Thalamocortical dysrhythmia detected by machine learning

Scalp EEG-based Pain Detection using Convolutional Neural Network

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83±0.09 and 0.81±0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.

Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.

Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.

Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

Brain Imaging Biomarkers for Chronic Pain

The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

Symptom dimensions to address heterogeneity in tinnitus

Tinnitus, the auditory phantom percept, is a well-known heterogenous disorder with multiple subtypes. Researchers and clinicians have tried to classify these subtypes according to clinical profiles, aetiologies, and response to treatment with little success. The occurrence of overlapping tinnitus subtypes suggests that the disorder exists along a continuum of severity, with no clear distinct boundaries. In this perspective, we propose a neuro-mechanical framework, viewing tinnitus as a dimensional disorder which is a complex interplay of its behavioural, biological and neurophysiological phenotypes. Moreover, we explore the potential of these dimensions as interacting networks without a common existing cause, giving rise to tinnitus. Considering tinnitus as partially overlapping, dynamically changing, interacting networks, each representing a different aspect of the unified tinnitus percept, suggests that the interaction of these networks determines the phenomenology of the tinnitus, ultimately leading to a dimensional spectrum, rather than a categorical subtyping. A combination of a robust theoretical framework and strong empirical evidence can advance our understanding of the functional mechanisms underlying tinnitus and ultimately, improve treatment strategies.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm.

METHOD

We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients.

RESULTS

Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%).

CONCLUSION

The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.

Changes in the spatiotemporal pattern of spontaneous activity across a cortical column after noise trauma

In the auditory modality, noise trauma has often been used to investigate cortical plasticity as it causes cochlear hearing loss. One limitation of these past studies, however, is that the effects of noise trauma have been mostly documented at the granular layer, which is the main cortical recipient of thalamic inputs. Importantly, the cortex is composed of six different layers each having its own pattern of connectivity and specific role in sensory processing. The present study aims at investigating the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity in primary auditory cortex of the anesthetized guinea pig. We show that spontaneous activity is dramatically altered across cortical layers after acute and chronic noise-induced hearing loss. First, spontaneous activity was globally enhanced across cortical layers, both in terms of firing rate and amplitude of spike-triggered average of local field potentials. Second, current source density on (spontaneous) spike-triggered average of local field potentials indicates that current sinks develop in the supra- and infragranular layers. These latter results suggest that supragranular layers become a major input recipient and that the propagation of spontaneous activity over a cortical column is greatly enhanced after acute and chronic noise-induced hearing loss. We discuss the possible mechanisms and functional implications of these changes.

Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:816-819. [PMID: 34891415 DOI: 10.1109/embc46164.2021.9629964]

Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are characterized by a large heterogeneity, which is a major obstacle in developing general treatments for this chronic disorder. As tinnitus patients often report severe constraints in their daily life, the lack of general treatments constitutes such a challenge that patients crave for any kind of promising method to cope with their tinnitus, even if it is not based on evidence. Another drawback constitutes the lack of objective measurements to determine the individual symptoms of patients. Many data sources are therefore investigated to learn more about the heterogeneity of tinnitus patients in order to develop methods to measure the individual situation of patients more objectively. As research assumes that tinnitus is caused by processes in the brain, electroencephalography (EEG) data are heavily investigated by researchers. Following this, we address the question whether EEG data can be used to classify tinnitus using a deep neural network. For this purpose, we analyzed 16,780 raw EEG samples from 42 subjects (divided into tinnitus patients and control group), with a duration of one second per sample. Four different procedures (with or without noise reduction and down-sampling or up-sampling) for automated preprocessing were used and compared. Subsequently, a neural network was trained to classify whether a sample refers to a tinnitus patient or the control group. We obtain a maximum accuracy in the test set of 75.6% using noise reduction and down-sampling. Our findings highlight the potential of deep learning approaches to detect EEG patterns for tinnitus patients as they are difficult to be recognized by humans.

Neuroinflammation as a possible link between attention-deficit/hyperactivity disorder (ADHD) and pain

Attention-deficit/hyperactivity disorder (ADHD) and pathological pain are two complex syndromes of multifactorial origin. Despite their prevalence and broad impacts, these conditions are seldom recognized and managed simultaneously. The co-existence of neuropsychiatric conditions (such as ADHD) and altered pain perception and chronic pain has been noted in children, and the comorbidity of ADHD and chronic pain is well documented in adults. Pathophysiological studies have suggested dysfunction of the dopaminergic system as a common neurochemical basis for comorbid ADHD and pain. Considerable evidence supports the role of neuroinflammation in the pathophysiology of both. We suggest that central neuroinflammation underlies altered pain perception and pain sensitization in persons with ADHD. Based on our hypothesis, targeting neuroinflammation may serve as a potential new therapeutic intervention to treat ADHD and comorbid pain in children and adolescents and a preventive strategy for the development of chronic pain in adults with ADHD.

Too Blind to See the Elephant? Why Neuroscientists Ought to Be Interested in Tinnitus

A curative therapy for tinnitus currently does not exist. One may actually exist but cannot currently be causally linked to tinnitus due to the lack of consistency of concepts about the neural correlate of tinnitus. Depending on predictions, these concepts would require either a suppression or enhancement of brain activity or an increase in inhibition or disinhibition. Although procedures with a potential to silence tinnitus may exist, the lack of rationale for their curative success hampers an optimization of therapeutic protocols. We discuss here six candidate contributors to tinnitus that have been suggested by a variety of scientific experts in the field and that were addressed in a virtual panel discussion at the ARO round table in February 2021. In this discussion, several potential tinnitus contributors were considered: (i) inhibitory circuits, (ii) attention, (iii) stress, (iv) unidentified sub-entities, (v) maladaptive information transmission, and (vi) minor cochlear deafferentation. Finally, (vii) some potential therapeutic approaches were discussed. The results of this discussion is reflected here in view of potential blind spots that may still remain and that have been ignored in most tinnitus literature. We strongly suggest to consider the high impact of connecting the controversial findings to unravel the whole complexity of the tinnitus phenomenon; an essential prerequisite for establishing suitable therapeutic approaches.

Enhanced Temporal Coupling between Thalamus and Dorsolateral Prefrontal Cortex Mediates Chronic Low Back Pain and Depression

Numerous neuroimaging studies have demonstrated that the brain plasticity is associated with chronic low back pain (cLBP). However, there is a lack of knowledge regarding the underlying mechanisms of thalamic pathways for chronic pain and psychological effects in cLBP caused by lumbar disc herniation (LDH). Combining psychophysics and magnetic resonance imaging (MRI), we investigated the structural and functional brain plasticity in 36 patients with LDH compared with 38 age- and gender-matched healthy controls. We found that (1) LDH patients had increased psychophysical disturbs (i.e., depression and anxiety), and depression (Beck-Depression Inventory, BDI) was found to be an outstanding significant factor to predict chronic pain (short form of the McGill Pain Questionnaire, SF-MPQ); (2) the LDH group showed significantly smaller fractional anisotropy values in the region of posterior corona radiate while gray matter volumes were comparable in both groups; (3) resting state functional connectivity analysis revealed that LDH patients exhibited increased temporal coupling between the thalamus and dorsolateral prefrontal cortex (DLPFC), which further mediate the relationship from chronic pain to depression. Our results emphasized that thalamic pathways underlying prefrontal cortex might play a key role in regulating chronic pain and depression of the pathophysiology of LDH.

Is the posterior cingulate cortex an on-off switch for tinnitus?: A comparison between hearing loss subjects with and without tinnitus

As the human brain works in a Bayesian manner to minimize uncertainty toward external stimuli, the deafferented brain may generate tinnitus in an attempt to fill in missing auditory information, e.g. due to hearing loss. However, not everybody with hearing loss develops tinnitus. Understanding the differences between people with hearing loss who develop tinnitus versus those who do not offers a unique opportunity to unravel critical brain areas involved in the generation of a phantom sound. In this study, we compared resting-state quantitative electroencephalography between hearing loss patients with (HL-T) and without tinnitus (HL-NT) to identify cortical oscillatory signatures that may reveal prerequisites for the selective development of tinnitus in subjects with hearing loss. We enrolled 65 subjects with HL-NT and 65 subjects with HL-T whose tinnitus handicap inventory scores were <16 (grade 1) to minimize the bias induced by distress-induced cortical activity changes. Subjects in the HL-T and HL-NT groups were matched in terms of the bilateral hearing threshold (0.25-8 kHz) using nearest neighbor method. Compared to the HL-NT group, the HL-T group showed significantly higher activity in the right parahippocampus for the beta 1 frequency band, in the left inferior parietal lobule (IPL) for the beta 2 frequency band, and in the right IPL for the beta 3- and gamma frequency bands. Functional connectivity analyses revealed that the HL-T group had significantly higher connectivity than the HL-NT group between both parahippocampal gyri and the right IPL for the delta frequency band, and between the left posterior cingulate cortex (PCC) and right IPL for the beta 2 frequency band. These results suggest that tinnitus may be perceived only if auditory memory stored in the parahippocampus is actively linked to the IPL-based "circuit breaker" system and the circuit breaker signal is connected to the PCC-based default mode network (DMN). Thus, when the circuit breaker system regards tinnitus secondary to peripheral deafferentation as a salient event and then the DMN regards tinnitus as a norm, subjects with hearing loss may consciously perceive tinnitus. The results of this study further refine the recently proposed Bayesian model and decipher the neurobiological mechanism of the selective development of tinnitus in subjects with hearing loss.

Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

Short-Wave Sensitive ("Blue") Cone Activation Is an Aggravating Factor for Visual Snow Symptoms

Background and Purpose: Visual Snow (VS) is a disorder characterised by the subjective perception of black-and-white visual static. The aetiology of this condition is not known. In our previous work we suggested that there is a link between short-wave (S or “blue” cone) signals and severity of visual snow symptoms. Therefore we aimed to further characterise this potential link.

Methods: Patients (n = 22) with classic VS based on the diagnostic criteria and healthy controls (n = 12), underwent Intuitive Colorimetry (IC) testing (Cerium Visual Technologies). Twelve hue directions (expressed as angle in CIE 1976 LUV space relative to D65) were rated on a five-point scale from preferred (relieving, positive score) to non-preferred (exacerbating, negative score), and overall preferred and non-preferred angles were chosen.

Results: A non-preferred violet region near the tritanopic confusion line / S-cone axis (267 deg.) was strongly associated with exacerbation of VS symptoms (range 250–310 deg, mean 276 ± 16, n = 20, Rayleigh p < 0.001). Two subjects with non-preferred region > 90 deg from mean were considered as outliers. Median rank at hue angle 270 deg was significantly lower than at angle 90 (−1.5 vs. 0.0, p < 0.001, Wilcoxon non-parametric rank-sum test). Patients showed preference for one of two spectral regions which relieved VS symptoms: orange-yellow (range 50–110 deg., mean 79 ± 24, n = 14) and turquoise-blue (range (210–250 deg., mean 234 ± 27, n = 8).

Conclusion: Our results show that visual snow symptoms are exacerbated by colour modulation that selectively increased levels of S-cone excitation. Because S-cone signals travel on primordial brain pathways that regulate cortical rhythms (koniocellular pathways) we hypothesis that these pathways contribute to the pathogenesis of this disorder.

Early impairment of thalamocortical circuit activity and coherence in a mouse model of Huntington's disease

Huntington's disease (HD) is a progressive, fatal neurodegenerative disorder characterized by motor, cognitive, and psychiatric disturbances. There is no known cure for HD, but its progressive nature allows for early therapeutic intervention. Currently, much of the research has focused on the striatum, however, there is evidence suggesting that disruption of thalamocortical circuits could underlie some of the early symptoms of HD. Loss of both cortical pyramidal neurons (CPNs) and thalamic neurons occurs in HD patients, and cognitive, somatosensory, and attention deficits precede motor abnormalities. However, the role of thalamocortical pathways in HD progression has been understudied. Here, we measured single unit activity and local field potentials (LFPs) from electrode arrays implanted in the thalamus and primary motor cortex of 4-5 month-old male and female Q175 mice. We assessed neuronal activity under baseline conditions as well as during presentation of rewards delivered via actuation of an audible solenoid valve. HD mice showed a significantly delayed licking response to the reward stimulus. At the same time, neuronal activation to the reward was delayed in thalamic neurons, CPNs and fast-spiking cortical interneurons (FSIs) of HD mice. In addition, thalamocortical coherence increased at lower frequencies in HD relative to wildtype mice. Together, these data provide evidence that impaired cortical and thalamic responses to reward stimuli, and impaired thalamocortical coherence, may play an important early role in motor, cognitive, and learning deficits in HD patients.

Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study

BACKGROUND

Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals.

OBJECTIVE

We developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome.

METHODS

35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment.

RESULTS

Baseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p < 0.0004), PC resonance in fronto-parietal (p = 0.001), and EC resonance in centro-posterior channels (p = 0.002). Subjects significantly improved with 10 sessions of rTMS treatment. Only decreased SCC SM resonance was significantly associated with clinical improvement (r = 0.35, p = 0.04). Subjects for whom 10 Hz SM SCC was highly ranked as an RF among all stimulation frequencies had better outcomes from 10 Hz treatment.

CONCLUSIONS

Resonance of 10 Hz stimulation measured using SCC correlated with both symptom severity and improvement with 10 Hz rTMS treatment. Research should determine whether this interrogation paradigm can identify individualized rTMS treatment frequencies.

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

The anatomy of pain and suffering in the brain and its clinical implications

Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. Chronic pain, with a prevalence of 20-30 % is the major cause of human suffering worldwide, because effective, specific and safe therapies have yet to be developed. It is unevenly distributed among sexes, with women experiencing more pain and suffering. Chronic pain can be anatomically and phenomenologically dissected into three separable but interacting pathways, a lateral 'painfulness' pathway, a medial 'suffering' pathway and a descending pain inhibitory pathway. One may have pain(fullness) without suffering and suffering without pain(fullness). Pain sensation leads to suffering via a cognitive, emotional and autonomic processing, and is expressed as anger, fear, frustration, anxiety and depression. The medial pathway overlaps with the salience and stress networks, explaining that behavioural relevance or meaning determines the suffering associated with painfulness. Genetic and epigenetic influences trigger chronic neuroinflammatory changes which are involved in transitioning from acute to chronic pain. Based on the concept of the Bayesian brain, pain (and suffering) can be regarded as the consequence of an imbalance between the two ascending and the descending pain inhibitory pathways under control of the reward system. The therapeutic clinical implications of this simple pain model are obvious. After categorizing the working mechanisms of each of the available treatments (pain killers, psychopharmacology, psychotherapy, neuromodulation, psychosurgery, spinal cord stimulation) to 1 or more of the 3 pathways, a rational combination can be proposed of activating the descending pain inhibitory pathway in combination with inhibition of the medial and lateral pathway, so as to rebalance the pain (and suffering) pathways.

Dynamic functional network connectivity associated with post-traumatic stress symptoms in COVID-19 survivors

Accumulating evidence shows that Coronavirus Disease 19 (COVID-19) survivors may encounter prolonged mental issues, especially post-traumatic stress symptoms (PTSS). Despite manifesting a plethora of behavioral or mental issues in COVID-19 survivors, previous studies illustrated that static brain functional networks of these survivors remain intact. The insignificant results could be due to the conventional statistic network analysis was unable to reveal information that can vary considerably in different temporal scales. In contrast, time-varying characteristics of the dynamic functional networks may help reveal important brain abnormalities in COVID-19 survivors. To test this hypothesis, we assessed PTSS and collected functional magnetic resonance imaging (fMRI) with COVID-19 survivors discharged from hospitals and matched controls. Results showed that COVID-19 survivors self-reported a significantly higher PTSS than controls. Tapping into the moment-to-moment variations of the fMRI data, we captured the dynamic functional network connectivity (dFNC) states, and three discriminative reoccurring brain dFNC states were identified. First of all, COVID-19 survivors showed an increased occurrence of a dFNC state with heterogeneous patterns between sensorimotor and visual networks. More importantly, the occurrence rate of this state was significantly correlated with the severity of PTSS. Finally, COVID-19 survivors demonstrated decreased topological organizations in this dFNC state than controls, including the node strength, degree, and local efficiency of the supplementary motor area. To conclude, our findings revealed the altered temporal characteristics of functional networks and their associations with PTSS due to COVID- 19. The current results highlight the importance of evaluating dynamic functional network changes with COVID-19 survivors.

The Psychiatric Symptomology of Visual Snow Syndrome

Objective: To characterise the psychiatric symptoms of visual snow syndrome (VSS), and determine their relationship to quality of life and severity of visual symptoms. Methods: One hundred twenty-five patients with VSS completed a battery of questionnaires assessing depression/anxiety, dissociative experiences (depersonalisation), sleep quality, fatigue, and quality of life, as well as a structured clinical interview about their visual and sensory symptoms. Results: VSS patients showed high rates of anxiety and depression, depersonalisation, fatigue, and poor sleep, which significantly impacted quality of life. Further, psychiatric symptoms, particularly depersonalisation, were related to increased severity of visual symptoms. The severity/frequency of psychiatric symptoms did not differ significantly due to the presence of migraine, patient sex, or timing of VSS onset (lifelong vs. later onset). Conclusion: Psychiatric symptoms are highly prevalent in patients with VSS and are associated with increased visual symptom severity and reduced quality of life. Importantly, patients with lifelong VSS reported lower levels of distress and milder self-ratings of visual symptoms compared to patients with a later onset, while being equally likely to experience psychiatric symptoms. This suggests that the psychiatric symptoms of VSS are not solely due to distress caused by visual symptoms. While no consistently effective treatments are available for the visual symptomology of VSS, psychiatric symptoms offer an avenue of treatment that is likely to significantly improve patient quality of life and ability to cope with visual symptoms.

Huygen F, C. de Vos C. Magnetoencephalography reveals increased slow-to-fast alpha power ratios in patients with chronic pain

INTRODUCTION

Objective disease markers are a key for diagnosis and personalized interventions. In chronic pain, such markers are still not available, and therapy relies on individual patients' reports. However, several pain studies have reported group-based differences in functional magnetic resonance imaging, electroencephalography, and magnetoencephalography (MEG).

OBJECTIVES

We aimed to explore spectral differences in resting-state MEG brain signals between patients with chronic pain and pain-free controls and to characterize the cortical and subcortical regions involved.

METHODS

We estimated power spectral density over 5 minutes of resting-state MEG recordings in patients with chronic pain and controls and derived 7 spectral features at the sensor and source levels: alpha peak frequency, alpha power ratio (power 7-9 Hz divided by power 9-11 Hz), and average power in theta, alpha, beta, low-gamma, and high-gamma bands. We performed nonparametric permutation t tests (false discovery rate corrected) to assess between-group differences in these 7 spectral features.

RESULTS

Twenty-one patients with chronic pain and 25 controls were included. No significant group differences were found in alpha peak frequency or average power in any frequency band. The alpha power ratio was significantly higher (P < 0.05) in patients with chronic pain at both the sensor and brain source levels. The brain regions showing significantly higher ratios included the occipital, parietal, temporal and frontal lobe areas, insular and cingulate cortex, and right thalamus.

CONCLUSION

The alpha power ratio is a simple, promising signal marker of chronic pain, affecting an expansive range of cortical and subcortical regions, including known pain-processing areas.

Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis

Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

Corticomuscular coupling analysis based on improved LSTM and transfer entropy

The study of functional corticomuscular coupling can reflect the interaction between the cerebral cortex and muscle tissue, thereby helping to understand how the brain controls muscle tissue and the effect of muscle movement on brain function. This study proposes a detection model of the coupling strength between the cortex and muscles. The detection model uses an adaptive selector to choose the optimal long short-term memory network, uses this network to extract the features of electroencephalography and electromyography, and finally transforms time characteristics into the frequency domain. The transfer entropy is used to represent the interaction intensity of signals in different frequency bands. Using this model, we analyze the coupling relationship between the cortex and muscles in the three movements of wrist flexion, wrist extension, and clench fist, and compare the model with traditional wavelet coherence analysis and deep canonical correlation analysis. The experimental results show that our model can not only express the bidirectional coupling relationship between different frequency bands but also suppress the possible false coupling that traditional methods may detect. Our research shows that the proposed model has great potential in medical rehabilitation, movement decoding, and other fields.

The balance between Bayesian inference and default mode determines the generation of tinnitus from decreased auditory input: A volume entropy-based study

Along with phantom pain, tinnitus, a phantom auditory perception occurring in the absence of an external acoustic stimulus, is one of the most representative phantom perceptions that develops in subjects with decreased peripheral sensory input. Although tinnitus is closely associated with peripheral hearing loss (HL), it remains unclear why only some individuals with HL develop tinnitus. In this study, we investigated the differences between 65 HL with tinnitus (HL‐T) and 104 HL with no tinnitus (HL‐NT) using a resting‐state electroencephalography data‐based volume entropy model of the brain network, by comparing the afferent node capacities, that quantify the contribution of each node to the spread of information, of all Brodmann areas. While the HL‐T group showed increased information flow in areas involved in Bayesian inference (the left orbitofrontal cortex, the left subgenual anterior cingulate cortex, and the left ventrolateral prefrontal cortex) and auditory memory storage (the right hippocampus/parahippocampus), the HL‐NT group showed increased afferent node capacity in hub areas of the default mode network (DMN; the right posterior cingulate cortex and the right medial temporal gyrus). These results suggest that the balance of activity between the Bayesian inferential network (updating missing auditory information by retrieving auditory memories from the hippocampus/parahippocampus) and DMN (maintaining the “silent status quo”) determines whether phantom auditory perception occurs in a brain with decreased peripheral auditory input.

Eye movement characteristics provide an objective measure of visual processing changes in patients with visual snow syndrome

Visual snow syndrome (VSS) is a poorly understood neurological disorder that features a range of disabling sensory changes. Visual processing changes revealed previously in VSS appear consistent with poor attentional control, specifically, with difficulty controlling environmentally driven shifts of attention. This study sought to confirm this proposal by determining whether these changes were similarly evident where attention is internally driven. Sixty seven VSS patients and 37 controls completed two saccade tasks: the endogenously cued saccade task and saccadic Simon task. The endogenously cued saccade task correctly (valid trial) or incorrectly (invalid trial) pre-cues a target location using a centrally presented arrow. VSS patients generated significantly shorter saccade latencies for valid trials (p = 0.03), resulting in a greater magnitude cue effect (p = 0.02), i.e. the difference in latency between valid and invalid trials. The saccadic Simon task presents a peripheral cue which may be spatially congruent or incongruent with the subsequent target location. Latencies on this task were comparable for VSS patients and controls, with a normal Simon effect, i.e. shorter latencies for saccades to targets spatially congruent with the preceding cue. On both tasks, VSS patients generated more erroneous saccades than controls towards non-target locations (Endogenously cued saccade task: p = 0.02, saccadic Simon task: p = 0.04). These results demonstrate that cued shifts of attention differentially affect saccade generation in VSS patients. We propose that these changes are not due to impairment of frontally-mediated inhibitory control, but to heightened saccade-related activity in visual regions. These results contribute to a VSS ocular motor signature that may provide clinical utility as well as an objective measure of dysfunction to facilitate future research.

The future of neuromodulation: smart neuromodulation

Introduction: The International Neuromodulation Society defines neuromodulation as the alteration of nerve activity through targeted delivery of a stimulus, such as electrical stimulation or chemical agents, to specific neurological sites in the body.Areas covered: In the near future (<5 years) increasingly complex implantable neuromodulation systems will enter the market. These devices are capable of closed-loop stimulation and the delivery of novel stimulation designs, pushing the need for upgradability. But what about the near-to-far future, meaning 5-10 years from now?Expert opinion: We propose that neuromodulation in the near to far future (5-10 years) will involve integration of adaptive network neuromodulation with predictive artificial intelligence, automatically adjusted by brain and external sensors, and controlled via cloud-based applications. The components will be introduced in a phased approach, culminating in a fully autonomous brain-stimulator-cloud interface. This may, in the long future (>10 years), lead to the brain of the future, a brain with integrated artificial intelligence.

Measuring the mental

Many philosophers have argued that the subjective character of conscious experience results in a fundamental deficit of third-person (henceforth: extrospective) access to first-person experience. By comparing extrospective measurement techniques with measurement techniques in the natural sciences, we will argue that extrospective methods suffer from no such deficit. After a rejection of some principled objections against extrospective methods, a historical comparison with the development of measurement techniques in the natural sciences will show that extrospective measuring methods are still in an early stage of development. However, they can be significantly improved by way of a bootstrapping strategy, similar to that which has proven successful in the development of physical measurement techniques. One reason to expect such improvement is the availability of multiple sources of evidence, which should allow for substantial advances in extrospective measurement techniques. Finally, we will discuss new developments in pain measurement in order to show that the bootstrapping strategy is already bearing fruit.

Reconfiguration of Directed Functional Connectivity Among Neurocognitive Networks with Aging: Considering the Role of Thalamo-Cortical Interactions

A complete picture of how subcortical nodes, such as the thalamus, exert directional influence on large-scale brain network interactions across age remains elusive. Using directed functional connectivity and weighted net causal outflow on resting-state fMRI data, we provide evidence of a comprehensive reorganization within and between neurocognitive networks (default mode: DMN, salience: SN, and central executive: CEN) associated with age and thalamocortical interactions. We hypothesize that thalamus subserves both modality-specific and integrative hub role in organizing causal weighted outflow among large-scale neurocognitive networks. To this end, we observe that within-network directed functional connectivity is driven by thalamus and progressively weakens with age. Secondly, we find that age-associated increase in between CEN- and DMN-directed functional connectivity is driven by both the SN and the thalamus. Furthermore, left and right thalami act as a causal integrative hub exhibiting substantial interactions with neurocognitive networks with aging and play a crucial role in reconfiguring network outflow. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings strengthen the hypothesis that the thalamus is a key causal hub balancing both within- and between-network connectivity associated with age and maintenance of cognitive functioning with aging.

Chronic pain as a brain imbalance between pain input and pain suppression

Chronic pain is pain that persists beyond the expected period of healing. The subjective experience of chronic pain results from pathological brain network interactions, rather than from persisting physiological sensory input of nociceptors. We hypothesize that pain is an imbalance between pain evoking dorsal anterior cingulate cortex and somatosensory cortex and pain suppression (i.e. pregenual anterior cingulate cortex). This imbalance can be measured objectively by current density ratios between pain input and pain inhibition. A balance between areas involved in pain input and pain suppression requires communication, which can be objectively identified by connectivity measures, both functional and effective connectivity. In patients with chronic neuropathic pain, electroencephalography is performed with source localization demonstrating that pain is reflected by an abnormal ratio between the dorsal anterior cingulate cortex, somatosensory cortex and pregenual anterior cingulate cortex. Functional connectivity demonstrates decreased communication between these areas, and effective connectivity puts the culprit at the dorsal anterior cingulate cortex, suggesting that the problem is related to abnormal behavioral relevance attached to the pain. In conclusion, chronic pain can be considered as an imbalance between pain input and pain suppression.

Preclinical Neuropathic Pain Assessment; the Importance of Translatability and Bidirectional Research

For patients suffering with chronic neuropathic pain the need for suitable novel therapies is imperative. Over recent years a contributing factor for the lack of development of new analgesics for neuropathic pain has been the mismatch of primary neuropathic pain assessment endpoints in preclinical vs. clinical trials. Despite continuous forward translation failures across diverse mechanisms, reflexive quantitative sensory testing remains the primary assessment endpoint for neuropathic pain and analgesia in animals. Restricting preclinical evaluation of pain and analgesia to exclusively reflexive outcomes is over simplified and can be argued not clinically relevant due to the continued lack of forward translation and failures in the clinic. The key to developing new analgesic treatments for neuropathic pain therefore lies in the development of clinically relevant endpoints that can translate preclinical animal results to human clinical trials. In this review we discuss this mismatch of primary neuropathic pain assessment endpoints, together with clinical and preclinical evidence that supports how bidirectional research is helping to validate new clinically relevant neuropathic pain assessment endpoints. Ethological behavioral endpoints such as burrowing and facial grimacing and objective measures such as electroencephalography provide improved translatability potential together with currently used quantitative sensory testing endpoints. By tailoring objective and subjective measures of neuropathic pain the translatability of new medicines for patients suffering with neuropathic pain will hopefully be improved.

Tinnitus and tinnitus disorder: Theoretical and operational definitions (an international multidisciplinary proposal)

As for hypertension, chronic pain, epilepsy and other disorders with particular symptoms, a commonly accepted and unambiguous definition provides a common ground for researchers and clinicians to study and treat the problem. The WHO's ICD11 definition only mentions tinnitus as a nonspecific symptom of a hearing disorder, but not as a clinical entity in its own right, and the American Psychiatric Association's DSM-V doesn't mention tinnitus at all. Here we propose that the tinnitus without and with associated suffering should be differentiated by distinct terms: "Tinnitus" for the former and "Tinnitus Disorder" for the latter. The proposed definition then becomes "Tinnitus is the conscious awareness of a tonal or composite noise for which there is no identifiable corresponding external acoustic source, which becomes Tinnitus Disorder "when associated with emotional distress, cognitive dysfunction, and/or autonomic arousal, leading to behavioural changes and functional disability.". In other words "Tinnitus" describes the auditory or sensory component, whereas "Tinnitus Disorder" reflects the auditory component and the associated suffering. Whereas acute tinnitus may be a symptom secondary to a trauma or disease, chronic tinnitus may be considered a primary disorder in its own right. If adopted, this will advance the recognition of tinnitus disorder as a primary health condition in its own right. The capacity to measure the incidence, prevalence, and impact will help in identification of human, financial, and educational needs required to address acute tinnitus as a symptom but chronic tinnitus as a disorder.

Thalamocortical Connectivity in Experimentally-Induced Migraine Attacks: A Pilot Study

In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.

Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence

Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.

Portable EEG monitoring for older adults with dementia and chronic pain - A feasibility study

Given the reduced ability of people with dementia to self-report pain, this study examined the feasibility of using a portable electroencephalography (EEG) headband (MUSE 2) as a pain measurement tool for long-term care residents with dementia. Ten minutes of resting-state EEG was acquired by MUSE 2 from people with dementia experiencing ongoing pain (n = 3) and without current pain (n = 1) over three days. The MUSE 2 was acceptable and feasible for use in people with dementia while challenges regarding software, data collection and analysis in using this device are reported. Compared to the resident not experiencing pain, EEG signals of residents with ongoing pain showed different EEG patterns, and this could be a potential biomarker to support pain measurement in people with dementia. Further research with larger sample size is warranted to verify study results.

Emerging Topics in the Behavioral Neuroscience of Tinnitus

This volume has highlighted the many recent advances in tinnitus theory, models, diagnostics, therapies, and therapeutics. But tinnitus knowledge is far from complete. In this chapter, contributors to the Behavioral Neuroscience of Tinnitus consider emerging topics and areas of research needed in light of recent findings. New research avenues and methods to explore are discussed. Issues pertaining to current assessment, treatment, and research methods are outlined, along with recommendations on new avenues to explore with research.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.

Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review

Background: Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.

Cortical Oscillatory Signatures Reveal the Prerequisites for Tinnitus Perception: A Comparison of Subjects With Sudden Sensorineural Hearing Loss With and Without Tinnitus

Just as the human brain works in a Bayesian manner to minimize uncertainty regarding external stimuli, a deafferented brain due to hearing loss attempts to obtain or "fill in" the missing auditory information, resulting in auditory phantom percepts (i.e., tinnitus). Among various types of hearing loss, sudden sensorineural hearing loss (SSNHL) has been extensively reported to be associated with tinnitus. However, the reason that tinnitus develops selectively in some patients with SSNHL remains elusive, which led us to hypothesize that patients with SSNHL with tinnitus (SSNHL-T) and those without tinnitus (SSNHL-NT) may exhibit different cortical activity patterns. In the current study, we compared resting-state quantitative electroencephalography findings between 13 SSNHL-T and 13 SSNHL-NT subjects strictly matched for demographic characteristics and hearing thresholds. By performing whole-brain source localization analysis complemented by functional connectivity analysis, we aimed to determine the as-yet-unidentified cortical oscillatory signatures that may reveal potential prerequisites for the perception of tinnitus in patients with SSNHL. Compared with the SSNHL-NT group, the SSNHL-T group showed significantly higher cortical activity in Bayesian inferential network areas such as the frontopolar cortex, orbitofrontal cortex (OFC), and pregenual anterior cingulate cortex (pgACC) for the beta 3 and gamma frequency bands. This suggests that tinnitus develops in a brain with sudden auditory deafferentation only if the Bayesian inferential network updates the missing auditory information and the pgACC-based top-down gatekeeper system is actively involved. Additionally, significantly increased connectivity between the OFC and precuneus for the gamma frequency band was observed in the SSNHL-T group, further suggesting that tinnitus derived from Bayesian inference may be linked to the default mode network so that tinnitus is regarded as normal. Taken together, our preliminary results suggest a possible mechanism for the selective development of tinnitus in patients with SSNHL. Also, these areas could serve as the potential targets of neuromodulatory approaches to preventing the development or prolonged perception of tinnitus in subjects with SSNHL.

Switching Tinnitus-On: Maps and source localization of spontaneous EEG

OBJECTIVE

To identify the spectrotemporal changes and sources in patients that could "turn on" tinnitus with multichannel electroencephalography (EEG) system.

METHODS

Multichannel EEG was recorded from six patients during the Tinnitus-On and Tinnitus-Off states. The EEG power spectrum and eLORETA-based sources were measured.

RESULTS

There was a global increase in delta and theta during Tinnitus-On plus large changes in alpha 1 and alpha 2. During the Tinnitus-On state, many new sources in delta, theta, alpha 1 and gamma bands emerged in the opposite hemisphere in the inferior temporal gyrus (Brodmann area, BA 20), middle temporal gyrus (BA 21), lateral perirhinal cortex (BA 36), ventral entorhinal cortex (BA 28) and anterior pole of the temporal gyrus (BA 38).

CONCLUSIONS

The emergence of new delta, theta and gamma band sources in the inferior temporal gyrus (BA 20), middle temporal gyrus (BA 21) and lateral perirhinal cortex (BA 36) plus the appearance of new delta and theta sources in the ventral entorhinal cortex (BA28) and anterior pole of the temporal lobe (BA 38) may comprise a network capable of evoking the phantom sound of tinnitus by simultaneously engaging brain regions involved in memory, sound recognition, and distress which together contribute to tinnitus severity.

SIGNIFICANCE

The sudden appearance of new sources of activity in the opposite hemisphere within the inferior temporal gyrus, middle temporal gyrus and perirhinal cortex may initiate the perception of tinnitus perception.

Common and distinct patterns of gray matter alterations in borderline personality disorder and posttraumatic stress disorder: A dual meta-analysis

BACKGROUND

Borderline personality disorder (BPD) and posttraumatic stress disorder (PTSD) are severe psychiatric disorders and often co-occur, either of the two will seriously threaten to public health. However, we lack ample evidences to understand the potential pathophysiologic mechanisms of gray matter (GM) alterations in the two disorders.

METHODS

We performed a meta-analysis in both BPD (15 datasets including 442 BPD subjects versus 441 healthy controls) and PTSD (11 datasets including 214 PTSD subjects versus 258 healthy controls) applying anisotropic effect-size-based algorithms (AES-SDM) method.

RESULTS

Conjunction analysis found relative GM volume reductions in both disorders in the orbitofrontal gyrus and anterior cingulate cortex, contrarily, differences were predominantly observed that GM volume increased in the posterior cingulate gyrus and precuneus in BPD subjects, and GM volume decreased in the amygdala-hippocampal fear circuit, fusiform gyrus in PTSD subjects.

CONCLUSIONS

Group comparisons and conjunction analyses in BPD and PTSD identified same regions of GM volume reductions in the orbitofrontal gyrus and anterior cingulate cortex, which may provide clues for the neurobiological mechanisms and clinical diagnosis underpinning two disorders.

NIRS measures in pain and analgesia: Fundamentals, features, and function

Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder

Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.

Longitudinal analysis of surgical outcome in subjects with pulsatile tinnitus originating from the sigmoid sinus

A dominant sigmoid sinus with either diverticulum or dehiscence (SS-Div/SS-Deh) is a common cause of pulsatile tinnitus (PT). For PT originating from SS-Div/SS-Deh, an etiology-specific and secure reconstruction using firm materials is vital for optimal outcomes. As a follow-up to our previous reports on transmastoid SS resurfacing or reshaping for SS-Div/SS-Deh, this study aimed to evaluate the long-term results of transmastoid resurfacing/reshaping. We retrospectively reviewed 20 PT patients who were diagnosed with SS-Div/SS-Deh, underwent transmastoid resurfacing/reshaping, and were followed up for more than 1 year postoperatively. For PT, immediate and long-term changes (> 1 year) in loudness and annoyance were analyzed using the visual analog scale (VAS). Additionally, pre and postoperative objective measurements of PT using transcanal sound recording and spectro-temporal analysis (TSR-STA), imaging results, and audiological findings were comprehensively analyzed. Significant improvements in PT were sustained or enhanced for > 1 year (median follow-up period: 37 months, range: 12–54 months). On TSR-STA, both peak and root mean square amplitudes decreased after surgery. Also, the average pure-tone threshold at 250 Hz improved after surgery. Thus, our long-term follow-up data confirmed that the surgical management of PT originating from SS-Div/SS-Deh is successful with regard to both objective and subjective measures.

A portable neurofeedback device for treating chronic subjective tinnitus: Feasibility and results of a pilot study

BACKGROUND

Several clinical studies have shown that neurofeedback (NFB) has the potential to significantly improve the quality of life of patients complaining of chronic subjective tinnitus. Yet the clinical applicability of such a therapeutic approach in the everyday practice has not been tested so far.

OBJECTIVE

This study aims at investigating the feasibility and efficacy of a semi-automated NFB intervention by means of a portable device that eventually could be used by the patients at home on an everyday basis. The duration of setup procedures is minimized through the use of a dry electrodes electroencephalography (EEG) headset and an automated user-interface.

METHODS

We conducted a pilot clinical study (non-controlled, single arm, NCT03773926). According to a predetermined power calculation, a homogeneous population of 33 subjects with strict inclusion criteria was enrolled. After inclusion, all patients underwent 10 NFB sessions lasting 50min each, over a period of 5 weeks and a 3-month follow-up period. According to previous studies, the NFB training aimed at increasing the alpha-band power (8-12Hz) in the EEG power spectrum on the averaged signal of leads FC1, FC2, F3 and F4. Tinnitus handicap inventory (THI) was used as a primary outcome measure. Secondary outcome measures were the visual analog scales (VAS) and the change of the alpha-band power within sessions and across training. Time points of assessment were before intervention (T1), after intervention (T2) and at the 3-month follow-up (T3).

RESULTS

Patient exhibited a clinically significant decrease of the THI score, with a 23% decrease (N=28) on average between T1 and T2 and a 31% decrease (N=25) between T1 and T3. A significant increase of the alpha-band power within sessions was observed. No significant increase of the alpha-band power across sessions was observed. For the 19 subjects where sufficient data were exploitable, a significant correlation was found between the evolution of the alpha-band training across sessions and the evolution of the THI between T1 and T2. The sessions were well tolerated and no adverse effect was reported.

CONCLUSION

This study suggests that neurofeedback has potential to suit everyday clinical practice with the goal to significantly reduce tinnitus intrusiveness. The merits and limitations of this NFB procedure are discussed, especially with respect to the choice of EEG electrodes to ensure a good signal quality.

Alterations of brain activity and functional connectivity in transition from acute to chronic tinnitus

The objective of this study was to investigate alterations to brain activity and functional connectivity in patients with tinnitus, exploring neural features in the transition from acute to chronic phantom perception. Twenty‐four patients with acute tinnitus, 23 patients with chronic tinnitus, and 32 healthy controls were recruited. High‐density electroencephalography (EEG) was used to explore changes in brain areas and functional connectivity in different groups. When compared with healthy subjects, acute tinnitus patients had a significant reduction in superior frontal cortex activity across all frequency bands, whereas chronic tinnitus patients had a significant reduction in the superior frontal cortex at beta 3 and gamma frequency bands as well as a significant increase in the inferior frontal cortex at delta‐band and superior temporal cortex at alpha 1 frequency band. When compared to the chronic tinnitus group, the acute tinnitus group activity was significantly increased in the middle frontal and parietal gyrus at the gamma‐band. Functional connectivity analysis showed that the chronic tinnitus group had increased connections between the parahippocampus gyrus, posterior cingulate cortex, and precuneus when compared with the healthy group. Alterations of local brain activity and connections between the parahippocampus gyrus and other nonauditory areas appeared in the transition from acute to chronic tinnitus. This indicates that the appearance and development of tinnitus is a dynamic process involving aberrant local neural activity and abnormal connectivity in multifunctional brain networks.

Vagus nerve stimulation for tinnitus: A review and perspective

Vagus nerve stimulation is a promising new tool in the treatment of chronic tinnitus. Current protocols involve pairing sounds, which exclude the tinnitus frequency, with simultaneous vagus nerve stimulation (VNS). This is based on extensive preclinical animal studies that demonstrate that pairing non-tinnitus sounds with VNS results in a tonotopic map plasticity. It is thought that by expanding the non-tinnitus sound representation, it is possible to overturn the expanded tonotopic map associated with the tinnitus frequency in these animal models. These findings have been translated into a clinical approach, where a clinically significant, but moderate improvement, in tinnitus distress and a modest benefit in tinnitus loudness perception has been shown. Yet, pairing tinnitus matched sound to VNS may produce tinnitus improvement by Pavlovian conditioning, in which the distressful tinnitus sound becomes associated with a relaxing "rest and digest" response from activation of the vagus nerve. If this hypothesis is correct, beneficial effects should be achieved with paired sounds that resemble the tinnitus sounds as much as possible. In conclusion, although the potential to use VNS to drive neural plasticity to reduce or eliminate the neural drivers of ongoing tinnitus is exciting, much work is needed to more completely understand the neural basis of tinnitus and to develop tailored therapies to address the suffering caused by this heterogeneous condition. Whether pairing of the vagus stimulation with non-tinnitus or tinnitus-matched sounds is essential is still to be determined.

Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines

BACKGROUND

Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention.

OBJECTIVE

The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury.

METHODS

We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic γ-aminobutyric acid concentration.

RESULTS

This study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021.

CONCLUSIONS

This clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large samples is not feasible.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943; https://bit.ly/2RY1jRx.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/20979.