AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Focused Ultrasound-mediated Liquid Biopsy in a Tauopathy Mouse Model.

Background Neurodegenerative disorders (such as Alzheimer disease) characterized by the deposition of various pathogenic forms of tau protein in the brain are collectively referred to as tauopathies. Identification of the molecular drivers and pathways of neurodegeneration is critical to individualized targeted treatment of these disorders. However, despite important advances in fluid biomarker detection, characterization of these molecular subtypes is limited by the blood-brain barrier. Purpose To evaluate the feasibility and safety of focused ultrasound-mediated liquid biopsy (sonobiopsy) in the detection of brain-derived protein biomarkers in a transgenic mouse model of tauopathy (PS19 mice). Materials and Methods Sonobiopsy was performed by sonicating the cerebral hemisphere in 2-month-old PS19 and wild-type mice, followed by measurement of plasma phosphorylated tau (p-tau) species (30 minutes after sonication in the sonobiopsy group). Next, spatially targeted sonobiopsy was performed by sonicating either the cerebral cortex or the hippocampus in 6-month-old PS19 mice. To detect changes in plasma neurofilament light chain (a biomarker of neurodegeneration) levels, blood samples were collected before and after sonication (15 and 45-60 minutes after sonication). Histologic staining was performed to evaluate tissue damage after sonobiopsy. The Shapiro-Wilk test, unpaired and paired t tests, and the Mann-Whitney U test were used. Results In the 2-month-old mice, sonobiopsy significantly increased the normalized levels of plasma p-tau species compared with the conventional blood-based liquid biopsy (p-tau-181-to-mouse tau [m-tau] ratio: 1.7-fold increase, P = .006; p-tau-231-to-m-tau ratio: 1.4-fold increase, P = .048). In the 6-month-old PS19 mice, spatially targeted sonobiopsy resulted in a 2.3-fold increase in plasma neurofilament light chain after sonication of the hippocampus and cerebral cortex (P < .001). After optimization of the sonobiopsy parameters, no excess microhemorrhage was observed in the treated cerebral hemisphere compared with the contralateral side. Conclusion This study showed the feasibility of sonobiopsy to release phosphorylated tau species and neurofilament light chain to the blood circulation, potentially facilitating diagnosis of neurodegenerative disorders. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fowlkes in this issue.

Relationship of neurite architecture to brain activity during task-based fMRI.

Functional MRI (fMRI) has been widely used to examine changes in neuronal activity during cognitive tasks. Commonly used measures of gray matter macrostructure (e.g., cortical thickness, surface area, volume) do not consistently appear to serve as structural correlates of brain function. In contrast, gray matter microstructure, measured using neurite orientation dispersion and density imaging (NODDI), enables the estimation of indices of neurite density (neurite density index; NDI) and organization (orientation dispersion index; ODI) in gray matter. Our study explored the relationship among neurite architecture, BOLD (blood-oxygen-level-dependent) fMRI, and cognition, using a large sample (n = 750) of young adults of the human connectome project (HCP) and two tasks that index more cortical (working memory) and more subcortical (emotion processing) targeting of brain functions. Using NODDI, fMRI, structural MRI and task performance data, hierarchical regression analyses revealed that higher working memory- and emotion processing-evoked BOLD activity was related to lower ODI in the right DLPFC, and lower ODI and NDI values in the right and left amygdala, respectively. Common measures of brain macrostructure (i.e., DLPFC thickness/surface area and amygdala volume) did not explain any additional variance (beyond neurite architecture) in BOLD activity. A moderating effect of neurite architecture on the relationship between emotion processing task-evoked BOLD response and performance was observed. Our findings provide evidence that neuro-/social-affective cognition-related BOLD activity is partially driven by the local neurite organization and density with direct impact on emotion processing. In vivo gray matter microstructure represents a new target of investigation providing strong potential for clinical translation.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Convergent effects of a functional C3 variant on brain atrophy, demyelination, and cognitive impairment in multiple sclerosis.

BACKGROUND: Complement system activation products are present in areas of neuroinflammation, demyelination, and neurodegeneration in brains of patients with multiple sclerosis (MS). C3 is a central element in the activation of complement cascades. A common coding variant in the C3 gene (rs2230199, C3R102G) affects C3 activity. OBJECTIVES: To assess the effects of rs2230199 on MS severity using clinical, cognitive, and imaging measures. METHODS: In total, 161 relapse-onset MS patients (Expanded Disability Status Scale (EDSS) ≤ 6) underwent physical assessments, cognitive tests (Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), and California Verbal Learning Test (CVLT)), and magnetic resonance imaging (MRI). Lesion volumes were quantified semi-automatically. Voxel-wise analyses were performed to assess the effects of rs2230199 genotype on gray matter (GM) atrophy ( n = 155), white matter (WM) fractional anisotropy (FA; n = 105), and WM magnetization transfer ratio (MTR; n = 90). RESULTS: While rs2230199 minor-allele dosage (C3-102G) showed no significant effect on EDSS and Multiple Sclerosis Functional Composite (MSFC), it was associated with worse cognitive performance ( p = 0.02), lower brain parenchymal fraction ( p = 0.003), and higher lesion burden ( p = 0.02). Moreover, voxel-wise analyses showed lower GM volume in subcortical structures and insula, and lower FA and MTR in several WM areas with higher copies of rs2230199 minor allele. CONCLUSION: C3-rs2230199 affects white and GM damage as well as cognitive impairment in MS patients. Our findings support a causal role for complement system activity in the pathophysiology of MS.

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.

Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan.

As humans age, a characteristic pattern of widespread neocortical dendritic disruption coupled with compensatory effects in hippocampus and other subcortical structures is shown in postmortem investigations. It is now possible to address age-related effects on gray matter (GM) neuritic organization and density in humans using multishell diffusion-weighted MRI and the neurite-orientation dispersion and density imaging (NODDI) model. In 45 healthy individuals across the adult lifespan (21-84 years), we used a multishell diffusion imaging and the NODDI model to assess the intraneurite volume fraction and neurite orientation-dispersion index (ODI) in GM tissues. We also determined the functional correlates of variations in GM microstructure by obtaining resting-state fMRI and behavioral data. We found a significant age-related deficit in neocortical ODI (most prominently in frontoparietal regions), whereas increased ODI was observed in hippocampus and cerebellum with advancing age. Neocortical ODI outperformed cortical thickness and white matter fractional anisotropy for the prediction of chronological age in the same individuals. Higher GM ODI sampled from resting-state networks with known age-related susceptibility (default mode and visual association networks) was associated with increased functional connectivity of these networks, whereas the task-positive networks tended to show no association or even decreased connectivity. Frontal pole ODI mediated the negative relationship of age with executive function, whereas hippocampal ODI mediated the positive relationship of age with executive function. Our in vivo findings align very closely with the postmortem data and provide evidence for vulnerability and compensatory neural mechanisms of aging in GM microstructure that have functional and cognitive impact in vivo.

Genome-wide variant by serum urate interaction in Parkinson's disease.

OBJECTIVE: Serum urate levels have been associated with risk for and progression of Parkinson's disease (PD). Urate-related compounds are therapeutic candidates in neuroprotective efforts to slow PD progression. A urate-elevating agent is currently under investigation as a potential disease-modifying strategy in people with PD. However, PD is a heterogeneous disorder, and genetic variation may explain divergence in disease severity and progression. METHODS: We conducted a genome-wide association study to identify gene variant × serum urate interaction effects on the striatal (123) I-ioflupane (DaTscan) binding ratio measured using single photon emission computed tomography in patients with possible PD from the Parkinson's Progression Markers Initiative (PPMI, n = 360). Follow-up analyses were conducted to assess gene variant × serum urate interaction effects on magnetic resonance imaging-derived regional brain volumes and clinical status. We then attempted to replicate our primary analysis in patients who entered the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) with a clinical diagnosis of PD (n = 349). RESULTS: Rs1109303 (T>G) variant within the INPP5K gene on chromosome 17p13.3 demonstrated a genome-wide significant interaction with serum urate level to predict striatal dopamine transporter density among all PPMI participants (n = 359) with possible PD (p = 2.01 × 10(-8) ; after excluding participants with SWEDD [scan without evidence of dopaminergic deficit]: p = 1.12 × 10(-9) ; n = 316). Independent of striatal dopamine transporter density, similar effects on brain atrophy, bradykinesia, anxiety, and depression were observed. No effect was present in the PRECEPT sample at baseline; however, in non-SWEDD PD participants in PRECEPT (n = 309), we observed a significant longitudinal genotype × serum urate interaction effect, consistent in direction with the PPMI sample, on progression of striatal dopamine transporter density over the 22-month follow-up. INTERPRETATION: Genetic profile combined with serum urate level can be used to predict disease severity and potential disease progression in patients with PD. These results may be relevant to therapeutic efforts targeting the urate pathway.

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease.

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

Cerebrospinal fluid-based spatial statistics: towards quantitative analysis of cerebrospinal fluid pseudodiffusivity.

BACKGROUND: Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity. However, few studies have investigated the relationship between the spatial patterns of CSF pseudodiffusivity and cognition. METHODS: We introduced a novel technique, CSF-based spatial statistics (CBSS), to automatically quantify CSF pseudodiffusivity in each sulcus, cistern and ventricle. Using cortical regions as landmarks, we segmented each CSF region. We retrospectively analyzed a cohort of 93 participants with varying degrees of cognitive impairment. RESULTS: We identified two groups of CSF regions whose pseudodiffusivity profiles were correlated with each other: one group displaying higher pseudodiffusivity and near large arteries and the other group displaying lower pseudodiffusivity and away from the large arteries. The pseudodiffusivity in the third ventricle positively correlated with short-term memory (standardized slope of linear regression = 0.38, adjusted p < 0.001) and long-term memory (slope = 0.37, adjusted p = 0.005). Fine mapping along the ventricles revealed that the pseudodiffusivity in the region closest to the start of the third ventricle demonstrated the highest correlation with cognitive performance. CONCLUSIONS: CBSS enabled quantitative spatial analysis of CSF pseudodiffusivity and suggested the third ventricle pseudodiffusivity as a potential biomarker of cognitive impairment.

Age-Related Alterations in Gray Matter Microstructure in Older People With Remitted Major Depression at Risk for Dementia.

Background: Major depressive disorder (MDD) in late life is a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease. However, studies of gray matter changes have produced varied estimates of which structures are implicated in MDD and dementia. Changes in gray matter volume and cortical thickness are macrostructural measures for the microstructural processes of free water accumulation and dendritic spine loss. Methods: We conducted multishell diffusion imaging to assess gray matter microstructure in 244 older adults with remitted MDD (n = 44), MCI (n = 115), remitted MDD+MCI (n = 61), or without psychiatric disorders or cognitive impairment (healthy control participants; n = 24). We estimated measures related to neurite density, orientation dispersion, and free water (isotropic volume fraction) using a biophysically plausible model (neurite orientation dispersion and density imaging). Results: Results showed that increasing age was correlated with an increase in isotropic volume fraction and a decrease in orientation dispersion index, which is consistent with neuropathology dendritic loss. In addition, this relationship between age and increased isotropic volume fraction was more disrupted in the MCI group than in the remitted MDD or healthy control groups. However, the association between age and orientation dispersion index was similar for all 3 groups. Conclusions: The findings suggest that the neurite orientation dispersion and density imaging measures could be used to identify biological risk factors for Alzheimer's disease, signifying both conventional neurodegeneration observed with MCI and dendritic loss seen in MDD.

The Spatial Patterns and Determinants of Cerebrospinal Fluid Circulation in the Human Brain.

The circulation of cerebrospinal fluid (CSF) is essential for maintaining brain homeostasis and clearance, and impairments in its flow can lead to various brain disorders. Recent studies have shown that CSF circulation can be interrogated using low b-value diffusion magnetic resonance imaging (low-b dMRI). Nevertheless, the spatial organization of intracranial CSF flow dynamics remains largely elusive. Here, we developed a whole-brain voxel-based analysis framework, termed CSF pseudo-diffusion spatial statistics (CΨSS), to examine CSF mean pseudo-diffusivity (MΨ), a measure of CSF flow magnitude derived from low-b dMRI. We showed that intracranial CSF MΨ demonstrates characteristic covariance patterns by employing seed-based correlation analysis. Importantly, we applied non-negative matrix factorization analysis to further elucidate the covariance patterns of CSF MΨ in a hypothesis-free, data-driven way. We identified distinct CSF spaces that consistently displayed unique pseudo-diffusion characteristics across multiple imaging datasets. Our study revealed that age, sex, brain atrophy, ventricular anatomy, and cerebral perfusion differentially influence MΨ across these CSF spaces. Notably, individuals with anomalous CSF flow patterns displayed incidental findings on multimodal neuroradiological examinations. Our work sets forth a new paradigm to study CSF flow, with potential applications in clinical settings.

First-in-human prospective trial of sonobiopsy in glioblastoma patients using neuronavigation-guided focused ultrasound.

Sonobiopsy is an emerging technology that combines focused ultrasound (FUS) with microbubbles to enrich circulating brain disease-specific biomarkers for noninvasive molecular diagnosis of brain diseases. Here, we report the first-in-human prospective trial of sonobiopsy in glioblastoma patients to evaluate its feasibility and safety in enriching circulating tumor biomarkers. A nimble FUS device integrated with a clinical neuronavigation system was used to perform sonobiopsy following an established clinical workflow for neuronavigation. Analysis of blood samples collected before and after FUS sonication showed enhanced plasma circulating tumor biomarker levels. Histological analysis of surgically resected tumors confirmed the safety of the procedure. Transcriptome analysis of sonicated and unsonicated tumor tissues found that FUS sonication modulated cell physical structure-related genes but evoked minimal inflammatory response. These feasibility and safety data support the continued investigation of sonobiopsy for noninvasive molecular diagnosis of brain diseases.

First-in-human prospective trial of sonobiopsy in high-grade glioma patients using neuronavigation-guided focused ultrasound.

Sonobiopsy is an emerging technology that combines focused ultrasound (FUS) with microbubbles to enrich circulating brain disease-specific biomarkers for noninvasive molecular diagnosis of brain diseases. Here, we report the first-in-human prospective trial of sonobiopsy in high-grade glioma patients to evaluate its feasibility and safety in enriching plasma circulating tumor biomarkers. A nimble FUS device integrated with a clinical neuronavigation system was used to perform sonobiopsy following an established clinical workflow for neuronavigation. Analysis of blood samples collected before and after FUS sonication showed that sonobiopsy enriched plasma circulating tumor DNA (ctDNA), including a maximum increase of 1.6-fold for the mononucleosome cell-free DNA (cfDNA) fragments (120-280 bp), 1.9-fold for the patient-specific tumor variant ctDNA level, and 5.6-fold for the TERT mutation ctDNA level. Histological analysis of surgically resected tumors confirmed the safety of the procedure. Transcriptome analysis of sonicated and nonsonicated tumor tissues found that FUS sonication modulated cell physical structure-related genes. Only 2 out of 17,982 total detected genes related to the immune pathways were upregulated. These feasibility and safety data support the continued investigation of sonobiopsy for noninvasive molecular diagnosis of brain diseases.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Patterns of fruit and vegetable consumption among Iranian adults: a SuRFNCD-2007 study.

The aim of the present study was to describe the patterns of fruit and vegetable (F&V) intake in a nationally representative sample of the Iranian population. The data collected in the Third National Surveillance of Risk Factors of Non-communicable Diseases (SuRFNCD-2007) were used. In a sample of 3702 Iranian adult participants, patterns of F&V consumption were assessed using the WHO STEPwise method. Low F&V consumption was defined as intake of less than five servings of fruit and/or vegetable daily according to the WHO guidelines. F&V consumption was compared among different age groups, sex and urban/rural areas using complex sample analysis. On average, 1·26 servings of fruit and 1·32 servings of vegetables were consumed daily. Taken together, Iranian adults consumed 2·58 F&V servings per d, with females eating more than males (P ≤ 0·001). Moreover, there was a trend towards lower consumption rates in older-age categories (P = 0·003). Prevalence of low F&V intake (less than five servings daily) was 87·5 % and also tended to be higher in older-age categories (P = 0·004). Prevalence of low intake did not differ significantly among men and women or urban and rural areas. A high prevalence of low F&V consumption in the Iranian adult population was documented. These findings may guide health policy makers in developing specific plans to encourage adequate F&V intake.

A review of bioeffects induced by focused ultrasound combined with microbubbles on the neurovascular unit.

Focused ultrasound combined with circulating microbubbles (FUS+MB) can transiently enhance blood-brain barrier (BBB) permeability at targeted brain locations. Its great promise in improving drug delivery to the brain is reflected by a rapidly growing number of clinical trials using FUS+MB to treat various brain diseases. As the clinical applications of FUS+MB continue to expand, it is critical to have a better understanding of the molecular and cellular effects induced by FUS+MB to enhance the efficacy of current treatment and enable the discovery of new therapeutic strategies. Existing studies primarily focus on FUS+MB-induced effects on brain endothelial cells, the major cellular component of BBB. However, bioeffects induced by FUS+MB expand beyond the BBB to cells surrounding blood vessels, including astrocytes, microglia, and neurons. Together these cell types comprise the neurovascular unit (NVU). In this review, we examine cell-type-specific bioeffects of FUS+MB on different NVU components, including enhanced permeability in endothelial cells, activation of astrocytes and microglia, as well as increased intraneuron protein metabolism and neuronal activity. Finally, we discuss knowledge gaps that must be addressed to further advance clinical applications of FUS+MB.

Sonobiopsy for minimally invasive, spatiotemporally-controlled, and sensitive detection of glioblastoma-derived circulating tumor DNA.

Though surgical biopsies provide direct access to tissue for genomic characterization of brain cancer, they are invasive and pose significant clinical risks. Brain cancer management via blood-based liquid biopsies is a minimally invasive alternative; however, the blood-brain barrier (BBB) restricts the release of brain tumor-derived molecular biomarkers necessary for sensitive diagnosis. Methods: A mouse glioblastoma multiforme (GBM) model was used to demonstrate the capability of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) to improve the diagnostic sensitivity of brain tumor-specific genetic mutations compared with conventional blood-based liquid biopsy. Furthermore, a pig GBM model was developed to characterize the translational implications of sonobiopsy in humans. Magnetic resonance imaging (MRI)-guided FUS sonication was performed in mice and pigs to locally enhance the BBB permeability of the GBM tumor. Contrast-enhanced T1-weighted MR images were acquired to evaluate the BBB permeability change. Blood was collected immediately after FUS sonication. Droplet digital PCR was used to quantify the levels of brain tumor-specific genetic mutations in the circulating tumor DNA (ctDNA). Histological staining was performed to evaluate the potential for off-target tissue damage by sonobiopsy. Results: Sonobiopsy improved the detection sensitivity of EGFRvIII from 7.14% to 64.71% and TERT C228T from 14.29% to 45.83% in the mouse GBM model. It also improved the diagnostic sensitivity of EGFRvIII from 28.57% to 100% and TERT C228T from 42.86% to 71.43% in the porcine GBM model. Conclusion: Sonobiopsy disrupts the BBB at the spatially-targeted brain location, releases tumor-derived DNA into the blood circulation, and enables timely collection of ctDNA. Converging evidence from both mouse and pig GBM models strongly supports the clinical translation of sonobiopsy for the minimally invasive, spatiotemporally-controlled, and sensitive molecular characterization of brain cancer.

Neurodevelopmental patterns of early postnatal white matter maturation represent distinct underlying microstructure and histology.

Cerebral white matter undergoes a rapid and complex maturation during the early postnatal period. Prior magnetic resonance imaging (MRI) studies of early postnatal development have often been limited by small sample size, single-modality imaging, and univariate analytics. Here, we applied nonnegative matrix factorization, an unsupervised multivariate pattern analysis technique, to T2w/T1w signal ratio maps from the Developing Human Connectome Project (n = 342 newborns) revealing patterns of coordinated white matter maturation. These patterns showed divergent age-related maturational trajectories, which were replicated in another independent cohort (n = 239). Furthermore, we showed that T2w/T1w signal variations in these maturational patterns are explained by differential contributions of white matter microstructural indices derived from diffusion-weighted MRI. Finally, we demonstrated how white matter maturation patterns relate to distinct histological features by comparing our findings with postmortem late fetal/early postnatal brain tissue staining. Together, these results delineate concise and effective representation of early postnatal white matter reorganization.

In Vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation.

Postmortem studies reveal that individuals with major neuropsychiatric disorders such as schizophrenia and autism spectrum disorder have gray matter microstructural abnormalities. These include abnormalities in neuropil organization, expression of proteins supporting neuritic and synaptic integrity, and myelination. Genetic and postmortem studies suggest that these changes may be causally linked to the pathogenesis of these disorders. Advances in diffusion-weighted magnetic resonance image (dMRI) acquisition techniques and biophysical modeling allow for the quantification of gray matter microstructure in vivo. While several biophysical models for imaging microstructural properties are available, one in particular, neurite orientation dispersion and density imaging (NODDI), holds great promise for clinical applications. NODDI can be applied to both gray and white matter and requires only a single extra shell beyond a standard dMRI acquisition. Since its development only a few years ago, the NODDI algorithm has been used to characterize gray matter microstructure in schizophrenia, Alzheimer's disease, healthy aging, and development. These investigations have shown that microstructural findings in vivo, using NODDI, align with postmortem findings. Not only do NODDI and other advanced dMRI-based modeling methods provide a window into the brain previously only available postmortem, but they may be more sensitive to certain brain changes than conventional magnetic resonance imaging approaches. This opens up exciting new possibilities for clinicians to more rapidly detect disease signatures and allows earlier intervention in the course of the disease. Given that neurites and gray matter microstructure have the capacity to rapidly remodel, these novel dMRI-based methods represent an opportunity to noninvasively monitor neuroplastic changes posttherapy within much shorter time scales.