Small Sample Size, Unprecedented Data Quality: Challenges & Opportunities in High-End Data Acquisition
Jonathan R. Polimeni1,2,3
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States


While there is a well-known trend towards large-scale neuroimaging studies, there is also mounting interest in single-subject MRI that enables the investigation of meaningful differences in brain structure and function between individuals. Single-subject MRI opens opportunities for advanced imaging strategies that are infeasible in large-scale studies, such as highly sampling individual brains to boost statistical power, and acquiring multiple averages of high-resolution data to achieve both high sensitivity and specificity. In this lecture I will survey specialized technologies for improving data quality, showcase example high-end datasets, discuss factors that limit data quality, and consider new methods to overcome these limits.

Motivation and goals

The motivation for this lecture is that, while there is a well-known trend towards increasing sample sizes and large-scale neuroimaging studies—both to increase statistical sensitivity (1) and enable inferences and predictions at the population level (2)—there is also increasing recognition that, as powerful and successful as these studies are, meaningful individual differences can often be lost. This has spurred renewed interest in single-subject neuroimaging studies. One important class of single-subject neuroimaging studies is high-resolution functional and anatomical MRI, where the features of interest are often far too small to be detected at the group level because spatial normalization and across-subject averaging will smear out these fine-scale details. In these small-sample-size studies, there are opportunities for advanced imaging strategies that are not feasible in large studies, and there are several exciting new technologies that can extend imaging capabilities beyond the current limits—many of which will soon be more widely available.

Therefore this lecture will attempt to make the case for the value of single-subject neuroimaging, and along the way will showcase some new high-end acquisition technologies that are now becoming available. The lecture will also highlight several themes encountered when thinking of high-end acquisition, and present several example datasets that achieve unprecedented quality.

The goal of this syllabus is to reference the literature cited in the lecture as well as other relevant studies that could not be included in the lecture due to lack of time. While this bibliography is not comprehensive, it focuses on recent work within the major themes of the lecture.

High-performance EPI

Improvement in fMRI spatial and temporal resolution is mainly motivated by the intrinsically high “biological resolution” of the technique. The precision, in both space and time, of blood flow regulation in response to neural activity is perhaps far better than previously believed, indicating that there are gains to be made by improving imaging resolution [3].

Perhaps the most important tool for increasing imaging resolution is parallel imaging. While this has long been known [4–6], recent developments have sought to improve the robustness of highly-accelerated EPI to enable more reliable and consistent data quality [7–10]. Although EPI Nyquist ghosts are a classic artifact, ghost correction is still under active development, with several newer methods utilizing parallel imaging techniques [11,12,21–28,13–20].

One new direction seen in high-resolution fMRI has been the use of distortion-matched anatomical reference data based on the same EPI readouts used for the functional acquisition [29]. These T1 EPI sequences are built upon a previously-described approach [30,31] extended to modern EPI technology such as Simultaneous Multi-Slice imaging [32–36]. Because this approach avoids the need for explicit distortion correction, which can fail, it is increasingly used high-resolution fMRI studies [37–42].

Another fMRI pulse sequence technology that has matured enormously in recent years is 3D-EPI [43–46], with one shot acquired per slice, which performs best when the phase variations across shots due to physiology can be corrected [47–52]. This readout allows for acceleration in two directions [53], and therefore enables more advanced CAIPI parallel imaging methods [54–57]. Today 3D-EPI is mainly used for high-resolution studies [38], but more applications to conventional resolutions are beginning to emerge [58,59].

Although 3D-EPI and SMS-EPI [60], especially blipped-CAIPI SMS-EPI [61], have many similarities [53], 3D-EPI fMRI techniques may outperform SMS-EPI when voxel sizes are sufficiently small that the data are thermal-noise dominated [38]. The abovementioned use of T1 anatomical reference data using EPI readouts has also been extended to 3D-EPI [41,62].

Overcoming sensitivity limits in single-subject fMRI

Typically high-resolution fMRI is applied in studies examining fine-scale organization of the brain such as cerebral cortical columns and layers, and for this reason these studies perform all analyses at the single-subject level. Not only does spatial normalization used in group-level studies result in loss of spatial resolution, but fine-scale features such as cortical columns are known to exhibit distinct patterns across subjects, therefore averaging across individuals would not be valid. Still, often high-resolution fMRI data have limited sensitivity, and the effect sizes of interest are small, therefore to achieve both high sensitivity and high spatial specificity many runs of fMRI data, sometimes multiple hours, are required.

It may be true that conventional fMRI studies are also sensitivity limited, and also can see more with the increased sensitivity that comes with more averages. For example, recent studies using conventional whole-brain fMRI acquisitions examined whole-brain activation in response to a simple visual stimulus after averaging, in individual participants, 100 runs across 10 sessions [63,64]. It was found that, after averaging this massive amount of data, activations were detected over nearly the entire brain. One of the main findings was responses in regions outside of the visual cortex whose time-courses did not resemble the stimulus timing, suggesting that in typical fMRI analyses performed on typical data may only detect responses that match the modeled activation time-course, and therefore meaningful, repeatable responses that do not match the model go undetected. This implies that false negatives may be pervasive in standard studies, which could be attributed either to high noise or to inappropriate response models or both. These important studies showcase new insights and model-free analysis approaches enabled by highly-powered studies in individual subjects.

While it is typical to acquire about 10 minutes of resting-state data to estimate functional connectivity, an influential pair of studies showed that consistent estimates are achieved only after 100 minutes of data [65,66]. While the exact amount of data required will depend on the specifics of the acquisition and analysis, this also suggests that conventional resting-state studies may not achieve consistent estimates of functional connectivity. These studies also showed that these highly-sampled individual subjects exhibited patterns of functional connectivity that differed from the group maps.

Along these same lines, a recent set of studies acquired resting-state fMRI data in individual subjects over 24 experimental sessions and investigated subject-specific functional connectivity maps from these data [67]. They identified a fractionation of the Default Network into two spatially distinct, adjacent, interdigitated subnetworks from the resting-state data, and these subnetworks also exhibited distinct responses to several tasks of high-level cognitive function. They similarly found a fractionation of the Fronto-Parietal Control Network and the dorsal Attention Network within association cortex, and demonstrated that these subnetworks also have complex inter-digitated relationships, however the subnetworks exhibit the same general progression across subjects. These subnetworks could also be seen in individual subjects from single experimental sessions at 7T, suggesting that 7T fMRI may in some cases provide sufficient sensitivity for single-subject fMRI [68].

One important practical consideration for longer resting-state fMRI sessions is changes in subject arousal. Since the probability of the subject remaining awake drops to nearly 50% after the first 10 minutes [69], and because functional connectivity patterns change with arousal [70], going forward studies may consider additional measures to quantify arousal. While resting-state fMRI studies often collect recordings of physiological signals using external sensors, these standard sensors are not suitable for assessing arousal. Simple behavioral measures are effective but run the risk of altering the measured brain networks. Simultaneous EEG is the gold-standard approach for quantifying arousal but it is still technically and logistically challenging. For these reasons more groups are opting for in-bore eye tracking to measure eye closures and movement, which may be an effective means to assess eye closures and arousal state [67,71].

Multi-session anatomical datasets: challenges and opportunities

One approach to examining brain anatomy at high resolutions is ex vivo MRI of human brain specimens, which can allow for resolutions at the 100-micron scale over the entire brain in a single multiple-day acquisition [72–75]. For in vivo imaging, however, scan times are limited, and image quality is limited by subject motion for moderately long acquisitions. Motion suppression can be partly achieved using classic approaches (such as careful placement of viscoelastic foam cushions, inflatable fixation cushions or vacuum fixation cushions), including providing tactile feedback to participants [76]. A new approach uses subject- and coil-specific custom head cases [77] which can be used to suppress head motion and improve fMRI data quality [78] although this approach may not be efficacious with all fMRI tasks [79].

There are several available approaches for motion correction, both prospective and retrospective, that can enable longer scan times to encode the high resolution with sufficient numbers of averages to attain sufficient SNR [80]. Retrospective correction with minimal sequence modification has enabled extremely high resolution scanning at 7T [81]. Prospective correction based on built-in navigators, where navigator is placed into a gap or dead-time in the sequence, has been shown to improve image quality and enables reacquisition of any portions of the data that are corrupted by motion [82–86]. The navigators must be designed so as to not affect parent sequence image contrast. Prospective motion correction based on camera systems can be utilized in a broad range of pulse sequences since there are fewer requirements compared to navigator-based approaches [87,88]. These methods have been applied to acquiring impressive multi-session ultra-high-resolution anatomical data sets [89] to achieve sufficient SNR.

There are many challenges in multi-session high-resolution acquisition, including differences in image artifacts and distortions that can vary across days, as well as real changes in the brain anatomy. Several sources of geometric distortion and intensity bias are head-position dependent—such as gradient nonlinearity, B0 inhomogeneity, proximity to transmit/receive coils, and differences in partial volume effects—and therefore care should be taken to achieve similar head positioning relative to the scanner across sessions. Subtle anatomical and physiological differences can manifest across sessions that can also cause discrepancy across datasets, including the well-documented time-of-day effects [90–93] as well as changes in metabolic state and hydration [94–96].

An example multi-session high-resolution anatomical dataset is the 760 μm isotropic diffusion dataset acquired with b=1,000 and 2,500 s/mm2 over nine two-hour sessions [97] using the recently-introduced “gSlider” method [98,99]. This dataset used a custom-made subject-specific head-case molded head holder fit to the 64-channel RF receive coil array both to suppress within-session motion and to help achieve consistent head positioning across sessions.

Advanced MRI instrumentation: sensing and control

Increasing channel counts in receive coil arrays is a cost-effective way of improving data quality through boosting parallel imaging performance and increasing SNR [100]. These highly-sensitive arrays can lead to position-dependent intensity biases and therefore care must be taken to suppress within-session motion and achieve consistent head positioning across session to minimize signal variation [101–103].

Other technologies have recently become more mature that can also contribute to improved imaging performance and sensitivity that fall into the category of field sensing and control. Field sensing using external field probes is a promising technology that allows for the magnetic field within the scanner to be measured dynamically during imaging to estimate field imperfections [104–109]. Once measured, these dynamic field changes can be used either to calibrate the system, or to inform corrections which are typically performed during the image reconstruction process. These imperfections, e.g. due to eddy currents, are typically measured for a specific sequence protocol on a phantom which are then used to correct the in vivo data. Non-cartesian acquisitions are particularly vulnerable to field imperfections (which alter the acquisition trajectory in k-space), and several recent examples have demonstrated stunning image quality for spiral acquisitions at 7 Tesla, including spirals for anatomical imaging [110] and single-shot spirals for fMRI [111,112]. Field probes have also recently been demonstrated to help with eddy current correction in cartesian EPI acquisition [113] and dynamic B0 shimming [114].

Field probes can also be used concurrently during in vivo imaging, in which case they can also measure field changes due to dynamic changes in physiology or head position [115–117]. It is also possible to utilize concurrent field monitoring for MR system feedback [118].

A complementary technology is field control through advanced B0 shimming. While conventional B­­0 shimming using spherical harmonic shim coils built into the gradient set has been sufficient in the past, there is a trend towards local arrays or inserts of shim coils closer to the head. These provide additional degrees of freedom to efficiently homogenize the field [119–121]. Recent examples demonstrate how the extra degrees of freedom in field control provided by multi-coil shim arrays can homogenize the B0 field in the human brain to reduce geometric distortion of EPI to the point where it closely resembles anatomical data, even at ultrahigh field strengths of 9.4T [122]. High-order spherical harmonic shimming using specialized shim inserts has also shown dramatic benefits for field homogenization [123,124]. A new “AC/DC” design has emerged that can integrate the shimming elements (“DC”) with the RF detector coils (“AC”) such that both share the same conductor and do not compete for precious real estate around the head [125,126]. Advances in Ultra-High-Field shimming have recently been reviewed [127].

One of the main reasons for improving B0 field homogeneity is to reduce distortions in EPI data, and these advanced shimming instruments dramatically improve field homogeneity compared to what can be achieved with conventional second-order spherical harmonic shims, which translates into reduced EPI distortion. An important implication of this is that, if distortion can be removed from the EPI data by improving the homogeneity of the B0 field, this means that one does not need to employ such high parallel imaging acceleration factors to mitigate distortion. This relieves some of the burden placed on the parallel imaging, allowing for lower acceleration factors and therefore higher SNR.

A promising new direction is to combine field sensing and field control using the degrees of freedom provided by high-channel-count AC/DC RF+B0 shim coil arrays. There are several methods for concurrently measuring field changes at relatively high spatial order through parallel imaging techniques [128–131]. These B0 navigator approaches are nicely compatible with B0 shimming with AC/DC arrays, since both the field sensing and field control may be possible with the same coil loops; and while the spatial complexity of the B0 field estimates is limited by the coil sensitivities, it is matched to what can be achieved with field control.

Special-purpose MRI systems

While most research and clinical MRI scanners were designed for general use, there is a trend towards high-end special-purpose MRI systems optimized for a specific form of imaging, such as functional or diffusion MR. The concept of a special-purpose scanner is not new [132–134], and recent efforts have again focused on gradient coil performance.

The performance of gradient coils was previously limited by engineering concerns, and these issues are now largely resolved, leaving patient safety (i.e., the bioeffects of rapidly switching, large magnetic fields) as the main limiting factor. Peripheral Nerve Stimulation (PNS) is strictly regulated in MRI, however smaller head gradient coil designs that cover only the subject’s head and neck offers a simple strategy for reducing PNS in brain imaging [135]. We are now seeing a renaissance for head gradient coils [136–142]. These coils can achieve high switching speeds required for high-resolution EPI. For this reason, there are several ongoing efforts to design an MRI scanner specifically for high-resolution functional MRI, using custom high-slew-rate gradient coils with large inner diameters to accommodate the additional instrumentation (RF+B0 shim coil array, field probes, motion-tracking cameras) needed to ensure high image quality, such as the “Nex Gen 7T” scanner developed by the Berkeley-MGH-UCSF-Vanderbilt-Maastricht group.

Similarly, recent efforts have been invested in building an MRI scanner specifically for enhanced diffusion imaging, which are also centered on high-performance gradient coils. These gradient coils are designed for high gradient strength, which is particularly beneficial for high b-value diffusion imaging. The “Connectom” gradient coil can achieve a gradient strength of 300 mT/m, which is seven times the strength of a gradient coil in a clinical MRI scanner [143,144]. There are several efforts that showcase the benefits of this exceptional gradient strength for microstructural imaging [145–149] including a publicly-available dataset including diffusion data at a b-value of b=10,000 s/mm2 [150,151].

Additional extreme performance gradient coils have been introduced, which further extend what can be achieved [134,152,153].

Finally, there is an ongoing trend towards higher magnetic field strengths beyond 10 Tesla to increase SNR and enhance image contrast. Exciting ongoing developments are the 10.5 T whole-body human scanner at the University of Minnesota, the 11.7 T head-only human scanner at the National Institutes of Health, and the 11.7 T whole-body human scanner at NeuroSpin. Not only will these systems provide unprecedented sensitivity, but these advanced systems are also technology drivers—much of the engineering and innovation that goes into helping these systems reach their potential, including both hardware and software advances, will be beneficial for users scanning at all field strengths.

Conclusions and outlook

Overall there are several trends that appear when considering high-end data acquisition. We see advanced EPI technologies allow robust high-resolution acquisitions, however single-subject fMRI still often sensitivity limited. We also see emerging instrumentation for additional sensing and control, and opportunities to combine these complementary technologies. What may be surprising is that “biological factors” such as subject safety, motion/physiology, or behavior, often limit performance rather than the capabilities of the instrumentation. We see an emergence of a new generation of scanners optimized for specific applications and a trend in high-end acquisition towards more application-specific instrumentation. Finally, we see that often the technology that is practical for use in small sample-size studies is not yet practical for everyday neuroimaging, however as new technologies prove their worth substantial investment is needed to make them streamlined, automatic and eventually routine to become usable for large-scale studies as well.


Acknowledgements: Supported in part by the NIH NIBIB (grants P41-EB015896, R01-EB019437 and R21-NS106706), by the BRAIN Initiative (NIH NIMH grants R01-MH111419 and R01-MH111438, and NIH NIBIB grant U01-EB025162), and by the MGH/HST Athinoula A. Martinos Center for Biomedical Imaging.


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