Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia

Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.

Toward the Language Oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language ‘oscillome.’ It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language ‘oscillogenome,’ for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution.

Higher-order language dysfunctions as a possible neurolinguistic endophenotype for schizophrenia: Evidence from patients and their unaffected first degree relatives

The purpose of the study was to examine the presence of pragmatic dysfunctions in first episode (FE) subjects and their healthy first degree relatives as a potential endophenotype for schizophrenia. Thirty-four FE patients, 34 parents of the patients (REL) and 32 healthy controls (HC) took part in the study. Pragmatic language functions were evaluated with the Right Hemisphere Language Battery, attention and executive functions were controlled, as well as age and education level. The parents differed from HC but not from their FE offspring with regard to overall level of language and communication and the general knowledge component of language processing. The FE participants differed from HC in comprehension of inferred meaning, emotional prosody, discourse dimensions, overall level of language and communication, language processing with regard to general knowledge and communication competences. The FE participants differed from REL regarding discourse dimensions. Our findings suggest that pragmatic dysfunctions may act as vulnerability markers of schizophrenia; their assessment may help in the diagnosis of early stages of the illness and in understanding its pathophysiology. In future research the adoptive and biological parents of schizophrenia patients should be compared to elucidate which language failures reflect genetic vulnerability and which ones environmental factors.

Bridging the Gap between Genes and Language Deficits in Schizophrenia: An Oscillopathic Approach

Schizophrenia is characterized by marked language deficits, but it is not clear how these deficits arise from the alteration of genes related to the disease. The goal of this paper is to aid the bridging of the gap between genes and schizophrenia and, ultimately, give support to the view that the abnormal presentation of language in this condition is heavily rooted in the evolutionary processes that brought about modern language. To that end we will focus on how the schizophrenic brain processes language and, particularly, on its distinctive oscillatory profile during language processing. Additionally, we will show that candidate genes for schizophrenia are overrepresented among the set of genes that are believed to be important for the evolution of the human faculty of language. These genes crucially include (and are related to) genes involved in brain rhythmicity. We will claim that this translational effort and the links we uncover may help develop an understanding of language evolution, along with the etiology of schizophrenia, its clinical/linguistic profile, and its high prevalence among modern populations.

White matter and cognition: making the connection

Whereas the cerebral cortex has long been regarded by neuroscientists as the major locus of cognitive function, the white matter of the brain is increasingly recognized as equally critical for cognition. White matter comprises half of the brain, has expanded more than gray matter in evolution, and forms an indispensable component of distributed neural networks that subserve neurobehavioral operations. White matter tracts mediate the essential connectivity by which human behavior is organized, working in concert with gray matter to enable the extraordinary repertoire of human cognitive capacities. In this review, we present evidence from behavioral neurology that white matter lesions regularly disturb cognition, consider the role of white matter in the physiology of distributed neural networks, develop the hypothesis that white matter dysfunction is relevant to neurodegenerative disorders, including Alzheimer's disease and the newly described entity chronic traumatic encephalopathy, and discuss emerging concepts regarding the prevention and treatment of cognitive dysfunction associated with white matter disorders. Investigation of the role of white matter in cognition has yielded many valuable insights and promises to expand understanding of normal brain structure and function, improve the treatment of many neurobehavioral disorders, and disclose new opportunities for research on many challenging problems facing medicine and society.

Language deficits in schizophrenia and autism as related oscillatory connectomopathies: An evolutionary account

Schizophrenia (SZ) and autism spectrum disorders (ASD) are characterised by marked language deficits, but it is not clear how these arise from gene mutations associated with the disorders. Our goal is to narrow the gap between SZ and ASD and, ultimately, give support to the view that they represent abnormal (but related) ontogenetic itineraries for the human faculty of language. We will focus on the distinctive oscillatory profiles of the SZ and ASD brains, in turn using these insights to refine our understanding of how the brain implements linguistic computations by exploring a novel model of linguistic feature-set composition. We will argue that brain rhythms constitute the best route to interpreting language deficits in both conditions and mapping them to neural dysfunction and risk alleles of the genes. Importantly, candidate genes for SZ and ASD are overrepresented among the gene sets believed to be important for language evolution. This translational effort may help develop an understanding of the aetiology of SZ and ASD and their high prevalence among modern populations.

Abnormal Neural Oscillations in Schizophrenia Assessed by Spectral Power Ratio of MEG During Word Processing

This study investigated spectral power of neural oscillations associated with word processing in schizophrenia. Magnetoencephalography (MEG) data were acquired from 12 schizophrenia patients and 10 healthy controls during a visual word processing task. Two spectral power ratio (SPR) feature sets: the band power ratio (BPR) and the window power ratio (WPR) were extracted from MEG data in five frequency bands, four time windows of word processing, and at locations covering whole head. Cluster-based nonparametric permutation tests were employed to identify SPRs that show significant between-group difference. Machine learning based feature selection and classification techniques were then employed to select optimal combinations of the significant SPR features, and distinguish schizophrenia patients from healthy controls. We identified three BPR clusters and three WPR clusters that show significant oscillation power difference between groups. These include the theta/delta, alpha/delta and beta/delta BPRs during base-to-encode and encode time windows, and the beta band WPR from base to encode and from encode to post-encode windows. Based on two WPR and one BPR features combined, over 95% cross-validation classification accuracy was achieved using three different linear classifiers separately. These features may have potential as quantitative markers that discriminate schizophrenia patients and healthy controls; however, this needs further validation on larger samples.

Induction Immunosuppression in High-risk Kidney Transplant Recipients

Kidney transplant remains the best type of renal replacement therapy in most patients with end-stage kidney disease, even in those with high immunologic risk. Immunosuppression in these patients is regarded as more complex, owing to the higher risk of both acute and chronic rejection. The advent of induction immunosuppression has resulted in a lower incidence of acute rejection and consequently improved short-term patient and allograft outcomes. Indeed, the use of these agents, especially in high-risk recipients, has become standard of care at most transplant centers. Transplant physicians are constantly faced with the challenge of estimating the recipients' immunologic risk and tailoring their immunosuppression accordingly. This review article aims to provide an up-to-date evaluation of the various studies available, which investigated the use of induction agents in kidney transplant, specifically in high-risk recipients. It evaluates the use of the most frequently used polyclonal antibody (rabbit antithymocyte globulin) versus the less commonly used monoclonal antibody alemtuzumab, superseded agents such as muromonab-CD3, and potentially emerging agents such as rituximab, bortezomib, and eculizumab. With this systematic review, we hope to inform the scientific community and facilitate this controversial decision through the implementation of robust scientific evidence.

The Oscillopathic Nature of Language Deficits in Autism: From Genes to Language Evolution

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders involving a number of deficits to linguistic cognition. The gap between genetics and the pathophysiology of ASD remains open, in particular regarding its distinctive linguistic profile. The goal of this article is to attempt to bridge this gap, focusing on how the autistic brain processes language, particularly through the perspective of brain rhythms. Due to the phenomenon of pleiotropy, which may take some decades to overcome, we believe that studies of brain rhythms, which are not faced with problems of this scale, may constitute a more tractable route to interpreting language deficits in ASD and eventually other neurocognitive disorders. Building on recent attempts to link neural oscillations to certain computational primitives of language, we show that interpreting language deficits in ASD as oscillopathic traits is a potentially fruitful way to construct successful endophenotypes of this condition. Additionally, we will show that candidate genes for ASD are overrepresented among the genes that played a role in the evolution of language. These genes include (and are related to) genes involved in brain rhythmicity. We hope that the type of steps taken here will additionally lead to a better understanding of the comorbidity, heterogeneity, and variability of ASD, and may help achieve a better treatment of the affected populations.

Global Synchronization of Multichannel EEG in Patients With Electrical Status Epilepticus in Sleep

In the research field of epilepsy, it is a challenge to understand the transition of brain activity to electrical status epilepticus in sleep (ESES). In this study, an S-estimator method is proposed to describe the course of global synchronization in multichannel electroencephalograph (EEG) from awake to sleep in 11 patients with ESES. The study confirms that there is a significant increase in spikes and global synchronization from awake to sleep. It is also found that global synchronization is strongly correlated with spikes. The proposed method has the potential of revealing the intrinsic features of EEG signals and the underlying brain dynamics in ESES.

Multisensory stimuli elicit altered oscillatory brain responses at gamma frequencies in patients with schizophrenia

Deficits in auditory and visual unisensory responses are well documented in patients with schizophrenia; however, potential abnormalities elicited from multisensory audio-visual stimuli are less understood. Further, schizophrenia patients have shown abnormal patterns in task-related and task-independent oscillatory brain activity, particularly in the gamma frequency band. We examined oscillatory responses to basic unisensory and multisensory stimuli in schizophrenia patients (N = 46) and healthy controls (N = 57) using magnetoencephalography (MEG). Time-frequency decomposition was performed to determine regions of significant changes in gamma band power by group in response to unisensory and multisensory stimuli relative to baseline levels. Results showed significant behavioral differences between groups in response to unisensory and multisensory stimuli. In addition, time-frequency analysis revealed significant decreases and increases in gamma-band power in schizophrenia patients relative to healthy controls, which emerged both early and late over both sensory and frontal regions in response to unisensory and multisensory stimuli. Unisensory gamma-band power predicted multisensory gamma-band power differently by group. Furthermore, gamma-band power in these regions predicted performance in select measures of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) test battery differently by group. These results reveal a unique pattern of task-related gamma-band power in schizophrenia patients relative to controls that may indicate reduced inhibition in combination with impaired oscillatory mechanisms in patients with schizophrenia.

EEG functional connectivity, axon delays and white matter disease

OBJECTIVE

Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists.

METHODS

Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework.

RESULTS

Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks.

CONCLUSIONS

Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence.

SIGNIFICANCE

White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments.

Role of myelin plasticity in oscillations and synchrony of neuronal activity

Conduction time is typically ignored in computational models of neural network function. Here we consider the effects of conduction delays on the synchrony of neuronal activity and neural oscillators, and evaluate the consequences of allowing conduction velocity (CV) to be regulated adaptively. We propose that CV variation, mediated by myelin, could provide an important mechanism of activity-dependent nervous system plasticity. Even small changes in CV, resulting from small changes in myelin thickness or nodal structure, could have profound effects on neuronal network function in terms of spike-time arrival, oscillation frequency, oscillator coupling, and propagation of brain waves. For example, a conduction delay of 5ms could change interactions of two coupled oscillators at the upper end of the gamma frequency range (∼100Hz) from constructive to destructive interference; delays smaller than 1ms could change the phase by 30°, significantly affecting signal amplitude. Myelin plasticity, as another form of activity-dependent plasticity, is relevant not only to nervous system development but also to complex information processing tasks that involve coupling and synchrony among different brain rhythms. We use coupled oscillator models with time delays to explore the importance of adaptive time delays and adaptive synaptic strengths. The impairment of activity-dependent myelination and the loss of adaptive time delays may contribute to disorders where hyper- and hypo-synchrony of neuronal firing leads to dysfunction (e.g., dyslexia, schizophrenia, epilepsy).

Dopamine, cognitive function, and gamma oscillations: role of D4 receptors

Cognitive deficits in individuals with schizophrenia (SCZ) are considered core symptoms of this disorder, and can manifest at the prodromal stage. Antipsychotics ameliorate positive symptoms but only modestly improve cognitive symptoms. The lack of treatments that improve cognitive abilities currently represents a major obstacle in developing more effective therapeutic strategies for this debilitating disorder. While D4 receptor (D4R)-specific antagonists are ineffective in the treatment of positive symptoms, animal studies suggest that D4R drugs can improve cognitive deficits. Moreover, recent work from our group suggests that D4Rs synergize with the neuregulin/ErbB4 signaling pathway, genetically identified as risk factors for SCZ, in parvalbumin (PV)-expressing interneurons to modulate gamma oscillations. These high-frequency network oscillations correlate with attention and increase during cognitive tasks in healthy subjects, and this correlation is attenuated in affected individuals. This finding, along with other observations indicating impaired GABAergic function, has led to the idea that abnormal neural activity in the prefrontal cortex (PFC) in individuals with SCZ reflects a perturbation in the balance of excitation and inhibition. Here we review the current state of knowledge of D4R functions in the PFC and hippocampus, two major brain areas implicated in SCZ. Special emphasis is given to studies focusing on the potential role of D4Rs in modulating GABAergic transmission and to an emerging concept of a close synergistic relationship between dopamine/D4R and neuregulin/ErbB4 signaling pathways that tunes the activity of PV interneurons to regulate gamma frequency network oscillations and potentially cognitive processes.