Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing a single step 3D interpolation at each individual time frame to recover a motion-free 4D fMRI image. Using only information from a 3D spatial neighborhood neglects the temporal structure of fMRI and useful information from neighboring timepoints. Here, we propose a novel technique based on four dimensional iterative reconstruction of the motion scattered fMRI slices. Quantitative evaluation of the proposed method on a cohort of real clinical fetal fMRI data indicates improvement of reconstruction quality compared to the conventional 3D interpolation approaches.Comment: Accepted by MICCAI 202

An open-source implementation of estimation and correction of head-motion and eddy-current distortions generalizable across diffusion MRI signal models

We develop an open-source tool for the retrospective estimation of inter-volume head-motion and eddy-current distortions, typically found in diffusion MRI (dMRI) data acquired with echo-planar imaging schemes. The implementation is “open-since-inception” to ensure transparency. By leveraging the widely used DIPY package and a user-friendly interface, researchers have at their disposal an implementation combining state-of-art approaches with substantial improvements that can efficiently leverage any compliant diffusion model(s) while simultaneously accounting for susceptibility distortions

Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge

Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples. To mitigate the recurrent bottleneck of voxel-wise label creation, we investigate the use of weak labels: these are oversized annotations which are considerably faster to create. We present a deep learning algorithm for aneurysm detection that exploits weak labels during training. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We created a retrospective dataset of 284 TOF-MRA subjects (170 females) out of which 157 are patients (with 198 aneurysms), and 127 are controls. Our open TOF-MRA dataset, the largest in the community, is released on OpenNEURO. To assess model generalizability, we participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). Weak labels were 4 times faster to generate than their voxel-wise counterparts. When using prior anatomical knowledge, our network achieved a sensitivity of 80% on the in-house data, with False Positive (FP) rate of 1.2 per patient. On the public challenge, sensitivity was 68% (FP rate = 2.5), ranking 4th/18 on the open leaderboard. We found no significant difference in sensitivity between aneurysm risk-of-rupture groups (p = 0.75), locations (p = 0.72), or sizes (p = 0.15). Our code is made available for reproducibility.Comment: Paper submitted to a Journa

High-density electric source imaging of interictal epileptic discharges: how many electrodes and which time point?

Objective: To assess the value of caudal EEG electrodes over cheeks and neck for high-density electric source imaging (ESI) in presurgical epilepsy evaluation, and to identify the best time point during averaged interictal epileptic discharges (IEDs) for optimal ESI accuracy. Methods: We retrospectively examined presurgical 257-channel EEG recordings of 45 patients with pharmacoresistant focal epilepsy. By stepwise removal of cheek and neck electrodes, averaged IEDs were downsampled to 219, 204, and 156 EEG channels. Additionally, ESI at the IED's half-rise was compared to other time points. The respective sources of maximum activity were compared to the resected brain area and postsurgical outcome. Results: Caudal channels had disproportionately more artefacts. In 30 patients with favourable outcome, the 204-channel array yielded the most accurate results with ESI maxima < 10 mm from the resection in 67% and inside affected sublobes in 83%. Neither in temporal nor in extratemporal cases did the full 257-channel setup improve ESI accuracy. ESI was most accurate at 50% of the IED's rising phase. Conclusion: Information from cheeks and neck electrodes did not improve high-density ESI accuracy, probably due to higher artefact load and suboptimal biophysical modelling. Significance: Very caudal EEG electrodes should be used for ESI with caution

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality

Quantitative Evaluation of Enhanced Multi-plane Clinical Fetal Diffusion MRI with a Crossing-Fiber Phantom

Diffusion Magnetic Resonance Imaging (dMRI) has become widely used to study in vivo white matter tissue properties non-invasively. However, fetal dMRI is greatly limited in Signal-to-Noise ratio and spatial resolution. Due to the uncontrollable fetal motion, echo planar imaging acquisitions often result in highly degraded images, hence the ability to depict precise diffusion MR properties remains unknown. To the best of our knowledge, this is the first study to evaluate diffusion properties in a fetal customized crossing-fiber phantom. We assessed the effect of scanning settings on diffusion quantities in a phantom specifically designed to mimic typical values in the fetal brain. Orthogonal acquisitions based on clinical fetal brain schemes were preprocessed for denoising, bias field inhomogeneity and distortion correction. We estimated the fractional anisotropy (FA) and mean diffusivity (MD) from the diffusion

Doctoral thesis recital (collaborative piano)

Brillance pour saxophone alto et piano (1974) / Ida Gotkovsky -- Sonata for Piano and Cello in A Major Op. 69 (1809) / Ludwig van Beethoven -- Carmen Fantasy (1994) / Alexander RosenblattMusicSupervisor not listed on recital program

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping

Connectome Mapper 3 implements, in accordance to the BIDS-App standard, full anatomical, diffusion, and resting/state functional MRI processing pipelines, from raw T1 / DWI / BOLD data to multi-resolution brain parcellation with corresponding connection matrices, based on a new version of the Lausanne parcellation atlas (Cammoun et al, 2012), aka Lausanne2018. This version mainly addresses all points raised by the JOSS review. What's Changed Updates Nipype has been updated from 1.7.0 to 1.8.0. (PR #184) See Nipype changelog) for more details. Bug fix Add missing cmp.stages.eeg to setup_pypi.py. (PR #166) Add missing package data for parcellation in setup_pypi.py. (PR #182) Use HTTPS instead of SSH for datalad clone in notebooks . (PR #181) Add missing condition to handle custom BIDS files with session. (PR #183) Integrate fix from Napari project for issues with menubar on Mac. (PR #174) Use the most recent PyQt5 instead of PySide2 (older) for graphical backend of cmpbidsappmanager, which provides a fix to run Qt-based GUI on MacOSX Big Sur. (PR #188) Documentation Correct conda env create instruction in the README. (PR #164) Refer to contributing guidelines in the README. (PR #167) Use sphinx-copybutton extension in the docs. (PR #168) Add notes about docker image and conda environment size and time to download. (PR #169) JOSS paper Integrate minor wording tweaks by @jsheunis. (PR #162) Add higher level summary and rename the old summary to "Overview of Functionalities". (PR #175) License The license has been updated to a pure 3-clause BSD license to comply with JOSS. (PR #163) Software development life cycle Migrate ubuntu 16.04 (now deprecated) to 20.04 on CircleCI. (PR #172) Contributors Sebastien Tourbier J.S. (Stephan) Heunis More... Full Changelog: https://github.com/connectomicslab/connectomemapper3/compare/v3.0.3...v3.0.

Abnormal directed connectivity of resting state networks in focal epilepsy

Objective Epilepsy diagnosis can be difficult in the absence of interictal epileptic discharges (IED) on scalp EEG. We used high-density EEG to measure connectivity in large‐scale functional networks of patients with focal epilepsy (Temporal and Extratemporal Lobe Epilepsy, TLE and ETLE) and tested for network alterations during resting wakefulness without IEDs, compared to healthy controls. We measured global efficiency as a marker of integration within networks. Methods We analysed 49 adult patients with focal epilepsy and 16 healthy subjects who underwent high-density-EEG and structural MRI. We estimated cortical activity using electric source analysis in 82 atlas-based cortical regions based on the individual MRI. We applied directed connectivity analysis (Partial Directed Coherence) on these sources and performed graph analysis: we computed the Global Efficiency on the whole brain and on each resting state network. We tested these features in different group of patients. Results Compared to controls, efficiency was increased in both TLE and ETLE (p<0.05). The somato-motor-network, the ventral-attention-network and the default-mode-network had a significantly increased efficiency (p<0.05) in both TLE and ETLE as well as TLE with hippocampal sclerosis. Significance During interictal scalp EEG epochs without IED, patients with focal epilepsy show brain functional connectivity alterations in the whole brain and in specific resting-state-networks. This higher integration reflects a chronic effect of pathological activity within these structures and complement previous work on altered information outflow. These findings could increase the diagnostic sensitivity of scalp EEG to identify epileptic activity in the absence of IED