Learning Optimal Biomarker-Guided Treatment Policy for Chronic Disorders

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in alpha and theta frequency bands have demonstrated some association with anti-depressant response, which is well-known to have low response rate. We aim to design an integrated pipeline that improves the response rate of major depressive disorder patients by developing an individualized treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. We first design an innovative automatic site-specific EEG preprocessing pipeline to extract features that possess stronger signals compared with raw data. We then estimate the conditional average treatment effect using causal forests, and use a doubly robust technique to improve the efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of EEG features as well as a significant average treatment effect, a result that cannot be obtained by conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind randomized controlled clinical trial, EMBARC

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups

Metodología para la selección automática de características de señales EEG utilizando algoritmos de aprendizaje de máquina aplicado al reconocimiento del procesamiento emocional en excombatientes

Los diferentes procesos de desmovilización y reincorporación a la vida en sociedad han conseguido que miles de excombatientes de diferentes grupos armados busquen retornar a la vida civil. Sin embargo, se ha reportado en la literatura que la experiencia de guerra causa en estas personas trastornos y desordenes psicológicos que les impiden completar su proceso de reintegración. Se ha encontrado en la literatura distintas alternativas para estudiar el comportamiento de personas que han participado en el conflicto armado, algunos de estos métodos abordan el problema desde la psicología, haciendo entrevistas y encuestas asistidas por expertos. En los últimos años estos estudios han sido apoyados cada vez más con técnicas de aprendizaje de máquina, haciendo análisis de registros electroencefalográficos (EEG), ya que el uso de los sensores para la adquisición de estas señales tiene un costo reducido y la prueba es no invasiva, lo cual facilita poner en práctica esta técnica. Además, los registros EEG tienen una muy buena resolución temporal (milisegundos), y mediante su análisis se ha mostrado una mejoría considerable en el rendimiento de la tarea de clasificación entre las clases (controles y excombatientes). La metodología desarrollada fue probada en dos bases de datos que evalúan el procesamiento emocional de controles y sujetos expuesto al conflicto. El primer conjunto de datos tiene como objetivo discriminar entre las clases utilizando una tarea de valencia contextual, y el segundo utiliza estímulos con imágenes anqueadas para distinguir entre sujetos que han recibido alta exposición y sujetos con baja exposición al conflicto armado colombiano. En esta tesis se plantea una metodología que utiliza dos formas de caracterización de registros EEG utilizando combinación de la representación de estas señales en tiempo, frecuencia y espacio. El primero de los métodos utiliza caracterización en tiempo-frecuencia empleando la transformada Wavelet en su forma discreta para descomponer las señales EEG. Después se extrajeron datos estadísticos sobre los coeficientes de detalle y aproximación, los cuales fueron utilizados como características. Por otro lado, se utilizó también información en frecuencia-espacio, haciendo análisis de conectividad funcional y aplicando la teoría de grafos a las conexiones encontradas en diferentes escalas de conectividad. Adicionalmente, se realizó un análisis de relevancia con tres métodos que permiten brindar mayor interpretabilidad a los resultados obtenidos y obtener una mayor tasa de clasificación al utilizar las características más relevantes. Los métodos utilizados son búsqueda exhaustiva, aprendizaje multi kernel (MKL), y selección de características con ANOVA. Finalmente, se realiza la clasificación de las características con una máquina de vectores de soporte, obteniendo el puntaje F1 como medida de evaluación. Los resultados sugieren que existe diferencia entre las clases de la tarea denominada como Flanker, consiguiendo hasta 94% de puntaje F1 en la tarea de clasificación. Para el caso de valencia contextual se tiene hasta un 85% en el puntaje F1 combinando la información espectral con MKL. En general, se obtuvo que el análisis por bandas de frecuencia obtiene a lo largo de las pruebas los resultados m as altos, aunque el análisis de relevancia con MKL es también consistente, y se observó que la banda en donde se dieron los mejores resultados fue en los rangos de frecuencia altos de B. Esto sugiere que los controles y pacientes expuestos al conflicto presentan una diferencia en los niveles de concentración y atención.The different demobilization process have bring thousands of colombian excombatants in searching for return to the civil life. However, it is reported that the war experience produces psychological disorders that prevent completing their reintegration process. The literature shows several alternatives to study the behavior of people with war experiences, some of these methods address the problem using psychology, i.e., making interviews by experts. In the last years, the studies have been helped by arti cial intelligence using electroencephalographic (EEG) signals, due to EEG is a non-invasive and low-cost study, which facilitates put into practice this technique. Also, EEG signals have an adequate time resolution (milliseconds), and with its analysis the classi cation task between excombatants and controls have improved. The developed methodology was evaluated in two di erent datasets, both assess the emotion processing in controls and subjects with high exposure to the armed con ict. The rst dataset aims to discriminate between classes using contextual valence. The second dataset uses stimuli with anker images to distinguish between subjects that have been highly exposed to the con ict and subjects with low exposure. In this thesis it is developed a methodology that uses two ways of EEG characterization making combinations of the representations of these signals in di erent domains as time, frequency, and space. The rst approach uses features in time-frequency domain employing decomposition with multiple discrete wavelets, then, statistics features are extracted from the decomposition coe cients. On the other hand, it is used frequency-space information making a functional connectivity analysis and applying graph theory over the connections found on the connectivity. Also, it was made a feature relevance analysis through three methods that give better interpretability of the data. The relevance analysis methods used are: Exhaustive search with frequency bands, weights assignment with MKL, and ANOVA feature selection technique. Finally, the classi cation was made using SVM, and evaluated with the F1 score metric. Results suggest that there is a di erence between the classes in the anker dataset, reaching a 94% of F1 score. For the contextual valence dataset, the F1 score achieves an 85% by combining the spectral information with MKL. In general the exhaustive search method showed the best scores among several tests, nevertheless the relevance analysis with MKL is the most regular method. Finally, it is shown that higher frequencies in the beta band are the most relevant ones, suggesting that the controls and subjects present di erences in the concentration and attention level

Automated rejection and repair of bad trials in MEG/EEG

peer reviewed© 2016 IEEE. We present an automated solution for detecting bad trials in magneto-/electroencephalography (M/EEG). Bad trials are commonly identified using peak-to-peak rejection thresholds that are set manually. This work proposes a solution to determine them automatically using cross-validation. We show that automatically selected rejection thresholds perform at par with manual thresholds, which can save hours of visual data inspection. We then use this automated approach to learn a sensor-specific rejection threshold. Finally, we use this approach to remove trials with finer precision and/or partially repair them using interpolation.We illustrate the performance on three public datasets. The method clearly performs better than a competitive benchmark on a 19-subject Faces dataset

The neuro-cognitive representation of word meaning resolved in space and time.

One of the core human abilities is that of interpreting symbols. Prompted with a perceptual stimulus devoid of any intrinsic meaning, such as a written word, our brain can access a complex multidimensional representation, called semantic representation, which corresponds to its meaning. Notwithstanding decades of neuropsychological and neuroimaging work on the cognitive and neural substrate of semantic representations, many questions are left unanswered. The research in this dissertation attempts to unravel one of them: are the neural substrates of different components of concrete word meaning dissociated? In the first part, I review the different theoretical positions and empirical findings on the cognitive and neural correlates of semantic representations. I highlight how recent methodological advances, namely the introduction of multivariate methods for the analysis of distributed patterns of brain activity, broaden the set of hypotheses that can be empirically tested. In particular, they allow the exploration of the representational geometries of different brain areas, which is instrumental to the understanding of where and when the various dimensions of the semantic space are activated in the brain. Crucially, I propose an operational distinction between motor-perceptual dimensions (i.e., those attributes of the objects referred to by the words that are perceived through the senses) and conceptual ones (i.e., the information that is built via a complex integration of multiple perceptual features). In the second part, I present the results of the studies I conducted in order to investigate the automaticity of retrieval, topographical organization, and temporal dynamics of motor-perceptual and conceptual dimensions of word meaning. First, I show how the representational spaces retrieved with different behavioral and corpora-based methods (i.e., Semantic Distance Judgment, Semantic Feature Listing, WordNet) appear to be highly correlated and overall consistent within and across subjects. Second, I present the results of four priming experiments suggesting that perceptual dimensions of word meaning (such as implied real world size and sound) are recovered in an automatic but task-dependent way during reading. Third, thanks to a functional magnetic resonance imaging experiment, I show a representational shift along the ventral visual path: from perceptual features, preferentially encoded in primary visual areas, to conceptual ones, preferentially encoded in mid and anterior temporal areas. This result indicates that complementary dimensions of the semantic space are encoded in a distributed yet partially dissociated way across the cortex. Fourth, by means of a study conducted with magnetoencephalography, I present evidence of an early (around 200 ms after stimulus onset) simultaneous access to both motor-perceptual and conceptual dimensions of the semantic space thanks to different aspects of the signal: inter-trial phase coherence appears to be key for the encoding of perceptual while spectral power changes appear to support encoding of conceptual dimensions. These observations suggest that the neural substrates of different components of symbol meaning can be dissociated in terms of localization and of the feature of the signal encoding them, while sharing a similar temporal evolution