Junqian Xu

The minimal preprocessing pipelines for the Human Connectome Project

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCPs acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.

The Human Connectome Project: A data acquisition perspective

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Temporally-independent functional modes of spontaneous brain activity

Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brains different functional networks spontaneously fluctuate in their activity level; each networks spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a regions activity pattern to reflect one networks activity some of the time, and another networks activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple “temporal functional modes,” including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIBs ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

Optical coherence tomography differs in neuromyelitis optica compared with multiple sclerosis

Background: Neuromyelitis optica (NMO) is associated with destructive inflammatory lesions, resulting in necrosis and axonal injury. Disability from multiple sclerosis (MS) is due to a combination of demyelination and varying axonal involvement. Optical coherence tomography (OCT), by measuring retinal nerve fiber layer (RNFL) as a surrogate of axonal injury, has potential to discriminate between these two conditions. Methods: Included were 22 subjects with NMO or NMO spectrum disorders and 47 with MS. Seventeen subjects with NMO and all with MS had a remote history of optic neuritis (ON) in at least one eye, at least 6 months before OCT. Linear mixed modeling was used to compare the two diagnoses for a given level of vision loss, while controlling for age, disease duration, and number of episodes of ON. Results: After ON, NMO was associated with a thinner mean RNFL compared to MS. This was found when controlling for visual acuity (56.7 vs 66.6 μm, p = 0.01) or for contrast sensitivity (61.2 vs 70.3 μm, p = 0.02). The superior and inferior quadrants were more severely affected in NMO than MS. Conclusions: Optic neuritis (ON) within neuromyelitis optica (NMO) is associated with a thinner overall average retinal nerve fiber layer compared to multiple sclerosis, with particular involvement of the superior and inferior quadrants. This suggests that NMO is associated with more widespread axonal injury in the affected optic nerves. Optical coherence tomography can help distinguish the etiology of these two causes of ON, and may be useful as a surrogate marker of axonal involvement in demyelinating disease.

Evaluation of slice accelerations using multiband echo planar imaging at 3 T.

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3 T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3 T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan.

Optical coherence tomography is less sensitive than visual evoked potentials in optic neuritis

Objectives: Determine the utility of optical coherence tomography (OCT) to detect clinical and subclinical remote optic neuritis (ON), its relationship to clinical characteristics of ON and visual function, and whether the retinal nerve fiber layer (RNFL) thickness functions as a surrogate marker of global disease severity. Methods: Cross-sectional study of 65 subjects with at least 1 clinical ON episode at least 6 months prior. Measures included clinical characteristics, visual acuity (VA), contrast sensitivity (CS), OCT, and visual evoked potentials (VEP). Results: Ninety-six clinically affected optic nerves were studied. The sensitivity of OCT RNFL after ON was 60%, decreasing further with mild onset and good recovery. VEP sensitivity was superior at 81% (p = 0.002). Subclinical ON in the unaffected eye was present in 32%. VEP identified 75% of all subclinically affected eyes, and OCT identified <20%. RNFL thickness demonstrated linear correlations with VA (r = 0.65) and CS (r = 0.72) but was unable to distinguish visual categories <20/50. RNFL was thinner with severe onset and disease recurrence but was unaffected by IV glucocorticoids. OCT measurements were not related to overall disability, ethnicity, sex, or age at onset. The greatest predictor for RNFL in the unaffected eye was the RNFL in the fellow affected eye. Conclusions: Visual evoked potentials (VEP) remains the preferred test for detecting clinical and subclinical optic neuritis. Optical coherence tomography (OCT) measures were unrelated to disability and demographic features predicting a worse prognosis in multiple sclerosis. OCT may provide complementary information to VEP in select cases, and remains a valuable research tool for studying optic nerve disease in populations.

Disability in optic neuritis correlates with diffusion tensor-derived directional diffusivities

Objective: To determine the potential of directional diffusivities from diffusion tensor imaging (DTI) to predict clinical outcome of optic neuritis (ON), and correlate with vision, optical coherence tomography (OCT), and visual evoked potentials (VEP). Methods: Twelve cases of acute and isolated ON were imaged within 30 days of onset and followed prospectively. Twenty-eight subjects with a remote clinical history of ON were studied cross-sectionally. Twelve healthy controls were imaged for comparison. DTI data were acquired at 3T with a surface coil and 1.3 × 1.3 × 1.3 mm3 isotropic voxels. Results: Normal DTI parameters (mean ± SD, μm2/ms) were axial diffusivity = 1.66 ± 0.18, radial diffusivity = 0.81 ± 0.26, apparent diffusion coefficient (ADC) = 1.09 ± 0.21, and fractional anisotropy (FA) = 0.43 ± 0.15. Axial diffusivity decreased up to 2.5 SD in acute ON. The decrease in axial diffusivity at onset correlated with visual contrast sensitivity 1 month (r = 0.59) and 3 months later (r = 0.65). In three subjects followed from the acute through the remote stage, radial diffusivity subsequently increased to >2.5 SD above normal, as did axial diffusivity and ADC. In remote ON, radial diffusivity correlated with OCT (r = 0.81), contrast sensitivity (r = 0.68), visual acuity (r = 0.56), and VEP (r = 0.54). Conclusion: In acute and isolated demyelination, axial diffusivity merits further investigation as a predictor of future clinical outcome. Diffusion parameters are dynamic in acute and isolated optic neuritis, with an initial acute decrease in axial diffusivity. In remote disease, radial diffusivity correlates with functional, structural, and physiologic tests of vision.