Binns, M. | Search bioRxiv

bioRxiv2024

Neural Mechanism Underlying Successful Classification of Amnestic Mild Cognitive Impairment Using Multi-Sensory-Evoked Potentials

Early detection of amnestic mild cognitive impairment (aMCI) is crucial for timely interventions. This study combines scalp recordings of lateralized auditory, visual, and somatosensory stimuli with a flexible and interpretable support vector machine learning pipeline to differentiate individuals diagnosed with aMCI from healthy controls. Event-related potentials (ERPs) and functional connectivity (FC) matrices from each modality successfully predicted aMCI. Reduced ERP amplitude in aMCI contributed to classification. The analysis of FC using phase-locking value revealed higher FC in aMCI than controls in frontal regions, which predicted worse cognitive performance, and lower FC in posterior regions from delta to alpha frequency. We observe optimal classification accuracy (96.1%), sensitivity (97.7%) and specificity (94.3%) when combining information from all sensory conditions than when using information from a single modality. The results highlight the clinical potential of sensory-evoked potentials in detecting aMCI, with optimal classification using both amplitude and oscillatory-based FC measures from multiple modalities.

neuroscience↗

bioRxiv2018

Climate, human influence, and the distribution limits of the invasive European earwig, Forficula auricularia, in Australia

BACKGROUNDBy modelling species-environment relationships of pest species, it is possible to understand potential limits to their distributions when they invade new regions, and their likely continued spread. The European earwig, Forficula auricularia, is a non-native invasive species in Australia that has been in the country for over 170 years. However, in the last few decades it has invaded new areas. Unlike in other countries, F. auricularia is a pest species of grains production in Australia. In this study we detail the Australian distribution of this species, adding new samples focussed around grain growing regions. Using this information we build global species distribution models for F. auricularia to better understand species-environment relationships.\n\nRESULTSOur models indicated that the distribution of F. auricularia is strongly associated with temperate through to semi-arid environments, a high winter rainfall and pronounced temperature seasonality. We identified regions that hold suitable, but as yet vacant, niche space for Australian populations, suggesting further potential for range expansion. Beyond climate, an index describing human influence on the landscape was important to understand the distribution limits of this pest. We identified regions where there was suitable climate space, but which F. auricularia has not occupied likely due to low levels of human impact.\n\nCONCLUSIONModelling the global distribution of a non-native pest species aided understanding of the regional distribution limits within Australia and highlighted the usefulness of human impact measures for modelling globally invasive insect species.

ecology↗