Brain connectivity mapping with diffusion MRI across individuals and species

The human brain is a highly complex organ that integrates functionally specialised subunits. Underpinning this complexity and functional specialisation is a network of structural connections, which may be probed using diffusion tractography, a unique, powerful and non-invasive MRI technique. Estimates of brain connectivity derived through diffusion tractography allow for explorations of how the brain’s functional subunits are inter-linked to subsequently produce experiences and behaviour. This thesis develops new diffusion tractography methodology for mapping brain connectivity, both across individuals and also across species; and explores frameworks for discovering associations of such brain connectivity features with behavioural traits. We build upon the hypothesis that connectional patterns can probe regions of functional equivalence across brains. To test this hypothesis we develop standardised and automated frameworks for mapping these patterns in very diverse brains, such as from human and non-human primates. We develop protocols to extract homologous fibre bundles across two species (human and macaque monkeys). We demonstrate robustness and generalisability of these protocols, but also their ability to capture individual variability. We also present investigations into how structural connectivity profiles may be used to inform us of how functionally-related features can be linked across different brains. Further, we explore how fully data-driven tractography techniques may be utilised for similar purposes, opening the door for future work on data-driven connectivity mapping. Subsequently, we explore how such individual variability in features that probe brain organisation are associated with differences in human behaviour. One approach to performing such explorations is the use of powerful multivariate statisitical techniques, such as canonical correlation analysis (CCA). After identifying issues in out-of-sample replication using multi-modal connectivity information, we perform comprehensive explorations into the robustness of such techniques and devise a generative model for forward predictions, demonstrating significant challlenges and limitations in their current applications. Specifically, we predict that the stability and generalisability of these techniques requires an order of magnitude more subjects than typically used to avoid overfitting and mis-interpretation of results. Using population-level data from the UK Biobank and confirmations from independent imaging modalities from the Human Connectome Project, we validate this prediction and demonstrate the direct link of CCA stability and generalisability with the number of subjects used per considered feature

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Brain connectivity mapping with diffusion MRI across individuals and species

The human brain is a highly complex organ that integrates functionally specialised subunits. Underpinning this complexity and functional specialisation is a network of structural connections, which may be probed using diffusion tractography, a unique, powerful and non-invasive MRI technique. Estimates of brain connectivity derived through diffusion tractography allow for explorations of how the brain’s functional subunits are inter-linked to subsequently produce experiences and behaviour. This thesis develops new diffusion tractography methodology for mapping brain connectivity, both across individuals and also across species; and explores frameworks for discovering associations of such brain connectivity features with behavioural traits. We build upon the hypothesis that connectional patterns can probe regions of functional equivalence across brains. To test this hypothesis we develop standardised and automated frameworks for mapping these patterns in very diverse brains, such as from human and non-human primates. We develop protocols to extract homologous fibre bundles across two species (human and macaque monkeys). We demonstrate robustness and generalisability of these protocols, but also their ability to capture individual variability. We also present investigations into how structural connectivity profiles may be used to inform us of how functionally-related features can be linked across different brains. Further, we explore how fully data-driven tractography techniques may be utilised for similar purposes, opening the door for future work on data-driven connectivity mapping. Subsequently, we explore how such individual variability in features that probe brain organisation are associated with differences in human behaviour. One approach to performing such explorations is the use of powerful multivariate statisitical techniques, such as canonical correlation analysis (CCA). After identifying issues in out-of-sample replication using multi-modal connectivity information, we perform comprehensive explorations into the robustness of such techniques and devise a generative model for forward predictions, demonstrating significant challlenges and limitations in their current applications. Specifically, we predict that the stability and generalisability of these techniques requires an order of magnitude more subjects than typically used to avoid overfitting and mis-interpretation of results. Using population-level data from the UK Biobank and confirmations from independent imaging modalities from the Human Connectome Project, we validate this prediction and demonstrate the direct link of CCA stability and generalisability with the number of subjects used per considered feature

Leak Energy Based Missing Feature Mask Generation for ICA and GSS and Its Evaluation with Simultaneous Speech Recognition

This paper addresses automatic speech recognition (ASR) for robots integrated with sound source separation (SSS) by using leak noise based missing feature mask generation. The missing feature theory (MFT) is a promising approach to improve noise-robustness of ASR. An issue in MFT-based ASR is automatic generation of the missing feature mask. To improve robot audition, we applied this theory to interface ASR and SSS which extracts a sound source originated from a specific direction by multiple microphones. In a robot audition system, it is a promising approach to use SSS as a pre-processor for ASR to be able to deal with any kind of noises. However, ASR usually assumes clean speech input, while speech extracted by SSS never fails to be distorted. MFT can be applied to cope with distortion in the extracted speech. In this case, we can assume that the noises included in extracted sounds are mainly leakages from other channels. Thus, we introduced leak noise based missing feature mask generation, which can generate a missing feature mask automatically by using information on leak noise obtained from other channels. To assess the effectiveness of the leak noise based missing feature mask generation, we used two methods for SSS: geometric source separation (GSS) and independent component analysis (ICA), and Multiband Julian for MFT based ASR. The two constructed systems, that is, GSS-based and ICA-based robot audition systems, were evaluated through recognition of simultaneous speech uttered by two speakers. As a result, we showed that the proposed leak noise based missing feature mask generation worked well in both systems. 1

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum