Souheil J. Inati

Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis

The brain is the bodys largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.

Integrated strategy for improving functional connectivity mapping using multiecho fMRI

Functional connectivity analysis of resting state blood oxygen level–dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals—a characteristic property of BOLD signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.

Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition.

To reduce the sensitivity of echo‐planar imaging (EPI) auto‐calibration signal (ACS) data to patient respiration and motion to improve the image quality and temporal signal‐to‐noise ratio (tSNR) of accelerated EPI time‐series data.

Initial investigation of the blood-brain barrier in MS lesions at 7 tesla

Background: We previously described two dynamics of contrast enhancement in scans of active multiple sclerosis lesions: Medium-sized, early lesions enhance centrifugally, whereas larger, slightly older lesions enhance centripetally. Due to technical limitations, our previous study did not characterize lesions < 5 mm in diameter, cortical enhancement, and anatomical structures within lesions. Objective: The objective of this paper is to obtain initial observations of these important aspects of lesion development on a 7 tesla scanner at high spatial resolution. Methods: We scanned eight patients, acquiring precontrast T2*-weighted scans, T1-weighted scans before and after contrast, and high-resolution dynamic contrast-enhanced scans during and up to 30 min after contrast. Results: We detected 15 enhancing lesions, obtaining dynamic data in 10: Five lesions < 4 mm enhanced centrifugally (initial central enhancement expanded outward), and five lesions > 4 mm enhanced centripetally (initial peripheral enhancement gradually filled the lesion). A leukocortical lesion initially showed enhancement in its white matter portion, which gradually spread into the cortex. Seventy-three percent of lesions were clearly perivenular. Conclusion: Most active lesions are perivenular, and the smallest lesions enhance centrifugally. This supports the idea that lesions grow outward from a central vein.

Distributed MRI reconstruction using gadgetron‐based cloud computing

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency.

Improvement of temporal signal-to-noise ratio of GRAPPA accelerated echo planar imaging using a FLASH based calibration scan

To demonstrate that the temporal signal‐to‐noise ratio (SNR) of generalized autocalibrating partially parallel acquisitions (GRAPPA) accelerated echo planar imaging (EPI) can be enhanced and made more spatially uniform by using a fast low angle shot (FLASH) based calibration scan.

Noise propagation in region of interest measurements

The purpose of this work was to develop and validate a technique for predicting the standard deviation (SD) associated with thermal noise propagation in region of interest measurements.

Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI.

Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.

Improved cardiac magnetic resonance thermometry and dosimetry for monitoring lesion formation during catheter ablation

A new real‐time MR‐thermometry pipeline was developed to measure multiple temperature images per heartbeat with 1.6×1.6×3 mm3 spatial resolution. The method was evaluated on 10 healthy volunteers and during radiofrequency ablation (RFA) in sheep.

ISMRM Raw data format: A proposed standard for MRI raw datasets

This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing.