Whole-brain Magnetic Resonance Imaging Research Articles

Whole-brain mapping of increased manganese levels in welders and its association with exposure and motor function

Although manganese (Mn) is a trace metal essential for humans, chronic exposure to Mn can cause accumulation of this metal ion in the brain leading to an increased risk of neurological and neurobehavioral health effects. This is a concern for welders exposed to Mn through welding fumes. While brain Mn accumulation in occupational settings has mostly been reported in the basal ganglia, several imaging studies also revealed elevated Mn in other brain areas. Since Mn functions as a magnetic resonance imaging (MRI) T1 contrast agent, we developed a whole-brain MRI approach to map in vivo Mn deposition differences in the brains of non-exposed factory controls and exposed welders. This is a cross-sectional analysis of 23 non-exposed factory controls and 36 exposed full-time welders from the same truck manufacturer. We collected high-resolution 3D MRIs of brain anatomy and R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Furthermore, we investigated the associations between excess Mn deposition and neuropsychological and motor test performance. Our results indicate that: (1) Using whole-brain MRI relaxometry methods we can generate excess Mn deposition maps in vivo, (2) excess Mn accumulation due to occupational exposure occurs beyond the basal ganglia in cortical areas associated with motor and cognitive functions, (3) Mn likely diffuses along white matter tracts in the brain, and (4) Mn deposition in specific brain regions is associated with exposure (cerebellum and frontal cortex) and motor metrics (cerebellum and hippocampus).

Simple procedure for assessing diffuse subarachnoid hemorrhage successfully created using filament perforation method in mice.

The murine model of subarachnoid hemorrhage (SAH) is a valuable experimental tool for investigating molecular and cellular mechanisms, and the endovascular filament perforation technique can be used to simulate prominent pathophysiological features observed after human SAH; however, current validation methods for assessing an appropriate SAH model are limited. Here, we introduce a simple procedure for selecting a mouse model of diffuse SAH. SAH was induced in 24 mice using a standard filament perforation method. After confirming survival at 24 h, SAH was scored 0-1 based on T2*-weighted images on whole-brain magnetic resonance imaging (MRI) and visual surveillance of the cisterna magna (CM) through the dura mater. The CM-based SAH grading correlated well with a reference parameter defined by extracted brain (r2 = 0.53, p < 0.0001). The receiver operating characteristic curve revealed a sensitivity of 85% and a specificity of 91% for detecting diffuse SAH, with a similar area under the curve (0.89 ± 0.06 [standard error of the mean]) as the MRI-based grading (0.72 ± 0.10, p = 0.12). Our data suggest that confirming an SAH clot in the CM is a valuable way to select a clinically relevant diffuse SAH model that can be used in future experimental studies.

Deep learning enabled fast 3D brain MRI at 0.055 tesla.

In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.

Correlation between neutrophil gelatinase phase lipocalin and cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is common in the elderly population. Neutrophil gelatinase-associated lipocalin (NGAL) is closely related to cardiovascular and cerebrovascular diseases. NGAL causes pathological changes, such as damage to the vascular endothelium, by causing inflammation, which results in other related diseases. The purpose of this study was to investigate whether serum NGAL levels could predict disease severity in patients with CSVD. The patients with CSVD who visited the Department of Neurology at the First Affiliated Hospital of Zhengzhou University between January 2018 and June 2022 were prospectively included. The total CSVD burden score was calculated using whole-brain magnetic resonance imaging (MRI), and the patients were divided into a mild group (total CSVD burden score < 2 points) and a severe group (total CSVD burden score ≥ 2 points). Age, sex, height, smoking and alcohol consumption history, medical history, and serological results of patients were collected to perform the univariate analysis. Multivariate logistic regression was used to analyze the risk factors that affect CSVD severity. The multiple linear regression method was used to analyze which individual CSVD markers (periventricular white matter hyperintensities, deep white matter hyperintensities, lacune, and cerebral microbleed) play a role in the association between total CSVD burden score and NGAL. A total of 427 patients with CSVD (140 in the mild group and 287 in the severe group) were included in the study. A multivariate logistic regression analysis showed that the following factors were significantly associated with CSVD severity: male sex [odds ratio(OR), 1.912; 95% confidence interval (CI), 1.150-3.179], age (OR, 1.046; 95% CI, 1.022-1.070), history of cerebrovascular disease (OR, 3.050; 95% CI, 1.764-5.274), serum NGAL level (OR, 1.005; 95% CI, 1.002-1.008), and diabetes (OR, 2.593; 95% CI, 1.424-4.722). A multivariate linear regression shows that periventricular white matter hyperintensities and cerebral microbleed are associated with serum NGAL concentrations (P < 0.05). Serum NGAL level is closely related to CSVD severity and is a risk factor for the burden of CSVD brain damage. Serum NGAL has high specificity in reflecting the severity of CSVD.

Brain volumes in alcohol use disorder: Do females and males differ? A whole-brain magnetic resonance imaging mega-analysis.

Emerging evidence suggests distinct neurobiological correlates of alcohol use disorder (AUD) between sexes, which however remain largely unexplored. This work from ENIGMA Addiction Working Group aimed to characterize the sex differences in gray matter (GM) and white matter (WM) correlates of AUD using a whole-brain, voxel-based, multi-tissue mega-analytic approach, thereby extending our recent surface-based region of interest findings on a nearly matching sample using a complementary methodological approach. T1-weighted magnetic resonance imaging (MRI) data from 653 people with AUD and 326 controls was analyzed using voxel-based morphometry. The effects of group, sex, group-by-sex, and substance use severity in AUD on brain volumes were assessed using General Linear Models. Individuals with AUD relative to controls had lower GM volume in striatal, thalamic, cerebellar, and widespread cortical clusters. Group-by-sex effects were found in cerebellar GM and WM volumes, which were more affected by AUD in females than males. Smaller group-by-sex effects were also found in frontotemporal WM tracts, which were more affected in AUD females, and in temporo-occipital and midcingulate GM volumes, which were more affected in AUD males. AUD females but not males showed a negative association between monthly drinks and precentral GM volume. Our results suggest that AUD is associated with both shared and distinct widespread effects on GM and WM volumes in females and males. This evidence advances our previous region of interest knowledge, supporting the usefulness of adopting an exploratory perspective and the need to include sex as a relevant moderator variable in AUD.

Tensor Distribution Regression Based on the 3D Conventional Neural Networks

Dear Editor, This letter presents a novel tensor-distribution-regression model based on 3D conventional neural networks (3D-TDR) with an application to clinical score prediction in aging-related diagnosis. The estimation of clinical scores of subjects using brain magnetic resonance imaging (MRI) helps understand the pathological stage of dementia. However, clinical scores prediction is still unsolved due to the reasons of: 1) Analyzing the whole-brain MRI is extremely difficult as the high-dimensional MRI data contains millions of voxels; 2) The clinical scores prediction is formulated as a one-dimensional regression issue in the current deep-learning-based algorithms, which ignores the implicit label information between subjects with different score levels. Motivated by the above discoveries, the proposed 3D-TDR model innovatively establishes the following three-fold ideas: a) incorporating a tensor regression layer (TRL) into a 3D conventional neural network (3D-CNN) to enable its extraction of more discriminative structural changes from the high-dimensional whole-brain magnetic resonance (MR) data; b) adopting the label distribution learning (LDL) to fully utilize the label correlation among the MR images, thus emphasizing the diversity of subjects' scores; and c) combining the TRL and LDL for an end-to-end deep learning framework, thereby achieving jointly low-rank feature extraction and clinical scores prediction. Experimental results on two real-world MRI datasets of two typical clinical prediction tasks indicate that the 3D-TDR outperforms the benchmark and state-of-the-art models. The proposed 3D-TDR model can achieve significant accuracy gain in dementia score and brain age prediction.

Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial

Parkinson’s disease (PD) is a serious neurological disease. Many studies have preseted regions of interest such as substantia nigra (SN) for PD detection from magnetic resonance imaging (MRI). However, the SN is not the only region with remarkable tissue changes in PD MRIs. Patients with Prodromal Parkinson’s Disease usually present with non-motor symptoms, and the associated brain regions may show varying degrees of damage on imaging. Therefore, exploring PD-related regions from whole-brain MRI is essential. In this study, we proposed an interpretable PD detection framework, including PD classification and feature region visualization. Specifically, we constructed a 3D ResNet model that could detect PD from whole-brain MRIs and discover other brain regions related to PD through 3D Gradient-weighted Class Activation Mapping (Grad-CAM) and Unified Parkinson’s Disease Rating Scale (UPDRS). We obtained T1-Weighted MRIs from the Parkinson’s Progression Markers Initiative (PPMI) database. The average classification accuracy of the 5-fold cross-validation and held-out dataset reached 96.1% and 94.5%, respectively. In addition, we used the 3D Grad-CAM framework to extract the weight of the feature map and obtain visual interpretation. The heat map highlighted the regions that were crucial for PD classification and found significant differences between PD and HC in frontal lobe related to linguistic semantic disorders. The UPDRS scores of PD and HC on the linguistic semantic function items were also remarkably different. Combined with previous studies, this work verified the significance of the frontal lobe and proved that the correlation between the frontal lobe and the pathogenesis of PD was explanatory.

Quantitative morphometric changes in vascular mild cognitive impairment patients: early diagnosis of dementia.

Vascular mild cognitive impairment (VMCI) is an early and reversible stage of dementia. Volume differences in regional gray matter may reveal the development and prognosis of VMCI. This study selected 2 of the most common types of VMCI, namely, periventricular white matter hyperintensities (PWMH, n = 14) and strategic single infarctions (SSI, n = 10), and used the voxel-based morphometry method to quantify their morphological characteristics. Meanwhile, age- and sex-matched healthy volunteers were included (n = 16). All the participants were neuropsychologically tested to characterize their cognitive function and underwent whole-brain magnetic resonance imaging scanning. Our results showed that the volumes of the bilateral temporal lobes and bilateral frontal gray matter were obviously diminished in the PWMH group. The atrophy volume difference was 4,086 voxels in the left temporal lobe, 4,154 voxels in the right temporal lobe, 1,718 voxels in the left frontal lobe, and 1,141 voxels in the right frontal lobe (P ≤ 0.001). Moreover, the characteristics of the gray matter atrophy associated with the PWMH were more similar to those associated with Alzheimer's disease than SSI, which further revealed the susceptibility for escalation from PWMH to dementia. In conclusion, PWMH patients and SSI patients have different morphological characteristics, which explain the different prognoses of VMCI.

Association between the Pittsburgh sleep quality index and white matter integrity in healthy adults: a whole-brain magnetic resonance imaging study.

The online version contains supplementary material available at 10.1007/s41105-022-00442-0.

A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site and are currently made with biopsy and histology. Here, we develop a deep-learning approach for accurate non-invasive digital histology with whole-brain magnetic resonance imaging (MRI) data. Contrast-enhanced T1-weighted and fast spoiled gradient echo brain MRI exams (n= 1,582) were preprocessed and input to the proposed deep-learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Tenfold cross-validation generated an overall area under the receiver operating characteristic curve (AUC) of 0.878 (95% confidence interval [CI]: 0.873,0.883). These data establish that whole-brain imaging features are discriminative enough to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes.

MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation.

Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis. Traditional neuroimaging analysis pipelines are implemented based on registration methods, which involve time-consuming optimization steps. Recent related deep learning methods speed up the segmentation pipeline but are limited to distinguishing fuzzy boundaries, especially encountering the multi-grained whole-brain segmentation task, where there exists high variability in size and shape among various anatomical regions. In this article, we propose a deep learning-based network, termed Multi-branch Residual Fusion Network, for the whole brain segmentation, which is capable of segmenting the whole brain into 136 parcels in seconds, outperforming the existing state-of-the-art networks. To tackle the multi-grained regions, the multi-branch cross-attention module (MCAM) is proposed to relate and aggregate the dependencies among multi-grained contextual information. Moreover, we propose a residual error fusion module (REFM) to improve the network's representations fuzzy boundaries. Evaluations of two datasets demonstrate the reliability and generalization ability of our method for the whole brain segmentation, indicating that our method represents a rapid and efficient segmentation tool for neuroimage analysis.

Association of body mass index and its classifications with gray matter volume in individuals with a wide range of body mass index group: A whole-brain magnetic resonance imaging study.

AimTo examine the association of body mass index (BMI) [kg/m2] and its classifications (underweight [BMI < 18.5], normal [18.5 ≤ BMI < 25], overweight [25 ≤ BMI < 30], and obese [BMI ≥ 30]) with brain structure in individuals with a wide range of BMI group.Materials and methodsThe participants included 382 right-handed individuals (mean age: 46.9 ± 14.3 years, 142 men and 240 women). The intelligence quotient was assessed using the Japanese Adult Reading Test. Voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) were performed to analyze the association of BMI and its classifications with gray and white matter structures, respectively.ResultsAccording to VBM, BMI was significantly and negatively correlated with the bilateral cerebellum exterior volumes. In group comparisons, the right cerebellum exterior volume was significantly lower in the overweight or obese group than in the underweight or normal group, while the bilateral cuneus and calcarine cortex, left cuneus, and left precuneus volume was significantly lower in the underweight group than in the non-underweight group. Sex-related stratification analyses for VBM revealed that BMI was significantly and negatively correlated with the bilateral cerebellum exterior volumes only in women. In group comparisons, the left cerebellum exterior volume was significantly lower in obese women than in non-obese women. The left thalamus proper and the right cerebellum exterior volumes were significantly lower in overweight or obese group than in underweight or normal group in men and women, respectively. The bilateral cuneus and calcarine cortex, left cuneus and carcarine cortex, and bilateral cuneus volume was significantly lower in underweight men than in non-underweight men. In contrast, there were no notable findings on DTI.ConclusionOur results suggest association of continuous BMI, being overweight or obese, and being underweight with decreased gray matter volume in individuals with a wide range of BMI group. Furthermore, sex-related differences are seen in the association of BMI and its classifications with regional gray matter volume reductions. Abnormally high or low BMIs may have a negative influence on regional gray matter volumes.

Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm

Glioma is the most common brain tumor in humans. Accurate stage estimation of the tumor is essential for treatment planning. The biopsy is the gold standard method for this purpose. However, it is an invasive procedure, which can prove fatal for patients, if a tumor is present deep inside the brain. Therefore, a magnetic resonance imaging (MRI) based non-invasive method is proposed in this paper for low-grade glioma (LGG) versus high-grade glioma (HGG) classification. To maximize the above classification performance, five pre-trained convolutional neural networks (CNNs) such as AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 are assembled using a majority voting mechanism. Segmentation methods require human intervention and additional computational efforts. It makes computer-aided diagnosis tools semi-automated. To analyze the performance effect of segmentation methods, three segmentation methods such as region of interest MRI segmentation (RSM) and skull-stripped MRI segmentation (SSM), and whole-brain MRI (WBM) (non-segmentation) data were compared using above mentioned algorithm. The highest classification accuracy of 99.06 ± 0.55 % was observed on the RSM data and the lowest accuracy of 98.43 ± 0.89 % was observed on the WSM data. However, only a 0.63 % improvement was found in the accuracy of the RSM data against the WBM data. This shows that deep learning models have an incredible ability to extract appropriate features from images. Furthermore, the proposed algorithm showed 2.85 %, 1.39 %, 1.26 %, 2.66 %, and 2.33 % improvement in the average accuracy of the above three datasets over the AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models, respectively.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks

BackgroundThere is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma.MethodsThis proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both “functional data” from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and “structural data” (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants.ResultsThe final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94–0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes.ConclusionsA fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data.Clinical Trial Numberhttp://ClinicalTrials.gov (No. NCT03482115).

Add-On MEmaNtine to Dopamine Antagonism to Improve Negative Symptoms at First Psychosis- the AMEND Trial Protocol.

BackgroundAntipsychotic drugs are primarily efficacious in treating positive symptoms by blocking the dopamine D2 receptor, but they fail to substantially improve negative symptoms and cognitive deficits. The limited efficacy may be attributed to the fact that the pathophysiology of psychosis involves multiple neurotransmitter systems. In patients with chronic schizophrenia, memantine, a non-competitive glutamatergic NMDA receptor antagonist, shows promise for ameliorating negative symptoms and improving cognition. Yet, it is unknown how memantine modulates glutamate levels, and memantine has not been investigated in patients with first-episode psychosis.AimsThis investigator-initiated double-blinded randomized controlled trial is designed to (1) test the clinical effects on negative symptoms of add-on memantine to antipsychotic medication, and (2) neurobiologically characterize the responders to add-on memantine.Materials and EquipmentAntipsychotic-naïve patients with first-episode psychosis will be randomized to 12 weeks treatment with [amisulpride + memantine] or [amisulpride + placebo]. We aim for a minimum of 18 patients in each treatment arm to complete the trial. Brain mapping will be performed before and after 12 weeks focusing on glutamate and neuromelanin in predefined regions. Regional glutamate levels will be probed with proton magnetic resonance spectroscopy (MRS), while neuromelanin signal will be mapped with neuromelanin-sensitive magnetic resonance imaging (MRI). We will also perform structural and diffusion weighted, whole-brain MRI. MRS and MRI will be performed at an ultra-high field strength (7 Tesla). Alongside, participants undergo clinical and neuropsychological assessments. Twenty matched healthy controls will undergo similar baseline- and 12-week examinations, but without receiving treatment.Outcome MeasuresThe primary endpoint is negative symptom severity. Secondary outcomes comprise: (i) clinical endpoints related to cognition, psychotic symptoms, side effects, and (ii) neurobiological endpoints related to regional glutamate- and neuromelanin levels, and structural brain changes.Anticipated ResultsWe hypothesize that add-on memantine to amisulpride will be superior to amisulpride monotherapy in reducing negative symptoms, and that this effect will correlate with thalamic glutamate levels. Moreover, we anticipate that add-on memantine will restore regional white matter integrity and improve cognitive functioning.PerspectivesBy combining two licensed, off-patent drugs, AMEND aims to optimize treatment of psychosis while investigating the memantine response. Alongside, AMEND will provide neurobiological insights to effects of dual receptor modulation, which may enable future stratification of patients with first-episode psychosis before initial antipsychotic treatment.Clinical Trial Registration[ClinicalTrials.gov], identifier [NCT04789915].

Learning residual motion correction for fast and robust 3D multiparametric MRI

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.

The Multicomponent Day-Care Program Prevents Volume Reduction in a Memory-Related Brain Area in Patients with Mild Cognitive Impairment

Introduction: Mild cognitive impairment (MCI) is considered an important period for interventions to prevent progression to dementia. Nonpharmacological interventions for MCI include exercise training, cognitive intervention, and music therapy. These play an important role in improving cognitive function, but their effects on brain plasticity in individuals with MCI are largely unknown. We investigated the effects of a multicomponent day-care program provided by the University of Tsukuba Hospital on the longitudinal brain volume changes in MCI patients. Methods: MCI patients who participated in the multicomponent day-care program and underwent whole-brain magnetic resonance imaging (MRI) twice during their participation (n = 14), were included. We divided them into two groups according to their attendance rate and conducted a between-group analysis of longitudinal volume changes in the whole cerebral cortex. Regional brain volumes derived from the patients’ MRI were calculated with Freesurfer 6.0.0. Results: The neuroimaging analysis demonstrated that the left rostral anterior cingulate cortex volume was significantly preserved in the high-attendance group compared to that of the low-attendance group. Conclusion: Our results suggest that continuous participation in a multicomponent day-care program could help prevent a volume reduction in memory-related brain areas in patients with MCI.

The Digital Brain Bank, an open access platform for post-mortem imaging datasets.

Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults.

IntroductionIntegrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a “metabolic map” of the brain in prodromal AD.MethodsIn 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort.ResultsThe multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05).ConclusionBy integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.

Peripheral inflammatory markers associated with brain volume reduction in patients with bipolar I disorder.

Neuroinflammation and brain structural abnormalities are found in bipolar disorder (BD). Elevated levels of cytokines and chemokines have been detected in the serum and cerebrospinal fluid of patients with BD. This study investigated the association between peripheral inflammatory markers and brain subregion volumes in BD patients. Euthymic patients with bipolar I disorder (BD-I) aged 20-45 years underwent whole-brain magnetic resonance imaging. Plasma levels of monocyte chemoattractant protein-1 (MCP-1), chitinase-3-like protein 1 (also known as YKL-40), fractalkine (FKN), soluble tumour necrosis factor receptor-1 (sTNF-R1), interleukin-1β, and transforming growth factor-β1 were measured on the day of neuroimaging. Clinical data were obtained from medical records and interviewing patients and reliable others. We recruited 31 patients with a mean age of 29.5 years. In multivariate regression analysis, plasma level YKL-40, a chemokine, was the most common inflammatory marker among these measurements displaying significantly negative association with the volume of various brain subareas across the frontal, temporal, and parietal lobes. Higher YKL-40 and sTNF-R1 levels were both significantly associated with lower volumes of the left anterior cingulum, left frontal lobe, right superior temporal gyrus, and supramarginal gyrus. A greater number of total lifetime mood episodes were also associated with smaller volumes of the right caudate nucleus and bilateral frontal lobes. The volume of brain regions known to be relevant to BD-I may be diminished in relation to higher plasma level of YKL-40, sTNF-R1, and more lifetime mood episodes. Macrophage and macrophage-like cells may be involved in brain volume reduction among BD-I patients.