MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning

A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (135 and 23 patients, respectively). It was compared to an approach based on lesion masks. The gamma mixture distribution of whole brain, white matter (WM) MWF was characterized with three variables: the mode (most frequent value) m1 of the gamma component shown to relate to lesion, the mode m2 of the component shown to be associated with normal appearing (NA) WM, and the mixing ratio (λ) between the two distributions. The lesion-mask approach relied on the mean MWF within lesion and within NAWM. A multivariate regression analysis was carried out to find the best predictors of cortical thickness for each group and for each approach. The gamma-mixture method was shown to outperform the lesion-mask approach in terms of adjusted R2, both for the RRMS and SPMS groups. The predictors of the final gamma-mixture models were found to be m1 (β = 1.56, p \u3c 0.005), λ (β = −0.30, p \u3c 0.0005) and age (β = −0.0031, p \u3c 0.005) for the RRMS group (adjusted R2 = 0.16), and m2 (β = 4.72, p \u3c 0.0005) for the SPMS group (adjusted R2 = 0.45). Further, a DICE coefficient analysis demonstrated that the lesion mask had more overlap to an ROI associated with m1, than to an ROI associated with m2 (p \u3c 0.00001), and vice versa for the NAWM mask (p \u3c 0.00001). These results suggest that during the relapsing phase, focal WM damage is associated with cortical thinning, yet in SPMS patients, global WM deterioration has a much stronger influence on secondary degeneration. Through these findings, we demonstrate the potential contribution of myelin loss on neuronal degeneration at different disease stages and the usefulness of our statistical reduction technique which is not affected by the typical bias associated with approaches based on lesion masks

Connectivity, not region-intrinsic properties, predicts regional vulnerability to progressive tau pathology in mouse models of disease

Abstract Spatiotemporal tau pathology progression is regarded as highly stereotyped within each type of degenerative condition. For instance, AD has a progression of tau pathology consistently beginning in the entorhinal cortex, the locus coeruleus, and other nearby noradrenergic brainstem nuclei, before spreading to the rest of the limbic system as well as the cingulate and retrosplenial cortices. Proposed explanations for the consistent spatial patterns of tau pathology progression, as well as for why certain regions are selectively vulnerable to exhibiting pathology over the course of disease generally focus on transsynaptic spread proceeding via the brain’s anatomic connectivity network in a cell-independent manner or on cell-intrinsic properties that might render some cell populations or regions uniquely vulnerable. We test connectivity based explanations of spatiotemporal tau pathology progression and regional vulnerability against cell-intrinsic explanation, using regional gene expression profiles as a proxy. We find that across both exogenously seeded and non-seeded tauopathic mouse models, the connectivity network provides a better explanation than regional gene expression profiles, even when such profiles are limited to specific sets of tau risk-related genes only. Our results suggest that, regardless of the location of pathology initiation, tau pathology progression is well characterized by a model positing entirely cell-type and molecular environment independent transsynaptic spread via the mouse brain’s connectivity network. These results further suggest that regional vulnerability to tau pathology is mainly governed by connectivity with regions already exhibiting pathology, rather than by cell-intrinsic factors

Spatial patterns of genome‐wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain

Unraveling the relationship between molecular signatures in the brain and their functional, architectonic and anatomic correlates is an important neuroscientific goal. It is still not well understood whether the diversity demonstrated by histological studies in the human brain is reflected in the spatial patterning of whole brain transcriptional profiles. Using genome-wide maps of transcriptional distribution of the human brain by the Allen Brain Institute, we test the hypothesis that gene expression profiles are specific to anatomically described brain regions. In this work, we demonstrate that this is indeed the case by showing that gene similarity clusters appear to respect conventional basal-cortical and caudal-rostral gradients. To fully investigate the causes of this observed spatial clustering, we test a connectionist hypothesis that states that the spatial patterning of gene expression in the brain is simply reflective of the fiber tract connectivity between brain regions. We find that although gene expression and structural connectivity are not determined by each other, they do influence each other with a high statistical significance. This implies that spatial diversity of gene expressions is a result of mainly location-specific features, but is influenced by neuronal connectivity, such that like cellular species preferentially connects with like cells

Additional file 1: Table S1. of Connectivity, not region-intrinsic properties, predicts regional vulnerability to progressive tau pathology in mouse models of disease

A list of genes used in the specific tau aggregation and expression factor related genes and noradrenergic neurotransmission related genes. The first column lists the gene abbreviations, the second lists the full gene name denoting basic function, and the third column gives the appropriate citation. Table S2. Regression and Multivariate Linear Models run with all 426, rather than only per-study selected regions. The entries under the “Bivariate Correlations” row correspond to the ΔR obtained from running the ND model with each row’s network from reported seedpoint. The four entries after the “Multivariate Linear Model” row represent the t-values and p-value thresholds obtained from ND model predictions or summed regional expression predictions after they were input as independent predictors into a Multivariate Linear Fit Model. *** p < 0.001, ** p < 0.01, * p < 0.05. (DOCX 132 kb

Additional file 2: Figure S1. of Connectivity, not region-intrinsic properties, predicts regional vulnerability to progressive tau pathology in mouse models of disease

Per study r-value chart and scatterplots for connectivity, gene expression profile, and spatial proximity with reported seed regions. (a) Bar chart of r-values, per study, between regional tauopathy data and proximity with the reported seed region in connectivity, gene expression profile, and spatial distance networks. We also show scatterplots of the relationship between proximity with the reported seed region across each network, as indicated by the title above each scatterplot, and regional tau pathology data from (b) DSAD homogenate injected P301S mice and (c) CBD homogenate injected P301S mice from [4], (d) P301S mice injected in the hippocampus and (e) caudoputamen with synthetic tau fibrils from [19], as well as hTau Alz17 mice injected with P301S purified tau tangles in the hippocampus from [9]. Figure S2. Per study r-value chart and scatterplots of regionally summed gene expression across tau aggregation and transcription promoting genes, as well as noradrenergic neurotransmission related genes. (a) Bar chart of r-values for connectivity proximity with reported seed regions, empirical seed regions, and the regionally summed gene expression values with regional tau pathology data. We also show the scatterplots depicting the relationship between the regionally summed gene expression levels with data from (b) DSAD homogenate injected P301S mice and (c) CBD homogenate injected P301S mice from [4], (d) P301S mice injected in the hippocampus and (e) caudoputamen with synthetic tau fibrils from [19], as well as hTau Alz17 mice injected with P301S purified tau tangles in the hippocampus from [9]. Figure S3. Scatterplots and βt curves for each of the relationships between ND modeling using connectivity, gene expression profile similarity, and spatial distance networks with regional tau pathology data, run from reported seedpoints using only study selected regions. The panels are the βt curves for end state tau deposition and regional slope of tauopathy increase, in that order, as well as the scatterplots for end state tau deposition on the top and regional slope of tauopathy increase on the bottom. They are presented in the following order according to study: (b) DSAD homogenate injected P301S mice and (c) CBD homogenate injected P301S mice from [4], (d) P301S mice injected in the hippocampus and (e) caudoputamen with synthetic tau fibrils from [19], as well as hTau Alz17 mice injected with P301S purified tau tangles in the hippocampus from [9]. Figure S4. Scatterplots and βt curves for each of the relationships between ND modeling using connectivity, gene expression profile similarity, and spatial distance networks with regional tau pathology data, run from reported seedpoints using all 426 ABA regions. The panels are the βt curves for end state tau deposition and regional slope of tauopathy increase, in that order, as well as the scatterplots for end state tau deposition on the top and regional slope of tauopathy increase on the bottom. They are presented in the following order according to study: (b) DSAD homogenate injected P301S mice and (c) CBD homogenate injected P301S mice from [4], (d) P301S mice injected in the hippocampus and (e) caudoputamen with synthetic tau fibrils from [19], as well as hTau Alz17 mice injected with P301S purified tau tangles in the hippocampus from [9]. Figure S5. Scatterplots for the correlation between data from the non-seeded mouse dataset obtained from [17], with ND modeling across networks and timepoints as well as regionally summed gene expression with final measured timepoint of regional tauopathy severity; analysis here is done using both only study selected regions and all 426 ABA regions. (a) The beta-t parameter optimization curves at 4 months, 6 months, and 8 months using ND modeling with connectivity and gene expression networks, with analysis done using only study selected regions. (b) The attendant scatterplots related to the beta-t parameter curves above at the final (8 month) timepoint. (c) Scatterplot of regional expression of specific tau and noradrenergic related gene sets with regional tau pathology, using only study selected regions in the analysis. (d) The beta-t parameters optimization curves at 4 months, 6 months, and 8 months using ND modeling with connectivity and gene expression networks, with analysis done using all 426 ABA regions. (e) The attendant scatterplots using the curves for the final (8 month) timepoint. (f) Scatterplot of regional expression of specific tau and noradrenergic related gene sets with regional tau pathology, using all 426 ABA regions in the analysis. (PDF 1.14 mb

Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model.

The brain's myelin content can be mapped by T2-relaxometry, which resolves multiple differentially relaxing T2 pools from multi-echo MRI. Unfortunately, the conventional fitting procedure is a hard and numerically ill-posed problem. Consequently, the T2 distributions and myelin maps become very sensitive to noise and are frequently difficult to interpret diagnostically. Although regularization can improve stability, it is generally not adequate, particularly at relatively low signal to noise ratio (SNR) of around 100-200. The purpose of this study was to obtain a fitting algorithm which is able to overcome these difficulties and generate usable myelin maps from noisy acquisitions in a realistic scan time. To this end, we restrict the T2 distribution to only 3 distinct resolvable tissue compartments, modeled as Gaussians: myelin water, intra/extra-cellular water and a slow relaxing cerebrospinal fluid compartment. We also impose spatial smoothness expectation that volume fractions and T2 relaxation times of tissue compartments change smoothly within coherent brain regions. The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions. Due to efficient design, the additional spatial aspect does not cause an increase in processing time. The proposed method was applied to fast spiral acquisitions on which conventional fitting gives uninterpretable results. While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry

Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer’s Disease

Alzheimer’s disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain’s connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model’s rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer’s disease assessment

Diffusion-Weighted and Dynamic Contrast-Enhanced MRI Derived Imaging Metrics for Stereotactic Body Radiotherapy of Pancreatic Ductal Adenocarcinoma: Preliminary Findings

We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusion-weighted (DW-) and dynamic contrast-enhanced magnetic resonance imaging (DCE)-MRI at pre-treatment (TX[0]), immediately after the first fraction of stereotactic body radiotherapy (D1-TX[1]), and 6 weeks post-TX (Post-TX[2]) in patients with pancreatic ductal adenocarcinoma. Ten enrolled patients (n = 10) underwent DW- and DCE-MRI examinations on a 3.0 T scanner. The apparent diffusion coefficient, ADC (mm2/s), was derived from DW imaging data using a monoexponential model. The tissue relaxation rate, R1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, Ktrans (min−1), extravascular extracellular volume fraction, ve, and mean lifetime of intracellular water protons, τi (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (Δ) in ADC, Ktrans, ve, and τi values between the magnetic resonance examinations. rADCΔ2–0 values were significantly greater than rADCΔ1-0 values (P = .009). rKtransΔ2–0 values were significantly lower than rKtrans Δ1-0 values (P = .048). rveΔ2-1 and rveΔ2-0 values were significantly different (P = .016). rτiΔ2-1 values were significantly lower than rτiΔ2-0 values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P = .18 and P = .14), skewness and kurtosis of Ktrans values (P = .14 for both) showed a leaning toward significant difference between patients who experienced local control (n = 2) and failed early (n = 4). DW- and DCE-MRI-derived quantitative metrics could be useful biomarkers to evaluate longitudinal changes to stereotactic body radiotherapy in patients with pancreatic ductal adenocarcinoma

List of parameters to be fitted, per voxel.

<p>Their initial guess and allowable range, used during constrained optimization, are also shown.</p