Dataset Information

Consistency of magnetoencephalographic functional connectivity and network reconstruction using a template versus native MRI for co-registration.

ABSTRACT:

Introduction

Studies using functional connectivity and network analyses based on magnetoencephalography (MEG) with source localization are rapidly emerging in neuroscientific literature. However, these analyses currently depend on the availability of costly and sometimes burdensome individual MR scans for co-registration. We evaluated the consistency of these measures when using a template MRI, instead of native MRI, for the analysis of functional connectivity and network topology.

Methods

Seventeen healthy participants underwent resting-state eyes-closed MEG and anatomical MRI. These data were projected into source space using an atlas-based peak voxel and a centroid beamforming approach either using (1) participants' native MRIs or (2) the Montreal Neurological Institute's template. For both methods, time series were reconstructed from 78 cortical atlas regions. Relative power was determined in six classical frequency bands per region and globally averaged. Functional connectivity (phase lag index) between each pair of regions was calculated. The adjacency matrices were then used to reconstruct functional networks, of which regional and global metrics were determined. Intraclass correlation coefficients were calculated and Bland-Altman plots were made to quantify the consistency and potential bias of the use of template versus native MRI.

Results

Co-registration with the template yielded largely consistent relative power, connectivity, and network estimates compared to native MRI.

Discussion

These findings indicate that there is no (systematic) bias or inconsistency between template and native MRI co-registration of MEG. They open up possibilities for retrospective and prospective analyses to MEG datasets in the general population that have no native MRIs available. Hum Brain Mapp, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. Hum Brain Mapp 39:104-119, 2018. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Douw L

PROVIDER: S-EPMC5725722 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Examination of intrinsic functional connectivity using functional MRI (fMRI) has provided important findings regarding dysconnectivity in schizophrenia. Extending these results using a complementary neuroimaging modality, magnetoencephalography (MEG), we present the first direct comparison of functional connectivity between schizophrenia patients and controls, using these two modalities combined. We developed a novel MEG approach for estimation of networks using MEG that incorporates spatial independent component analysis (ICA) and pairwise correlations between independent component timecourses, to estimate intra- and intern-network connectivity. This analysis enables group-level inference and testing of between-group differences. Resting state MEG and fMRI data were acquired from a large sample of healthy controls (n=45) and schizophrenia patients (n=46). Group spatial ICA was performed on fMRI and MEG data to extract intrinsic fMRI and MEG networks and to compensate for signal leakage in MEG. Similar, but not identical spatial independent components were detected for MEG and fMRI. Analysis of functional network connectivity (FNC; i.e., pairwise correlations in network (ICA component) timecourses) revealed a differential between-modalities pattern, with greater connectivity among occipital networks in fMRI and among frontal networks in MEG. Most importantly, significant differences between controls and patients were observed in both modalities. MEG FNC results in particular indicated dysfunctional hyperconnectivity within frontal and temporal networks in patients, while in fMRI FNC was always greater for controls than for patients. This is the first study to apply group spatial ICA as an approach to leakage correction, and as such our results may be biased by spatial leakage effects. Results suggest that combining these two neuroimaging modalities reveals additional disease-relevant patterns of connectivity that were not detectable with fMRI or MEG alone.

Project description:Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.

Project description:The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

Project description:BackgroundFibromyalgia (FM) is a disabling chronic pain syndrome with unknown pathophysiology. Functional magnetic resonance imaging studies on FM have suggested altered brain connectivity between the insula and the default mode network (DMN). However, this connectivity change has not been characterized through direct neural signals for exploring the embedded spectrotemporal features and the pertinent clinical relevance.MethodsWe recorded the resting-state magnetoencephalographic activities of 28 patients with FM and 28 age- and sex-matched controls, and analyzed the source-based functional connectivity between the insula and the DMN at 1-40 Hz by using the minimum norm estimates and imaginary coherence methods. We also measured the connectivity between the DMN and the primary visual (V1) and somatosensory (S1) cortices as intrapatient negative controls. Connectivity measurement was further correlated with the clinical parameters of FM.ResultsCompared with the controls, patients with FM reported more tender points (15.2±2.0 vs. 5.9±3.7) and higher total tenderness score (TTS; 29.1±7.0 vs. 7.7±5.5; both p < 0.001); they also had decreased insula-DMN connectivity at the theta band (4-8 Hz; left, p = 0.007; right, p = 0.035), but displayed unchanged V1-DMN and S1-DMN connectivity (p > 0.05). When patients with FM and the controls were combined together, the insula-DMN theta connectivity was negatively correlated with the number of tender points (left insula, r = -0.428, p = 0.001; right insula, r = -0.4, p = 0.002) and TTS score (left insula, r = -0.429, p = 0.001; right insula, r = -0.389, p = 0.003). Furthermore, in patients with FM, the right insula-DMN connectivity at the beta band (13-25 Hz) was negatively correlated with the number of tender points (r = -0.532, p = 0.004) and TTS (r = -0.428, p = 0.023), and the bilateral insula-DMN connectivity at the delta band (1-4 Hz) was negatively correlated with FM Symptom Severity (left: r = -0.423, p = 0.025; right: r = -0.437, p = 0.020) and functional disability (Fibromyalgia Impact Questionnaire; left: r = -0.415, p = 0.028; right: r = -0.374, p = 0.050).ConclusionsWe confirmed the frequency-specific reorganization of the insula-DMN connectivity in FM. The clinical relevance of this connectivity change may warrant future studies to elucidate its causal relationship and potential as a neurological signature for FM.

Project description:Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.