Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer’s disease (AD). There is much interest in which dMRI measures are most strongly correlated with (1) AD diagnosis, (2) clinical measures of AD severity, such as the clinical dementia rating (CDR), and (3) biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 healthy controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 brain regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were strongly associated with age and AD severity, but also with amyloid positivity. All harmonization methods gave comparable results when assessing associations with age, dementia and amyloid load, while enabling data integration across multiple scanners and protocols.
This study used advanced diffusion-weighted MRI (dMRI) to examine the association between exogenous sex-hormone exposure and the brain’s white matter aging trajectories in a large population-based sample of women. To investigate the effect of pre- and post-menopausal sex hormones on brain aging, cross-sectional brain dMRI data from the UK Biobank was analyzed using 3 diffusion models: conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted and averaged across the whole brain, including fractional anisotropy, isotropic volume fraction, intracellular volume fraction and orientation dispersion index. We used general linear models and fractional polynomial regressions to characterize age-related trajectories in white matter measures following hormone therapy (HT) and oral contraceptive (OC) use in women (HT analysis: N=8,301; OC analysis: N=8,913). Sex hormone treatment (HT and OC) was statistically associated with the aging trends in white matter measures. Estrogen therapy alone appeared to exert a neuroprotective effect on age-related white matter processes, compared to HT containing both estrogen and progestin therapy - which was associated with accelerated aging-related processes in women. These results support the hypothesis that exogenous sex hormone exposure may impact white matter aging; white matter metrics may also be sensitive to sex hormone levels in women. Furthermore, we discuss the necessity to test alternative models for lifespan trajectories beyond popular linear and quadratic models, especially when dealing with large samples. Fractional polynomial models may provide a more adaptive alternative to linear or quadratic models.
The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intracellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extracellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.
Julio Villalon-Reina, Christopher R. K. Ching, Deydeep Kothapalli, Daqiang Sun, Talia Nir, Amy Lin, Jennifer Forsyth, Leila Kushan, Ariana Vajdi, Maria Jalbrzikowski, Laura Hansen, Rachel Jonas, Therese van Amelsvoort, Geor Bakker, Wendy Kates, Kevin Antshel, Wanda Fremont, Linda Campbell, Kathryn McCabe, Eileen Daly, Maria Gudbrandsen, Clodagh Murphy, Declan Murphy, Michael Craig, Beverly Emanuel, Donna McDonald-McGinn, Kosha Ruparel, David Roalf, Raquel Gur, J. Eric Schmitt, Tony Simon, Naomi Goodrich-Hunsaker, Courtney Durdle, Joanne Doherty, Adam Cunningham, Marianne van den Bree, David E. J. Linden, Michael Owen, Hayley Moss, Neda Jahanshad, Carrie Bearden, Paul Thompson
Diffusion MRI (dMRI) is widely used to study the brain’s white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation.
Mild traumatic brain injury (mTBI) is characterized clinically by a closed head injury involving differential or rotational movement of the brain inside the skull. Over 3 million mTBIs occur annually in the United States alone. Many of the individuals who sustain an mTBI go on to recover fully, but around 20% experience persistent symptoms. These symptoms often last for many weeks to several months. The thalamus, a structure known to serve as a global networking or relay system for the rest of the brain, may play a critical role in neurorehabiliation and its integrity and connectivity after injury may also affect cognitive outcomes. To examine the thalamus, conventional tractography methods to map corticothalamic pathways with diffusion-weighted MRI (DWI) lead to sparse reconstructions that may contain false positive fibers that are anatomically inaccurate. Using a specialized method to zero in on corticothalamic pathways with greater robustness, we noninvasively examined corticothalamic fiber projections using DWI, in 68 service members. We found significantly lower fractional anisotropy (FA), a measure of white matter microstructural integrity, in pathways projecting to the left pre- and postcentral gyri – consistent with sensorimotor deficits often found post-mTBI. Mapping of neural circuitry in mTBI may help to further our understanding of mechanisms underlying recovery post-TBI.
In this paper, a Bayesian super resolution (SR) method obtains high resolution (HR) brain Diffusion-Weighted Magnetic Resonance Imaging (DMRI) images from degraded low resolution (LR) images. Under a Bayesian formulation, the unknown HR image, the acquisition process and the unknown parameters are modeled as stochastic processes. The likelihood model is modeled using a Gaussian distribution to estimate the error between the a linear representation and the observations. The prior is introduced as a Multivariate Gaussian Distribution, for which the inverse of the covariance matrix is approximated by Laplacian-like functions that model the local relationships, capturing thereby non-homogeneous relationships between neighbor intensities. Experimental results show the method outperforms the base line by 2.56 dB when using PSNR as a metric of quality in a set of 35 cases.
Fractional anisotropy derived from the single-tensor model (FADTI) in diffusion MRI (dMRI) is the most widely used metric to characterize white matter (WM) micro-architecture in disease, despite known limitations in regions with extensive fiber crossing. Models such as the tensor distribution function (TDF), which represents the diffusion profile as a probabilistic mixture of tensors, have been proposed to reconstruct multiple underlying fibers. Although complex HARDI acquisition protocols are rare in clinical studies, the TDF and TDF-derived scalar FA metric (FATDF) have been shown to be advantageous even for data with modest angular resolution. However, further evaluation and validation of the metric are necessary. Here we compared the test-retest reliability of FATDF and FADTI in clinical quality data by computing the intra-class correlation (ICC) between dMRI scans collected 3 months apart. When FATDF and FADTI were calculated at various angular resolutions, FATDF ICC in both the corpus callosum and in a full axial slice were consistently more stable across scans, as compared to FADTI.
Emily Dennis, Jeffry Alger, Talin Babikian, Faisal Rashid, Julio Villalon-Reina, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher Giza, Robert Asarnow, Paul Thompson
Traumatic brain injury (TBI) causes extensive damage to the white matter (WM) of the brain, which can be evaluated with diffusion-weighted magnetic resonance imaging (dMRI). Diffusion MRI can be used to map the WM tracts and their integrity, but offers limited understanding of the biochemical basis of any differences. Magnetic resonance spectroscopy (MRS) measures neural metabolites that reflect neuronal health, inflammation, demyelination, and other consequences of TBI. We combined whole-brain MRS with dMRI to investigate WM dysfunction following pediatric TBI, using “tract-based spectroscopy”. Deficits in N-acetylaspartate (NAA) correspond to regions of deficits in WM integrity, but choline showed minimal overlap with WM deficits. NAA is a marker of neuronal health, while choline is an inflammatory marker. A partial F-test showed that MRS measures improved our ability to predict long-term cognitive function. This is the first paper to combine MRS with dMRI-derived tracts on a whole-brain scale, offering insights into the biochemical correlates of WM tract dysfunction, following injury and potentially in other WM disorders.
Echo-planar imaging (EPI) is commonly used for diffusion-weighted imaging (DWI) but is susceptible to nonlinear geometric distortions arising from inhomogeneities in the static magnetic field. These inhomogeneities can be measured and corrected using a fieldmap image acquired during the scanning process. In studies where the fieldmap image is not collected, these distortions can be corrected, to some extent, by nonlinearly registering the diffusion image to a corresponding anatomical image, either a T1- or T2-weighted image. Here we compared two EPI distortion correction pipelines, both based on nonlinear registration, which were optimized for the particular weighting of the structural image registration target. The first pipeline used a 3D nonlinear registration to a T1-weighted target, while the second pipeline used a 1D nonlinear registration to a T2-weighted target. We assessed each pipeline in its ability to characterize high-level measures of brain connectivity in Parkinson’s disease (PD) in 189 individuals (58 healthy controls, 131 people with PD) from the Parkinson’s Progression Markers Initiative (PPMI) dataset. We computed a structural connectome (connectivity map) for each participant using regions of interest from a cortical parcellation combined with DWI-based whole-brain tractography. We evaluated test-retest reliability of the connectome for each EPI distortion correction pipeline using a second diffusion scan acquired directly after the participants’ first. Finally, we used support vector machine (SVM) classification to assess how accurately each pipeline classified PD versus healthy controls using each participants’ structural connectome.
Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. High angular resolution diffusion imaging (HARDI) samples diffusivity at a large number of spherical angles, to better resolve neural fibers that mix or cross. Here, we implemented a framework for advanced mathematical analysis of mouse 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm2), also known as hybrid diffusion imaging (HYDI). Using q-ball imaging (QBI) at ultra-high field strength (7 Tesla), we computed diffusion and fiber orientation distribution functions (dODF, fODF) to better detect crossing fibers. We also computed a quantitative anisotropy (QA) index, and deterministic tractography, from the peak orientation of the fODFs. We found that the signal to noise ratio (SNR) of the QA was significantly higher in single and multi-shell reconstructed data at the lower b-values (b=1000, 3000, 4000 s/mm2) than at higher b-values (b=8000, 12000 s/mm2); the b=1000 s/mm2 shell increased the SNR of the QA in all multi-shell reconstructions, but when used alone or in <5-shell reconstruction, it led to higher angular error for the major fibers, compared to 5-shell HYDI. Multi-shell data reconstructed major fibers with less error than single-shell data, and was most successful at reducing the angular error when the lowest shell was excluded (b=1000 s/mm2). Overall, high-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease.
The detection, segmentation and quantification of multiple sclerosis (MS) lesions on magnetic resonance images (MRI) has been a very active field for the last two decades because of the urge to correlate these measures with the effectiveness of pharmacological treatment. A myriad of methods has been developed and most of these are non specific for the type of lesions and segment the lesions in their acute and chronic phases together. On the other hand, radiologists are able to distinguish between several stages of the disease on different types of MRI images. The main motivation of the work presented here is to computationally emulate the visual perception of the radiologist by using modeling principles of the neuronal centers along the visual system. By using this approach we are able to detect the lesions in the majority of the images in our population sample. This type of approach also allows us to study and improve the analysis of brain networks by introducing a priori information.
This paper introduces an automated method for finding diagnostic regions-of-interest (RoIs) in histopathological
images. This method is based on the cognitive process of visual selective attention that arises during a
pathologist's image examination. Specifically, it emulates the first examination phase, which consists in a coarse
search for tissue structures at a "low zoom" to separate the image into relevant regions.1 The pathologist's
cognitive performance depends on inherent image visual cues - bottom-up information - and on acquired clinical
medicine knowledge - top-down mechanisms -. Our pathologist's visual attention model integrates the latter two
components. The selected bottom-up information includes local low level features such as intensity, color, orientation
and texture information. Top-down information is related to the anatomical and pathological structures
known by the expert. A coarse approximation to these structures is achieved by an oversegmentation algorithm,
inspired by psychological grouping theories. The algorithm parameters are learned from an expert pathologist's
segmentation. Top-down and bottom-up integration is achieved by calculating a unique index for each of the
low level characteristics inside the region. Relevancy is estimated as a simple average of these indexes. Finally,
a binary decision rule defines whether or not a region is interesting. The method was evaluated on a set of 49
images using a perceptually-weighted evaluation criterion, finding a quality gain of 3dB when comparing to a
classical bottom-up model of attention.
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