Investigating microstructural changes between in vivo and perfused ex vivo marmoset brains using oscillating gradient and b-tensor encoded diffusion MRI at 9.4 T.

PURPOSE: To investigate microstructural alterations induced by perfusion fixation in brain tissues using advanced diffusion MRI techniques and estimate their potential impact on the application of ex vivo models to in vivo microstructure. METHODS: We used oscillating gradient spin echo (OGSE) and b-tensor encoding diffusion MRI to examine in vivo and ex vivo microstructural differences in the marmoset brain. OGSE was used to shorten effective diffusion times, whereas b-tensor encoding allowed for the differentiation of isotropic and anisotropic kurtosis. Additionally, we performed Monte Carlo simulations to estimate the potential microstructural changes in the tissues. RESULTS: We report large changes (˜50%-60%) in kurtosis frequency dispersion (OGSE) and in both anisotropic and isotropic kurtosis (b-tensor encoding) after perfusion fixation. Structural MRI showed an average volume reduction of about 10%. Monte Carlo simulations indicated that these alterations could likely be attributed to extracellular fluid loss possibly combined with axon beading and increased dot compartment signal fraction. Little evidence was observed for reductions in axonal caliber. CONCLUSION: Our findings shed light on advanced MRI parameter changes that are induced by perfusion fixation and potential microstructural sources for these changes. This work also suggests that caution should be exercised when applying ex vivo models to infer in vivo tissue microstructure, as significant differences may arise.

A Neuropathology Case Report of a Woman with Down Syndrome who Remained Cognitively Stable.

In this neuropathology case report, we present findings from an individual with Down syndrome (DS) who remained cognitively stable despite Alzheimer's disease (AD) neuropathology. Clinical assessments, fluid biomarkers, neuroimaging, and neuropathological examinations were conducted to characterize her condition. Notably, her ApoE genotype was E2/3, which is associated with a decreased risk of dementia. Neuroimaging revealed stable yet elevated amyloid profiles and moderately elevated tau levels, while neuropathology indicated intermediate AD neuropathologic change with Lewy body pathology and cerebrovascular pathology. Despite the presence of AD pathology, the participant demonstrated intact cognitive functioning, potentially attributed to factors such as genetic variations, cognitive resilience, and environmental enrichment. The findings suggest a dissociation between clinical symptoms and neuropathological changes, emphasizing the complexity of AD progression in DS. Further investigation into factors influencing cognitive resilience in individuals with DS, including comorbidities and social functioning, is warranted. Understanding the mechanisms underlying cognitive stability in DS could offer insights into resilience to AD neuropathology in people with DS and in the general population and inform future interventions.

Correlating hippocampal and amygdala volumes with neuropathological burden in neurodegenerative diseases using 7T postmortem MRI.

Numerous research groups worldwide have focused on postmortem imaging to bridge the resolution gap between clinical neuroimaging and neuropathology data. We developed a standardized protocol for brain embedding, imaging, and processing, facilitating alignment between antemortem MRI, postmortem MRI, and pathology to observe brain atrophy and structural damage progression over time. Using 7T postmortem ex vivo MRI, we explore the potential correlation of amygdala and hippocampal atrophy with neuropathological burden in both Down syndrome (DS) and Alzheimer's disease (AD) cohorts. Using 7T postmortem ex vivo MRI scans from 66 cases (12 DS and 54 AD) alongside a subset of antemortem scans (n=17), we correlated manually segmented hippocampal and amygdala volumes, adjusted for age, sex, and ApoE4 status, with pathological indicators such as Thal phase, Braak stage, limbic-predominant age-related TDP-43 encephalopathy (LATE) stage, hippocampal sclerosis (HS), and Lewy body (LB) stage. A significant correlation was observed between postmortem and antemortem volumes for the hippocampus, but a similar trend observed for the amygdala did not reach statistical significance. DS individuals exhibited notably smaller hippocampal and amygdala volumes compared to AD subjects. In DS, lower hippocampal and amygdala volumes correlated with more severe Braak stage, without significant associations with Thal phase. LATE and HS pathologies were uncommon in DS cases but trended toward smaller hippocampal volumes. In AD, lower hippocampal volume associated with dementia duration, advanced Thal phase, Braak stage, LATE stage, and HS presence, whereas reduced amygdala volume correlated mainly with severe LATE stage and HS, but not with Thal or Braak stages. No significant LB correlation was detected in either DS or AD cohorts. Hippocampal volume in AD appears influenced by both AD and LATE pathologies, while amygdala volume seems primarily influenced by LATE. In DS, smaller hippocampal volume, relative to AD, appears primarily influenced by tau pathology.

Characterization of pulsations in the brain and cerebrospinal fluid using ultra-high field magnetic resonance imaging.

The development of innovative non-invasive neuroimaging methods and biomarkers is critical for studying brain disease. Imaging of cerebrospinal fluid (CSF) pulsatility may inform the brain fluid dynamics involved in clearance of cerebral metabolic waste. In this work, we developed a methodology to characterize the frequency and spatial localization of whole brain CSF pulsations in humans. Using 7 Tesla (T) human magnetic resonance imaging (MRI) and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture pulsations of the CSF signal. Physiological data were simultaneously collected and compared with the 7 T MR data. The primary components of signal pulsations were identified using spectral analysis, with the most evident frequency bands identified around 0.3, 1.2, and 2.4 Hz. These pulsations were mapped spatially and temporally onto the MR image domain and temporally onto the physiological measures of electrocardiogram and respiration. We identified peaks in CSF pulsations that were distinct from peaks in grey matter and white matter regions. This methodology may provide novel in vivo biomarkers of disrupted brain fluid dynamics.

An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys.

BACKGROUND: High-precision neurosurgical targeting in nonhuman primates (NHPs) often requires presurgical anatomical mapping with noninvasive neuroimaging techniques (MRI, CT, PET), allowing for translation of individual anatomical coordinates to surgical stereotaxic apparatus. Given the varied tissue contrasts that these imaging techniques produce, precise alignment of imaging-based coordinates to surgical apparatus can be cumbersome. MRI-compatible stereotaxis with radiopaque fiducial markers offer a straight-forward and reliable solution, but existing commercial options do not fit in conformal head coils that maximize imaging quality. NEW METHOD: We developed a compact MRI-compatible stereotaxis suitable for a variety of NHP species (Macaca mulatta, Macaca fascicularis, and Cebus apella) that allows multimodal alignment through technique-specific fiducial markers. COMPARISON WITH EXISTING METHODS: With the express purpose of compatibility with clinically available MRI, CT, and PET systems, the frame is no larger than a human head, while allowing for imaging NHPs in the supinated position. This design requires no marker implantation, special software, or additional knowledge other than the operation of a common large animal stereotaxis. RESULTS: We demonstrated the applicability of this 3D-printable apparatus across a diverse set of experiments requiring presurgical planning: 1) We demonstrate the accuracy of the fiducial system through a within-MRI cannula insertion and subcortical injection of a viral vector. 2) We also demonstrated accuracy of multimodal (MRI and CT) alignment and coordinate transfer to guide a surgical robot electrode implantation for deep-brain electrophysiology. CONCLUSIONS: The computer-aided design files and engineering drawings are publicly available, with the modular design allowing for low cost and manageable manufacturing.

Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study.

BACKGROUND: Previous studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. OBJECTIVE: Using 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. METHODS: High-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. RESULTS: Smaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. CONCLUSION: Ultrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

Manual segmentation of the paraventricular nucleus of the hypothalamus and the dorsal and ventral bed nucleus of stria terminalis using multimodal 7 Tesla structural MRI: probabilistic atlases for a stress-control triad.

The paraventricular nucleus of the hypothalamus (PVN) is uniquely capable of proximal control over autonomic and neuroendocrine stress responses, and the bed nucleus of the stria terminalis (BNST) directly modulates PVN function, as well as playing an important role in stress control itself. The dorsal BNST (dBNST) is predominantly preautonomic, while the ventral BNST (vBNST) is predominantly viscerosensory, receiving dense noradrenergic signaling. Distinguishing the dBNST and vBNST, along with the PVN, may facilitate our understanding of dynamic interactions among these regions. T1-weighted MPRAGE and high resolution gradient echo (GRE) modalities were acquired at 7T. GRE was coregistered to MPRAGE and segmentations were performed in MRIcroGL based on their Atlas of the Human Brain depictions. The dBNST, vBNST and PVN were manually segmented in 25 participants; 10 images were rated by 2 raters. These segmentations were normalized and probabilistic atlases for each region were generated in MNI space, now available as resources for future research. We found moderate-high inter-rater reliability [n = 10; Mean Dice (SD); PVN = 0.69 (0.04); dBNST = 0.77 (0.04); vBNST = 0.62 (0.04)]. Probabilistic atlases were reverse normalized into native space for six additional participants that were segmented but not included in the original 25. We also found moderate to moderate-high reliability between the probabilistic atlases and manual segmentations [n = 6; Mean Dice (SD); PVN = 0.55 (0.12); dBNST = 0.60 (0.10); vBNST = 0.47 (0.12 SD)]. By isolating these hypothalamic and BNST subregions using ultra-high field MRI modalities, more specific delineations of these regions can facilitate greater understanding of mechanisms underlying stress-related function and psychopathology.

Characterization of oscillations in the brain and cerebrospinal fluid using ultra-high field magnetic resonance imaging.

Development of innovative non-invasive neuroimaging methods and biomarkers are critical for studying brain disease. In this work, we have developed a methodology to characterize the frequency responses and spatial localization of oscillations and movements of cerebrospinal fluid (CSF) flow in the human brain. Using 7 Tesla human MRI and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture CSF oscillations and movements. Physiological data was simultaneously collected and correlated with the 7T MR data. The primary components of CSF oscillations were identified using spectral analysis (with frequency bands identified around 0.3Hz, 1.2Hz and 2.4Hz) and were mapped spatially and temporally onto the MR image domain and temporally onto the physiological domain. The developed methodology shows a good consistency and repeatability (standard deviation of 0.052 and 0.078 for 0.3Hz and 1.2Hz bands respectively) in-vivo for potential brain dynamics and CSF flow and clearance studies.

Gene Expressions Preferentially Influence Cortical Thickness of Human Connectome Project Atlas Parcellated Regions in First-Episode Antipsychotic-Naïve Psychoses.

Altered gene expressions may mechanistically link genetic factors with brain morphometric alterations. Existing gene expression studies have examined selected morphometric features using low-resolution atlases in medicated schizophrenia. We examined the relationship of gene expression with cortical thickness (CT), surface area (SA), and gray matter volume (GMV) of first-episode antipsychotic-naïve psychosis patients (FEAP = 85) and 81 controls, hypothesizing that gene expressions often associated with psychosis will differentially associate with different morphometric features. We explored such associations among schizophrenia and non-schizophrenia subgroups within FEAP group compared to controls. We mapped 360 Human Connectome Project atlas-based parcellations on brain MRI on to the publicly available brain gene expression data from the Allen Brain Institute collection. Significantly correlated genes were investigated using ingenuity pathway analysis to elucidate molecular pathways. CT but not SA or GMV correlated with expression of 1137 out of 15 633 genes examined controlling for age, sex, and average CT. Among these ≈19%, ≈39%, and 8% of genes were unique to FEAP, schizophrenia, and non-schizophrenia, respectively. Variants of 10 among these 1137 correlated genes previously showed genome-wide-association with schizophrenia. Molecular pathways associated with CT were axonal guidance and sphingosine pathways (common to FEAP and controls), selected inflammation pathways (unique to FEAP), synaptic modulation (unique to schizophrenia), and telomere extension (common to NSZ and healthy controls). We demonstrate that different sets of genes and molecular pathways may preferentially influence CT in different diagnostic groups. Genes with altered expressions correlating with CT and associated pathways may be targets for pathophysiological investigations and novel treatment designs.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses.

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal "attacks" (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing.

PURPOSE: The expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI. Here, we investigate the extent that ℓ 1 $$ {\ell}_1 $$ -wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade-offs between spatial resolution, readout time and SNR for single-shot spiral DWI at 7T. The reconstructions were performed using our open-source graphics processing unit-enabled reconstruction toolbox, "MatMRI," that allows inclusion of the different components of the expanded encoding model, with or without CS. METHODS: In vivo accelerated single-shot spirals were acquired with five acceleration factors (R) (2×-6×) and three in-plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS-based regularization on image quality when compared to a known ground truth. RESULTS: In vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean-squared error) over the range of R investigated. CONCLUSION: The expanded encoding model and CS regularization are complementary tools for single-shot spiral diffusion MRI, which enables both higher spatial resolutions and higher R.

Estimation of free water-corrected microscopic fractional anisotropy.

Water diffusion anisotropy MRI is sensitive to microstructural changes in the brain that are hallmarks of various neurological conditions. However, conventional metrics like fractional anisotropy are confounded by neuron fiber orientation dispersion, and the relatively low resolution of diffusion-weighted MRI gives rise to significant free water partial volume effects in many brain regions that are adjacent to cerebrospinal fluid. Microscopic fractional anisotropy is a recent metric that can report water diffusion anisotropy independent of neuron fiber orientation dispersion but is still susceptible to free water contamination. In this paper, we present a free water elimination (FWE) technique to estimate microscopic fractional anisotropy and other related diffusion indices by implementing a signal representation in which the MRI signal within a voxel is assumed to come from two distinct sources: a tissue compartment and a free water compartment. A two-part algorithm is proposed to rapidly fit a set of diffusion-weighted MRI volumes containing both linear- and spherical-tensor encoding acquisitions to the representation. Simulations and in vivo acquisitions with four healthy volunteers indicated that the FWE method may be a feasible technique for measuring microscopic fractional anisotropy and other indices with greater specificity to neural tissue characteristics than conventional methods.

Inducing Vascular Grammars for Anomaly Classification in Brain Angiograms.

As machine learning is used to make strides in medical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macroscale vascular formations, for producing assessments of medical conditions with relatively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a recursive subgraph mining algorithm. The distribution of rule instances in the data from which they are induced is then used as an intermediary representation enabling common classification and anomaly detection approaches to identify indicative rules with relatively small data sets. The method is applied to seven-tesla time-of-flight angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reliably. This application demonstrates the power of automated structured intermediary representations for assessing nuanced biological form relationships, and the strength of shape grammars, in particular for identifying indicative patterns in complex vascular networks.

Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis.

Background: Magnetic resonance (MR) scans are routine clinical procedures for monitoring people with multiple sclerosis (PwMS). Patient discomfort, timely scheduling, and financial burden motivate the need to accelerate MR scan time. We examined the clinical application of a deep learning (DL) model in restoring the image quality of accelerated routine clinical brain MR scans for PwMS. Methods: We acquired fast 3D T1w BRAVO and fast 3D T2w FLAIR MRI sequences (half the phase encodes and half the number of slices) in parallel to conventional parameters. Using a subset of the scans, we trained a DL model to generate images from fast scans with quality similar to the conventional scans and then applied the model to the remaining scans. We calculated clinically relevant T1w volumetrics (normalized whole brain, thalamic, gray matter, and white matter volume) for all scans and T2 lesion volume in a sub-analysis. We performed paired t-tests comparing conventional, fast, and fast with DL for these volumetrics, and fit repeated measures mixed-effects models to test for differences in correlations between volumetrics and clinically relevant patient-reported outcomes (PRO). Results: We found statistically significant but small differences between conventional and fast scans with DL for all T1w volumetrics. There was no difference in the extent to which the key T1w volumetrics correlated with clinically relevant PROs of MS symptom burden and neurological disability. Conclusion: A deep learning model that improves the image quality of the accelerated routine clinical brain MR scans has the potential to inform clinically relevant outcomes in MS.

Analysis of hippocampal subfields in sickle cell disease using ultrahigh field MRI.

Sickle cell disease (SCD) is an inherited hemoglobinopathy that causes organ dysfunction, including cerebral vasculopathy and neurological complications. Hippocampal segmentation with newer and advanced 7 Tesla (7T) MRI protocols has revealed atrophy in specific subregions in other neurodegenerative and neuroinflammatory diseases, however, there is limited evidence of hippocampal involvement in SCD. Thus, we explored whether SCD may be also associated with abnormalities in hippocampal subregions. We conducted 7T MRI imaging in individuals with SCD, including the HbSS, HbSC and HbS/beta thalassemia genotypes (n = 53), and healthy race and age-matched controls (n = 47), using a customized head coil. Both T1- and T2-weighted images were used for automatic segmentation of the hippocampal subfields. Individuals with SCD had, on average, significantly smaller volume of the region including the Dentate Gyrus and Cornu Ammonis (CA) 2 and 3 as compared to the control group. Other hippocampal subregions also showed a trend towards smaller volumes in the SCD group. These findings support and extend previous reports of reduced volume in the temporal lobe in SCD patients. Further studies are necessary to investigate the mechanisms that lead to structural changes in the hippocampus subfields and their relationship with cognitive performance in SCD patients.

Improved 7 Tesla transmit field homogeneity with reduced electromagnetic power deposition using coupled Tic Tac Toe antennas.

Recently cleared by the FDA, 7 Tesla (7 T) MRI is a rapidly growing technology that can provide higher resolution and enhanced contrast in human MRI images. However, the increased operational frequency (~ 297 MHz) hinders its full potential since it causes inhomogeneities in the images and increases the power deposition in the tissues. This work describes the optimization of an innovative radiofrequency (RF) head coil coupled design, named Tic Tac Toe, currently used in large scale human MRI scanning at 7 T; to date, this device was used in more than 1,300 neuro 7 T MRI scans. Electromagnetic simulations of the coil were performed using the finite-difference time-domain method. Numerical optimizations were used to combine the calculated electromagnetic fields produced by these antennas, based on the superposition principle, resulting in homogeneous magnetic field distributions at low levels of power deposition in the tissues. The simulations were validated in-vivo using the Tic Tac Toe RF head coil system on a 7 T MRI scanner.

A comprehensive electromagnetic evaluation of an MRI anthropomorphic head phantom.

Electromagnetic simulations are an important tool for the safety assessment of RF coils. They are a useful resource for MRI RF coil designers, especially when complemented with experimental measurements and testing using physical phantoms. Regular-shaped (spherical/cylindrical) homogeneous phantoms are the MRI standard for RF testing but are somewhat inaccurate when compared with anthropomorphic anatomies, especially at high frequencies. In this work, using a recently developed anthropomorphic heterogeneous human head phantom, studies were performed to analyze the scattering parameters (S-parameters) and the electric and magnetic field distributions using (1) the B1+ field mapping method on a 7 T human MRI scanner and (2) numerical full-wave electromagnetic simulations. All studies used the following: a recently developed six-compartment refillable 3D-printed anthropomorphic head phantom (developed from MRI scans obtained in vivo), where the phantom itself is filled in its entirety with either heterogeneous loading, or homogeneous brain or water loading, in vivo imaging, and a commercial homogeneous spherical water phantom. Our results determined that the calculated S-parameters for all the anthropomorphic head phantom models were comparable to the model that is based on the volunteer (within 17% difference of the reflection coefficient value) but differed for the commercial homogeneous spherical water phantom (within 45% difference). The experimentally measured B1+ field maps of the anthropomorphic heterogeneous and homogeneous brain head phantoms were most comparable to the in vivo measured values. The numerical simulations also show that both the anthropomorphic homogeneous water and brain phantom models were less accurate in terms of electric field intensities/distributions when compared with the segmented in-vivo-based head model and the anthropomorphic heterogeneous head phantom model. The presented data highlights the differences between the physical phantoms/phantom models, and the in vivo measurements/segmented in-vivo-based head model. The results demonstrate the usefulness of 3D-printed anthropomorphic phantoms for RF coil evaluation and testing.

White Matter Integrity Underlying Depressive Symptoms in Dementia Caregivers.

OBJECTIVE: We sought to determine whether the aspects of white matter connectivity implicated in major depression also relate to mild depressive symptoms in family dementia caregivers (dCGs). METHODS: Forty-one dCGs (average age=69 years, standard deviation=6.4) underwent a 7 Tesla 64-direction (12-minute) diffusion-weighted imaging sequence. We compared the fractional anisotropy (FA) of 11 white matter features between dCGs with (n=20) and without (n=21) depressive symptoms (Patient Health Questionnaire-9 scores ≥5). RESULTS: Caregivers reporting depression symptoms had lower FA in tracts connecting to the posterior cingulate cortex (Cohen's d = -0.9) and connecting dorsolateral prefrontal with rostral cingulate regions (Cohen's d = -1.2). CONCLUSIONS: Posterior cingulate and dorsolateral prefrontal-to-rostral cingulate white matter, implicated in prior studies of major depression, appear relevant to mild depression in dCGs.

Neuroimaging of Small Vessel Disease in Late-Life Depression.

Cerebral small vessel disease is associated with late-life depression, cognitive impairment, executive dysfunction, distress, and loss of life for older adults. Late-life depression is becoming a substantial public health burden, and a considerable number of older adults presenting to primary care have significant clinical depression. Even though white matter hyperintensities are linked with small vessel disease, white matter hyperintensities are nonspecific to small vessel disease and can co-occur with other brain diseases. Advanced neuroimaging techniques at the ultrahigh field magnetic resonance imaging are enabling improved characterization, identification of cerebral small vessel disease and are elucidating some of the mechanisms that associate small vessel disease with late-life depression.