Biology subjects

Ferre, J.-C.

Publications and source records attributed to Ferre, J.-C..

3 recordsLinked to original sources

bioRxiv2021

Multi T1-weighted contrast imaging and T1 mapping with Compressed sensing FLAWS at 3T

The Fluid And White matter Suppression (FLAWS) MRI sequence allows for the acquisition of multiple T1-weighted contrasts in a single sequence acquisition. However, its acquisition time is prohibitive for use in clinical practice when the k-space is linearly downsampled and reconstructed using the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique. This study proposes a FLAWS sequence optimization tailored to allow for the acquisition of FLAWS images with a Cartesian phyllotaxis k-space undersampling and compressed sensing (CS) reconstruction at 3T. The CS FLAWS sequence parameters were determined using a method previously employed to optimize FLAWS imaging at 1.5T and 7T. In-vivo experiments show that the proposed CS FLAWS optimization allows to reduce the FLAWS sequence acquisition time from 8 mins to 6 mins without decreasing the FLAWS image quality. In addition, this study demonstrates for the first time that T1-weighted imaging with low B1 sensitivity and T1 mapping can be performed with the FLAWS sequence at 3T for both GRAPPA and CS reconstructions. The FLAWS T1 mapping was validated using in-silico, in-vitro and in-vivo experiments with comparison against the inversion recovery turbo spin echo and MP2RAGE T1 mappings. These new results suggest that the recent advances in FLAWS imaging allow to combine the MP2RAGE imaging benefits (T1-weigthed imaging with low B1 sensitivity and T1 mapping) and with the previous version of FLAWS imaging benefits (multi T1-weighted contrast imaging) in a single 6 mins sequence acquisition. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗

bioRxiv2020

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↗

bioRxiv2018

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, ...), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

neuroscience↗