Dataset Information

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

ABSTRACT: Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).

SUBMITTER: Schrouff J

PROVIDER: S-EPMC5797202 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Schrouff Jessica J Monteiro J M JM Portugal L L Rosa M J MJ Phillips C C Mourão-Miranda J J

Neuroinformatics 20180101 1

Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In th ...[more]

PMID: 29297140

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Project description:There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.

Project description:Introduction Medical schools in the United Kingdom are under increasing pressure to provide more streamlined, applicable teaching due to rising numbers of trainee doctors but are failing to meet their educational need for otolaryngology. The recent novel coronavirus disease 2019 (COVID-19) pandemic has placed additional pressures on medical schools to adapt the medium over which the curriculum is delivered. The use of tactile learning with three-dimensional models and distanced learning via videoconferencing may provide alternative teaching methods to meet otolaryngology undergraduate learning requirements. This pilot study aimed to assess the differences in undergraduate student attitudes toward tactile learning via nontactile distanced learning and review their acceptability among this cohort. Methods Two groups of medical students observed a single educational event on the larynx and management of the airway. The learning opportunity was delivered in a lecture format with the lecturer demonstrating on an anatomical model of the larynx. Group one (tactile group) had an identical model to interact with during the lecture and were present within the lecture theater; group two (nontactile group) did not and observed the lecture via video link. Students were asked to rank their opinion to several statements about the session based on an 11-point Likert's scale and give qualitative feedback. Results All ranked feedback was mainly positive. Tactile learning was statistically equivalent to nontactile learning based on the ranked feedback from the students, except for "improvement in anatomical knowledge," for which the students believed tactile learning was superior ( p = 0.017). A variety of qualitative feedback was received by both groups. Conclusion This pilot study provides evidence for the acceptability among students of the use of nontactile distanced learning to deliver the otolaryngology undergraduate curriculum compared with tactile learning. This can provide the basis for larger studies to assess the educational impact of these different teaching methods.

Dataset Information

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Publications

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

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