3 research outputs found

    Using machine learning to resolve the neural basis of alcohol dependence

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    Alcohol dependence is a psychiatric disorder with a lifetime prevalence of over 10% and a leading cause of morbidity and premature death. A better understanding of the neural mechanisms underlying alcohol dependence to improve prevention, diagnosis and treatment is thus of great societal interest. Recent advancements in the analysis of neuroimaging data based on machine learning have opened new paths to a better quantitative understanding of the disorder. The present habilitation reviews both works with a focus on improving machine learning methodology and empirical works in which machine learning was applied to investigate the neural basis of alcohol dependence. The methodological works advanced several aspects of machine learning in neuroimaging. In particular, they introduced i) a novel classifier (weighted robust distance – WeiRD), which operates parameter-free, computationally efficient and enables a transparent inspection of feature importances, ii) a method to preprocess neuroimaging data based on multivariate noise normalization, which yielded a substantial improvement in classification performance compared to previous the state-of-the-art, and iii) a novel method to reintroduce meaningful graded information into discretized classification accuracies by utilizing classifier decision values. Drawing on a large neuroimaging dataset of alcohol-dependent patients and controls from the LeAD-study (www.lead-studie.de; clinical trial number: NCT01679145), machine learning methods were applied in empirical works to investigate structural and functional alterations in alcohol dependence. Structural damage associated with alcohol dependence were investigated from two conceptually different angles. A first study was aimed at providing the first quantitative evidence for a long-standing hypothesis about the damaging effects of alcohol – the premature aging hypothesis. To this end, a machine learning model was trained on the relationship between grey-matter pattern information and chronological age in a healthy control group and then applied to the sample of alcohol-dependent patients. The predicted ‘brain age’ of patients was found to be was several years higher than their chronological age, thus not only providing quantitative evidence for brain aging in alcohol dependence, but also showing that these aging effects are indeed substantial in relation to the human lifespan. The second study used machine learning to quantify the predictive accuracy of grey-matter pattern information for the diagnosis and a severity measure (lifetime consumption) of alcohol dependence. On average, machine learning models correctly predicted the diagnosis in three of four subjects and accurately estimated the amount of lifetime alcohol consumption. Closer inspection of the prediction model indicated an important role of dorsal anterior cingulate cortex. Comparison with an experienced radiologist, who, like the classifier, was provided with the structural brain scans of the subjects, demonstrated superior performance of computer-based classification and in addition a more effective consideration of demographic information (age and gender). Finally, a third study used functional magnetic resonance imaging to investigate a specific hypothesis about reduced goal-directed learning in alcohol dependence as well as its relation to relapse after detoxification. Computational modelling in combination with machine learning revealed that the interaction of model-based learning and high alcohol expectancies was predictive of diagnosis (patients versus controls) and treatment outcome (abstainers versus relapsers). This finding was paralleled by a signature of model-based learning in medial prefrontal cortex, which was reduced in patients relative to controls and in relapsers relative to abstainers. In sum, the works presented in this habilitation advance machine learning methods for neuroimaging and show that these methods yield novel insights into the neural basis of alcohol dependence. An emerging theme across the three empirical studies on alcohol dependence is the disturbance of executive frontal brain structure and function, supporting a top-down rather than bottom-up view for the aetiology of alcohol dependence

    A machine learning approach to taking EEG-based brain-computer interfaces out of the lab

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    Despite being a subject of study for almost three decades, non-invasive brain- computer interfaces (BCIs) are still trapped in the laboratory. In order to move into more common use, it is necessary to have systems that can be reliably used over time with a minimum of retraining. My research focuses on machine learning methods to minimize necessary retraining, as well as a data science approach to validate processing pipelines more robustly. Via a probabilistic transfer learning method that scales well to large amounts of data in high dimensions it is possible to reduce the amount of calibration data needed for optimal performance. However, a good model still requires reliable features that are resistant to recording artifacts. To this end we have also investigated a novel feature of the electroencephalogram which is predictive of multiple types of brain-related activity. As cognitive neuroscience literature suggests, shifts in the peak frequency of a neural oscillation – hereafter referred to as frequency modulation – can be predictive of activity in standard BCI tasks, which we validate for the first time in multiple paradigms. Finally, in order to test the robustness of our techniques, we have built a codebase for reliable comparison of pipelines across over fifteen open access EEG datasets

    Bayesian joint detection-estimation in functional MRI with automatic parcellation and functional constraints

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    Brain parcellation into a number of hemodynamically homogeneous regions (parcels) is a challenging issue in fMRI analyses. An automatic inference for the parcels from the fMRI data was proposed in the framework of the joint parcellation detection estimation (JPDE) model. However, this model still requires appropriate prior information about the number of parcels and their shapes provided through an initial parcellation, which is a challenging task since it generally depends on the subject. In this thesis, we present novel approaches for hemodynamic brain parcellation. These approaches are motivated by the fact that the hemodynamic response function varies across brain regions and sessions within subjects, and even among subjects and groups. The proposed approaches belong to one of two main categories, the subject-level and group-level fMRI data analysis models. For the subjectlevel fMRI data analysis, we propose three models to automatically estimate the optimum number of parcels and their shapes directly from fMRI data. The first one is formulated as a model selection procedure added to the framework of the classical JPDE model in which we compute the free energy for the candidate models, each with different number of parcels, and then select the one that maximizes this energy. To overcome the computational intensity associated with the first approach, we propose a second method which relies on a Bayesian non-parametric model where a combination of a Dirichlet process mixture model and a hidden Markov random field is used to allow for unlimited number of parcels and then estimate the optimal one. Finally to avoid the computational complexity associated with the estimation of the interaction parameter of the Markov field in the second approach, we make use of a well known clustering algorithm (the mean shift) and embed it in the framework of the JPDE model to automatically infer the number of parcels by estimating the modes of the underlying multivariate distribution. All the proposed subject-level approaches are validated using synthetic and real data. The obtained results are consistent across approaches in terms of the detection of the elicited activity. Moreover, the second and the third approaches manage to discriminate the hemodynamic response function profiles according to different criteria such as the full width at half maximum and the time to peak. Regarding the group-level fMRI analysis, we propose two new models that are able to estimate group-level parcellation and hemodynamic response function profiles. The JPDE model is extended to allow for this group-level estimation by considering data coming from all the subjects resulting in a multisubject joint parcellation detection estimation model. However, in real data experiment, it is noticed that the smoothness of the estimated HRFs is sensitive to one of the hyperparameters. Hence, we resort to the second model that performs inter and intra subject analysis providing estimation at both the single and group-levels. A thorough comparison is conducted between the two models at the group-level where the results are coherent. At the subject-level, a comparison is conducted between the proposed inter and intra subject analysis model and the JPDE one. This comparison indicates that the HRF estimates using our proposed model are more accurate as they are closer to the canonical HRF shape in the right motor cortex. Finally, the estimation of the unknown variables, the parameters and the hyperparameters in all of the proposed approaches is addressed from a Bayesian point of view using a variational expectation maximization strategy
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