6,496 research outputs found
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
Interictal Network Dynamics in Paediatric Epilepsy Surgery
Epilepsy is an archetypal brain network disorder. Despite two decades of research
elucidating network mechanisms of disease and correlating these with outcomes, the clinical
management of children with epilepsy does not readily integrate network concepts. For
example, network measures are not used in presurgical evaluation to guide decision making
or surgical management plans.
The aim of this thesis was to investigate novel network frameworks from the perspective of
a clinician, with the explicit aim of finding measures that may be clinically useful and
translatable to directly benefit patient care. We examined networks at three different scales,
namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and
micro (single unit networks) scales, consistently finding network abnormalities in children
being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical
translation, using frameworks such as IDEAL to robustly assess the impact of these new
technologies on management and outcomes.
The thesis sets up a platform from which promising computational technology, that utilises
brain network analyses, can be readily translated to benefit patient care
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital
healthcare services to clients through remote sensing, continuous monitoring,
and data analytics. Smart eHealth applications sense input data from multiple
modalities, transmit the data to edge and/or cloud nodes, and process the data
with compute intensive machine learning (ML) algorithms. Run-time variations
with continuous stream of noisy input data, unreliable network connection,
computational requirements of ML algorithms, and choice of compute placement
among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth
applications. In this chapter, we present edge-centric techniques for optimized
compute placement, exploration of accuracy-performance trade-offs, and
cross-layered sense-compute co-optimization for ML-driven eHealth applications.
We demonstrate the practical use cases of smart eHealth applications in
everyday settings, through a sensor-edge-cloud framework for an objective pain
assessment case study
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have
paved the way for unprecedented opportunities for understanding the complexity
of the brain and its emulation by computational systems. Cutting-edge
advancements in neuroscience research have revealed the intricate relationship
between brain structure and function, while the success of artificial neural
networks highlights the importance of network architecture. Now is the time to
bring them together to better unravel how intelligence emerges from the brain's
multiscale repositories. In this review, we propose the Digital Twin Brain
(DTB) as a transformative platform that bridges the gap between biological and
artificial intelligence. It consists of three core elements: the brain
structure that is fundamental to the twinning process, bottom-layer models to
generate brain functions, and its wide spectrum of applications. Crucially,
brain atlases provide a vital constraint, preserving the brain's network
organization within the DTB. Furthermore, we highlight open questions that
invite joint efforts from interdisciplinary fields and emphasize the
far-reaching implications of the DTB. The DTB can offer unprecedented insights
into the emergence of intelligence and neurological disorders, which holds
tremendous promise for advancing our understanding of both biological and
artificial intelligence, and ultimately propelling the development of
artificial general intelligence and facilitating precision mental healthcare
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