Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets

Calcium channel blockade with nimodipine reverses MRI evidence of cerebral oedema following acute hypoxia

Acute cerebral hypoxia causes rapid calcium shifts leading to neuronal damage and death. Calcium channel antagonists improve outcomes in some clinical conditions, but mechanisms remain unclear. In 18 healthy participants we: (i) quantified with multiparametric MRI the effect of hypoxia on the thalamus, a region particularly sensitive to hypoxia, and on the whole brain in general; (ii) investigated how calcium channel antagonism with the drug nimodipine affects the brain response to hypoxia. Hypoxia resulted in a significant decrease in apparent diffusion coefficient (ADC), a measure particularly sensitive to cell swelling, in a widespread network of regions across the brain, and the thalamus in particular. In hypoxia, nimodipine significantly increased ADC in the same brain regions, normalizing ADC towards normoxia baseline. There was positive correlation between blood nimodipine levels and ADC change. In the thalamus, there was a significant decrease in the amplitude of low frequency fluctuations (ALFF) in resting state functional MRI and an apparent increase of grey matter volume in hypoxia, with the ALFF partially normalized towards normoxia baseline with nimodipine. This study provides further evidence that the brain response to acute hypoxia is mediated by calcium, and importantly that manipulation of intracellular calcium flux following hypoxia may reduce cerebral cytotoxic oedem

Faster permutation inference in brain imaging.

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models

Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) – one of the most widely used techniques for the exploratory analysis of fMRI data – has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB's ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL

High resolution diffusion-weighted imaging in fixed human brain using diffusion-weighted steady state free precession

High resolution diffusion tensor imaging and tractography of ex vivo brain specimens has the potential to reveal detailed fibre architecture not visible on in vivo images. Previous ex vivo diffusion imaging experiments have focused on animal brains or small sections of human tissue since the unfavourable properties of fixed tissue (including short T(2) and low diffusion rates) demand the use of very powerful gradient coils that are too small to accommodate a whole, human brain. This study proposes the use of diffusion-weighted steady-state free precession (DW-SSFP) as a method of extending the benefits of ex vivo DTI and tractography to whole, human, fixed brains on a clinical 3 T scanner. DW-SSFP is a highly efficient pulse sequence; however, its complicated signal dependence precludes the use of standard diffusion tensor analysis and tractography. In this study, a method is presented for modelling anisotropy in the context of DW-SSFP. Markov Chain Monte Carlo sampling is used to estimate the posterior distributions of model parameters and it is shown that it is possible to estimate a tight distribution on the principal axis of diffusion at each voxel using DW-SSFP. Voxel-wise estimates are used to perform tractography in a whole, fixed human brain. A direct comparison between 3D diffusion-weighted spin echo EPI and 3D DW-SSFP-EPI reveals that the orientation of the principal diffusion axis can be inferred on with a higher degree of certainty using a 3D DW-SSFP-EPI even with a 68% shorter acquisition time

Early brain injury and cognitive impairment after aneurysmal subarachnoid haemorrhage

The first 72 h following aneurysm rupture play a key role in determining clinical and cognitive outcomes after subarachnoid haemorrhage (SAH). Yet, very little is known about the impact of so called “early brain injury” on patents with clinically good grade SAH (as defined as World Federation of Neurosurgeons Grade 1 and 2). 27 patients with good grade SAH underwent MRI scanning were prospectively recruited at three time-points after SAH: within the first 72 h (acute phase), at 5–10 days and at 3 months. Patients underwent additional, comprehensive cognitive assessment 3 months post-SAH. 27 paired healthy controls were also recruited for comparison. In the first 72 h post-SAH, patients had significantly higher global and regional brain volume than controls. This change was accompanied by restricted water diffusion in patients. Persisting abnormalities in the volume of the posterior cerebellum at 3 months post-SAH were present to those patients with worse cognitive outcome. When using this residual abnormal brain area as a region of interest in the acute-phase scans, we could predict with an accuracy of 84% (sensitivity 82%, specificity 86%) which patients would develop cognitive impairment 3 months later, despite initially appearing clinically indistinguishable from those making full recovery. In an exploratory sample of good clinical grade SAH patients compared to healthy controls, we identified a region of the posterior cerebellum for which acute changes on MRI were associated with cognitive impairment. Whilst further investigation will be required to confirm causality, use of this finding as a risk stratification biomarker is promising

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 FMRIB's 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