ORCID Profile
0000-0003-3776-6782
Current Organisation
National University of Ireland Galway
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Frontiers Media SA
Date: 21-06-2016
Publisher: Center for Open Science
Date: 08-08-2019
Abstract: Understanding how people rate their confidence is critical for characterizing a wide range of perceptual, memory, motor, and cognitive processes. However, as in many other fields, progress has been slowed by the difficulty of collecting new data and the unavailability of existing data. To address this issue, we created a large database of confidence studies spanning a broad set of paradigms, participant populations, and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analyzed in multiple software packages. Each dataset is further accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (available at osf.io/s46pr) contained 145 datasets with data from over 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. We show the usefulness of this large collection of datasets in four different analyses that provide precise estimation for several foundational confidence-related effects and lead to new findings that depend on the availability of large quantity of data. This Confidence Database will continue to enable new discoveries and can serve as a blueprint for similar databases in related fields.
Publisher: Springer Science and Business Media LLC
Date: 03-02-2020
Publisher: Cambridge University Press (CUP)
Date: 13-02-2017
DOI: 10.1017/S0033291717000058
Abstract: Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the ‘rich-club’, represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses. We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks. Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus. This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Denis O'Hora.