PURPOSE: Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS: The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS: Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m) ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
Studies using diffusion tensor imaging (DTI) have documented alterations in the attention and executive system in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). While abnormalities in the frontal lobe have also been reported, the associated white matter fiber bundles have not been investigated comprehensively due to the complexity in tracing them through fiber crossings. Furthermore, most studies have used a non-specific DTI model to understand white matter abnormalities. We present results from a first study that uses a multi-shell diffusion MRI (dMRI) data set coupled with an advanced multi-fiber tractography algorithm to probe microstructural measures related to axonal/cellular density and volume of fronto-striato-thalamic pathways in children with ADHD (N = 30) and healthy controls (N = 28). Head motion was firstly examined as a priority in order to assure that no group difference existed. We investigated 45 different white matter fiber bundles in the brain. After correcting for multiple comparisons, we found lower axonal/cellular packing density and volume in ADHD children in 8 of the 45 fiber bundles, primarily in the right hemisphere as follows: 1) Superior longitudinal fasciculus-II (SLF-II) (right), 2) Thalamus to precentral gyrus (right), 3) Thalamus to superior-frontal gyrus (right), 4) Caudate to medial orbitofrontal gyrus (right), 5) Caudate to precentral gyrus (right), 6) Thalamus to paracentral gyrus (left), 7) Caudate to caudal middlefrontal gyrus (left), and 8) Cingulum (bilateral). Our results demonstrate reduced axonal/cellular density and volume in certain frontal lobe white matter fiber tracts, which sub-serve the attention function and executive control systems. Further, our work shows specific microstructural abnormalities in the striato-thalamo-cortical connections, which have not been previously reported in children with ADHD.
PURPOSE: To develop and validate a CT harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols.
METHODS: We propose to reduce the effects of spatially-variant noise such as non-uniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially-variant variance of noise, with an autocalibration technique that reduces the non-uniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, non-homogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n=54 subjects; and GE, n=50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with high-dose and low-dose protocols.
RESULTS: The harmonization reduces the effect of non-homogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74:20%, enabling comparable results between highdose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30 percentage points and showing that the low-dose vs. high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs. high-dose comparisons of non-harmonized data.
CONCLUSION: The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models and reconstruction techniques. This article is protected by copyright. All rights reserved.
The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.
RATIONALE: There is increasing evidence that aberrant processes occurring in the airways may precede the development of idiopathic pulmonary fibrosis (IPF); however, there has been no prior confirmatory data derived from imaging studies.
OBJECTIVES: To assess quantitative measures of airway wall thickness (AWT) in populations characterized for interstitial lung abnormalities (ILA) and for IPF.
METHODS: Computed tomographic imaging of the chest and measures of AWT were available for 6,073, 615, 1,167, and 38 participants from COPDGene (Genetic Epidemiology of COPD study), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study), and the Framingham Heart Study (FHS) and in patients with IPF from the Brigham and Women's Hospital Herlihy Registry, respectively. To evaluate these associations, we used multivariable linear regression to compare a standardized measure of AWT (the square root of AWT for airways with an internal perimeter of 10 mm [Pi10]) and characterizations of ILA and IPF by computed tomographic imaging of the chest.
RESULTS: In COPDGene, ECLIPSE, and FHS, research participants with ILA had increased measures of Pi10 compared with those without ILA. Patients with IPF had mean measures of Pi10 that were even greater than those noted in research participants with ILA. After adjustment for important covariates (e.g., age, sex, race, body mass index, smoking behavior, and chronic obstructive pulmonary disease severity when appropriate), research participants with ILA had increased measures of Pi10 compared with those without ILA (0.03 mm in COPDGene, 95% confidence interval [CI], 0.02-0.03; P < 0.001; 0.02 mm in ECLIPSE, 95% CI, 0.005-0.04; P = 0.01; 0.07 mm in FHS, 95% CI, 0.01-0.1; P = 0.01). Compared with COPDGene participants without ILA older than 60 years of age, patients with IPF were also noted to have increased measures of Pi10 (2.0 mm, 95% CI, 2.0-2.1; P < 0.001). Among research participants with ILA, increases in Pi10 were correlated with reductions in lung volumes in some but not all populations.
CONCLUSIONS: These results demonstrate that measurable increases in AWT are consistently noted in research participants with ILA and in patients with IPF. These findings suggest that abnormalities of the airways may play a role in, or be correlated with, early pathogenesis of pulmonary fibrosis.
Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation. SNPs from 168 loci with P values less than 5 × 10 in the largest GWAS meta-analysis of EABD were analyzed. Eighty-four loci overlapped eQTL, with 12 of these loci showing greater than 80% likelihood of harboring a single, shared GWAS and eQTL causal variant. Seventeen cell types were enriched for overlap between EABD loci and Roadmap Epigenomics marks (permutation P < 0.05), with the strongest enrichment observed in CD4, CD8, and regulatory T cells. We selected a putative causal variant, rs7962469, associated with ACVR1B expression in lung tissue for additional functional investigation, and reporter assays confirmed allele-specific regulatory activity for this variant in human bronchial epithelial and Jurkat immune cell lines. ACVR1B expression levels exhibit a nominally significant association with emphysema distribution. EABD-associated loci are preferentially enriched in regulatory elements of multiple cell types, most notably T-cell subsets. Multiple EABD loci colocalize to regulatory elements that are active across multiple tissues and cell types, and functional analyses confirm the presence of an EABD-associated functional variant that regulates ACVR1B expression, indicating that transforming growth factor-β signaling plays a role in the EABD phenotype. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).
There is a need for methods that distinguish Parkinson's disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), which have similar characteristics in the early stages of the disease. In this prospective study, we evaluate mapping of apparent susceptibility based on susceptibility weighted imaging (SWI) for differential diagnosis. We included 134 patients with PD, 11 with PSP, 10 with MSA and 44 healthy controls. SWI data were processed into maps of apparent susceptibility. In PSP, apparent susceptibility was increased in the red nucleus compared to all other groups, and in globus pallidus, putamen, substantia nigra and the dentate nucleus compared to PD and controls. In MSA, putaminal susceptibility was increased compared to PD and controls. Including all studied regions and using discriminant analysis between PSP and PD, 100% sensitivity and 97% specificity was achieved, and 91% sensitivity and 90% specificity in separating PSP from MSA. Correlations between putaminal susceptibility and disease severity in PD could warrant further research into using susceptibility mapping for monitoring disease progression and in clinical trials. Our study indicates that susceptibility in deep nuclei could play a role in the diagnosis of atypical parkinsonism, especially in PSP.
Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.
BACKGROUND: 22q11.2 Deletion Syndrome (22q11DS) is a genetic, neurodevelopmental disorder characterized by a chromosomal deletion and a distinct cognitive profile. Although abnormalities in the macrostructure of the cortex have been identified in individuals with 22q11DS, it is not known if there are additional microstructural changes in gray matter regions in this syndrome, and/or if such microstructural changes are associated with cognitive functioning.
METHODS: This study employed a novel diffusion MRI measure, the Heterogeneity of Fractional Anisotropy (HFA), to examine variability in the microstructural organization of the cortex in healthy young adults (N = 30) and those with 22q11DS (N = 56). Diffusion MRI, structural MRI, clinical and cognitive data were acquired.
RESULTS: Compared to controls, individuals with 22q11DS evinced increased HFA in cortical association (p = .003, d = 0.86) and paralimbic (p < .0001, d = 1.2) brain areas, whereas no significant differences were found between the two groups in primary cortical brain areas. Additionally, increased HFA of the right paralimbic area was associated with poorer performance on tests of response inhibition, i.e., the Stroop Test (rho = -0.37 p = .005) and the Gordon Diagnostic System Vigilance Commission (rho = -0.41 p = .002) in the 22q11DS group. No significant correlations were found between HFA and cognitive abilities in the healthy control group.
CONCLUSIONS: These findings suggest that cortical microstructural disorganization may be a neural correlate of response inhibition in individuals with 22q11DS. Given that the migration pattern of neural crest cells is disrupted at the time of early brain development in 22q11DS, we hypothesize that these neural alterations may be neurodevelopmental in origin, and reflect cortical dysfunction associated with cognitive deficits.
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
Instead of assuming a constant relationship between brain abnormalities and memory impairment, we aimed to examine the stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in the Alzheimer's disease (AD) continuum. We assessed grey matter volume, white matter (WM) microstructural measures (free-water (FW) and FW-corrected fractional anisotropy), and functional connectivity of the default mode network (DMN) in 54 amnestic mild cognitive impairment (aMCI) and 46 AD. We employed a novel sparse varying coefficient model to investigate how the associations between abnormal brain measures and memory impairment varied throughout disease continuum. We found lower functional connectivity in the DMN was related to worse memory across AD continuum. Higher widespread white matter FW and lower fractional anisotropy in the fornix showed a stronger association with memory impairment in the early aMCI stage; such WM-memory associations then decreased with increased dementia severity. Notably, the effect of the DMN atrophy occurred in early aMCI stage, while the effect of the medial temporal atrophy occurred in the AD stage. Our study provided evidence to support the hypothetical progression models underlying memory dysfunction in AD cascade and underscored the importance of FW increases and DMN degeneration in early stage of memory deficit.
Diffusion-attenuated MR signal for heterogeneous media has been represented as a sum of signals from anisotropic Gaussian sub-domains to the extent that this approximation is permissible. Any effect of macroscopic (global or ensemble) anisotropy in the signal can be removed by averaging the signal values obtained by differently oriented experimental schemes. The resulting average signal is identical to what one would get if the micro-domains are isotropically (e.g., randomly) distributed with respect to orientation, which is the case for "powdered" specimens. We provide exact expressions for the orientationally-averaged signal obtained via general gradient waveforms when the microdomains are characterized by a general diffusion tensor possibly featuring three distinct eigenvalues. This extends earlier results which covered only axisymmetric diffusion as well as measurement tensors. Our results are expected to be useful in not only multidimensional diffusion MR but also solid-state NMR spectroscopy due to the mathematical similarities in the two fields.
Alterations in parietal and temporal white matter microstructure derived from diffusion tensor imaging occur in preclinical and clinical Alzheimer's disease. Amyloid beta (Aβ) deposition and such white matter alterations are two pathological hallmarks of Alzheimer's disease. However, the relationship between these pathologies is not yet understood, partly since conventional diffusion MRI methods cannot distinguish between cellular and extracellular processes. Thus, we studied Aβ-associated longitudinal diffusion MRI changes in Aβ-positive (N = 21) and Aβ-negative (N = 51) cognitively normal elderly obtained from the Alzheimer's Disease Neuroimaging Initiative dataset using linear mixed models. Aβ-positivity was based on Alzheimer's Disease Neuroimaging Initiative amyloid-PET recommendations using a standardized uptake value ratio cut-off of 1.11. We used free-water imaging to distinguish cellular and extracellular changes. We found that Aβ-positive subjects had increased baseline right uncinate fasciculus free-water fraction (FW), associated with worse baseline Alzheimer's disease assessment scale scores. Furthermore, Aβ-positive subjects showed faster decrease in fractional anisotropy (FW-corrected) in the right uncinate fasciculus and faster age-dependent right inferior longitudinal fasciculus FW increases over time. Right inferior longitudinal fasciculus FW increases were associated with greater memory decline. Importantly, these results remained significant after controlling for gray and white matter volume and hippocampal volume. This is the first study to illustrate the influence of Aβ burden on early longitudinal (in addition to baseline) white matter changes in cognitively normal elderly individuals at-risk of Alzheimer's disease, thus underscoring the importance of longitudinal studies in assessing microstructural alterations in individuals at risk of Alzheimer's disease prior to symptoms onset.
BACKGROUND: Cor pulmonale (right ventricular dilation) and cor pulmonale parvus (right ventricular shrinkage) are both described in chronic obstructive pulmonary disease (COPD). The identification of emphysema as a shared risk factor suggests that additional disease characterization is needed to understand these widely divergent cardiac processes. Here, we explored the relationship between CT measures of emphysema and distal pulmonary arterial morphology with RV volume, as well as their association with exercise capacity and mortality in ever-smokers with COPD enrolled in the COPDGene Study.
METHODS: Epicardial (myocardium and chamber) RV volume (RVEV), distal pulmonary arterial blood vessel volume (arterial BV5: vessels <5mm2 in cross section) as well as objective measures of emphysema were extracted from 3,506 COPDGene CT scans. Multivariable linear and Cox regression models as well as the log rank test were used to explore the association between emphysema, arterial BV5 and RVEV with exercise capacity (6-MWD) and all-cause mortality.
RESULTS: The RVEV was approximately 10% smaller in GOLD 4 vs GOLD 1 COPD (P<0.0001). In multivariable modeling, a 10mL decrease in arterial BV5 (pruning) was associated with a 1mL increase in RVEV. For a given amount of emphysema, relative preservation of the arterial BV5 was associated with a smaller RVEV. An increased RVEV was associated with reduced 6-MWD and in those with arterial pruning an increased mortality.
CONCLUSIONS: Pulmonary arterial pruning is associated with clinically significant increases in right ventricular volume in smokers with COPD and is related to exercise capacity and mortality in COPD.
Alterations in cognitive performance have been noted in nondemented subjects with elevated accumulation of amyloid-β (Aβ) fibrils. However, it is not yet understood whether brain function is already influenced by Aβ deposition during the very earliest stages of the disease. We therefore investigated associations between [18F]Flutemetamol PET, resting-state functional connectivity, gray and white matter structure and cognitive performance in 133 cognitively normal elderly that exhibited normal global Aβ PET levels. [18F]Flutemetamol uptake in regions known to accumulate Aβ fibrils early in preclinical AD (i.e., mainly certain parts of the default-mode network) was positively associated with dynamic but not static functional connectivity (r = 0.77). Dynamic functional connectivity was further related to better cognitive performance (r = 0.21-0.72). No significant associations were found for Aβ uptake with gray matter volume or white matter diffusivity. The findings demonstrate that the earliest accumulation of Aβ fibrils is associated with increased functional connectivity, which occurs before any structural alterations. The enhanced functional connectivity may reflect a compensatory mechanism to maintain high cognitive performance in the presence of increasing amyloid accumulation during the earliest phases of AD.
RATIONALE: Cigarette smoke exposure is a risk factor for many lung diseases, and histologic studies suggest tobacco-related vasoconstriction and vessel loss plays a role in the development of emphysema. However, it remains unclear how tobacco affects the pulmonary vasculature in general populations with a typical range of tobacco exposure, and whether these changes are detectable by radiographic methods.
OBJECTIVE: To determine whether tobacco exposure in a generally healthy population manifests as lower pulmonary blood vessel volumes and vascular pruning on imaging.
METHODS: 2,410 Framingham Heart Study participants with demographic data and smoking history underwent volumetric whole-lung computed tomography (CT) from 2008-2011. Automated algorithms calculated the total volume of all intrapulmonary vessels (TBV), smaller peripheral vessels (BV5), and the relative fraction of small vessels (BV5/TBV). Tobacco exposure was assessed as smoking status, cumulative pack-years, and second-hand exposure. We constructed multivariable linear regression models to evaluate associations of cigarette exposure and pulmonary blood vessel volume measures, adjusting for demographic covariates including age, sex, height, weight, education, occupation, and median neighborhood income.
RESULTS: All metrics of tobacco exposure (including smoking status, pack-years, and second-hand exposure) were consistently associated with higher absolute pulmonary blood vessel volume, higher small vessel volume, and/or higher small vessel fraction. For example, ever-smokers had a 4.6mL higher TBV (95% CI: 2.9-6.3, p<0.0001), 2.1mL higher BV5 (95% CI: 1.3-2.9, p<0.0001), and 0.28 percentage-point higher BV5/TBV (95% CI: 0.03-0.52, p=0.03) compared to never-smokers. These associations remained significant after adjustment for percent-predicted FEV, cardiovascular comorbidities, and did not differ based on presence or absence of airflow obstruction.
CONCLUSIONS: Using CT imaging, we found that cigarette exposure was associated with higher pulmonary blood vessel volumes, especially in the smaller peripheral vessels. While histologically, tobacco-related vasculopathy is characterized by vessel narrowing and loss, our results suggest that radiographic vascular pruning may not be a surrogate of these pathologic changes.
Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.
Aptitude for and proficiency in acquiring new languages varies in the human population but their neural bases are largely unknown. We investigated the influence of cortical thickness on language learning predictors measured by the LLAMA tests and a pitch-change discrimination test. The LLAMA tests are first language-independent assessments of language learning aptitude for vocabulary, phonetic working memory, sound-symbol correspondence (not used in this study), and grammatical inferencing. Pitch perception proficiency is known to predict aptitude for learning new phonology. Results show a correlation between scores in a grammatical meaning-inferencing aptitude test and cortical thickness of Broca's area (r(30) = 0.65, p = 0.0202) and other frontal areas (r(30) = 0.66, p = 0.0137). Further, a correlation was found between proficiency in discriminating pitch-change direction and cortical thickness of the right Broca homologue (r(30) = 0.57, p = 0.0006). However, no correlations were found for aptitude for vocabulary learning or phonetic working memory. Results contribute to locating cortical regions important for language-learning aptitude.
INTRODUCTION: Abnormalities in the corpus callosum (CC) and the lateral ventricles (LV) are hallmark features of schizophrenia. These abnormalities have been reported in chronic and in first episode schizophrenia (FESZ). Here we explore further associations between CC and LV in FESZ using diffusion tensor imaging (DTI).
METHODS: . Sixteen FESZ patients and 16 healthy controls (HC), matched on age, gender, and handedness participated in the study. Diffusion and structural imaging scans were acquired on a 3T GE Signa magnet. Volumetric measures for LV and DTI measures for five CC subdivisions were completed in both groups. In addition, two-tensor tractography, the latter corrected for free-water (FA), was completed for CC. Correlations between LV and DTI measures of the CC were examined in both groups, while correlations between DTI and clinical measures were examined in only FESZ.
RESULTS: Results from two-tensor tractography demonstrated decreased FA and increased trace and radial diffusivity (RD) in the five CC subdivisions in FESZ compared to HC. Central CC diffusion measures in FESZ were significantly correlated with volume of the LV, i.e., decreased FA values were associated with larger LV volume, while increased RD and trace values were associated with larger LV volume. In controls, correlations were also significant, but they were in the opposite direction from FESZ. In addition, decreased FA in FESZ was associated with more positive symptoms.
DISCUSSION: Partial volume corrected FA, RD, and trace abnormalities in the CC in FESZ suggest possible de- or dys-myelination, or changes in axonal diameters, all compatible with neurodevelopmental theories of schizophrenia. Correlational findings between the volume of LV and diffusion measures in FESZ reinforce the concept of a link between abnormalities in the LV and CC in early stages of schizophrenia and are also compatible with neurodevelopmental abnormalities in this population.