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Data used to create the atlas for In vivo structural MRI-based atlas of human thalamic nuclei
No full textThese are the 20 WMn MPRAGE datasets used to create the structural thalamic atlas (which can be found here https://zenodo.org/record/3966531). Of the 20 datasets, 11 are MS patients (ms1, ms2 etc) and 9 are healthy controls (ctrl1, ctrl2 etc). The format is as follows. Each tgz file contains a WMnMPRAGE_bias_corr.nii.gz which is the bias corrected WMn MPRAGE 7T data set in NifTi format and a directory called sanitized_rois which contains the manual delineation of 11 thalamic nuclei, the mammillothalamic tract (MTT) and the whole thalamus in NifTi format.
This is part of a submission to Scientific Data titled In vivo structural MRI-based atlas of human thalamic nuclei by Saranathan, Manojkumar; Iglehart, Charles; Monti, Martin; Tourdias, Thomas; Rutt, Brian.
Copyright Manojkumar Saranathan 2020
For research use only
Structural changes in thalamic nuclei across prodromal and clinical Alzheimer's disease
No full textBackground: Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer's disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum. Objective: The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS). Methods: MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA. Results: There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects. Conclusion: This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes
How recent learning shapes the brain: Memory-dependent functional reconfiguration of brain circuits
Full text linkThe process of storing recently encoded episodic mnestic traces so that they are available for subsequent retrieval is accompanied by specific brain functional connectivity (FC) changes. In this fMRI study, we examined the early processing of memories in twenty-eight healthy participants performing an episodic memory task interposed between two resting state sessions. Memory performance was assessed through a forced-choice recognition test after the scanning sessions. We investigated resting state system configuration changes via Independent Component Analysis by cross-modeling baseline resting state spatial maps onto the post-encoding resting state, and post-encoding resting state spatial maps onto baseline. We identified both persistent and plastic components of the overall brain functional configuration between baseline and post-encoding. While FC patterns within executive, default mode, and cerebellar circuits persisted from baseline to post-encoding, FC within the visual circuit changed. A significant session × performance interaction characterized medial temporal lobe and prefrontal cortex FC with the visual circuit, as well as thalamic FC within the executive control system. Findings reveal early-stage FC changes at the system-level subsequent to a learning experience and associated with inter-individual variation in memory performance
Atrophy patterns of deep gray matter nuclei in Alzheimer's disease and frontotemporal dementia
No full textBackgroundWhile cortical atrophy has been widely studied in dementia, emerging evidence highlights the role of subcortical degeneration, particularly in deep gray matter structures such as the thalamus, claustrum, and basal nuclei, in both Alzheimer's disease (AD) and frontotemporal dementia (FTD). However, disease-specific subcortical patterns remain undercharacterized.ObjectiveTo quantify deep gray matter atrophy across the AD continuum (mild cognitive impairment and AD) and three FTD subtypes (bvFTD, svFTD, PNFA), and to assess longitudinal atrophy, cognitive associations, and diagnostic classification.MethodsWe applied a novel segmentation pipeline (sTHOMAS) to T1-weighted MRI data from 380 participants in the ADNI cohort and 274 participants in the FTLDNI cohort, with longitudinal follow-up available for 237 participants. Group differences were assessed using ANCOVA (adjusted for age and sex), followed by post hoc comparisons and effect size estimation (Cohen's d). Neuropsychological associations were examined using partial correlations. A hierarchical Random Forest model was trained to classify diagnostic groups.ResultsPronounced atrophy was observed in the mediodorsal, anteroventral, pulvinar thalamic nuclei, and nucleus accumbens, and claustrum in both AD and FTD, but was significantly greater in bvFTD. Longitudinal analysis revealed the fastest progression in bvFTD. Classification achieved 96.8% accuracy (AUC = 0.99) for AD versus FTD and 77.7% accuracy (AUC = 0.83) for PNFA versus svFTD. Subcortical atrophy correlated to executive, language, and semantic deficits.ConclusionsAtrophy in the mediodorsal, pulvinar, anteroventral thalamic nuclei, nucleus accumbens, and claustrum distinguishes AD from FTD and differentiates FTD subtypes. These subcortical structures represent promising biomarkers for diagnosis and monitoring of neurodegeneration
Straegies For Rapid MR Imaging
No full textIn MR imaging, techniques for acquisition of reduced data (Rapid MR imaging) are being explored to obtain high-quality images to satisfy the conflicting requirements of simultaneous high spatial and temporal resolution, required for functional studies. The term “rapid” is used because reduction in the volume of data acquisition leads to faster scans. The objective is to obtain high acceleration factors, since it indicates the ability of the technique to yield high-quality images with reduced data (in turn, reduced acquisition time). Reduced data acquisition in conventional (sequential) MR scanners, where a single receiver coil is used, can be achieved either by acquiring only certain k-space regions or by regularly undersampling the entire data in k-space. In parallel MR scanners, where multiple receiver coils are used to acquire high-SNR data, reduced data acquisition is typically accomplished using regular undersampling.
Optimal region selection in the 3D k-space (restricted to ky - kz plane, since kx is the readout direction) needs to satisfy “maximum energy compaction” and “minimum acquisition” requirements. In this thesis, a novel star-shaped truncation window is proposed to increase the achievable acceleration factor. The proposed window monotonically cuts down the acquisition of the number of k-space samples with lesser energy. The truncation window samples data within a star-shaped region centered around the origin in the ky - kz plane. The missing values are extrapolated using generalized series modeling-based methods. The proposed method is applied to several real and synthetic data sets. The superior performance of the proposed method is illustrated using the standard measures of error images and uptake curve comparisons. Average values of slope error in estimating the enhancement curve are obtained over 5 real data sets of breast and abdomen images, for an acceleration factor of 8. The proposed method results in a slope error of 5%, while the values obtained using rectangular and elliptical windows are 12% and 10%, respectively.
k-t BLAST, a popular method used in cardiac and functional brain imaging, involves regular undersampling. However, the method suffers from drawbacks such as separate training scan, blurred training estimates and aliased phase maps. In this thesis, variations to k-t BLAST have been proposed to overcome the drawbacks. The proposed improved k-t BLAST incorporates variable-density sampling scheme, phase information from the training map and utilization of generalized-series extrapolated training map. The advantage of using a variable density sampling scheme is that the training map is obtained from the actual acquisition instead of a separate pilot scan. Besides, phase information from the training map is used, in place of phase from the aliased map; generalized series extrapolated training map is used instead of the zero-padded training map, leading to better estimation of the unacquired values. The existing technique and the proposed variations are applied on real fMRI data volumes. Improvement in PSNR of activation maps of up to 10 dB. Besides, a reduction of 10% in RMSE is obtained over the entire time series of fMRI images. The peak improvement of the proposed method over k-t BLAST is 35%, averaged over 5 data sets.
Most image reconstruction techniques in parallel MR imaging utilize the knowledge of coil sensitivities for image reconstruction, along with assumptions of image reconstruction functions. The thesis proposes an image reconstruction technique that neither needs to estimate coil sensitivities nor makes any assumptions on the image reconstruction function. The proposed cartesian parallel imaging using neural networks, called “Composite image Reconstruction And Unaliasing using Neural Networks” (CRAUNN), is a novel approach based on the observation that the aliasing patterns remain the same irrespective of whether the k-space acquisition consists of only low frequencies or the entire range of k-space frequencies. In the proposed approach, image reconstruction is obtained using the neural network framework. Data acquisition follows a variable-density sampling scheme, where low k-space frequencies are densely sampled, while the rest of the k-space is sparsely sampled. The blurred, unaliased images obtained using the densely sampled low k-space data are used to train the neural network. Image is reconstructed by feeding to the trained network, the aliased images, obtained using the regularly undersampled k-space containing the entire range of k-space frequencies. The proposed approach has been applied to the Shepp-Logan phantom as well as real brain MRI data sets. A visual error measure for estimating the image quality used in compression literature, called SSIM (Structural SIMilarity) index is employed. The average SSIM for the noisy Shepp-Logan phantom (SNR = 10 dB) using the proposed method is 0.68, while those obtained using GRAPPA and SENSE are 0.6 and 0.42, respectively. For the case of the phantom superimposed with fine grid-like structure, the average SSIM index obtained with the proposed method is 0.7, while those for GRAPPA and SENSE are 0.5 and 0.37, respectively.
Image reconstruction is more challenging with reduced data acquired using non-cartesian trajectories since aliasing introduced is not localized. Popular technique for non-cartesian parallel imaging CGSENSE suffers from drawbacks like sensitivity to noise and requirement of good coil estimates, while radial/spiral GRAPPA requires complete identical scans to obtain reconstruction kernels for specific trajectories. In our work, the proposed neural network based reconstruction method, CRAUNN, has been shown to work for general non-cartesian acquisitions such as spiral and radial too. In addition, the proposed method does not require coil estimates, or trajectory-specific customized reconstruction kernels. Experiments are performed using radial and spiral trajectories on real and synthetic data, and compared with CGSENSE. Comparison of error images shows that the proposed method has far lesser residual aliasing compared to CGSENSE. The average SSIM index for reconstructions using CRAUNN with spirally and radially undersampled data, are comparable at 0.83 and 0.87, respectively. The same measure for reconstructions using CGSENSE are 0.67 and 0.69, respectively. The average RMSE for reconstructions using CRAUNN with spirally and radially undersampled data, are comparable at 11.1 and 6.1, respectively. The same measure for reconstructions using CGSENSE are 16 and 9.18, respectively
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Fast Automatic Segmentation of Thalamic Nuclei
No full textFast, automated segmentation of the thalamic nuclei in the brain has long been desired as it provides for direct visualization of the target for certain procedures like Deep Brain Stimulation (DBS) that target a specific nucleus. It is also beneficial in the study of other pathologies that pertain to different nuclei. In this thesis, a novel approach to fast automated segmentation of thalamic nuclei called Shortened Template and THalamus for Optimal Multi Atlas Segmentation (ST THOMAS) was developed using the multi-atlas segmentation approach. It was designed with a focus on robustness and speed by making use of an averaged template for registration and cropping the inputs and the template. The performance of ST THOMAS was first evaluated on 7T MRI data by comparing with manual delineation (ground truth) by an expert neuroradiologist. Dice coefficients and Volumetric Similarity Indices were used as metrics. To extend the applicability of this method, 3T MRI data were also evaluated. Finally, applications to real time ventralintermideiate (VIM) nucleus targeting for DBS and study of the effects of alcoholism are demonstrated
Thalamic nuclei changes in early and late onset Alzheimer's disease
Full text linkAlzheimer's disease (AD) is the most common cause of dementia worldwide. Increasing evidence points to the thalamus as an important hub in the clinical symptomatology of the disease, with the ‘limbic thalamus’ been described as especially vulnerable. In this work, we examined thalamic atrophy in early-onset AD (EOAD) and late-onset AD (LOAD) compared to young and old healthy controls (YHC and OHC, respectively) using a recently developed cutting-edge thalamic nuclei segmentation method. A deep learning variant of Thalamus Optimized Multi Atlas Segmentation (THOMAS) was used to parcellate 11 thalamic nuclei per hemisphere from T1-weighted MRI in 88 biomarker-confirmed AD patients (49 EOAD and 39 LOAD) and 58 healthy controls (41 YHC and 17 OHC) with normal AD biomarkers. Nuclei volumes were compared among groups using MANCOVA. Further, Pearson's correlation coefficient was computed between thalamic nuclear volume and cortical—subcortical regions, CSF tau levels, and neuropsychological scores. The results showed widespread thalamic nuclei atrophy in EOAD and LOAD compared to their respective healthy control groups, with EOAD showing additional atrophy in the centromedian and ventral lateral posterior nuclei compared to YHC. In EOAD, increased thalamic nuclei atrophy was associated with posterior parietal atrophy and worse visuospatial abilities, while LOAD thalamic nuclei atrophy was preferentially associated with medial temporal atrophy and worse episodic memory and executive function. Our findings suggest that thalamic nuclei may be differentially affected in AD according to the age at symptoms onset, associated with specific cortical—subcortical regions, CSF total tau and cognition
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