A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

Modelling beyond Regression Functions: an Application of Multimodal Regression to Speed-Flow Data

An enormous amount of publications deals with smoothing in the sense of nonparametric regression. However, nearly all of the literature treats the case where predictors and response are related in the form of a function y=m(x)+noise. In many situations this simple functional model does not capture adequately the essential relation between predictor and response. We show by means of speed-flow diagrams, that a more general setting may be required, allowing for multifunctions instead of only functions. It turns out that in this case the conditional modes are more appropriate for the estimation of the underlying relation than the commonly used mean or the median. Estimation is achieved using a conditional mean-shift procedure, which is adapted to the present situation

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Affordances and Safe Design of Assistance Wearable Virtual Environment of Gesture

Safety and reliability are the main issues for designing assistance wearable virtual environment of technical gesture in aerospace, or health application domains. That needs the integration in the same isomorphic engineering framework of human requirements, systems requirements and the rationale of their relation to the natural and artifactual environment.To explore coupling integration and design functional organization of support technical gesture systems, firstly ecological psychologyprovides usa heuristicconcept: the affordance. On the other hand mathematical theory of integrative physiology provides us scientific concepts: the stabilizing auto-association principle and functional interaction.After demonstrating the epistemological consistence of these concepts, we define an isomorphic framework to describe and model human systems integration dedicated to human in-the-loop system engineering.We present an experimental approach of safe design of assistance wearable virtual environment of gesture based in laboratory and parabolic flights. On the results, we discuss the relevance of our conceptual approach and the applications to future assistance of gesture wearable systems engineering

Neural correlates of the processing of co-speech gestures

In communicative situations, speech is often accompanied by gestures. For example, speakers tend to illustrate certain contents of speech by means of iconic gestures which are hand movements that bear a formal relationship to the contents of speech. The meaning of an iconic gesture is determined both by its form as well as the speech context in which it is performed. Thus, gesture and speech interact in comprehension. Using fMRI, the present study investigated what brain areas are involved in this interaction process. Participants watched videos in which sentences containing an ambiguous word (e.g. She touched the mouse) were accompanied by either a meaningless grooming movement, a gesture supporting the more frequent dominant meaning (e.g. animal) or a gesture supporting the less frequent subordinate meaning (e.g. computer device). We hypothesized that brain areas involved in the interaction of gesture and speech would show greater activation to gesture-supported sentences as compared to sentences accompanied by a meaningless grooming movement. The main results are that when contrasted with grooming, both types of gestures (dominant and subordinate) activated an array of brain regions consisting of the left posterior superior temporal sulcus (STS), the inferior parietal lobule bilaterally and the ventral precentral sulcus bilaterally. Given the crucial role of the STS in audiovisual integration processes, this activation might reflect the interaction between the meaning of gesture and the ambiguous sentence. The activations in inferior frontal and inferior parietal regions may reflect a mechanism of determining the goal of co-speech hand movements through an observation-execution matching process