Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function

Automatic Activation of Phonological Templates for Native but Not Nonnative Phonemes: An Investigation of the Temporal Dynamics of Mu Activation

Models of speech perception suggest a dorsal stream connecting the temporal and inferior parietal lobe with the inferior frontal gyrus. This stream is thought to involve an auditory-motor loop that translates acoustic information into motor/articulatory commands and is further influenced by decision making processes that involve maintenance of working memory or attention. Parsing out dorsal stream’s speech specific mechanisms from memory related ones in speech perception poses a complex problem. Here I argue that these processes may be disentangled from the viewpoint of the temporal dynamics of sensorimotor neural activation around a speech perception related event. Methods: Alpha (~10Hz) and beta (~20Hz) spectral components of the mu () rhythm, localized to sensorimotor regions, have been shown to index somatosensory and motor activity, respectively. In the present work, event related spectral perturbations (ERSP) of the EEG -rhythm were analyzed, while manipulating two factors: active/passive listening, and perception of native/nonnative phonemes. Active and passive speech perception tasks were used as indexes of memory load employed, while native and. nonnative perception were used as indexes of automatic top-down coding for sensory analysis. Results: Statistically significant differences were found in the oscillatory patterns of components between active and passive speech perception conditions with greater alpha and beta event related desynchronization (ERD) after stimuli offset in active speech perception. When compared to listening to noise, passive speech perception presented significantly (pFDR Conclusion: These findings suggest that neural processes within the dorsal auditory stream are functionally and automatically involved in speech perception mechanisms. While its early activity (shortly after stimuli onset) seems to be importantly involved with the instantiation of predictive motor/articulatory internal models that help constraining speech discrimination, its later activity (post-stimulus offset) seems essential in the maintenance of working memory processes

The oscillatory fingerprints of self-prioritization : Novel markers in spectral EEG for self-relevant processing

Funding Information: The research reported in this article was supported by a Grant from the Deutsche Forschungsgemeinschaft (SCHA 2253/1–1). Open Access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis

This tutorial paper introduces a common framework for the evaluation of widely used frequency-domain measures of coupling (coherence, partial coherence) and causality (directed coherence, partial directed coherence) from the parametric representation of linear multivariate (MV) processes. After providing a comprehensive time-domain definition of the various forms of connectivity observed in MV processes, we particularize them to MV autoregressive (MVAR) processes and derive the corresponding frequency-domain measures. Then, we discuss the theoretical interpretation of these MVAR-based connectivity measures, showing that each of them reflects a specific time-domain connectivity definition and how this results in the description of peculiar aspects of the information transfer in MV processes. Furthermore, issues related to the practical utilization of these measures on real-time series are pointed out, including MVAR model estimation and significance assessment. Finally, limitations and pitfalls arising from model mis-specification are discussed, indicating possible solutions and providing practical recommendations for a safe computation of the connectivity measures. An example of estimation of the presented measures from multiple EEG signals recorded during a combined visuomotor task is also reported, showing how evaluation of coupling and causality in the frequency domain may help describing specific neurophysiological mechanisms

Connectivity Analysis of Brain States and Applications in Brain-Computer Interfaces

Human brain is organized by a large number of functionally correlated but spatially distributed cortical neurons. Cognitive processes are usually associated with dynamic interactions among multiple brain regions. Therefore, the understanding of brain functions requires the inves- tigation of the brain interaction patterns. This thesis contains two main aspects. The first aspect focuses on the neural basis for cognitive processes through the use of brain connectivity analysis. The second part targets on assessing brain connectivity patterns in realistic scenarios, e.g., in-car BCI and stroke patients. In the first part, we explored the neural correlates of error-related brain activity. We recorded scalp electroencephalogram (EEG) from 15 healthy subjects while monitoring the movement of a cursor on a computer screen, yielding particular brain connectivity patterns after monitoring external errors. This supports the presence of common role of medial frontal cortex in coordinating cross-regional activity during brain error processes, independent of their causes, either self-generated or external events. This part also included the investigation of the connectivity during left/right hand motor imagery, including 9 healthy subjects, which demonstrated particular intrahemispheric and interhemispheric information flows in two motor imagery tasks, i.e., the ÎŒ rhythm is highly modulated in intrahemispheric, whereas β and γ are modulated in interhemispheric interactions. This part also explored the neural correlates of reaction time during driving. An experiment with 15 healthy subjects in car simulator was designed, in which they needed to perform lane change to avoid collision with obstacles. Significant neural modulations were found in ERP (event-related potential), PSD (power spectral density), and frontoparietal network, which seems to reflect the underlying information transfer from sensory representation in the parietal cortex to behavioral adjusting in the frontal cortex. In the second part, we first explored the feasibility of using BCI as driving assistant system, in which visual stimuli were presented to evoke error/correct related potentials, and were classified to infer driverâs preferred turning direction. The system was validated in a car simulator with 22 subjects, and 7 joined online tests. The system was also tested in real car, yielding similar brain patterns and comparable classification accuracy. The second part also carried out the brain connectivity analysis in stroke patients.We performed exploratory study to correlate the recovery effects of BCI therapy, through the quantification of connectivity between healthy and lesioned hemispheres. The results indicate the benefits of BCI therapy for stroke patients, i.e., brain connectivity are more similar as healthy patterns, increased (decreased) flow from the damaged (undamaged) to the undamaged (damaged) cortex. Briefly, this thesis presents exploratory studies of brain connectivity analysis, investigating the neural basis of cognitive processes, and its contributions in the decoding phase. In particular, such analysis is not limited to laboratory researches, but also extended to clinical trials and driving scenarios, further supporting the findings observed in the ideal condition

A subject-specific neurofeedback approach for cognitive enhancement

La técnica de neurofeedback (NF) permite el aprendizaje de la auto-regulación de la actividad cerebral, por la cual los usuarios pueden aprender a modificar (hasta un cierto grado) patrones de la actividad cerebral. Un punto clave del NF en la práctica es la técnica de registro de la actividad cerebral, donde el electroencefalograma (EEG) es la más usada debido a que es no invasiva, portátil y tiene una buena resolución temporal. La investigación en neurociencia ha reportado en repetidas ocasiones la relación de determinadas funciones cognitivas y desórdenes psiquiátricos con las oscilaciones del EEG. Por lo tanto, no es sorprendente que la regulación de estas oscilaciones conlleve efectos en comportamiento. Por ejemplo, muchos estudios han aplicado esta técnica de NF para aumentar funciones cognitivas como memoria de trabajo, atención y habilidades visuoespaciales, usualmente evaluadas en usuarios sanos. Además se ha aplicado NF para el tratamiento de desórdenes psiquiátricos tales como el trastorno por déficit de atención con hiperactividad (TDAH), depresión, epilepsia y tinitus, entre otros. El EEG es una señal no estacionaria que presenta una variabilidad inherente entre usuarios en los correlatos de procesos mentales. Sin embargo, la gran mayoría de técnicas de NF desarrolladas hasta el momento son genéricas en el sentido de que no se adaptan a los patrones individuales de EEG de cada usuario. En este sentido ha habido una tendencia en los últimos años hacia el uso de técnicas de NF específicas por medio de la adaptación de algunos métodos involucrados en el procedimiento. Esta tesis aborda el diseño de una técnica de NF dentro de un framework unificado, y muestra su viabilidad por medio de la implementación de tres estudios de NF consistentes en el incremento de la actividad en alpha superior para mejora cognitiva, evaluada en usuarios sanos, pacientes con depresión mayor y niños con TDAH. Una disciplina que viene a la mente cuando pensamos en cómo individualizar la técnica de NF son las interfaces cerebro-computador (BCIs). Las BCIs es una tecnología reciente cuyo objetivo es abrir un canal de comunicación entre un humano y un dispositivo usando únicamente la actividad cerebral para mejor la calidad de vida de personas con graves deficiencias motoras. Un punto clave de las BCIs es que los métodos de procesamiento de la señal se adaptan a cada usuario y momento de utilización de la tecnología. Esto se logra usualmente por medio de una fase de calibración ejecutada antes de la operación en línea. En esta fase de calibración los usuarios realizan tareas mentales que permiten medir algunos correlatos de EEG que son usados para calcular filtros individualizados, por ejemplo en términos de filtrado de artefactos y detección de tareas mentales. Después estos filtros se aplican en línea al EEG para decodificar las tareas mentales, accionando en consecuencia el dispositivo. Tomando el framework de BCI como referencia, proponemos los siguientes métodos individualizados: (1) un método de filtrado de artefactos en tiempo real (usando separación ciega de fuentes) para eliminar los artefactos de los patrones cerebrales de interés; (2) un método novedoso para la individualización de los patrones cerebrales de acuerdo a la combinación de registros de EEG en dos condiciones (estado de reposo y tarea activa); (3) un método para calcular el nivel de trabajo de los patrones cerebrales (baseline) por sujeto y sesión; y (4) una variedad de métodos y métricas para evaluar los efectos del NF en los patrones cerebrales (post-análisis). Para evaluar la viabilidad y validez experimental de esta técnica de NF, llevamos a cabo una implementación de un protocolo de NF basado en el incremento de la actividad en alpha superior para mejora cognitiva, evaluada en tres estudios distintos de NF involucrando usuarios sanos, pacientes con depresión y niños con TDAH. Estos estudios investigaron si individuos sanos, con depresión y ADHD son capaces de incrementar la potencia en alpha superior por medio de NF y, si es así, si estos efectos están relacionados con efectos en comportamiento (rendimiento cognitivo o escalas clínicas). 1. El primer estudio investigó los efectos de una única sesión de NF en usuarios sanos (N = 19) en un diseño experimental con falso feedback. Este estudio mostró un incremento en la potencia en alpha superior en la tarea activa (inmediatamente después del NF) así como un incremento en potencia en alpha superior durante la tarea de rotación mental (intervalo preestímulo), únicamente para el grupo experimental. Ambos grupos mejoraron en rendimiento cognitivo, con una mejora superior para el grupo experimental. Sin embargo una única sesión parece insuficiente para producir diferencias significativas entre grupos. 2. El segundo estudio investigó los efectos de ocho sesiones de NF en pacientes con depresión (N = 60) en un estudio controlado. Este estudio mostró un incremento en la potencia en alpha superior en la tarea activa (pre-post estudio) para el grupo experimental. Estos efectos no estuvieron restringidos espacialmente o espectralmente al parámetro de NF. Se encontró un incremento de actividad a nivel de las fuentes cerebrales en alpha para el grupo experimental, localizado en el giro cingulado anterior (sgACC, BA 25). El grupo experimental mostró un incremento en rendimiento así como un incremento en velocidad de procesamiento medido por un test de memoria de trabajo después del NF, sugiriendo por tanto que los síntomas cognitivos de pacientes con depresión pueden aliviarse por medio de este procedimiento. 3. El tercer estudio investigó los efectos de 18 sesiones de NF en niños con TDAH (N = 20) en un estudio preliminar no controlado. Este estudio mostró un incremento en la potencia (relativa y absoluta) en alpha superior en la tarea activa (pre-post estudio). Mientras que los cambios pre-post estudio estuvieron restringidos mayormente a la banda alpha superior, los efectos dentro de la sesión mostraron un decremento en potencia absoluta en las bajas frecuencias (se debe notar que los niños con TDAH usualmente tienen un exceso de actividad en bajas frecuencias). Los padres indicaron una mejora clínica en los niños con respecto a inatención e hiperactividad/impulsividad, y los tests neuropsicológicos mostraron una mejora en memoria de trabajo. En resumen, estos resultados muestran que la técnica de NF se adaptó a la gran variabilidad de los patrones cerebrales entre sujetos y sesiones. Además, estas tres poblaciones fueron capaces de auto-regular los patrones cerebrales con una consecuente mejora tanto en rendimiento cognitivo y como en escalas clínicas (en el caso de niños con TDAH). Aunque la principal contribución de esta tesis está en los métodos y en la implementación de una técnica individualizada de NF, los estudios de NF aquí presentados son novedosos en sí mismos y los resultados que se extraen de ellos constituyen una contribución añadida de esta tesis.Neurofeedback (NF) promotes the learning of the self-regulation of brain activity, where subjects can learn to shape (to a certain degree) some patterns of brain activity. A key practical point of NF is the recording technique of brain activity, where the electroencephalogram (EEG) is the most widely used one as it is non-invasive, portable and presents a good temporal resolution. Neuroscience research has repeatedly reported the relation of cognitive functions and some psychiatric disorders to EEG oscillations. Thus, it is not surprising that the regulation of EEG oscillations yields behavioral effects. For instance, a large body of research has applied NF for the enhancement of cognitive functions such as working memory, attention and visuospatial abilities, usually applied to healthy subjects. NF has been also applied for the treatment of psychiatric disorders such as attention-deficit/hyperactive disorder (ADHD), depression, epilepsy and tinnitus, among others. EEG is a non-stationary signal that presents an inherent variability among subjects in the EEG correlates of brain processes. However, the large majority of NF procedures developed to date are subject-generic in the sense that they are not adapted to the individual EEG patterns of each subject. In this direction, there has been a trend in recent years towards the use of subject-specific NF procedures by adapting some methods involved in that procedure. This thesis addresses the design of a subject-specific NF approach in a unified framework, and shows its feasibility by implementing three different NF studies of upper alpha up-regulation for cognitive enhancement in healthy subjects, patients with major depressive disorder and children diagnosed with ADHD. One discipline that comes to mind when thinking about how to individualize EEG-based NF procedures is the brain-computer interfaces (BCIs). BCIs is a recent technology whose objective is to open a communication channel between a human and a device using only brain activity to improve the quality of life of people with severe motor disability. A key point of BCIs is that the signal processing methods are adapted for each subject and time of use of the technology. This is commonly achieved by a calibration phase before the online operation. In this calibration phase, the subjects perform metal tasks that allow to measure some EEG correlates that are used to compute subject-specific filters, for example in terms of filtering the EEG artifacts and detecting the mental tasks. These filters are then applied during the online operation phase to the ongoing EEG to decode the mental tasks, actuating the devices accordingly. Taking the BCI framework as a reference, we propose the following individualized methods: (1) a real-time artifact filtering method (using blind source separation) to remove the artifacts from the brain patterns of interest; (2) a novel method for the individualization of the brain patterns according to the combination of EEG recordings in two conditions (resting state and task-related activity); (3) a method for computing the baseline working level of the brain patterns per subject and session; and (4) a variety of methods and metrics to assess the effects of NF on the brain patterns (post-analysis). In order to demonstrate the feasibility and experimental validity of this subject-specific NF approach, we carried out an implementation of a NF protocol of upper alpha up-regulation for cognitive enhancement, evaluated in three different NF studies involving healthy subjects, depressed patients and ADHD children. These studies investigated whether healthy, depressed and ADHD individuals could learn to increase the individual upper alpha power by means of NF, and whether these effects were related to behavioral effects on either cognition or clinical outcome. 1. The first study investigated the effects of a single NF session on healthy participants (N = 19) following a sham-controlled experimental design. This study showed increased upper alpha power in task-related activity (immediately after training), as well as increased pre-stimulus upper alpha power during the execution of a mental rotation task, apparen only for the experimental group. Both groups improved cognitive performance, with a more prominent improvement for the experimental group. However a single session seems to be insufficient to yield significant differences between groups. 2. The second study investigated the effects of eight NF sessions on depressed patients (N = 60) in a controlled study. This study showed increased upper alpha power in task-related activity (pre-post study) for the experimental group, not spatially or spectrally restricted to the trained parameter. A current density increase appeared at brain source level in alpha for the experimental group, localized in the subgenual anterior cingulate cortex (sgACC, BA 25). The experimental group showed increased performance as well as improved processing speed in a working memory test after the training, thus suggesting that the cognitive symptoms of depressed patients could be alleviated by this typeof procedure. 3. The third study investigated the effects of 18 NF sessions on ADHD children (N = 20) in a preliminary uncontrolled study. This study showed increased relative and absolute upper alpha power in task-related activity (pre-post study). While the pre-post study effects were mainly restricted to upper alpha, within-session analysis showed an absolute power decrease in slow-frequency oscillations (note that ADHD children commonly show an excess of slow-frequency activity). Parents rated a clinical improvement in children regarding inattention and hyperactivity/impulsivity, and neurophysiological tests showed an improvement in working memory. In summary, these results show that the NF technique was able to accommodate the large variability of the brain patterns among subjects and over sessions. In addition, these three populations were able to self-regulate the targeted brain patterns with a consequent improvement in cognitive performance and clinical outcome (in the case of ADHD children). Although the main contribution of the thesis is on the methods and on the implementation of the subject-specific NF procedure, the NF studies herein presented are novel and the results extracted from them constitute an added contribution of this thesis

Does extensive motor learning trigger local sleep?

After prolonged learning we all have experienced a reduction of alertness, resulting in errors that we would normally not make. Despite this being a common situation in everyday life, the reasons for this phenomenon are unclear. A possible explanation is that the regions of the brain which are involved in the learning, go off-line trying to partially recover. This event is defined as local sleep and it has been detected in animals and sleep-deprived humans performing learning tasks. Local sleep is a sleep-like electrophysiological activity occurring locally, while the rest of the brain is fully awake, and producing performance deterioration. However, since all the studies included both lack of sleep and learning, it is uncertain whether such phenomenon is related to sleep deprivation or if it is the consequence of prolonged learning. Further, local sleep has not been related to electrophysiological changes occurring during the task. This thesis aimed to assess, for the first time in well rested subjects, whether local sleep and performance decline occur because of prolonged learning. Specifically, the goal was to discriminate between sustained practice and learning, as to determine whether learning is required to cause local sleep. Also, a 90-minute nap was evaluated to establish whether sleep is necessary to counterbalance neuronal fatigue and performance decrease. The starting hypothesis was that local sleep is a plasticity-related phenomenon affecting performance and requiring learning to be triggered. Consequently, sleep would be a prerequisite to counterbalance performance and electrophysiological changes. High-Density EEG and behavioral data of 78 healthy young subjects were collected during and after two learning tasks performed for three hours: a visual sequence learning task, and a visuo-motor rotation task, randomly selected. Afterward, subjects were divided in two groups: those who slept for one hour and a half and those who remained awake and quietly rested for the same amount of time before being tested for electrophysiological and behavioral changes. Moreover, to discriminate between the effects of prolonged learning and practice, 11 additional subjects performed a control condition consisting in planar upper limb reaching movements instead of the above-mentioned learning tasks. In detail, the power spectrum of the EEG activity during the task and at rest with eyes opened was divided into five ranges to determine frequency changes of the EEG activity: delta 1 to 4 Hz; theta 4 to 8 Hz; alpha 8 to 13 Hz, beta 13 to 25 Hz, gamma 25 to 55 Hz. Additionally, movement-related beta activity of 35 young subjects was analyzed to find a relationship between task related oscillations and performance indices, as the modulatory activity during practice may reflect plasticity-related phenomena that can describe the occurrence of local sleep. Finally, 13 young subjects were compared to a dataset of 13 older participants who performed planar upper limb reaching movements to determine whether beta oscillations were affected by age. Specifically, beta activity was assessed during reaching movements in different brain regions, in terms of topography, magnitude, and peak frequency. Results demonstrated that sustained learning produced electrophysiological changes both at rest and during the task. In fact, resting state was characterized by a progressive slowing of the EEG activity over areas overlapping with those engaged during the task. Precisely, we detected task-related activity mainly in the high-frequency ranges (gamma and beta right temporo-parietal activity for the visual sequence learning task; alpha and beta activity over a fontal and left parietal areas for the visuo-motor rotation); the same areas were characterized by a progressive increase of the low frequency EEG activity at rest ranging from alpha, beta after one hour of practice, to theta after three one-hour blocks. The control task did not trigger such EEG slowing, as reaching movements without learning did only left an alpha, beta trace in the resting state over a cluster reflecting the motor area contralateral to the movement. Further, continuous learning triggered performance deteriorations only in tests sharing the same neural substrate of the previously performed task. In other words, the visuo-motor learning task only affected performance in a motor test consisting in random reaching movements; conversely, visual sequence learning altered performance on a visual working memory test, but did not influence reaching movements. Also, the control condition did not affect performance in any of the two exercises. Performance decline, learning ability and local sleep were partially renormalized by a 90-minute nap but not by an equivalent period of wake. As such, the global EEG activity, computed as the mean power of all the electrodes, was not affected by either 90 minutes of sleep or quiet wake. However, the regions characterized by low frequency at rest benefited from the sleep period, as the low frequencies content partially decreased after the nap but not after quiet wake. Task related beta activity during motor practice presented similar magnitude and timing patterns in different brain areas, with a progressive increase with practice, in both young and older subjects, despite the older subjects performing slower, less accurate movements. Intriguingly, the motor areas showed a post movement beta synchronization having a peak between 15 and 18 Hz, as opposed to a frontal area that has it between 23 and 29 Hz. Finally, results did not reveal any direct relationship between EEG beta oscillations and performance indices. Altogether, these results indicate that local sleep and performance decrease can be triggered by prolonged learning in well rested subjects; furthermore, some amount of sleep can partially renormalize learning ability, EEG activity and performance. Also, differences in the brainnoscillations during motor activity can express separate processes underlying motor planning, execution and skills acquisition. The present study adds some important knowledge in the field of local sleep; in fact, it suggests that such phenomenon is triggered by sustained learning rather than sleep deprivation, thus being a plasticity-related phenomenon. Finally, the role of sleep on counterbalancing local sleep has been proved, despite additional studies are required to establish whether a full night of sleep rather than a specific amount of time is needed to fully restore learning ability and electrophysiological activity. In conclusion, the present findings are of importance in all the fields where sustained learning is required, such as rehabilitative programs, sport and military trainings, and must be taken into account when plasticity plays a fundamental role in the acquisition of new skills