Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Abstract

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

Keywords: Diffusion MRI; Human Connectome Project (HCP); axon diameter; brain; clinical applications; data sharing; fiber tracking; high b-value; human connectome scanner; peripheral nerve stimulation; preprocessing; radio frequency coil; sequence; tissue microstructure; white matter.

Fig. 1.. Benefits of strong gradients for diffusion MRI.

High gradient amplitudes up to 300 mT/m on the Connectome system (bottom) achieve a given diffusion-encoding gradient area in less time compared to conventional gradient systems (top), as illustrated through the pulsed gradient spin echo diffusion MRI sequence. Benefits of strong diffusion-encoding gradients include shortening the entire diffusion-encoding period and echo time (TE), and hence increasing the signal-to-noise ratio (SNR) by reducing signal loss due to T2 decay. The larger gradient amplitudes also enable stronger diffusion encoding (i.e., larger diffusion-encoding gradient areas, larger q- values and b-values) to be achieved with shorter diffusion times, providing higher “diffusion resolution” to improve the capability of resolving smaller length-scales for probing tissue microstructure and for resolving complex white matter structures such as crossing fibers. RF = radiofrequency, δ = diffusion-encoding gradient pulse duration, Δ = diffusion time.

Fig. 2.. Illustration of the different length scales accessible by diffusion MRI in the brain.

Macrostructure refers to structures on the whole-brain and regional level, while microstructure refers to structures on the microscopic level, e.g., cells and axons. Mesostructure resides in the intermediate, millimeter to sub-millimeter regime, on the order of the typical MRI voxel size. Figure contents adapted from (Reisert et al., 2017).

Fig. 3.. Distribution of the four 300 mT/m gradient strength MRI systems installed worldwide.

Figure contents adapted from news announcements for the Cardiff University, Max Planck Institute for Human Cognitive and Brain Sciences, and Fudan University.

Fig. 4.. Peripheral Nerve Stimulation (PNS) Simulations.

A: Experimental and simulated PNS threshold curves given as minimum stimulating gradient amplitude AG as a function of rise time for the y-axis of the Connectome gradient (experiments in blue, simulation in red) and the Prisma gradient (in grayscale). PNS thresholds were obtained for a trapezoidal bipolar train with 0.5 ms flat top duration. B: Simulated activation maps plotted as PNS oracle hot-spots (reciprocal PNS thresholds) in the male model for a head-imaging position and for a trapezoidal rise time of 0.5 ms. The overall nerve activation induced by the Connectome y-axis gradient was substantially lower than that of the Prisma y-axis gradient. The activation hot-spots in both coils occurred in the shoulders (suprascapular nerve) and close to the cervical spine (intercostal nerves).

Fig. 5.. Select array coils developed for the Connectome MRI scanner.

Starting in 2011 with a 32-channel head coil, Connectome coil technology has evolved from a 60-channel head-neck array, a 64-channel brain array, and a 48-channel ex vivo brain coil all the way to a 64-channel head coil with an integrated field monitoring system in 2021. Figure adapted from (Gruber et al., 2014; Keil et al., 2013; Mahmutovic et al., 2021; Scholz et al., 2021).

Fig. 6.. Combined brain and cervical-spine tractography.

Data was obtained from 1.5 mm isotropic diffusion acquisition using the 60-channel head-neck array coil.

Fig. 7.. Illustration of spiral image reconstruction using concurrent field monitoring.

(A) Zero-order phase and higher-order dynamic field effects; (B) first-order read trajectory. Examples of temporal SNR (tSNR) maps (calculated from a series of 20 repetitions) obtained with the 64-ch coil using (C) spiral and (D) EPI acquisitions. Images reconstructed from the spiral acquisitions using concurrently monitored field information do not show evident distortions. Figure adapted from (Mahmutovic et al., 2021).

Fig. 8.. Schematic illustration of diffusion-encoding gradients and induced eddy currents.

In the following diagrams, the x-axis represents time and the y-axis represents gradient strength. a) If the nominal temporal gradient profile (red dotted line) follows the desired shape, the actual gradient profile (blue continuous line) is distorted by eddy currents (yellow dashed line). b) This can be compensated by gradient pre-emphasis, i.e., overshooting the gradient profile. For demonstration purposes, the eddy current amplitude is exaggerated. c) Stejskal-Tanner diffusion encoding with eddy currents for a single time constant (with rescaled amplitude for better visibility). The short vertical dash between the two diffusion encoding lobes indicates the position of a 180° pulse. d) Twice refocused spin echo diffusion encoding with timing optimized to null the eddy current of this time constant. This method requires two RF pulses to achieve a high b-value and eddy current reduction.

Fig. 9.. Maps of the deviation in diffusion encoding from the nominal prescribed b-value and corresponding histogram of the calculated variance across the whole brain volume.

The diffusion gradient direction [1, 0, 0] was used to generate the map. Figure adapted from (Eichner et al., 2019).

Fig. 10.. Effects of distortion correction with double versus single interpolation.

The left and middle panels compare enlarged views of a b = 0 image corrected using two successive interpolation steps versus a single concatenated interpolation. Adapted from (Eichner et al., 2019).

Fig. 11.. Illustration of the AxTract framework.

(a) Ground truth directions used to generate the data with their lengths scaled by the axon diameter index α. (b) Estimated fiber ODFs (fODFs), (c) fiber ODF peaks and (d) fiber ODF peaks with their lengths scaled by α, (e) show valid connections (VC) and invalid connections (IC) for AxTract. (f) VC and IC for conventional deterministic tractography (CDT) are provided for comparison. Figure adapted from (Girard et al., 2017).

Fig. 12.

Illustration of diffusion models and key concepts. The concept of compartmentalization was initially proposed by Stanisz et al. (a), following which the AxCaliber aims to estimate the diameter distribution of the restricted water compartment (b). Variations of the AxCaliber model was proposed, such as modifications to account for fiber dispersions (c) and crossings (d). Realistic axons are very different from ideal cylinders, instead, there are plenty of beadings and undulations (f) and structural disorder are commonly seen along neurites (g). In gray matter, a sphere (e) or dot (h) compartment is incorporated into the physical model to account for additional signal component present in the measurement. Figure adapted from the papers labeled therein.

Fig. 13.. Illustration of various diffusion encoding schemes.

In each row, an exemplary diffusion weighting gradient waveform was shown on the left (x, y, and z components are shown in blue, green, and red, respectively) and its corresponding b-tensor shape was shown on the right. The diffusion encoding schemes shown here include: a) single diffusion encoding (SDE) (standard Stejskal-Tanner), b) double diffusion encoding (DDE), c) tiple diffusion encoding (TDE), (d-g) q-vector trajectory encoding, d) spherical tensor encoding (STE) or isotropic encoding, e) planar tensor encoding, f) prolate encoding, g) oblate encoding.

Fig. 14.. The concept of the multi-contrast diffusion experiments.

(a) Illustration of the concept of multi-contrast encoding space with each MRI parameter as one dimension; (b) the distribution of different tissue types in the multi-dimensional space of tissue properties (figure adapted from de Almeida Martins et al., 2020, permission pending); (c) examples of the contrast dimensions include, but are not limited to, diffusion weighting directions, b-value, TE, delay time (TD), inversion time (TI), diffusion encoding schemes, etc. Figure adapted from (Tax et al., 2021b).

Fig. 15.. Whole-brain ex-vivo diffusion MRI at 550 micrometer isotropic resolution using b-values up to 10000s/mm².

Axial, coronal, and sagittal views of a given diffusion direction are shown in (a), and the corresponding averaged DWI are displayed in (b). High-quality submillimeter diffusion MRI allows mapping diffusivity with unprecedented quality in fine anatomical structures often inaccessible in in-vivo settings, as seen in the internal capsule and transverse fibers in the pons (c), and anisotropic diffusivity in the primary and somatosensory cortex (d). Resolving fiber architecture of the hippocampus is achievable using this high-quality, high-spatial-resolution dataset, as can be seen in (e). Adapted from (Ramos-Llorden et al., 2021).