfields in learning and memory. In particular, it has been suggested that encoding and retrieval is associated with slow gamma (25-55 Hz) and fast gamma (60-100 Hz) oscillations, respectively, which appear in a nested manner at specific phases of the ongoing theta oscillations (4-12 Hz). However, the relationship between memory demand and the theta phase of gamma oscillations remains unclear.

A Model of Semantic Completion in Generative Episodic Memory.

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete.

Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm.

Retrieved memories of past events are often inaccurate. The scenario construction model (SCM) postulates that during encoding, only the gist of an episode is stored in the episodic memory trace and during retrieval, information missing from that trace is constructed from semantic information. The current study aimed to find behavioural evidence for semantic construction in a realistic, yet controlled setting by introducing a completely new paradigm and adjusted memory tests that measure semantic construction.

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.

Recordings from resting-state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of "ground truth" has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas.

Dynamical differential covariance recovers directional network structure in multiscale neural systems.

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements.

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion.

Neocortical localization and thalamocortical modulation of neuronal hyperexcitability contribute to Fragile X Syndrome.

Fragile X Syndrome (FXS) is a monogenetic form of intellectual disability and autism in which well-established knockout (KO) animal models point to neuronal hyperexcitability and abnormal gamma-frequency physiology as a basis for key disorder features. Translating these findings into patients may identify tractable treatment targets. Using source modeling of resting-state electroencephalography data, we report findings in FXS, including 1) increases in localized gamma activity, 2) pervasive changes of theta/alpha activity, indicative of disrupted thalamocortical modulation coupled with elevated gamma power, 3) stepwise moderation of low and high-frequency abnormalities based on female sex, and 4) relationship of this physiology to intellectual disability and neuropsychiatric symptoms.

Habituation and Dishabituation in Motor Behavior: Experiment and Neural Dynamic Model.

Does motor behavior early in development have the same signatures of habituation, dishabituation, and Spencer-Thompson dishabituation known from infant perception and cognition? And do these signatures explain the choice preferences in A not B motor decision tasks? We provide new empirical evidence that gives an affirmative answer to the first question together with a unified neural dynamic model that gives an affirmative answer to the second question.In the perceptual and cognitive domains, habituation is the weakening of an orientation response to a stimulus over perceptual experience. Switching to a novel stimulus leads to dishabituation, the re-establishment of the orientation response.

Corrigendum: Seeing What I Did (Not): Cerebral and Behavioral Effects of Agency and Perspective on Episodic Memory Re-activation.

[This corrects the article DOI: 10.3389/fnbeh.2021.

Hold that pose: capturing cervical dystonia's head deviation severity from video.

Objective: Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures.

It Takes Two: Interpersonal Neural Synchrony Is Increased after Musical Interaction.

Music's deeply interpersonal nature suggests that music-derived neuroplasticity relates to interpersonal temporal dynamics, or synchrony. Interpersonal neural synchrony (INS) has been found to correlate with increased behavioral synchrony during social interactions and may represent mechanisms that support them. As social interactions often do not have clearly delineated boundaries, and many start and stop intermittently, we hypothesize that a neural signature of INS may be detectable following an interaction.

The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study.

The cerebellum is involved in the acquisition and consolidation of learned fear responses. Knowledge about its contribution to extinction learning, however, is sparse. Extinction processes likely involve erasure of memories, but there is ample evidence that at least part of the original memory remains.

EEG Correlates of Active Stopping and Preparation for Stopping in Chronic Tic Disorder.

Motor inhibition is an important cognitive process involved in tic suppression. As the right frontal lobe contains important inhibitory network nodes, we characterized right superior, middle, and inferior frontal gyral (RSFG, RMFG, RIFG) event-related oscillations during motor inhibition in youth with chronic tic disorders (CTD) versus controls. Fourteen children with CTD and 13 controls (10-17 years old) completed an anticipated-response stop signal task while dense-array electroencephalography was recorded.

Different Language Modalities Yet Similar Cognitive Processes in Arithmetic Fact Retrieval.

Does experience with signed language impact the neurocognitive processes recruited by adults solving arithmetic problems? We used event-related potentials (ERPs) to identify the components that are modulated by operation type and problem size in Deaf American Sign Language (ASL) native signers and in hearing English-speaking participants. Participants were presented with single-digit subtraction and multiplication problems in a delayed verification task. Problem size was manipulated in small and large problems with an additional extra-large subtraction condition to equate the overall magnitude of large multiplication problems.

Hierarchical Network Connectivity and Partitioning for Reconfigurable Large-Scale Neuromorphic Systems.

We present an efficient and scalable partitioning method for mapping large-scale neural network models with locally dense and globally sparse connectivity onto reconfigurable neuromorphic hardware. Scalability in computational efficiency, i.e.

Head tremor in cervical dystonia: Quantifying severity with computer vision.

Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective observation. Technological developments offer alternatives for measuring HT severity that are objective and amenable to automation.

Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-based software system) to quantify oscillatory and directional aspects of HT from video recordings during a clinical examination and to test its convergent validity with clinical rating scales.

Seeing What I Did (Not): Cerebral and Behavioral Effects of Agency and Perspective on Episodic Memory Re-activation.

Intuitively, we assume that we remember episodes better when we actively participated in them and were not mere observers. Independently of this, we can recall episodes from either the first-person perspective (1pp) or the third-person perspective (3pp). In this functional magnetic resonance imaging (fMRI) study, we tested whether agency and perspective modulate neural activity during memory retrieval and subsequently enhance memory performance.

High-power gamma-related delta phase alteration in schizophrenia patients at rest.

Aim: Information processing is supported by the cortico-cortical transmission of neural oscillations across brain regions. Recent studies have demonstrated that the rhythmic firing of neural populations is not random but is governed by interactions with other frequency bands. Specifically, the amplitude of gamma-band oscillations is associated with the phase of lower frequency oscillations in support of short and long-range communications among networks.