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Bannier, E.

Publications and source records attributed to Bannier, E..

3 recordsLinked to original sources

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's Disease

Impaired memory is a hallmark of prodromal Alzheimers Disease (AD). Prior knowledge associated with the memoranda has proved to have a powerful effect on memory in healthy subjects. Yet, barely nothing is known about its effect in early AD. We used functional MRI to ask whether prior knowledge enhanced memory encoding in early AD and whether the nature of prior knowledge mattered. Early AD patients and healthy controls underwent a task-based fMRI experiment, being scanned while learning face-scene associations. Famous faces carried Pre-Experimental Knowledge (PEK) while unknown faces repeatedly familiarized prior to learning carried Experimental Knowledge (EK). As expected, PEK increased subsequent memory in healthy elderly. However, patients did not benefit from PEK. Partly non-overlapping brain networks supported PEK vs. EK encoding in healthy controls. Patients displayed impaired activation in a right subhippocampal region where activity predicted successful associative memory formation of PEK stimuli. These findings call for a thorough consideration of how prior knowledge impacts learning and suggest a possible underestimation of the extent of associative memory impairment in early AD. HighlightsO_LILearning is impaired in prodromal AD, but we currently ignore whether prior knowledge available at encoding promotes learning in AD as it does in healthy controls. C_LIO_LIPatients with AD failed to benefit from pre-experimental prior knowledge (famous faces) by comparison with experimental knowledge (unknown but familiarized faces). C_LIO_LIfMRI responses at study revealed distinct networks underlying associative encoding for both pre-experimental and experimental knowledge. C_LIO_LIA subsequent memory effect found in control subjects for associations carrying pre-experimental knowledge in the right subhippocampal structures, including the perirhinal cortex, was absent in patients. C_LIO_LIPre-experimental knowledge-based associative encoding relies on brain regions specifically targeted by early tau pathology. C_LIO_LIUsing unfamiliar materials to probe memory in early AD might underestimate learning impairment. C_LI

neuroscience↗

Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration.

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. Our group is the second in the world to have integrated EEG and fMRI for NF, therefore this dataset is unique of its kind. We believe that it will be a valuable tool to O_LIAdvance and test methodologies to integrate complementary neuroimaging techniques (design and validation of methods of multi-modal data integration at various scales) C_LIO_LIImprove the quality of Neurofeedback provided C_LIO_LIImprove methodologies for de-noising EEG acquired under MRI C_LIO_LIInvestigate the neuromarkers of motor-imagery using multi-modal information C_LI

neuroscience↗

Automated Electrodes Detection during simultaneous EEG/fMRI

1The coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localisation of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localise EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure.

neuroscience↗