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Aliko, S.

Publications and source records attributed to Aliko, S..

2 recordsLinked to original sources

Reorganization of the neurobiology of language after sentence overlearning

There is a widespread assumption that there are a static set of language regions in the brain. Yet, people still regularly produce familiar formulaic expressions when those regions are severely damaged. This suggests that the neurobiology of language varies with the extent of word sequence learning and might not be fixed. We test the hypothesis that perceiving sentences is mostly supported by sensorimotor regions involved in speech production and not language regions after overlearning. Twelve participants underwent two sessions of behavioural testing and functional magnetic resonance imaging (fMRI), separated by 15 days. During this period, they repeated two sentences 30 times each, twice a day. In both fMRI sessions, participants passively listened to those two sentences and novel sentences. Lastly, they spoke novel sentences. Behavioural results confirm that participants overlearned sentences. Correspondingly, there was an increase or recruitment of sensorimotor regions involved in sentence production and a reduction in activity or inactivity for overlearned sentences in regions involved in listening to novel sentences. The global network organization of the brain changed by [~]45%, mostly through lost connectivity. Thus, there was a profound reorganization of the neurobiology of speech perception after overlearning towards sensorimotor regions not considered in most contemporary models and away from the language regions posited by those models. These same sensorimotor regions are generally preserved in aphasia and Alzheimers disease, perhaps explaining residual abilities with formulaic language. These and other results warrant reconsidering static neurobiological models of language.

neuroscience

A 'Naturalistic Neuroimaging Database' for understanding the brain using ecological stimuli

Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of resting-state data for which the functions of networks cannot be verifiably labelled. We make a Naturalistic Neuroimaging Database (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

neuroscience