Automatic deep learning segmentation of the hippocampus on high-resolution diffusion magnetic resonance imaging and its application to the healthy lifespan.

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with limited brain coverage of 20 axial-oblique slices aligned along its long axis. However, manual segmentation is too laborious for large population studies, and it cannot be automatically segmented directly on the diffusion images using traditional T1 or T2 image-based methods because of the limited brain coverage and different contrast. An automatic method is proposed here that segments the hippocampus directly on high-resolution diffusion images based on an extension of well-known deep learning architectures like UNet and UNet++ by including additional dense residual connections. The method was trained on 100 healthy participants with previously performed manual segmentation on the 1-mm DTI, then evaluated on typical healthy participants (n = 53), yielding an excellent voxel overlap with a Dice score of ~ 0.90 with manual segmentation; notably, this was comparable with the inter-rater reliability of manually delineating the hippocampus on diffusion magnetic resonance imaging (MRI) (Dice score of 0.86). This method also generalized to a different DTI protocol with 36% fewer acquisitions. It was further validated by showing similar age trajectories of volumes, fractional anisotropy, and mean diffusivity from manual segmentations in one cohort (n = 153, age 5-74 years) with automatic segmentations from a second cohort without manual segmentations (n = 354, age 5-90 years). Automated high-resolution diffusion MRI segmentation of the hippocampus will facilitate large cohort analyses and, in future research, needs to be evaluated on patient groups.

High-resolution diffusion tensor imaging and T2 mapping detect regional changes within the hippocampus in multiple sclerosis.

Hippocampus demyelinating lesions in multiple sclerosis (MS) have been frequently observed in ex vivo histopathological studies; however, they are difficult to image and quantify in vivo. Diffusion tensor imaging (DTI) and T2 mapping could potentially detect such regional in vivo changes if acquired with sufficient spatial resolution. The goal here was to evaluate whether there are focal hippocampal abnormalities in 43 MS patients (35 relapsing-remitting, eight secondary progressive) with and without cognitive impairment (CI) versus 43 controls using high-resolution 1 mm isotropic DTI, as well as complementary methods of T2-weighted and T2 mapping at 3 T. Abnormal hippocampus regions were identified voxel-by-voxel by using mean diffusivity (MD)/T2 thresholds and avoiding voxels attributed to cerebrospinal fluid. When compared with controls, averaged left/right whole hippocampus MD was higher in both MS groups, while lower fractional anisotropy (FA) and volume, and higher T2 relaxometry and T2-weighted signal values, were only significant in CI MS. The hippocampal MD and T2 images/maps were not uniformly affected and focal regions of elevated MD/T2 were evident in MS patients. Both CI and not CI MS groups showed greater proportional areas of the hippocampus with elevated MD, whereas only the CI group showed a greater proportional area of elevated T2 relaxation times or T2-weighted signal. Higher T2 relaxometry and T2-weighted signal values of elevated regions correlated with greater disability and whole hippocampus FA negatively correlated with physical fatigue. High-resolution hippocampus DTI and T2 mapping with less partial volume effects showed whole hippocampus abnormalities with regional elevations of MD/T2 in MS, which could be interpreted as potentially from demyelination, neuron loss, and/or inflammation, and which overall were more extensive in the hippocampus of patients with larger total brain lesion volumes and CI.

A Normative Brain MRI Database of Neurotypical Participants from 5 to 90 Years of Age.

Brain magnetic resonance imaging (MRI) studies of clinical populations often require comparison to a normative 'control' cohort, usually of similar age/sex, scanned with the same protocol. The goal here was to create a normative brain MRI database of common quantitative methods to be used in comparisons with a variety of neurological disorders across the lifespan. 378 neurotypical controls (aged 5-90 years; median 31 years; 216 females, 162 males) completed brain MRI, cognitive testing, clinical assessment, and a demographics questionnaire. In addition, this large normative sample will yield novel insight into healthy brain development and aging.

High-resolution diffusion tensor imaging identifies hippocampal volume loss without diffusion changes in individuals with prenatal alcohol exposure.

BACKGROUND: Magnetic resonance imaging (MRI) studies of prenatal alcohol exposure (PAE) commonly report reduced hippocampal volumes, which animal models suggest may result from microstructural changes that include cell loss and altered myelination. Diffusion tensor imaging (DTI) is sensitive to microstructural changes but has not yet been used to study the hippocampus in PAE. METHODS: Thirty-six healthy controls (19 females; 8 to 24 years) and 19 participants with PAE (8 females; 8 to 23 years) underwent high-resolution (1 mm isotropic) DTI, anatomical T1-weighted imaging, and cognitive testing. Whole-hippocampus, head, body, and tail subregions were manually segmented to yield DTI metrics (mean, axial, and radial diffusivities-MD, AD, and RD; fractional anisotropy-FA), volumes, and qualitative assessments of hippocampal morphology and digitations. Automated segmentation of T1-weighted images was used to corroborate manual whole-hippocampus volumes. RESULTS: Gross morphology and digitation counts were similar in both groups. Whole-hippocampus volumes were 18% smaller in the PAE than the control group on manually traced diffusion images, but automated T1-weighted image segmentations were not significantly different. Subregion segmentation on DTI revealed reduced volumes of the body and tail, but not the head. There were no significant differences in diffusion metrics between groups for any hippocampal region. Correlations between age and volume were not significant in either group, whereas negative correlations between age and whole-hippocampus MD/AD/RD, and head/body (but not tail) MD/AD/RD were significant in both groups. There were no significant effects of sex, group by age, or group by sex for any hippocampal metric. In controls, seven positive linear correlations were found between hippocampal volume and cognition; five of these were left lateralized and included episodic and working memory, and two were right lateralized and included working memory and processing speed. In PAE, left tail MD positively correlated with executive functioning, and right head MD negatively correlated with episodic memory. CONCLUSIONS: Reductions of hippocampal volumes and altered relationships with memory suggest disrupted hippocampal development in PAE.

High resolution diffusion tensor imaging of the hippocampus across the healthy lifespan.

The human hippocampus is difficult to image given its small size, location, shape, and complex internal architecture. Structural magnetic resonance imaging (MRI) has shown age-related hippocampal volume changes that vary along the anterior-posterior axis. Diffusion tensor imaging (DTI) provides complementary measures related to microstructure, but there are few hippocampus DTI studies investigating change with age in healthy participants, and all have been limited by low spatial resolution. The current study uses high resolution 1 mm isotropic DTI of 153 healthy volunteers aged 5-74 years to investigate diffusion and volume trajectories of the hippocampus (whole, head, body, and tail) and correlations with memory. Hippocampal volume showed age-related changes that differed between head (peaking at midlife), body (no changes), and tail (decreasing across the age span). Fractional anisotropy (FA) and mean, axial, and radial diffusivities (MD, AD, RD) yielded peaks or minima, respectively, at ~30-35 years in all three subregions of the hippocampus. Greater magnitude changes were observed during development than in aging. Age trajectories for both volume and DTI were similar between males and females. Correlations between tests of memory and FA and/or volume were significant in younger subjects (5-17 years), but not in 18-49 year olds or 50-74 year olds. MD was significantly correlated with memory performance in 18-49 year olds, but not in other age groups. Given the diffusion-weighted image contrast and resolution, head digitations could be examined revealing that the majority of subjects had 3-4 (48%) or 2 (32%) bilaterally with no effect of age. One millimeter isotropic DTI yielded high quality diffusion-weighted maps of the human hippocampus that showed regionally specific age effects and cognitive correlations along the anterior-posterior axis from 5 to 74 years.

Validating presupposed versus focused text information.

There is extensive evidence that readers continually validate discourse accuracy and congruence, but that they may also overlook conspicuous text contradictions. Validation may be thwarted when the inaccurate ideas are embedded sentence presuppositions. In four experiments, we examined readers' validation of presupposed ("given") versus new text information. Throughout, a critical concept, such as a truck versus a bus, was introduced early in a narrative. Later, a character stated or thought something about the truck, which therefore matched or mismatched its antecedent. Furthermore, truck was presented as either given or new information. Mismatch target reading times uniformly exceeded the matching ones by similar magnitudes for given and new concepts. We obtained this outcome using different grammatical constructions and with different antecedent-target distances. In Experiment 4, we examined only given critical ideas, but varied both their matching and the main verb's factivity (e.g., factive know vs. nonfactive think). The Match × Factivity interaction closely resembled that previously observed for new target information (Singer, 2006). Thus, readers can successfully validate given target information. Although contemporary theories tend to emphasize either deficient or successful validation, both types of theory can accommodate the discourse and reader variables that may regulate validation.

Comparisons between multi-component myelin water fraction, T1w/T2w ratio, and diffusion tensor imaging measures in healthy human brain structures.

Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures [T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = -0.49, ρ = -0.62), MD vs. MWF (r = -0.22, ρ = -0.29), RD vs. FA (r = -0.62, ρ = -0.75) and MD vs. FA (r = -0.22, ρ = -0.18)]. However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues - with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).