Abnormal Entropy Modulation of the EEG Signal in Patients With Schizophrenia During the Auditory Paired-Stimulus Paradigm

The complexity change in brain activity in schizophrenia is an interesting topic clinically. Schizophrenia patients exhibit abnormal task-related modulation of complexity, following entropy of electroencephalogram (EEG) analysis. However, complexity modulation in schizophrenia patients during the sensory gating (SG) task, remains unknown. In this study, the classical auditory paired-stimulus paradigm was introduced to investigate SG, and EEG data were recorded from 55 normal controls and 61 schizophrenia patients. Fuzzy entropy (FuzzyEn) was used to explore the complexity of brain activity under the conditions of baseline (BL) and the auditory paired-stimulus paradigm (S1 and S2). Generally, schizophrenia patients showed significantly higher FuzzyEn values in the frontal and occipital regions of interest (ROIs). Relative to the BL condition, the normalized values of FuzzyEn of normal controls were decreased greatly in condition S1 and showed less variance in condition S2. Schizophrenia patients showed a smaller decrease in the normalized values in condition S1. Moreover, schizophrenia patients showed significant diminution in the suppression ratios of FuzzyEn, attributed to the higher FuzzyEn values in condition S1. These results suggested that entropy modulation during the process of sensory information and SG was obvious in normal controls and significantly deficient in schizophrenia patients. Additionally, the FuzzyEn values measured in the frontal ROI were positively correlated with positive scores of Positive and Negative Syndrome Scale (PANSS), indicating that frontal entropy was a potential indicator in evaluating the clinical symptoms. However, negative associations were found between the FuzzyEn values of occipital ROIs and general and total scores of PANSS, likely reflecting the compensation effect in visual processing. Thus, our findings provided a deeper understanding of the deficits in sensory information processing and SG, which contribute to cognitive deficits and symptoms in patients with schizophrenia

Improving the quality of combined EEG-TMS neural recordings: artifact removal and time analysis

The combination of TMS (transcranial magnetic stimulation) and EEG (electroencephalography) allows a functional assessment of cortical regions in a controlled, non-invasive way and without the need of having subjects under study perform a task. Thanks to this combination, a characterization of the cerebral signals of schizophrenic patients (16) and healthy controls (15), through the TMS-evoked potentials (TEPs), will be performed. Two protocols will be evaluated: SICI (short-interval intracortical inhibition) and LICI (long-interval intracortical inhibition), which activate different inhibitory-type receptors (GABA-A and GABA-B, respectively). In both protocols, there are signals with a single TMS-pulses (SP) or with paired-pulses (two pulses, PP). Thus, besides the characterization, the different results obtained will be compared between type of pulses, protocols and groups of study. The methodology followed is the typical one for this type of signals: removal of the TMS-pulse(s); ICA (independent component analysis) application to delete the artefactuated or noisy components; signal reconstruction with the good components and bad channel and bad trial rejection. Once the pre-processing is finished and the signal is clean, the TMS-evoked potentials are obtained. For the purpose of finding the differences between PP and SP, the former signal is subtracted from the latter and a modulation ratio is computed. The TEPs found are P25, N30, P50, N100, P140, N160 and P200. Regarding the differences between types of pulses, the observed that the TEP amplitudes for LICI PP are lower than the ones for LICI SP. However, this difference is not clearly seen in SICI protocol. As to the differences between subjects, it seems that the controls have greater inhibition than the patients. In conclusion, the results are remarkably similar to the ones expected. Due to the small sample size and the fact that it is still a poorly studied filed, some results do not match completely with the ones in the literature, but they follow the same tendencyLa combinació de la TMS (estimulació magnètica transcranial, per les seves sigles en anglès) i l’electroencefalograma permet una avaluació funcional directa de regions corticals d’una manera controlada, no invasiva i sense necessitat de realitzar una tasca per part del subjecte d’estudi. És amb la combinació d’aquestes dues tècniques que es vol caracteritzar les senyal cerebrals de persones amb esquizofrènia (16) i persones sanes (15), obtenint els potencials evocats pel TMS (TEP). S’avaluen dos protocols: SICI (short-interval intracortical inhibition) i LICI (long-interval intracortical inhibition), que activen diferents receptors de tipus inhibitori (GABA-A i GABA-B, respectivament). Dins d’aquests protocols, hi ha senyals amb un sol pols de TMS (SP) o amb dos polsos (PP). Així doncs, a part de la caracterització de la senyal, també es comparen els resultats entre els diferents tipus de polsos (1 ó 2), els protocols i els dos grups d’estudi. La metodologia seguida és la típica per aquest tipus de senyals: eliminació del pols o polsos; aplicació d’ICA (anàlisis de components independents), per tal d’eliminar aquelles components artefactuades i/o sorolloses; reconstrucció de la senyal amb les components bones i eliminació de canals i experiments sorollosos. Un cop s’ha fet el pre-processament de la senyal i aquesta està neta, es busquen els potencials evocats pel pols(os) del TMS. Per buscar les diferències entre PP i SP, es resta la primera senyal a la segona i es calcula un rati de modulació. S’han trobat els TEPs P25, N30, P50, N100, P140, N160 i P200. Referent a les diferències entre tipus de polsos, s’observa que les amplituds dels TEPs és menor en LICI PP que en LICI SP. Tanmateix, aquesta diferencia no es veu tan clara en el protocol SICI. Pel que fa les diferències entre subjectes, sembla que els controls (les persones sanes) tenen més inhibició que no pas els pacients. En conclusió, els resultats obtinguts són bastant propers als esperats. Degut a la mida reduïda de la mostra i a que es tracta d’un camp encara poc estudiat, els resultats no són exactament els de la literatura però sí que van en la mateixa líniaLa combinación de la TMS (estimulación magnética transcranial, por sus siglas en inglés) y el electroencefalograma permite una avaluación funcional directa de regiones corticales de una manera controlada, no invasiva y sin necesidad de realizar una tarea por parte del sujeto de estudio. Es con la combinación de ambas técnicas que se quiere caracterizar las señales cerebrales de personas con esquizofrenia (16) y personas sanas (15), obteniendo los potenciales evocados por el TMS (TEPs). Se evalúan dos protocolos: SICI (short-interval intracortical inhibition) y LICI (long-interval intracortical inhibition), que activan diferentes receptores de tipo inhibitorio (GABA-A y GABA-B; respectivamente). Dentro de estos protocolos, hay señales con un solo pulso de TMS (SP) o con dos pulsos (PP). Así pues, a parte de la caracterización de la señal, también se comparan los resultados entre los diferentes tipos de pulsos, los protocolos y los dos grupos de estudio. La metodología seguida es la típica para este tipo de señales: eliminación del pulso o pulsos; aplicación de ICA (análisis de componentes independientes), con el fin de eliminar las componentes artefactuadas y/o ruidosas; reconstrucción de la señal con las componentes buenas y eliminación de canales y experimentos ruidosos. Una vez hecho el pre-procesado de la señal y ésta está limpia, se buscan los TEPs. Para buscar las diferencias entre PP y SP, se resta la primera señal a la segunda y se calcula una ratio de modulación. Se han encontrado los TEPs P25, N30, P50, N100, P140, N169 y P200. Referente a las diferencias entre tipos de pulsos, se observa que las amplitudes de los TEPs son menores en LICI PP que en LICI SP. Sin embargo, esta diferencia no se ve tan clara en SICI. En cuanto a las diferencias entre sujetos, parece ser que los controles (personas sanas) tienen más inhibición que los pacientes. En conclusión, los resultados son muy similares a los esperados. Debido al tamaño reducido de la muestra y a que es un campo poco estudiado, los resultados no son exactamente iguales a los de la literatura, pero sí que van en la misma líneaObjectius de Desenvolupament Sostenible::3 - Salut i Benesta

Characterization of Neural Activity using Complex Network Theory. Application to the Identification of the Altered Neural Substrates in Schizophrenia

La esquizofrenia es un desorden psiquiátrico caracterizado por alteraciones en el pensamiento y en la capacidad de respuesta emocional. Comprende una gran variedad de síntomas, sin embargo, no está claro que todos compartan un sustrato neurológico común. Por ello, el objetivo de esta Tesis Doctoral es desarrollar un marco de referencia desde la perspectiva de la Teoría de Redes Complejas para investigar las interacciones neurales alteradas de la esquizofrenia haciendo uso de la señal electroencefalográfica. Así, dos bases de datos independientes de registros electroencefalográficos fueron registras durante una tarea cognitiva. Nuestros hallazgos son consistentes con estudios previos al tiempo que muestran una hiperactivación del intervalo de estímulo previa a una reorganización neural disminuida durante la cognición, principalmente asociado a caminos neurales secundarios. Los hallazgos de esta Tesis ponen de manifiesto la gran heterogeneidad de la esquizofrenia, posiblemente asociada a la existencia de subgrupos dentro de la misma.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation

Objective: Higher mental functions depend on global cerebral functional coordination. Our aim was to study fast modulation of functional networks in schizophrenia that has not been previously assessed. Methods: Graph-theory was used to analyze the electroencephalographic (EEG) activity during an odd-ball task in 57 schizophrenia patients (18 first episode patients, FEPs) and 59 healthy controls. Clustering coefficient (CLC), characteristic path length (PL) and small-worldness (SW) were computed at baseline ([−300 0] ms prior to stimulus delivery) and response ([150 450] ms post-stimulus) windows. Clinical and cognitive assessments were performed. Results: CLC, PL and SW showed a significant modulation between baseline and response in controls but not in patients. Patients obtained higher CLC and SW at baseline, lower CLC and higher PL at response, and diminished modulation of CLC and SW as compared to controls. In patients, CLC and SW modulation were inversely associated to cognitive performance in executive tasks and directly associated to working memory. Similar patterns were observed in FEPs. CLC and SW during the baseline were inversely associated to their respective modulation magnitudes. Conclusions: Our results are coherent with a hyper-segregated network at baseline (higher CLC) and a decreased modulation of the functional connectivity during cognition in schizophrenia.This work was supported by the Instituto Carlos III (PI11/02708, PI11/02203 and PI15/00299) and the Gerencia Regional de Salud de Castilla y León (GRS 1134/A/15 and GRS 1263/A/16) grants; the ‘MINECO and FEDER (TEC2014-53196-R), ‘Consejería de Educación de la Junta de Castilla y León’ (VA037U16); and predoctoral fellowships to A. Lubeiro (‘Consejería de Educación Junta de Castilla y León’) and to J. Gomez-Pilar (University of Valladolid)

Characterization of dynamical neural activity by means of EEG data: application to schizophrenia

Schizophrenia is a disabling, chronic and severe mental illness characterized by disintegration of the process of thinking, contact with reality and emotional responsiveness. Schizophrenia has been related to an aberrant assignment of salience to external objects and internal representations. In addition, schizophrenia has been identified as a dysconnection syndrome, which is associated with a reduced capacity to integrate information among different brain regions. Relevance attribution likely involves diverse cerebral regions and their interconnections. As a consequence, many efforts have been devoted to identifying abnormalities in the cortical connections and their relation to schizophrenia symptoms and cognitive performance. Neural oscillations are one of the largest contributing mechanism for enabling coordinated activity during normal brain functioning. Alterations in neural oscillations and cognitive processing in schizophrenia have long been assessed using electroencephalographic (EEG) recordings (i.e. time-varying voltages on the human scalp generated by the electrical activity on the cerebral cortex). Event-related potentials (ERP) depict EEG data as a response to a cognitive task. ERP analyses are used to gain further insights into the neural mechanisms underlying cognitive dysfunctions. In this Doctoral Thesis, a 3-stimulus auditory-oddball paradigm was used for examining cognitive processing as response to both relevant and irrelevant stimuli. A total of 69 ERP recordings were analyzed in the research papers included in the Thesis, which comprises 20 chronic schizophrenia patients, 11 first episode patients and 38 healthy controls. This Doctoral Thesis is focused on the study, design and application of biomedical signal processing methodologies in order to facilitate the understanding of cognitive processes altered by the schizophrenia. EEG data were examined using a two-level analysis: (I) local activation studies to quantify functional segregation of the brain network, by means of spectral analysis and by assessing neural source generators of P3a and P3b components; and (II) EEG interactions studies to explore functional integration across brain regions, including pair-wise couplings and exploring hierarchical organization of neural rhythms.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field

Multichannel Characterization of Brain Activity in Neurological Impairments

Hundreds of millions of people worldwide suffer from various neurological and psychiatric disorders. A better understanding of the underlying neurophysiology and mechanisms for these disorders can lead to improved diagnostic techniques and treatments. The objective of this dissertation is to create a novel characterization of multichannel EEG activity for selected neurological and psychiatric disorders based on available datasets. Specifically, this work provides spatial, spectral, and temporal characterizations of brain activity differences between patients/animal models and healthy controls, with focus on modern techniques that quantify cortical connectivity, which is widely believed to be abnormal in such disorders. Exploring the functional brain networks in these patients can provide a better understanding of the pathophysiology and brain network integrity of the respective disorders. This can allow for the assessment of neural mechanism deficits and possibly lead to developing a model for enhancement in the biology of neural interactions in these patients. This unique electrophysiological information may also contribute to the development of target drugs, novel treatments, and genetic studies. Moreover, the outcomes not only provide potential biomarkers for the diagnosis of respective disorders but also can serve as biofeedback for neurotherapy and also development of more sophisticated BCIs

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activ- ity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific con- tributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field

When expectations do not reflect reality: do event-related-potential amplitudes for self-generated sounds reflect auditory prediction errors?

The ability to anticipate upcoming situations avoids us from being overwhelmed by the vast number of stimuli we experience in our perceptual world. Predictions are vast and various, involving cognitive, motor, and sensory computations. Some specific predictions of particular interest for the current work help us distinguish between stimuli generated by ourselves from environmentally caused stimuli. Numerous studies show that differences in the amplitude of N1 and P2, two ERP components in the EEG signal, appear to reflect the distinction between these types of stimuli. Nevertheless, predictions are not always reliable, but sometimes they fail to represent the upcoming situation. An update of the cognitive models from which they arise is fundamental to be capable of not making the same error in the future. This holds possible through the formation of prediction errors; like the word itself implies, such signals bring the error back to the areas of interest to allow a revision of the cognitive model. This pilot study aims to analyze the ERP variations linked to prediction errors after self-generated actions when the expectations of the subject are unfulfilled.The ability to anticipate upcoming situations avoids us from being overwhelmed by the vast number of stimuli we experience in our perceptual world. Predictions are vast and various, involving cognitive, motor, and sensory computations. Some specific predictions of particular interest for the current work help us distinguish between stimuli generated by ourselves from environmentally caused stimuli. Numerous studies show that differences in the amplitude of N1 and P2, two ERP components in the EEG signal, appear to reflect the distinction between these types of stimuli. Nevertheless, predictions are not always reliable, but sometimes they fail to represent the upcoming situation. An update of the cognitive models from which they arise is fundamental to be capable of not making the same error in the future. This holds possible through the formation of prediction errors; like the word itself implies, such signals bring the error back to the areas of interest to allow a revision of the cognitive model. This pilot study aims to analyze the ERP variations linked to prediction errors after self-generated actions when the expectations of the subject are unfulfilled