Vocal differentiation parallels development of auditory saccular sensitivity in a highly soniferous fish

Vocal differentiation is widely documented in birds and mammals but has been poorly investigated in other vertebrates, including fish, which represent the oldest extant vertebrate group. Neural circuitry controlling vocal behaviour is thought to have evolved from conserved brain areas that originated in fish, making this taxon key to understanding the evolution and development of the vertebrate vocal-auditory systems. This study examines ontogenetic changes in the vocal repertoire and whether vocal differentiation parallels auditory development in the Lusitanian toadfish Halobatrachus didactylus (Batrachoididae). This species exhibits a complex acoustic repertoire and is vocally active during early development. Vocalisations were recorded during social interactions for four size groups (fry: <2 cm; small juveniles: 2-4 cm; large juveniles: 5-7 cm; adults >25 cm, standard length). Auditory sensitivity of juveniles and adults was determined based on evoked potentials recorded from the inner ear saccule in response to pure tones of 75-945 Hz. We show an ontogenetic increment in the vocal repertoire from simple broadband-pulsed 'grunts' that later differentiate into four distinct vocalisations, including low-frequency amplitude-modulated 'boatwhistles'. Whereas fry emitted mostly single grunts, large juveniles exhibited vocalisations similar to the adult vocal repertoire. Saccular sensitivity revealed a three-fold enhancement at most frequencies tested from small to large juveniles; however, large juveniles were similar in sensitivity to adults. We provide the first clear evidence of ontogenetic vocal differentiation in fish, as previously described for higher vertebrates. Our results suggest a parallel development between the vocal motor pathway and the peripheral auditory system for acoustic social communication in fish.

TRANSFORMATIONS OF TASK-DEPENDENT PLASTICITY FROM A1 TO HIGHER-ORDER AUDITORY CORTEX

Everyday, humans and animals navigate complex acoustic environments, where multiple sound sources overlap. Somehow, they effortlessly perform an acoustic scene analysis and extract relevant signals from background noise. Constant updating of the behavioral relevance of ambient sounds requires the representation and integration of incoming acoustical information with internal representations such as behavioral goals, expectations and memories of previous sound-meaning associations. Rapid plasticity of auditory representations may contribute to our ability to attend and focus on relevant sounds. In order to better understand how auditory representations are transformed in the brain to incorporate behavioral contextual information, we explored task-dependent plasticity in neural responses recorded at four levels of the auditory cortical processing hierarchy of ferrets: the primary auditory cortex (A1), two higher-order auditory areas (dorsal PEG and ventral-anterior PEG) and dorso-lateral frontal cortex. In one study we explored the laminar profile of rapid-task related plasticity in A1 and found that plasticity occurred at all depths, but was greatest in supragranular layers. This result suggests that rapid task-related plasticity in A1 derives primarily from intracortical modulation of neural selectivity. In two other studies we explored task-dependent plasticity in two higher-order areas of the ferret auditory cortex that may correspond to belt (secondary) and parabelt (tertiary) auditory areas. We found that representations of behaviorally-relevant sounds are progressively enhanced during performance of auditory tasks. These selective enhancement effects became progressively larger as you ascend the auditory cortical hierarchy. We also observed neuronal responses to non-auditory, task-related information (reward timing, expectations) in the parabelt area that were very similar to responses previously described in frontal cortex. These results suggests that auditory representations in the brain are transformed from the more veridical spectrotemporal information encoded in earlier auditory stages to a more abstract representation encoding sound behavioral meaning in higher-order auditory areas and dorso-lateral frontal cortex.

Feasibility of resting-state microstates neurofeedback

Despite major progress, currently available treatment options for patients suffering from schizophrenia remain suboptimal. Antipsychotic medication is one such option, and is helpful in acute phases of the disease. However, antipsychotics cause significant side-effects that often require additional medication, and can even trigger the discontinuation of treatment. Taken together, along with the fact that 20-30% of patients are medication-resistant, it is clear that new medical care options should be developed for patients with schizophrenia. Besides medication, an emerging option to treat psychiatric symptoms is through the use of neurofeedback. This technique has proven efficacy for other disorders and, more importantly, has also proven to be feasible in patients with schizophrenia. One of the major advantages of this approach is that it allows for the influence of brain states that otherwise would be inaccessible; i.e. the physiological markers underlying psychotic symptoms. EEG resting-state microstates are a very interesting electrophysiological marker of schizophrenia symptoms. Precisely, a specific class of resting-state microstates, namely microstate class D, has consistently been found to show a temporal shortening in patients with schizophrenia compared to controls, and this shortening is correlated with the presence positive psychotic symptoms. Under the scope of biological psychiatry, appropriate treatment of psychotic symptoms can be expected to modify the underlying physiological markers accompanying behavioral manifestations of a disease. We reason that if abnormal temporal parameters of resting-state microstates seem to be related to positive symptoms in schizophrenia, regulating this EEG feature might be helpful as a treatment for patients. The goal of this thesis was to prove the feasibility of microstate class D contribution self-regulation via neurofeedback. Given that no other study has attempted to regulate microstates via neurofeedback, we first tested its feasibility in a population of healthy subjects. In the first paper we describe the methodological characteristics of the neurofeedback protocol and its implementation. Neurofeedback performance was assessed by means of linear mixed effects modeling, which provided a complete profile of the neurofeedback’s training response within and between-subjects. The protocol included 20 training sessions, and each session contained three conditions: baseline (resting-state) and two active conditions: training (auditory feedback upon self-regulation performance) and transfer (self-regulation with no feedback). With linear modeling we obtained performance indices for each of them as follows: baseline carryover (baseline increments time-dependent) and learning and aptitude for each of the active conditions. Learning refers to the increase/decrease of the microstate class D contribution, time-dependent during each active condition, and aptitude refers to the constant difference of the microstate class D contribution between each active condition and baseline independent of time. The indices provided are discussed in terms of tailoring neurofeedback treatment to individual profiles so that it can be applied in future studies or clinical practice. In our sample of participants, neurofeedback proved feasible, as all participants at least showed positive results in one of the aforementioned learning indices. Furthermore, between-subjects we observed that the contribution of microstate class D across-sessions increased by 0.42% during baseline, 1.93% during training trials, and 1.83% during transfer. This range is expected to be effective in treating psychotic symptoms in patients. In the second paper presented in this thesis, we explored the possible predictors of neurofeedback success among psychological variables measured with questionnaires. An interesting finding was the negative correlation between “motivational incongruence” and some of the neurofeedback performance indices. Even though this finding requires replication, we discuss it in terms of the interfering effects of incompatible psychological processes with neurofeedback training requirements. In the third paper, we present a meta-analysis on all available studies that have related resting-state microstate abnormalities and schizophrenia. We obtained medium effect sizes for two microstate classes, namely C and D. Combining the meta-analysis results with the fact that microstate class D abnormalities are correlated with the presence of positive symptoms in patients with schizophrenia, these results add further support for the training of this precise microstate. Overall, the results obtained in this study encourage the implementation of this protocol in a population of patients with schizophrenia. However, future studies will have to show whether patients will be able to successfully self-regulate the contribution of microstate class D and, if so, whether this regulation will have an impact on symptomatology.

Single pulse TMS-EEG reveals no electrophysiological abnormality in adults with high-functioning Autism Spectrum Disorder

 Objective: Neuroimaging and electrophysiological research have revealed a range of neural abnormalities in autism spectrum disorder (ASD), but a comprehensive understanding remains elusive. We utilized a novel methodology among individuals with ASD and matched controls, combining transcranial magnetic stimulation (TMS) with concurrent electroencephalogram (EEG) recording (TMS-EEG) to explore cortical function and connectivity in three sites implicated in the neuropathophysiology of ASD (dorsolateral prefrontal cortex, primary motor cortex, and temporoparietal junction). As there is evidence for neurobiological gender differences in ASD, we also examined the influence of biological sex.

Methods: TMS pulses were applied to each of the three sites (right lateralized) during 20-channel EEG recording.

Results: We did not identify any differences in the EEG response to TMS between ASD and control groups. This finding remained when data were stratified by sex. Nevertheless, traits and characteristics associated with ASD were correlated with the neurophysiological response to TMS.

Conclusion: While TMS-EEG did not appear to clarify the neuropathophysiology of ASD, the relationships identified between the neurophysiological response to TMS and clinical characteristics warrant further investigation.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure-sample entropy (SampEn)-and a more recently proposed complexity measure-distribution entropy (DistEn)-were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

The effect of notch filtering on the waveform of the newborn auditory brainstem response

OBJECTIVE: To identify whether the use of a notch filter significantly affects the morphology or characteristics of the newborn auditory brainstem response (ABR) waveform and so inform future guidance for clinical practice. DESIGN: Waveforms with and without the application of a notch filter were recorded from babies undergoing routine ABR tests at 4000, 1000 and 500 Hz. Any change in response morphology was judged subjectively. Response latency, amplitude, and measurements of response quality and residual noise were noted. An ABR simulator was also used to assess the effect of notch filtering in conditions of low and high mains interference. RESULTS: The use of a notch filter changed waveform morphology for 500 Hz stimuli only in 15% of tests in newborns. Residual noise was lower when 4000 Hz stimuli were used. Response latency, amplitude, and quality were unaffected regardless of stimulus frequency. Tests with the ABR stimulator suggest that these findings can be extended to conditions of high level mains interference. CONCLUSIONS: A notch filter should be avoided when testing at 500 Hz, but at higher frequencies appears to carry no penalty.

Does cochlear implantation improve speech recognition in children with auditory neuropathy spectrum disorder? A systematic review

OBJECTIVE: Cochlear implantation (CI) is a standard treatment for severe-profound sensorineural hearing loss (SNHL). However, consensus has yet to be reached on its effectiveness for hearing loss caused by auditory neuropathy spectrum disorder (ANSD). This review aims to summarize and synthesize current evidence of the effectiveness of CI in improving speech recognition in children with ANSD. DESIGN: Systematic review. STUDY SAMPLE: A total of 27 studies from an initial selection of 237. RESULTS: All selected studies were observational in design, including case studies, cohort studies, and comparisons between children with ANSD and SNHL. Most children with ANSD achieved open-set speech recognition with their CI. Speech recognition ability was found to be equivalent in CI users (who previously performed poorly with hearing aids) and hearing-aid users. Outcomes following CI generally appeared similar in children with ANSD and SNHL. Assessment of study quality, however, suggested substantial methodological concerns, particularly in relation to issues of bias and confounding, limiting the robustness of any conclusions around effectiveness. CONCLUSIONS: Currently available evidence is compatible with favourable outcomes from CI in children with ANSD. However, this evidence is weak. Stronger evidence is needed to support cost-effective clinical policy and practice in this area.

The Comparative Effectiveness of EEG Biomarkers in Antidepressant Response and Illness Prediction in Major Depressive Disorder

Thesis (Ph.D, Neuroscience Studies) -- Queen's University, 2016-08-27 00:55:35.782

An in-depth characterisation of neonatal seizures by early continuous video-EEG analysis

Introduction Seizures are harmful to the neonatal brain; this compels many clinicians and researchers to persevere further in optimizing every aspects of managing neonatal seizures. Aims To delineate the seizure profile between non-cooled versus cooled neonates with hypoxic-ischaemic encephalopathy (HIE), in neonates with stroke, the response of seizure burden to phenobarbitone and to quantify the degree of electroclinical dissociation (ECD) of seizures. Methods The multichannel video-EEG was used in this research study as the gold standard to detect seizures, allowing accurate quantification of seizure burden to be ascertained in term neonates. The entire EEG recording for each neonate was independently reviewed by at least 1 experienced neurophysiologist. Data were expressed in medians and interquartile ranges. Linear mixed models results were presented as mean (95% confidence interval); p values <0.05 were deemed as significant. Results Seizure burden in cooled neonates was lower than in non-cooled neonates [60(39-224) vs 203(141-406) minutes; p=0.027]. Seizure burden was reduced in cooled neonates with moderate HIE [49(26-89) vs 162(97-262) minutes; p=0.020] when compared with severe HIE. In neonates with stroke, the background pattern showed suppression over the infarcted side and seizures demonstrated a characteristic pattern. Compared with 10 mg/kg, phenobarbitone doses at 20 mg/kg reduced seizure burden (p=0.004). Seizure burden was reduced within 1 hour of phenobarbitone administration [mean (95% confidence interval): -14(-20 to -8) minutes/hour; p<0.001], but seizures returned to pre-treatment levels within 4 hours (p=0.064). The ECD index in cooled, non-cooled neonates with HIE, stroke and in neonates with other diagnoses were 88%, 94%, 64% and 75% respectively. Conclusions Further research exploring the treatment effects on seizure burden in the neonatal brain is required. A change to our current treatment strategy is warranted as we continue to strive for more effective seizure control, anchored with use of the multichannel EEG as the surveillance tool.

Behavioural differences between EEG-defined subgroups of children with Attention-Deficit/Hyperactivity Disorder

Objective: This study investigated the presence and nature of EEG clusters within a clinically-referred sample of children with Attention-Deficit/Hyperactivity Disorder (AD/HD), and whether behavioural differences exist between clusters.

Method: Participants were 155 boys with AD/HD and 109 age- and gender-matched controls. EEG was recorded during an eyes-closed resting condition and Fourier transformed to provide estimates for total power, and relative delta, theta, alpha, and beta. EEG data were grouped into 3 regions, and subjected to Cluster Analysis. Behavioural data for each cluster were compared against the remaining AD/HD subjects.

Results: Four EEG clusters were found. These were characterised by (a) elevated beta activity, (b) elevated theta with deficiencies of alpha and beta, (c) elevated slow wave with less fast wave activity, and (d) elevated alpha. An exploratory analysis of behavioural correlates with these EEG subtypes indicated the presence of interesting trends that need further investigation.

Conclusions: This study found that the AD/HD EEG profiles reported in past studies are robust and not substantially affected by the inclusion of children with other comorbid conditions. The observed group differences in behavioural profiles indicated that different patterns of EEG activity have importance in determining behaviour.

Significance: This is the first study to link behavioural profiles of children with AD/HD to specific EEG abnormalities.