Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia.

Introduction: Silent pauses are regarded as integral components of the temporal organization of speech. However, it has also been hypothesized that they serve as markers for internal cognitive processes, including word access, monitoring, planning, and memory functions. Although existing evidence across various pathological populations underscores the importance of investigating silent pauses' characteristics, particularly in terms of frequency and duration, there is a scarcity of data within the domain of post-stroke aphasia. Methods: The primary objective of the present study is to scrutinize the frequency and duration of silent pauses in two distinct narrative tasks within a cohort of 32 patients with chronic post-stroke aphasia, in comparison with a control group of healthy speakers. Subsequently, we investigate potential correlation patterns between silent pause measures, i.e., frequency and duration, across the two narrative tasks within the patient group, their performance in neuropsychological assessments, and lesion data. Results: Our findings showed that patients exhibited a higher frequency of longer-duration pauses in both narrative tasks compared to healthy speakers. Furthermore, within-group comparisons revealed that patients tended to pause more frequently and for longer durations in the picture description task, while healthy participants exhibited the opposite trend. With regard to our second research question, a marginally significant interaction emerged between performance in semantic verbal fluency and the narrative task, in relation to the location of silent pauses-whether between or within clauses-predicting the duration of silent pauses in the patient group. However, no significant results were observed for the frequency of silent pauses. Lastly, our study identified that the duration of silent pauses could be predicted by distinct Regions of Interest (ROIs) in spared tissue within the left hemisphere, as a function of the narrative task. Discussion: Overall, this study follows an integrative approach of linguistic, neuropsychological and neuroanatomical data to define silent pauses in connected speech, and illustrates interrelations between cognitive components, temporal aspects of speech, and anatomical indices, while it further highlights the importance of studying connected speech indices using different narrative tasks.

Connectivity steered graph Fourier transform for motor imagery BCI decoding.

OBJECTIVE: Graph signal processing (GSP) concepts are exploited for brain activity decoding and particularly the detection and recognition of a motor imagery (MI) movement. A novel signal analytic technique that combines graph Fourier transform (GFT) with estimates of cross-frequency coupling (CFC) and discriminative learning is introduced as a means to recover the subject's intention from the multichannel signal. APPROACH: Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis. A personalized decoding scheme supporting a binary decision, either 'left versus right' or 'rest versus MI', is crafted from a small set of training trials. Electroencephalographic (EEG) activity from 12 volunteers recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition is used to introduce and validate our methodology. In addition, the introduced methodology was further validated based on dataset IVa of BCI III competition. MAIN RESULTS: Our GFT-domain decoding scheme achieves nearly optimal performance and proves superior to alternative techniques that are very popular in the field. SIGNIFICANCE: At a conceptual level, our work suggests a fruitful way to introduce network neuroscience in BCI research. At a more practical level, it is characterized by efficiency. Training is realized using a small number of exemplar trials and decoding requires very simple operations that leaves room for real-time implementation.

Discriminative codewaves: a symbolic dynamics approach to SSVEP recognition for asynchronous BCI.

OBJECTIVE: Steady-state visual evoked potential (SSVEP) is a very popular approach to establishing a communication pathway in brain-computer interfaces (BCIs), without any training requirements for the user. Brain activity recorded over occipital regions, in association with stimuli flickering at distinct frequencies, is used to predict the gaze direction. High performance is achieved when the analysis of multichannel signal is guided by the driving signals. This study introduces an efficient way of identifying the attended stimulus without the need to register the driving signals. APPROACH: Regional brain response is described as a dynamical trajectory towards one of the 'attractors' associated with the brainwave entrainment induced by the attended stimulus. A condensed description for each single-trial response is provided by means of discriminative vector quantization, and different trajectories are disentangled based on a simple classification scheme that uses templates and confidence intervals derived from a small training dataset. MAIN RESULTS: Experiments, based on two different datasets, provided evidence that the introduced approach compares favorably to well-established alternatives, regarding the information transfer rate. SIGNIFICANCE: Our approach relies on (but not restricted to) single sensor traces, incorporates a novel description of brainwaves based on semi-supervised learning, and its great advantage stems from its potential for self-paced BCI.

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes.

We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

Exploiting the temporal patterning of transient VEP signals: a statistical single-trial methodology with implications to brain-computer interfaces (BCIs).

BACKGROUND: When visual evoked potentials (VEPs) are deployed in brain-computer interfaces (BCIs), the emphasis is put on stimulus design. In the case of transient VEPs (TVEPs) brain responses are never treated individually, i.e. on a single-trial (ST) basis, due to their poor signal quality. Therefore their main characteristic, which is the emergence during early latencies, remains unexplored. NEW METHOD: Following a pattern-analytic methodology, we investigated the possibility of using single-trial TVEP responses to differentiate between the different spatial locations where a particular visual stimulus appeared and decide whether it was attended or unattended by the subject. RESULTS: Covert spatial attention modulates the temporal patterning of TVEPs in such a way that a brief ST-segment, from a single synthesized sensor, is sufficient for a Mahalanobis-Taguchi (MT) system to decode subject's intention. COMPARISON WITH EXISTING METHOD(S): In contrast to previous VEP-based approaches, stimulus-related information and user's intention are being decoded from transient ST-signals via exploiting aspects of brain response in the temporal domain. CONCLUSIONS: We demonstrated that in the TVEP signals there is sufficient discriminative information, coming in the form of a temporal code. We were able to introduce an efficient scheme that can fully exploit this information for the benefit of online classification. The measured performance brings high expectations for incorporating these ideas in BCI-control.

A tensorial approach to access cognitive workload related to mental arithmetic from EEG functional connectivity estimates.

The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parietooccipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.

Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG.

In this study we investigate systematic patterns of rapidly changing sensor-level interdependencies in resting MEG data obtained from 23 children experiencing reading difficulties (RD) and 27 non-impaired readers (NI). Three-minute MEG time series were band-passed and subjected to blind source separation (BSS) prior to estimating sensor interdependencies using the weighted phase synchronization measure (wPLI). Dynamic sensor-level network properties were then derived for two network metrics (global and local efficiency). The temporal decay of long-range temporal correlations in network metrics (LRTC) was quantified using the scaling exponent (SE) in detrended fluctuation analysis (DFA) plots. Having established the reliability of SE estimates as robust descriptors of network dynamics, we found that RD students displayed significantly reduced (a) overall sensor-level network organization across all frequency bands (global efficiency), and (b) temporal correlations between sensors covering the left temporoparietal region and the remaining sensors in the ß3 band (local efficiency). Importantly, both groups displayed scale-free global network connectivity dynamics. The direct application of DFA to MEG signals failed to reveal significant group differences. Results are discussed in relation to prior evidence for disrupted temporoparietal functional circuits for reading in developmental reading disability.

On the quantization of time-varying phase synchrony patterns into distinct functional connectivity microstates (FCµstates) in a multi-trial visual ERP paradigm.

The analysis of functional brain connectivity has been supported by various techniques encompassing spatiotemporal interactions between distinct areas and enabling the description of network organization. Different brain states are known to be associated with specific connectivity patterns. We introduce here the concept of functional connectivity microstates (FCµstates) as short lasting connectivity patterns resulting from the discretization of temporal variations in connectivity and mediating a parsimonious representation of coordinated activity in the brain. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of FCµstates can be derived so as to encapsulate both the inter-subject and inter-trial response variability and further provide novel insights into cognition. The main practical advantage of our approach lies in the fact that time-varying connectivity analysis can be simplified significantly by considering each FCµstate as prototypical connectivity pattern, and this is achieved without sacrificing the temporal aspects of dynamics. Multi-trial datasets from a visual ERP experiment were employed so as to provide a proof of concept, while phase synchrony was emphasized in the description of connectivity structure. The power of FCµstates in knowledge discovery is demonstrated through the application of network topology descriptors. Their time-evolution and association with event-related responses is explored.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses.

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

Analyzing functional brain connectivity by means of commute times: a new approach and its application to track event-related dynamics.

There is growing interest in studying the association of functional connectivity patterns with particular cognitive tasks. The ability of graphs to encapsulate relational data has been exploited in many related studies, where functional networks (sketched by different neural synchrony estimators) are characterized by a rich repertoire of graph-related metrics. We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG). CT is a measure of the time taken for a random walk to setout and return between a pair of nodes on a graph. Its computation is considered here as a robust and accurate integration, over the FCG, of the individual pairwise measurements of functional coupling. To demonstrate the benefits from our approach, we attempted the characterization of time evolving connectivity patterns derived from EEG signals recorded while the subject was engaged in an eye-movement task. With respect to standard ways, which are currently employed to characterize connectivity, an improved detection of event-related dynamical changes is noticeable. CTs appear to be a promising technique for deriving temporal fingerprints of the brain's dynamic functional organization.

Single-trial variability in early visual neuromagnetic responses: an explorative study based on the regional activation contributing to the N70m peak.

Cortical activity evoked by repeated identical sensory stimulation is extremely variable. The source of this variability is often assigned to "random ongoing background activity" which is considered to be irrelevant to the processing of the stimuli and can therefore be eliminated by ensemble averaging. In this work, we studied the single-trial variability in neuromagnetic responses elicited by circular checkerboard pattern stimuli with radii of 1.8 degrees, 3.7 degrees, and 4.5 degrees. For most of the MEG sensors over the occipital areas, the averaged signal showed a clear early (N70m) response following the stimulus onset and this response was modulated by the checkerboard size. A data-driven spatial filter was used to extract one of the many possible composite time courses of single-trial activity corresponding to the complex of N70m generators. Pattern analysis principles were then employed to analyze, classify, and handle the extracted temporal patterns. We explored whether these patterns correspond to distinct response modes, which could characterize the evoked response better than the averaged signal and over an extended range of latencies around N70m. A novel scheme for detecting and organizing the structure in single-trial recordings was utilized. This served as a basis for comparisons between runs with different checkerboard sizes and provided a causal interpretation of variability in terms of regional dynamics, including the relatively weak activation in primary visual cortex. At the level of single trial activity, the polymorphic response to a simple stimulus is generated by a coupling of polymodal areas and cooperative activity in striate and extrastriate areas. Our results suggest a state-dependent response with a wide range of characteristic time scales and indicate the ongoing activity as a marker of the responsiveness state.

The human primary somatosensory cortex response contains components related to stimulus frequency and perception in a frequency discrimination task.

Somatosensory stimulation of primary somatosensory cortex (SI) using frequency discrimination offers a direct, well-defined and accessible way of studying cortical decisions at the locus of early input processing. Animal studies have identified and classified the neuronal responses in SI but they have not yet resolved whether during prolonged stimulation the collective SI response just passively reflects the input or actively participates in the comparison and decision processes. This question was investigated using tomographic analysis of single trial magnetoencephalographic data. Four right-handed males participated in a frequency discrimination task to detect changes in the frequency of an electrical stimulus applied to the right-hand digits 2+3+4. The subjects received approximately 600 pairs of stimuli with Stim1 always at 21 Hz, while Stim2 was either 21 Hz (50%) or varied from 22 to 29 Hz in steps of 1 Hz. Both stimuli were 1 s duration, separated by a 1 s interval of no stimulation. The left-SI was the most consistently activated area and showed the first activation peak at 35-48 ms after Stim1 onset and sustained activity during both stimulus periods. During the Stim2 period, we found that the left-SI activation started to differ significantly between two groups of trials (21 versus 26-29 Hz) within the first 100 ms and this difference was sustained and enhanced thereafter (approximately 600 ms). When only correct responses from the above two groups were used, the difference was even higher at later latencies (approximately 650 ms). For one subject who had enough trials of same perception to different input frequencies, e.g. responded 21 Hz to Stim2 at 21 Hz (correct) and 26-29 Hz (error), we found the sustained difference only before 650 ms. Our results suggest that SI is involved with the analysis of an input frequency and related to perception and decision at different latencies.

Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses.

OBJECTIVES: A general framework for identifying and describing structure in a given sample of evoked response single-trial signals (STs) is introduced. The approach is based on conceptually simple geometrical ideas and enables the convergence of pattern analysis and non-linear time series analysis. METHODS: Classical steps for analyzing the STs by waveform are first employed and the ST-analysis is transferred to a multidimensional space, the feature space, the geometry of which is systematically studied via multidimensional scaling (MDS) techniques giving rise to semantic maps. The structure in the feature space characterizes the trial-to-trial variability and this is utilized to probe functional connectivity between two brain areas. The underlying dynamic process responsible for the emerged structure can be described by a multidimensional trajectory in the feature space. This in turn enables the detection of dynamical interareal coupling as similarity between the corresponding trajectories. RESULTS AND CONCLUSIONS: The utility of semantic maps was demonstrated using magnetoencephalographic data from a simple auditory paradigm. The coupling of ongoing activity and evoked response is vividly demonstrated and contrasted with the apparent deflection from zero baseline that survives averaging. Prototypes are easily identified as the end points of distinct paths in the semantic map representation, and their neighborhood is populated by STs with distinct properties not only in the latencies where the evoked response is expected to be strong, but also and very significantly in the prestimulus period. Finally our results provide evidence for interhemispheric binding in the (4-8 Hz) range and dynamical coupling at faster time scales.

Exploratory data analysis of evoked response single trials based on minimal spanning tree.

OBJECTIVE: An exploratory data analysis framework, based on minimal spanning tree, is proposed as a means to support the analysis of single trial (ST) electrophysiological signals. The core of this framework is the compact description of the input ST sample in a form of content-dependent ordered lists. Based on the established hierarchies, efficient ways to increase the SNR, extract prototypical responses, visualize possible self-organization trends in the sample and track the course of evoked response along the trial-to-trial dimension, are proposed. METHOD: Magnetoencephalographic auditory evoked responses were used for demonstrating and validating the introduced framework. RESULTS AND CONCLUSION: The results demonstrate the benefits, from this intelligent manipulation of STs, in understanding and enhancing the actual evoked signal. Specifically we find support for stimulus-induced phase-resetting hypothesis in the 3-20 Hz band, the existence of trials void of the prototypical evoked response, and an order across the single trial set hinting at an underlying process with long time scale.

Beat-to-beat wavelet variance of the QRS complex as a marker of arrhythmogenic substrate in ventricular tachycardia patients.

This study proposes a wavelet transform based technique to assess the beat-to-beat variation of the QRS signal in post-myocardial infarction patients with sustained monomorphic ventricular tachycardia. Recent electrophysiological investigations suggested that the diminished synchrony between the normal myocardium and the scarred arrhythmogenic tissue bordering a myocardial infarction area gives rise to beat-variable ECG signal components. Using a mathematical model of small variations in a largely repetitive waveform, we show that the inherent alignment errors (trigger jitter) of the high-resolution ECG (HRECG) can artificially increase the value of the time-domain beat-to-beat variance, making it less valuable as a marker of beat-variable signal components. To overcome this drawback, we propose the wavelet based approach which discriminates between the different factors responsible for the beat variability (the alignment error and the beat-variable signal components). The Morlet wavelet transform is performed on HRECG signals from normal individuals (control group) and postmyocardial infarction patients with documented ventricular tachycardia. Electrical variability is quantitatively assessed via the beat-to-beat wavelet variance measurements. A marker of arrhythmogenic induced variance which achieves a good performance in discrimination of ventricular tachycardia patients from normal subjects was found between 200 Hz and 300 Hz. This finding is in agreement with the proposed mathematical model which states that the useful part of the time-frequency map is shifted upward in a precise mathematical way, as the variance induced by the beat-variable arrhythmogenic signals depend on the frequency characteristics of the first derivative of these signals. We conclude that the dynamics of the arrhythmogenic substrate as revealed by the beat-to-beat wavelet variance can be a new estimator of ventricular tachycardia risk.

Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals.

This technical note describes a robust version of moving averages, that enables reliable monitoring of the evoked potential (EP) signals. A cluster analysis (CA) procedure is introduced to robustify the signal averaging (SA). It is implemented via a Hopfield neural network (HNN), which performs selection of the trials forming a cluster around the current state of the EP signal. The core of this cluster serves as an estimate of the instantaneous EP. The effectiveness of the method, indicated by application to real data, and its computation efficiency, due to the use of simple matrix operations, makes it very promising for clinical observations.

An intelligent system for the tracking--localization of changes & exploratory analysis--of long-term ECG.

An interactive methodology for the analysis of long-term ECG is introduced. It is an anthropomimetic technique and consists of three parts. At the first stage, the clinicians' scan of the ECG traces is imitated and changes in the shape of QRS are quantified. In the sequel, a clinician involves in the interpretation of the most prominent changes providing the patient-dependent prototypes for the subsequent machine learning procedure. Finally, a classification scheme incorporates the portion of medical knowledge needed to explore the whole patient's ECG. This scheme, being very robust to noise, presents excellent generalization properties and can serve as a reliable automation in a future examination of the certain subject.

Cardiac electrical instability of the arrhythmogenic tissue substrate assessed by means of wavelet transform.

The unstable activation wavefront from the tissue responsible for the production of ventricular tachycardia (VT) gives rise to beat-variable signals components that are eluded during the averaging step of high resolution ECG (HRECG). We used a mathematical model of small variations in a largely repetitive waveform to evaluate the beat-to-beat variance of the HRECG signal. The ability of the Morlet Wavelet Transform to discriminate the different factors responsible for the beat-variability (the alignment error and the beat-variable signal component) has been assessed on simulated signals. The performance evaluation on real ECG signals from normal subjects and patients with a documented history of ventricular tachycardia showed that the dynamics of the arrhythmogenic substrate as revealed by wavelet transform offers a significant improvement in ventricular tachycardia risk assessment.

Data dependent weighted averages for recording of evoked potential signals.

Two new filters are proposed to replace conventional on-line artifact rejection routines. They are based on an algorithm that computes a weight for each trial according to its similarity to the rest. The robustness of the two filters and their capacity to reduce recording time significantly were verified experimentally.

Latency measurement improvement of P100 complex in visual evoked potentials by FMH filters.

The ensemble average of Pattern Shift Visual Evoked Potentials (PSVEP) signals is seriously affected by random latency variations encountered in each individual sweep which is modeled as a continuous signal with linear segments and well-shaped triangular peaks. This effect is causing the smoothed peaks of the averaged PSVEP waveforms. It is our objective to restore the degraded peaks and provide accurate information about their exact location. The method used is based on nonlinear filtering of the FIR-Median Hybrid (FMH) type and is recommended as a postfiltering process to the well-known averaging methods of recovering PSVEP signals from noise by time-locking to stimuli. The new technique, tested in signals from clinical observations, has proven very promising.