A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections.

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene-neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene-neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain.

The Japan Monkey Centre Primates Brain Imaging Repository of high-resolution postmortem magnetic resonance imaging: The second phase of the archive of digital records.

A comparison of neuroanatomical features of the brain between humans and our evolutionary relatives, nonhuman primates, is key to understanding the human brain system and the neural basis of mental and neurological disorders. Although most comparative MRI studies of human and nonhuman primate brains have been based on brains of primates that had been used as subjects in experiments, it is essential to investigate various species of nonhuman primates in order to elucidate and interpret the diversity of neuroanatomy features among humans and nonhuman primates. To develop a research platform for this purpose, it is necessary to harmonize the scientific contributions of studies with the standards of animal ethics, animal welfare, and the conservation of brain information for long-term continuation of the field. In previous research, we first developed a gated data-repository of anatomical images obtained using 9.4-T ex vivo MRI of postmortem brain samples from 12 nonhuman primate species, and which are stored at the Japan Monkey Centre. In the present study, as a second phase, we released a collection of T2-weighted images and diffusion tensor images obtained in nine species: white-throated capuchin, Bolivian squirrel monkey, stump-tailed macaque, Tibet monkey, Sykes' monkey, Assamese macaque, pig-tailed macaque, crested macaque, and chimpanzee. Our image repository should facilitate scientific discoveries in the field of comparative neuroscience. This repository can also promote animal ethics and animal welfare in experiments with nonhuman primate models by optimizing methods for in vivo and ex vivo MRI scanning of brains and supporting veterinary neuroradiological education. In addition, the repository is expected to contribute to conservation, preserving information about the brains of various primates, including endangered species, in a permanent digital form.

Regulation of sensorimotor gating via Disc1/Huntingtin-mediated Bdnf transport in the cortico-striatal circuit.

Sensorimotor information processing underlies normal cognitive and behavioral traits and has classically been evaluated through prepulse inhibition (PPI) of a startle reflex. PPI is a behavioral dimension deregulated in several neurological and psychiatric disorders, yet the mechanisms underlying the cross-diagnostic nature of PPI deficits across these conditions remain to be understood. To identify circuitry mechanisms for PPI, we performed circuitry recording over the prefrontal cortex and striatum, two brain regions previously implicated in PPI, using wild-type (WT) mice compared to Disc1-locus-impairment (LI) mice, a model representing neuropsychiatric conditions. We demonstrated that the corticostriatal projection regulates neurophysiological responses during the PPI testing in WT, whereas these circuitry responses were disrupted in Disc1-LI mice. Because our biochemical analyses revealed attenuated brain-derived neurotrophic factor (Bdnf) transport along the corticostriatal circuit in Disc1-LI mice, we investigated the potential role of Bdnf in this circuitry for regulation of PPI. Virus-mediated delivery of Bdnf into the striatum rescued PPI deficits in Disc1-LI mice. Pharmacologically augmenting Bdnf transport by chronic lithium administration, partly via phosphorylation of Huntingtin (Htt) serine-421 and its integration into the motor machinery, restored striatal Bdnf levels and rescued PPI deficits in Disc1-LI mice. Furthermore, reducing the cortical Bdnf expression negated this rescuing effect of lithium, confirming the key role of Bdnf in lithium-mediated PPI rescuing. Collectively, the data suggest that striatal Bdnf supply, collaboratively regulated by Htt and Disc1 along the corticostriatal circuit, is involved in sensorimotor gating, highlighting the utility of dimensional approach in investigating pathophysiological mechanisms across neuropsychiatric disorders.

Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder. Integrity of white matter microstructure plays a key role in the neural mechanism of ADHD presentations. However, the relationships between specific behavioural dimensions and white matter microstructure are less well known. This study aimed to identify associations between white matter and a broad set of clinical features across children and adolescent with and without ADHD using a data-driven multivariate approach. METHOD: We recruited a total of 130 children (62 controls and 68 ADHD) and employed regularized generalized canonical correlation analysis to characterize the associations between white matter and a comprehensive set of clinical measures covering three domains, including symptom, cognition and behaviour. We further applied linear discriminant analysis to integrate these associations to explore potential developmental effects. RESULTS: We delineated two brain-behaviour dimensional associations in each domain resulting a total of six multivariate patterns of white matter microstructural alterations linked to hyperactivity-impulsivity and mild affected; executive functions and working memory; externalizing behaviour and social withdrawal, respectively. Apart from executive function and externalizing behaviour sharing similar white matter patterns, all other dimensions linked to a specific pattern of white matter microstructural alterations. The multivariate dimensional association scores showed an overall increase and normalization with age in ADHD group while remained stable in controls. CONCLUSIONS: We found multivariate neurobehavioral associations exist across ADHD and controls, which suggested that multiple white matter patterns underlie ADHD heterogeneity and provided neural bases for more precise diagnosis and individualized treatment.

Nemo-like kinase reduces mutant huntingtin levels and mitigates Huntington's disease.

Nemo-like kinase (NLK), an evolutionarily conserved serine/threonine kinase, is highly expressed in the brain, but its function in the adult brain remains not well understood. In this study, we identify NLK as an interactor of huntingtin protein (HTT). We report that NLK levels are significantly decreased in HD human brain and HD models. Importantly, overexpression of NLK in the striatum attenuates brain atrophy, preserves striatal DARPP32 levels and reduces mutant HTT (mHTT) aggregation in HD mice. In contrast, genetic reduction of NLK exacerbates brain atrophy and loss of DARPP32 in HD mice. Moreover, we demonstrate that NLK lowers mHTT levels in a kinase activity-dependent manner, while having no significant effect on normal HTT protein levels in mouse striatal cells, human cells and HD mouse models. The NLK-mediated lowering of mHTT is associated with enhanced phosphorylation of mHTT. Phosphorylation defective mutation of serine at amino acid 120 (S120) abolishes the mHTT-lowering effect of NLK, suggesting that S120 phosphorylation is an important step in the NLK-mediated lowering of mHTT. A further mechanistic study suggests that NLK promotes mHTT ubiquitination and degradation via the proteasome pathway. Taken together, our results indicate a protective role of NLK in HD and reveal a new molecular target to reduce mHTT levels.

MRSCloud: A cloud-based MRS tool for basis set simulation.

PURPOSE: The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T. METHODS: The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio. RESULTS: Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96. CONCLUSIONS: Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

A Machine Learning-Based Approach to Discrimination of Tauopathies Using [18 F]PM-PBB3 PET Images.

BACKGROUND: We recently developed a positron emission tomography (PET) probe, [18 F]PM-PBB3, to detect tau lesions in diverse tauopathies, including mixed three-repeat and four-repeat (3R + 4R) tau fibrils in Alzheimer's disease (AD) and 4R tau aggregates in progressive supranuclear palsy (PSP). For wider availability of this technology for clinical settings, bias-free quantitative evaluation of tau images without a priori disease information is needed. OBJECTIVE: We aimed to establish tau PET pathology indices to characterize PSP and AD using a machine learning approach and test their validity and tracer capabilities. METHODS: Data were obtained from 50 healthy control subjects, 46 patients with PSP Richardson syndrome, and 37 patients on the AD continuum. Tau PET data from 114 regions of interest were subjected to Elastic Net cross-validation linear classification analysis with a one-versus-the-rest multiclass strategy to obtain a linear function that discriminates diseases by maximizing the area under the receiver operating characteristic curve. We defined PSP- and AD-tau scores for each participant as values of the functions optimized for differentiating PSP (4R) and AD (3R + 4R), respectively, from others. RESULTS: The discriminatory ability of PSP- and AD-tau scores assessed as the area under the receiver operating characteristic curve was 0.98 and 1.00, respectively. PSP-tau scores correlated with the PSP rating scale in patients with PSP, and AD-tau scores correlated with Mini-Mental State Examination scores in healthy control-AD continuum patients. The globus pallidus and amygdala were highlighted as regions with high weight coefficients for determining PSP- and AD-tau scores, respectively. CONCLUSIONS: These findings highlight our technology's unbiased capability to identify topologies of 3R + 4R versus 4R tau deposits. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.

Multi-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure-based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI-rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure-based analysis showed widespread DTI abnormalities in FEP and rs-fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof-of-concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub-groups.

Systematic volumetric analysis predicts response to CSF drainage and outcome to shunt surgery in idiopathic normal pressure hydrocephalus.

OBJECTIVES: Idiopathic normal pressure hydrocephalus (INPH) is a neurodegenerative disorder characterized by excess cerebrospinal fluid (CSF) in the ventricles, which can be diagnosed by invasive CSF drainage test and treated by shunt placement. Here, we aim to investigate the diagnostic and prognostic power of systematic volumetric analysis based on brain structural MRI for INPH. METHODS: We performed a retrospective study with a cohort of 104 probable INPH patients who underwent CSF drainage tests and another cohort of 41 INPH patients who had shunt placement. High-resolution T1-weighted images of the patients were segmented using an automated pipeline into 283 structures that are grouped into different granularity levels for volumetric analysis. Volumes at multi-granularity levels were used in a recursive feature elimination model to classify CSF drainage responders and non-responders. We then used pre-surgical brain volumes to predict Tinetti and MMSE scores after shunting, based on the least absolute shrinkage and selection operator. RESULTS: The classification accuracy of differentiating the CSF drainage responders and non-responders increased as the granularity increased. The highest diagnostic accuracy was achieved at the finest segmentation with a sensitivity/specificity/precision/accuracy of 0.89/0.91/0.84/0.90 and an area under the curve of 0.94. The predicted post-surgical neurological scores showed high correlations with the ground truth, with r = 0.80 for Tinetti and r = 0.88 for MMSE. The anatomical features that played important roles in the diagnostic and prognostic tasks were also illustrated. CONCLUSIONS: We demonstrated that volumetric analysis with fine segmentation could reliably differentiate CSF drainage responders from other INPH-like patients, and it could accurately predict the neurological outcomes after shunting. KEY POINTS: • We performed a fully automated segmentation of brain MRI at multiple granularity levels for systematic volumetric analysis of idiopathic normal pressure hydrocephalus (INPH) patients. • We were able to differentiate patients that responded to CSF drainage test with an accuracy of 0.90 and area under the curve of 0.94 in a cohort of 104 probable INPH patients, as well as to predict the post-shunt gait and cognitive scores with a coefficient of 0.80 for Tinetti and 0.88 for MMSE. • Feature analysis showed the inferior lateral ventricle, bilateral hippocampus, and orbital cortex are positive indicators of CSF drainage responders, whereas the posterior deep white matter and parietal subcortical white matter were negative predictors.

The association of neuropsychiatric symptoms with regional brain volumes from patients in a tertiary multi-disciplinary memory clinic.

BACKGROUND: To examine the interaction between structural brain volume measures derived from a clinical magnetic resonance imaging (MRI) and occurrence of neuropsychiatric symptoms (NPS) in outpatient memory clinic patients. METHODS: Clinical and neuroimaging data were collected from the medical records of outpatient memory clinic patients who were seen by neurologists, geriatric neuropsychiatrists, and geriatricians. MRI scan acquisition was carried out on a 3 T Siemens Verio scanner at Johns Hopkins Bayview Medical Center. Image analyses used an automated multi-label atlas fusion method with a geriatric atlas inventory to generate 193 anatomical regions from which volumes were measured. Regions of interest were generated a priori based on previous literature review of NPS in dementia. Regional volumes for agitation, apathy, and delusions were carried forward in a linear regression analysis. RESULTS: Seventy-two patients had clinical and usable neuroimaging data that were analyzed and grouped by Mini-Mental State Exam (MMSE). Neuropsychiatric Inventory Questionnaire (NPI-Q) agitation was inversely associated with rostral anterior cingulate cortex (ACC) bilaterally and left subcallosal ACC volumes in the moderate severity group. Delusions were positively associated with left ACC volumes in both severe and mild groups but inversely associated with the right dorsolateral prefrontal cortex (DLPFC) in the moderate subgroup. CONCLUSIONS: Agitation, apathy, and delusions are associated with volumes of a priori selected brain regions using clinical data and clinically acquired MRI scans. The ACC is an anatomic region common to these symptoms, particularly agitation and delusions, which closely mirror the findings of research-quality studies and suggest its importance as a behavioral hub.

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility.

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ±â€¯0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ±â€¯0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ±â€¯0.08 to manual labels and 0.89 ±â€¯0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.

Diffusion MRI fiber tractography of the brain.

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

ASL-MRICloud: An online tool for the processing of ASL MRI data.

Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud-based tool for ASL data processing, referred to as ASL-MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL-MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL-MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo-continuous ASL and pulsed ASL, and can process single-delay and multi-delay ASL data. The outputs of ASL-MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel-wise masks indicating regions with potential hyper-perfusion and hypo-perfusion, and an image quality index. The ASL tool is also integrated with a T1 -based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region-of-interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Low-Frequency Right Repetitive Transcranial Magnetic Stimulation for the Treatment of Depression After Traumatic Brain Injury: A Randomized Sham-Controlled Pilot Study.

OBJECTIVE: Major depression is the most common psychiatric sequela of traumatic brain injury (TBI), but effective treatment continues to be a challenge, with few studies providing guidance. METHODS: In a pilot study, the authors evaluated the effect size of low-frequency right-sided (LFR) repetitive transcranial magnetic stimulation (rTMS), compared with sham treatment, over the right dorsolateral prefrontal cortex (DLPFC) in patients (N=30) with TBI depression and co-occurring neuropsychiatric symptoms, including suicidal thoughts, anxiety, posttraumatic stress disorder, sleep disturbance, behavioral problems, and cognitive dysfunction. Exploratory analyses of diffusion tensor imaging pre- and postintervention were performed to determine the effect size of LFR rTMS on white matter integrity. RESULTS: Small (Hedge's g=0.19) and highly variable effects of LRF rTMS over right DLPFC in TBI depression were observed. Similarly, the effect of LFR rTMS for treatment of comorbid neuropsychiatric symptoms varied from small to moderate. CONCLUSIONS: These findings suggest that the observed effects of LFR rTMS over the right DLPFC in TBI depression and co-occurring neuropsychiatric symptoms are small, at best, and, preliminarily, that low-frequency right DLPFC stimulation has limited potential in this patient population. However, studies employing different rTMS parameters (e.g., type, location, frequency, duration) or other participant characteristics (e.g., TBI severity, chronicity, comorbidity, concurrent treatment) may potentially yield different responses.

Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years.

Recent evidence indicates that measures from cerebrospinal fluid, MRI scans and cognitive testing obtained from cognitively normal individuals can be used to predict likelihood of progression to mild cognitive impairment several years later, for groups of individuals. However, it remains unclear whether these measures are useful for predicting likelihood of progression for an individual. The increasing focus on early intervention in clinical trials for Alzheimer's disease emphasizes the importance of improving the ability to identify which cognitively normal individuals are more likely to progress over time, thus allowing researchers to efficiently screen participants, as well as determine the efficacy of any treatment intervention. The goal of this study was to determine which measures, obtained when individuals were cognitively normal, predict on an individual basis, the onset of clinical symptoms associated with a diagnosis of mild cognitive impairment due to Alzheimer's disease. Cognitively normal participants (n = 224, mean baseline age = 57 years) were evaluated with a range of measures, including: cerebrospinal fluid amyloid-ß and phosphorylated-tau, hippocampal and entorhinal cortex volume, cognitive tests scores and APOE genotype. They were then followed to determine which individuals developed mild cognitive impairment over time (mean follow-up = 11 years). The primary outcome was progression from normal cognition to the onset of clinical symptoms of mild cognitive impairment due to Alzheimer's disease at 5 years post-baseline. Time-dependent receiver operating characteristic analyses examined the sensitivity and specificity of individual measures, and combinations of measures, as predictors of the outcome. Six measures, in combination, were the most parsimonious predictors of transition to mild cognitive impairment 5 years after baseline (area under the curve = 0.85; sensitivity = 0.80, specificity = 0.75). The addition of variables from each domain significantly improved the accuracy of prediction. The incremental accuracy of prediction achieved by adding individual measures or sets of measures successively to one another was also examined, as might be done when enrolling individuals in a clinical trial. The results indicate that biomarkers obtained when individuals are cognitively normal can be used to predict which individuals are likely to develop clinical symptoms at 5 years post-baseline. As a number of the measures included in the study could also be used as subject selection criteria in a clinical trial, the findings also provide information about measures that would be useful for screening in a clinical trial aimed at individuals with preclinical Alzheimer's disease.

In vivo assessment of the placental anatomy and perfusion in a mouse model of intrauterine inflammation.

BACKGROUND: Magnetic resonance imaging (MRI) provides useful markers to examine placental function. MRI features of placental injury due to intrauterine inflammation-a common risk during pregnancy, are not well known. PURPOSE: To investigate the capability of structural MRI and intravoxel incoherent motion (IVIM) imaging in examining acute placental injury in a mouse model of intrauterine inflammation, as well as gestation-dependent placental changes. STUDY TYPE: Prospective study. ANIMAL MODEL: Pregnant CD1 mice were scanned on embryonic day 15 (E15, n = 40 placentas from six dams) and E17. On E17, mice were subjected to intrauterine injury by exposure to lipopolysaccharide (LPS, n = 25 placentas from three dams) or sham injury (n = 25 placentas from three dams). FIELD STRENGTH/SEQUENCE: In vivo MRI was performed on an 11.7T Bruker scanner, using a fast spin-echo sequence and a diffusion-weighted echo-planar imaging (EPI) sequence. ASSESSMENT: T2 -weighted MRI was acquired to evaluate placental volume. IVIM imaging was performed in a restricted field-of-view using 15 b-values from 10-800 s/mm2 , based on which, the pseudodiffusion fraction (f), pseudodiffusion coefficient (D*), and tissue water coefficient (D) were estimated with a two-step fitting procedure. STATISTICAL TESTS: Two-way analysis of variance (ANOVA) was used to evaluate the group differences. RESULTS: The placental volume increased by ∼21% from E15 to E17 (P < 0.01), and a 15% volume loss was observed at 6 hours after LPS exposure (P < 0.01). IVIM parameters (f, D*, and f·D*) were similar between the E15 and E17 sham groups (P > 0.05), which was significantly reduced in the LPS-exposed placentas compared to the shams (P < 0.001). D values decreased from E15 to E17 (P < 0.05), which were further reduced after LPS exposure (P < 0.05). Changes in placental area and vascular density were histologically identified in the LPS-exposed group, along with gestation-dependent changes. DATA CONCLUSION: Our results suggested structural MRI and IVIM measurements are potential markers for detecting acute placental injury after intrauterine inflammation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1260-1267.

The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains.

Diffusion tensor imaging (DTI) is used extensively in neuroscience to noninvasively estimate white matter (WM) microarchitecture. However, the diffusion signal is inherently ambiguous because it infers WM structure from the orientation of water diffusion and cannot identify the biological sources of diffusion changes. To compare inferred WM estimates to directly labeled axonal elements, we performed a novel within-subjects combination of high-resolution ex vivo DTI with two-photon laser microscopy of intact mouse brains rendered optically transparent by Clear Lipid-exchanged, Anatomically Rigid, Imaging/immunostaining compatible, Tissue hYdrogel (CLARITY). We found that myelin basic protein (MBP) immunofluorescence significantly correlated with fractional anisotropy (FA), especially in WM regions with coherent fiber orientations and low fiber dispersion. Our results provide evidence that FA is particularly sensitive to myelination in WM regions with these characteristics. Furthermore, we found that radial diffusivity (RD) was only sensitive to myelination in a subset of WM tracts, suggesting that the association of RD with myelin should be used cautiously. This combined DTI-CLARITY approach illustrates, for the first time, a framework for using brain-wide immunolabeling of WM targets to elucidate the relationship between the diffusion signal and its biological underpinnings. This study also demonstrates the feasibility of a within-subject combination of noninvasive neuroimaging and tissue clearing techniques that has broader implications for neuroscience research.

Early white matter abnormalities, progressive brain pathology and motor deficits in a novel knock-in mouse model of Huntington's disease.

White matter abnormalities have been reported in premanifest Huntington's disease (HD) subjects before overt striatal neuronal loss, but whether the white matter changes represent a necessary step towards further pathology and the underlying mechanism of these changes remains unknown. Here, we characterized a novel knock-in mouse model that expresses mouse HD gene homolog (Hdh) with extended CAG repeat- HdhQ250, which was derived from the selective breeding of HdhQ150 mice. HdhQ250 mice manifest an accelerated and robust phenotype compared with its parent line. HdhQ250 mice exhibit progressive motor deficits, reduction in striatal and cortical volume, accumulation of mutant huntingtin aggregation, decreased levels of DARPP32 and BDNF and altered striatal metabolites. The abnormalities detected in this mouse model are reminiscent of several aspects of human HD. In addition, disturbed myelination was evident in postnatal Day 14 HdhQ250 mouse brain, including reduced levels of myelin regulatory factor and myelin basic protein, and decreased numbers of myelinated axons in the corpus callosum. Thinner myelin sheaths, indicated by increased G-ratio of myelin, were also detected in the corpus callosum of adult HdhQ250 mice. Moreover, proliferation of oligodendrocyte precursor cells is altered by mutant huntingtin both in vitro and in vivo. Our data indicate that this model is suitable for understanding comprehensive pathogenesis of HD in white matter and gray matter as well as developing therapeutics for HD.

Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that progressively affects motor, cognitive, and emotional functions. Structural MRI studies have demonstrated brain atrophy beginning many years prior to clinical onset ("premanifest" period), but the order and pattern of brain structural changes have not been fully characterized. In this study, we investigated brain regional volumes and diffusion tensor imaging (DTI) measurements in premanifest HD, and we aim to determine (1) the extent of MRI changes in a large number of structures across the brain by atlas-based analysis, and (2) the initiation points of structural MRI changes in these brain regions. We adopted a novel multivariate linear regression model to detect the inflection points at which the MRI changes begin (namely, "change-points"), with respect to the CAG-age product (CAP, an indicator of extent of exposure to the effects of CAG repeat expansion). We used approximately 300 T1-weighted and DTI data from premanifest HD and control subjects in the PREDICT-HD study, with atlas-based whole brain segmentation and change-point analysis. The results indicated a distinct topology of structural MRI changes: the change-points of the volumetric measurements suggested a central-to-peripheral pattern of atrophy from the striatum to the deep white matter; and the change points of DTI measurements indicated the earliest changes in mean diffusivity in the deep white matter and posterior white matter. While interpretation needs to be cautious given the cross-sectional nature of the data, these findings suggest a spatial and temporal pattern of spread of structural changes within the HD brain. Hum Brain Mapp 38:5035-5050, 2017. © 2017 Wiley Periodicals, Inc.

Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases.

Purpose To determine the improvement of radiologist efficiency and performance in the detection of bone metastases at serial follow-up computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. CT image pairs (previous and current scans of the torso) in 60 patients with cancer (primary lesion location: prostate, n = 14; breast, n = 16; lung, n = 20; liver, n = 10) were included. These consisted of 30 positive cases with a total of 65 bone metastases depicted only on current images and confirmed by two radiologists who had access to additional imaging examinations and clinical courses and 30 matched negative control cases (no bone metastases). Previous CT images were semiautomatically registered to current CT images by the algorithm, and TS images were created. Seven radiologists independently interpreted CT image pairs to identify newly developed bone metastases without and with TS images with an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Reading time was recorded, and usefulness was evaluated with subjective scores of 1-5, with 5 being extremely useful and 1 being useless. Significance of these values was tested with the Wilcoxon signed-rank test. Results The subtraction images depicted various types of bone metastases (osteolytic, n = 28; osteoblastic, n = 26; mixed osteolytic and blastic, n = 11) as temporal changes. The average reading time was significantly reduced (384.3 vs 286.8 seconds; Wilcoxon signed rank test, P = .028). The average figure-of-merit value increased from 0.758 to 0.835; however, this difference was not significant (JAFROC analysis, P = .092). The subjective usefulness survey response showed a median score of 5 for use of the technique (range, 3-5). Conclusion TS images obtained from serial CT scans using nonrigid registration successfully depicted newly developed bone metastases and showed promise for their efficient detection. © RSNA, 2017 Online supplemental material is available for this article.