Research articleVisibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering
Introduction
Stuttering is a disruption in speech categorized by verbal dysfluency such as interjection, repetition and prolongation of syllables and words during communication [41,62,79]. Recent neurological studies have focused on the underlying mechanisms of stuttering. Subjects with persistent developmental stuttering (PDS) have shown structural and functional abnormalities in the cortex based on Magnetoencephalography (MEG) and imaging studies such as an abnormal planum temporale asymmetry and different hemispheric speech-motor processing [36,44]. PDS is associated with the abnormal neural activities in subcortical lesions [58,69]. Thus, auditory brainstem response (ABR) is a suitable clinical tool to recognize brainstem timing deficits.
Graña et al. employ resting state effective connectivity to discriminate auditory hallucination [37]. The scalp-recorded ABR is an auditory evoked potential extracted from the electrical activity generated by a large population of neurons along the auditory brainstem and recorded by electrodes placed on the scalp usually in response to simple auditory sounds (e.g., tones, click) [20,22]. The ABR is a noninvasive clinical test used to evaluate the auditory brainstem function in various clinical applications such as developmental and learning disorders [13], determining hearing loss [74], hearing screening of newborn [60], intraoperative monitoring [40], auditory threshold estimation [85] and hearing aid fittings [54].
Recently, the response of the brainstem to more complex stimuli such as speech or music has been studied [19,45]. The s-ABR includes transient and steady-state responses. Transient and non-periodic features of the stimulus are reflected in the transient responses, while steady-state time-locked responses are observed in the sustained responses [47,48,75]. The s-ABR is an objective tool suitable for evaluation of developmental and learning disorders, auditory processing problems and auditory neuropathy [19,45].
In previous studies, different linear time and frequency methods have been applied to examine s-ABR time series including amplitude and peak latency, cross-correlation, Fourier analysis, and cross-phaseogram [72,75,76]. These methods, however, cannot extract the intricate details of the s-ABR signals adequately. Furthermore, most existing quantitative methods used to evaluate the dynamics of s-ABR series assume the stationarity of the s-ABR time series where the mean and variance of signal do not change with time [67]. Recent studies, however, have brought to attention the non-linear and non-stationary nature of s-ABR series, thus limiting the utility of the existing methods for examining complexity [63,71].
Using peak latency and Fourier analysis, Tahaei et al. [77] report significant differences for the onset and offset transient peaks in PDS compared with the control group. These methods, however, fail to reveal the exact temporal dynamic features of the underlying signals, thus motivating the authors to explore the application of nonlinear methods [56]. In recent years, researchers have employed signal processing, nonlinear science and chaos theory, and machine learning to develop powerful models for computer-aided diagnosis of various neurological and psychiatric disorders such as epilepsy [24,29,33,73], Autism Spectrum Disorder (ASD) [7,9,16,17], Attention Deficit Hyperactivity Disorder (ADHD) [8,12], Mild Cognitive Impairment (MCI) [3,53,55], the Alzheimer’s disease (AD) [6,15,18,39,42], Parkinson’s disease (PD) [84], Creutzfeld-Jakob disease [61], depression including Major Depressive Disorder (MDD) [2,5,11], and sleep studies [1,21,27], seizure detection [26], and seizure control [65]. Mozaffarilegha et al. used Ensemble Empirical Mode Decomposition (EEMD) to evaluate the chaotic features of s-ABR signals [63].
In order to examine the fractality and complexity in signals, a number of nonlinear methods have been proposed [51,80,83]. Among them is detrended fluctuation analysis (DFA) used to compute the Hurst exponent in order to identify the fractality and the long-range correlation behavior in different signals [68]. Long-range correlation or long-range dependence is a phenomenon arising in the analysis of time series. It shows how the statistical correlation between two points decreases when the time intervals increase. On the other hand, a visibility graph (VG) is an alternative approach to calculating the Hurst exponent [49].
In recent years, graph theory has been used to model and study brain connectivity [34,59,82]. The VG can convert fractal signals into scale-free graphs [50]. The degree of fractality of the signals can be identified using the degree of scale-freeness. It has been demonstrated that different biological time series including electrocardiography (ECG) [14,35] and electroencephalography (EEG) [4,32,78,80] behave as scale-invariant processes. Ahmadlou and Adeli present visibility graph similarity as a new way of measuring complexity in coupled dynamic systems and to evaluate brain activities of neurodegenerative disorders such as autism [4].
In this research, VG and the concept of fractality based on nonlinear science and chaos theory is explored to study and evaluate the complexity of the s-ABR time series in order to capture its intrinsic multiscale dynamics. To the best of our knowledge, the VG has not been used on the s-ABR series in the literature. The VG of the s-ABR series is proposed to differentiate adults with PDS from the control group. Differential complexity between normal and PDS subjects is quantified using Graph index complexity (GIC). The model is applied to 14 individuals with PDS and 15 normal subjects.
Section snippets
Participants
This experiment was performed and data were collected by the first author under a protocol approved by the review board of the Iran University of Medical Sciences. All participants in this study signed the informed consent. The data were collected from 14 right-handed individuals with PDS (6 women and 8 men), aged 15–33 years (mean ± SD = 26.50 ± 1.04). A speech-language pathologist made clinical diagnoses of developmental stuttering. To evaluate the severity of stuttering, we used the Persian
Results
The research was performed on an Intel(R) Core(TM) i5 CPU, M 450 @ 2.40 GHz with microcode of 0 × 1, 2400.000 cpu MHz and 3072 KB cache size. The MATLAB R2016b was used to implement the methodology. The CPU computation time was 0.09 s.
Fig. 5 shows the Log–Log plot of the cumulative degree distribution p(k) of the series of s-ABR for healthy and PDS subjects, where k indicates the order of a node, i.e. the number of edges connected to a node, P is the probability distribution of edges
Discussions
In this study, we have examined the large-scale dynamics of neural oscillations in the auditory system in the brainstem level of normal and subjects with PDS. The long-range power-law correlations have been found in a wide range of complex biological systems, including heart rate [14], medullary sympathetic neurons in neurophysiology [70] and long memory in human coordination [30] exhibiting the fractal dynamics of biological systems. On the other hand, recent studies have discovered long-range
Conclusion
In this paper, the VG algorithm is used to quantify the long-range correlations in s-ABR signals. The s-ABR series of PDS group were compared with normal subjects. The results reveal the promising ability of GIC for assessment of abnormal activation of brainstem level in PDS group. The GIC values of subjects with stuttering are higher than the normal group confirmed the evidence of insufficiency of the cortical activities in PDS subjects. It is also observed that the VGs of all s-ABR series
References (86)
- et al.
Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task
Clin. Neurophysiol.
(2014) - et al.
Visibility graph similarity: a new measure of generalized synchronization in coupled dynamic systems
Phys. D: Nonlinear Phenom.
(2012) - et al.
Fractality analysis of frontal brain in major depressive disorder
Int. J. Psychophysiol.
(2012) - et al.
Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder
J. Neurosci. Methods
(2012) - et al.
Improved visibility graph fractality with application for the diagnosis of autism spectrum disorder
Phys. A: Stat. Mech. Appl.
(2012) - et al.
Which attention-deficit/hyperactivity disorder children will be improved through neurofeedback therapy? A graph theoretical approach to neocortex neuronal network of ADHD
Neurosci. Lett.
(2012) - et al.
Oxidative stress, mitochondrial dysfunction and neurodegenerative diseases; a mechanistic insight
Biomed. Pharmacother.
(2015) - et al.
Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation
Clin. Neurophysiol.
(2016) - et al.
Planum temporale asymmetry in people who stutter
J. Fluen. Disord.
(2018) - et al.
Management of vestibular schwannoma: a pilot case series with postoperative ABR monitoring
Clin. Neurol. Neurosurg.
(2016)
What is a complex graph?
Phys. A: Stat. Mech. Appl.
Speech-evoked auditory brainstem responses in children with hearing loss
Int. J. Pediatr. Otorhinolaryngol.
Brainstem origins for cortical ‘what’ and ‘where’ pathways in the auditory system
Trends Neurosci.
Human frequency-following responses: representation of steady-state synthetic vowels
Hear. Res.
Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus
Neuroscience
Brainstem responses to speech syllables
Clin. Neurophysiol.
Cross-phaseogram: objective neural index of speech sound differentiation
J. Neurosci. Methods
EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures
J. Neurosci. Methods
Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia
Behav. Brain Res.
Nonlinear dynamics measures for automated EEG-based sleep stage detection
Eur. Neurol.
A novel depression diagnosis index using nonlinear features in EEG signals
Eur. Neurol.
Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease
Alzheimer Dis. Assoc. Disord.
Graph theoretical analysis of organization of functional brain networks in ADHD
Clin. EEG Neurosci.
New diagnostic EEG markers of the Alzheimer’s disease using visibility graph
J. Neural Transm.
Spatiotemporal analysis of relative convergence of EEGs reveals differences between brain dynamics of depressive women and men
Clin. EEG Neurosci.
Learning disabilities in different types of attention deficit hyperactivity disorders and its relation to cortical and brainstem function
J. Neurol. Res.
Quantitative assessment of heart rate dynamics during meditation: an ECG based study with multi-fractality and visibility graph
Front. Physiol.
Autism: cause factors, early diagnosis and therapies
Rev. Neurosci.
Automated diagnosis of autism: in search of a mathematical marker
Rev. Neurosci.
Clinical neurophysiological and automated EEG-based diagnosis of the Alzheimer’s disease
Eur. Neurol.
Methodological approaches to recording speech auditory brainstem responses: effect of stimulus duration, background, consonant, and number of repetitions
J. Hear. Sci.
Noise cancellation for brainstem auditory evoked potentials
IEEE Trans. Biomed. Eng.
Physiological ripples associated with sleep spindles differ in waveform morphology from epileptic ripples
Int. J. Neural Syst.
Auditory Evoked Potentials: Basic Principles and Clinical Application
Deep learning-based crack damage detection using convolutional neural networks
Comput.-Aided Civ. Infrastruct. Eng.
Multi-biosignal analysis for epileptic seizure monitoring
Int. J. Neural Syst.
Abnormal speech sound representation in persistent developmental stuttering
Neurology
Online automated seizure detection in temporal lobe epilepsy patients using single-lead ecg
Int. J. Neural Syst.
An automated quiet sleep detection approach in preterm infants as a gateway to assess brain maturation
Int. J. Neural Syst.
A realistic seizure prediction study based on multiclass SVM
Int. J. Neural Syst.
Fractal dynamics, variability, and coordination in human locomotion
Kinesiol. Rev.
A Pareto-based ensemble with feature and instance selection for learning from multi-class imbalanced datasets
Int. J. Neural Syst.
Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG
Int. J. Neural Syst.
Cited by (25)
Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
2022, Computers in Biology and MedicineCitation Excerpt :The current research focuses on the three-dimensional simulation of tumor growth, with and without a prescription, to identify the differential benefits of the anti-angiogenic drugs bevacizumab, ranibizumab, and brolucizumab and their efficacy in inhibiting tumor growth. Vilanova et al. [16] studied a combination of computational and imaging models of tumor-induced angiogenesis using graph theory [17–19] to overcome some challenges in tumor growth treatment. Additionally, Tang et al. [20] developed pressure-based 3D computational modeling of tumor growth and angiogenesis to evaluate chemotherapy drug responses.
Study of the brainstem auditory evoked potential with speech stimulus in the pediatric population with and without oral language disorders: a systematic review
2020, Brazilian Journal of OtorhinolaryngologyCitation Excerpt :Studies agree that these difficulties can compromise language development skills and interfere with the individual’s social communication. The application of BAEP-speech in the population with persistent developmental stuttering was identified only in the study of Mozaffarilegha et al.,19 which used graphic visibility and fractality to observe the complexity of the responses to the exam in individuals with oral language pathology and with typical development. The study group showed greater complexity in the graphic index, compared to the group without alterations.
Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure
2024, Computer-Aided Civil and Infrastructure EngineeringA graph-based method for quantifying crack patterns on reinforced concrete shear walls
2024, Computer-Aided Civil and Infrastructure EngineeringCollaborative passenger flow control for an urban rail transit network
2024, Computer-Aided Civil and Infrastructure Engineering