Multimodal Brain Magnetic Resonance Imaging Research Articles

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Discovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study.

Introduction: Brain age prediction using neuroimaging data and machine learning algorithms holds significant promise for gaining insights into the development of neurodegenerative diseases. The estimation of brain age may be influenced not only by the imaging modality but also by multidomain clinical factors. However, the degree to which various clinical factors in individuals are associated with brain structure, as well as the comprehensive relationship between these factors and brain aging, is not yet clear. Methods: In this study, multimodal brain magnetic resonance imaging data and longitudinal clinical information were collected from 964 participants in a population-based cohort with 16 years of follow-up in northern China. We developed a machine learning-based algorithm to predict multimodal brain age and compared the estimated brain age gap (BAG) differences among the 5 groups characterized by varying exposures to these high-risk clinical factors. We then estimated modality-specific brain age in the hypertension group based on hypertension-related regional imaging metrics. Results: The results revealed a significantly larger BAG estimated from multimodal neuroimaging in subjects with 4 or 5 risk factors compared to other groups, suggesting an acceleration of brain aging under cumulative exposure to multiple risk factors. The estimated T1-based BAG exhibited a significantly higher level in the hypertensive subjects compared to the normotensive individuals. Conclusion: Our study provides valuable insights into a range of health factors across lifestyle, metabolism, and social context that are reflective of brain aging and also contributes to the advancement of interventions and public health initiatives targeted at the general population aimed at promoting brain health.

Cognitive status modifies the association between elevated cerebrospinal fluid phosphorylated tau and longitudinal cerebral blood flow changes over a 7‐year follow‐up period

AbstractBackgroundAlzheimer’s disease (AD) is associated with widespread reductions in cerebral blood flow (CBF), but the precise link between AD pathology and CBF is unclear. Hypoperfusion is a late‐stage event associated with tau, but not amyloid, pathology. We assessed whether baseline levels of cerebrospinal fluid (CSF)‐measured core AD pathology [β‐amyloid42 (Aβ42), phosphorylated tau (p‐tau)] related to CBF changes over a 7‐year period and if associations differed by baseline cognitive status.MethodsVanderbilt Memory and Aging Project participants free of clinical dementia or stroke at enrollment [n = 153, 72±6 years, 37% mild cognitive impairment (MCI), 33% female] underwent lumbar puncture to obtain CSF at study entry and serial multimodal 3T brain magnetic resonance imaging over a 7‐year follow‐up period (mean = 5.4 years). Enzyme‐linked immunosorbent assays quantified CSF Aβ42 and p‐tau181. Pseudo‐continuous arterial spin‐labeling captured CBF in total and lobar grey matter regions of interest (ROI). Linear mixed‐effects models related biomarker x time to longitudinal CBF variables adjusting for baseline age and cognitive status, sex, race/ethnicity, education, Framingham Stroke Risk Profile (minus age), ROI volume, APOE‐ε4 status, and time. Models were repeated testing a cognitive status x biomarker x time interaction term.ResultsIn main effect models, neither CSF Aβ42 (p‐values&gt;0.31) nor p‐tau (p‐values&gt;0.28) related to CBF outcomes. Aβ42 did not interact with cognitive status on CBF outcomes (p‐values&gt;0.37), but p‐tau interacted with baseline cognitive status on longitudinal frontal, temporal, parietal, occipital, and total grey matter CBF trajectories (p‐values&lt;0.005). Among cognitively unimpaired participants, higher p‐tau was associated with slower reductions in frontal (β = 0.01, p = 0.03), parietal (β = 0.01, p = 0.04), occipital (β = 0.01, p = 0.02), and total grey matter CBF (β = 0.01, p = 0.04). Among individuals with MCI, higher p‐tau was associated with faster reductions in frontal (β = ‐0.03, p = 0.008), temporal (β = ‐0.02, p = 0.01), parietal (β = ‐0.04, p = 0.002), occipital (β = ‐0.03, p = 0.0004), and total grey matter CBF (β = ‐0.03, p = 0.005).ConclusionsResults linking CSF p‐tau to CBF decline in MCI support the theory that grey matter hypoperfusion is a later‐stage event tied to neurofibrillary tangle pathology. Associations between higher p‐tau and slower rates of CBF decline among cognitively unimpaired individuals warrants further investigation. Funding: R01‐AG034962, K24‐AG046373, F31‐AG079640, T32‐AG058524, P20‐AG068082

Arterial spin‐labeling spatial coefficient of variation is associated with cerebral small vessel disease

AbstractBackgroundCerebral small vessel disease (SVD) is the most common pathology to co‐occur with Alzheimer’s disease but pathophysiologic mechanisms leading to SVD are poorly understood. The arterial spin‐labeling (ASL) spatial coefficient of variation (sCoV), a proxy marker of arterial transit time (ATT), may better reflect SVD‐related hemodynamic disturbances than ASL‐derived cerebral blood flow (CBF). We investigated whether grey matter CBF and grey matter sCoV each related to neuroimaging markers of SVD, including white matter hyperintensities (WMHs), basal ganglia enlarged perivascular spaces (ePVS), cerebral microbleeds (CMBs), and lacunes.MethodsVanderbilt Memory and Aging Project participants free of clinical dementia and stroke at study entry (n = 312, 73±7 years, 39% mild cognitive impairment, 41% female) underwent multimodal 3T brain magnetic resonance imaging to quantify ASL parameters and SVD burden. Pseudo‐continuous ASL assessed CBF in total grey matter. Grey matter sCoV was calculated as σ(CBFGrey Matter)/μ(CBFGrey Matter). WMHs and ePVS volumes were quantified using automated methods and ePVS volumes were standardized to basal ganglia volume. CMBs and lacune counts were quantified by an expert rater. Ordinary least squares regression models cross‐sectionally related grey matter CBF and sCoV individually to log‐transformed burden of each SVD marker. Models were adjusted for age, sex, race/ethnicity, education, cognitive status, Framingham Stroke Risk Profile (minus age), intracranial volume, grey matter volume, and apolipoprotein E‐ε4 status.ResultsGrey matter CBF was unrelated to all SVD outcomes (p‐values&gt;0.25). Higher grey matter sCoV was associated with greater WMHs (β = 2.1, p = 0.001), ePVS (β = 0.004, p = 0.0003), and lacunar infarcts (β = 1.4, p = 0.0006) but not CMBs (β = 1.0, p = 0.06).ConclusionsOur findings align with previous results showing that grey matter sCoV is a valuable marker of SVD. In contrast to previous work, we found an association between higher grey matter sCoV and higher basal ganglia ePVS burden. Potentially, our use of volumetric ePVS quantification rather than qualitative ePVS burden increased our ability to detect this association. As sCoV is a proxy marker of ATT, future work should consider quantifying sCoV or ATT in addition to CBF to better characterize SVD‐related hemodynamic disturbances. Funding: R01‐AG034962, K24‐AG046373, F31‐AG079640, P20‐AG068082

CSF‐plasma albumin ratio differs by sex in community dwelling older adults

AbstractBackgroundBlood‐brain barrier (BBB) permeability is associated with Alzheimer’s disease (AD) and neurodegeneration. Cerebrospinal fluid (CSF)/plasma albumin ratio (QAlb) is a biomarker commonly used to approximate BBB and blood‐CSF barrier (BCSFB) integrity. While sex‐based differences in AD risk are well‐established, limited work has evaluated these differences with QAlb and neurodegeneration and neuropsychological outcomes. Here, we examine if sex modifies the association of QAlb with neuropsychological and structural neuroimaging markers of neurodegeneration in community‐dwelling older adults.MethodVanderbilt Memory and Aging Project participants (n = 152, 72±7 years, 33% female) completed a fasting blood draw, lumbar puncture, neuropsychological assessment, and multimodal 3T brain magnetic resonance imaging (MRI) to capture grey matter volumes and white matter hyperintensities (WMHs). Ordinary least squares regressions cross‐sectionally related sex to QAlb. Follow‐up models tested a sex x QAlb interaction with neuropsychological and MRI outcomes. Models adjusted for age, sex, race/ethnicity, education, cognitive status, Framingham Stroke Risk Profile score (minus age), apolipoprotein (APOE)‐e4 status, and (for MRI outcomes) intracranial volume.ResultFemale sex was associated with lower QAlb (p&lt;0.001). However, sex did not interact with QAlb on any neuropsychological (p‐values&gt;0.7) or neuroimaging outcomes (p‐values&gt;0.2). Models stratified by sex were also null for neuropsychological (p‐values&gt;0.93) and neuroimaging outcomes (p‐values&gt;0.11).ConclusionElevated QAlb is believed to reflect disrupted brain‐barrier integrity such that blood‐based albumin (which lacks an active transport mechanism across the BBB) can enter the brain parenchyma and travel to the CSF. We found that female sex is associated with lower QAlb levels but does not interact with early cognitive or neuroimaging markers of AD‐associated neurodegeneration. The strong association between female sex and higher brain‐barrier integrity highlights the importance of considering sex differences when applying QAlb in clinical and research settings. Funding: R01‐AG034962, K24‐AG046373, T32‐AG058524, P20‐AG068082

APOE4 allele-specific associations between diet, multimodal biomarkers, and cognition among Puerto Rican adults in Massachusetts.

Apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's Disease (AD), and the ε4 allele (APOE4) may interact with lifestyle factors that relate to brain structural changes, underlying the increased risk of AD. However, the exact role of APOE4 in mediating interactions between the peripheral circulatory system and the central nervous system, and how it may link to brain and cognitive aging requires further elucidation. In this analysis, we investigated the association between APOE4 carrier status and multimodal biomarkers (diet, blood markers, clinical diagnosis, brain structure, and cognition) in the context of gene-environment interactions. Participants were older adults from a longitudinal observational study, the Boston Puerto Rican Health Study (BPRHS), who self-identified as of Puerto Rican descent. Demographics, APOE genotype, diet, blood, and clinical data were collected at baseline and at approximately 12th year, with the addition of multimodal brain magnetic resonance imaging (MRI) (T1-weighted and diffusion) and cognitive testing acquired at 12-year. Measures were compared between APOE4 carriers and non-carriers, and associations between multimodal variables were examined using correlation and multivariate network analyses within each group. A total of 156 BPRHS participants (mean age at imaging = 68 years, 77% female, mean follow-up 12.7 years) with complete multimodal data were included in the current analysis. APOE4 carriers (n = 43) showed reduced medial temporal lobe (MTL) white matter (WM) microstructural integrity and lower mini-mental state examination (MMSE) score than non-carriers (n = 113). This pattern was consistent with an independent sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) of n = 283 non-Hispanic White adults without dementia (mean age = 75, 40% female). Within BPRHS, carriers showed distinct connectivity patterns between multimodal biomarkers, characterized by stronger direct network connections between baseline diet/blood markers with 12-year blood/clinical measures, and between blood markers (especially lipids and cytokines) and WM. Cardiovascular burden (i.e., hypertension and diabetes status) was associated with WM integrity for both carriers and non-carriers. APOE4 carrier status affects interactions between dietary factors, multimodal blood biomarkers, and MTL WM integrity across ~12 years of follow-up, which may reflect increased peripheral-central systems crosstalk following blood-brain barrier breakdown in carriers.

Brain age prediction across the human lifespan using multimodal MRI data.

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

Combining multimodal magnetic resonance brain imaging and machine learning to unravel neurocognitive function in non-neuropsychiatric systemic lupus erythematosus.

To study whether multimodal brain MRI comprising permeability and perfusion measures coupled with machine learning can predict neurocognitive function in young patients with SLE without neuropsychiatric manifestations. SLE patients and healthy controls (HCs) (≤40 years of age) underwent multimodal structural brain MRI that comprised voxel-based morphometry (VBM), magnetization transfer ratio (MTR) and dynamic contrast-enhanced (DCE) MRI in this cross-sectional study. Neurocognitive function assessed by Automated Neuropsychological Assessment Metrics was reported as the total throughput score (TTS). Olfactory function was assessed. A machine learning-based model (i.e. glmnet) was constructed to predict TTS. Thirty SLE patients and 10 HCs were studied. Both groups had comparable VBM, MTR, olfactory bulb volume (OBV), olfactory function and TTS. While after correction for multiple comparisons the uncorrected increase in the blood-brain barrier (BBB) permeability parameters compared with HCs did not remain evident in SLE patients, DCE-MRI perfusion parameters, notably an increase in right amygdala perfusion, was positively correlated with TTS in SLE patients (r = 0.636, false discovery rate P < 0.05). A machine learning-trained multimodal MRI model comprising alterations of VBM, MTR, OBV and DCE-MRI parameters mainly in the limbic system regions predicted TTS in SLE patients (r = 0.644, P < 0.0005). Multimodal brain MRI demonstrated increased right amygdala perfusion that was associated with better neurocognitive performance in young SLE patients without statistically significant BBB leakage and microstructural abnormalities. A machine learning-constructed multimodal model comprising microstructural, perfusion and permeability parameters accurately predicted neurocognitive performance in SLE patients.

Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range

Magnetic resonance imaging (MRI) is a non-invasive neuroimaging technique that is useful for identifying normal developmental and aging processes and for data sharing. Marmosets have a relatively shorter life expectancy than other primates, including humans, because they grow and age faster. Therefore, the common marmoset model is effective in aging research. The current study investigated the aging process of the marmoset brain and provided an open MRI database of marmosets across a wide age range. The Brain/MINDS Marmoset Brain MRI Dataset contains brain MRI information from 216 marmosets ranging in age from 1 and 10 years. At the time of its release, it is the largest public dataset in the world. It also includes multi-contrast MRI images. In addition, 91 of 216 animals have corresponding high-resolution ex vivo MRI datasets. Our MRI database, available at the Brain/MINDS Data Portal, might help to understand the effects of various factors, such as age, sex, body size, and fixation, on the brain. It can also contribute to and accelerate brain science studies worldwide.

Naoxin'an capsules protect brain function and structure in patients with vascular cognitive impairment.

Introduction: Vascular cognitive impairment (VCI) is one of the most common types of dementia. Naoxin'an capsule (NXA), a traditional Chinese medicine compound, has been used to treat VCI for a long time in the clinic. Previous studies proved that the NXA capsules could ameliorate the cerebral mitochondrion deficits of VCI animals. This study aimed to investigate the protectiveness of NXA on human brain structure and function in patients with VCI. Methods: In total, 100 VCI patients were enrolled in this 24-week trial and randomly divided into the NXA capsules group (n = 50) and the ginkgo biloba capsules control group (n = 50). Before and after the treatment, cognitive behavior tests and multimodal brain magnetic resonance imaging were analyzed to comprehensively evaluate the effectiveness of NXA treatment on VCI patients after 24 weeks. Results: We found that the NXA group significantly improved overall cognitive ability (Alzheimer's Disease Assessment Scale-Cognitive section, p = 0.001; Mini-Mental Status Examination, p = 0.003), memory (Rey-Osterrieth Complex Figure test, p < 0.001) and executive function (Trail Making Test-A, p = 0.024) performance after treatment compared with the control group. For brain function, the degree of centrality in the left middle frontal gyrus, right postcentral gyrus, and left supplementary motor area increased in the NXA group and decreased in the ginkgo biloba group after treatment. The fractional amplitude of low-frequency fluctuation (fALFF) of the left precentral and right superior parietal gyrus increased, and the fALFF of the right parahippocampal and left inferior temporal gyrus decreased in the NXA group after treatment. For brain structure, the gray matter density of the left postcentral gyrus increased in the NXA group after treatment, and the total volume of white matter hyperintensity showed a decreasing trend but was not statistically significant. Furthermore, the improvement effect of NXA on executive function was associated with changes in brain function. Conclusion: These findings suggest that the NXA capsules improved cognitive performance and multiregional brain function, as well as gray matter structure in the postcentral gyrus.

Multi-Modal Brain Tumor Data Completion Based on Reconstruction Consistency Loss.

Multi-modal brain magnetic resonance imaging (MRI) data has been widely applied in vison-based brain tumor segmentation methods due to its complementary diagnostic information from different modalities. Since the multi-modal image data is likely to be corrupted by noise or artifacts during the practical scanning process, making it difficult to build a universal model for the subsequent segmentation and diagnosis with incomplete input data, image completion has become one of the most attractive fields in the medical image pre-processing. It can not only assist clinicians to observe the patient's lesion area more intuitively and comprehensively, but also realize the desire to save costs for patients and reduce the psychological pressure of patients during tedious pathological examinations. Recently, many deep learning-based methods have been proposed to complement the multi-modal image data and provided good performance. However, current methods cannot fully reflect the continuous semantic information between the adjacent slices and the structural information of the intra-slice features, resulting in limited complementation effects and efficiencies. To solve these problems, in this work, we propose a novel generative adversarial network (GAN) framework, named as random generative adversarial network (RAGAN), to complete the missing T1, T1ce, and FLAIR data from the given T2 modal data in real brain MRI, which consists of the following parts: (1) For the generator, we use T2 modal images and multi-modal classification labels from the same sample for cyclically supervised training of image generation, so as to realize the restoration of arbitrary modal images. (2) For the discriminator, a multi-branch network is proposed where the primary branch is designed to judge whether the certain generated modal image is similar to the target modal image, while the auxiliary branch is to judge whether its essential visual features are similar to those of the target modal image. We conduct qualitative and quantitative experimental validations on the BraTs2018 dataset, generating 10,686 MRI data in each missing modality. Real brain tumor morphology images were compared with synthetic brain tumor morphology images using PSNR and SSIM as evaluation metrics. Experiments demonstrate that the brightness, resolution, location, and morphology of brain tissue under different modalities are well reconstructed. Meanwhile, we also use the segmentation network as a further validation experiment. Blend synthetic and real images into a segmentation network. Our segmentation network adopts the classic segmentation network UNet. The segmentation result is 77.58%. In order to prove the value of our proposed method, we use the better segmentation network RES_UNet with depth supervision as the segmentation model, and the segmentation accuracy rate is 88.76%. Although our method does not significantly outperform other algorithms, the DICE value is 2% higher than the current state-of-the-art data completion algorithm TC-MGAN.

Evaluation of neostriatum changes in Crohn’s disease: a multimodal brain magnetic resonance imaging study

Evaluation of neostriatum changes in Crohn’s disease: a multimodal brain magnetic resonance imaging study

Automatic delineation of hippocampus in CT images based on deep learning and dosimetry study in whole brain radiotherapy

ObjectiveThe artificial intelligence algorithm based on deep learning, combined with multi-modal brain magnetic resonance imaging (MRI) and computed tomography (CT) images, was used to develop an automatic delineation technology of the hippocampal structure from CT images to provide an efficient and accurate automatic delineation method for hippocampal protection during cranial radiotherapy. MethodsThe MR and CT images of 20 patients with brain metastases were collected. After registering the MR and CT images, the hippocampus was delineated. Aunet, Unet, and Pix2pix deep learning models were trained on the CT-MRI dataset, and the differences between three automatically segmented hippocampus and the hippocampus manually segmented by senior chief physicians were calculated. The VMAT plan was designed separately for each patient. ResultsThe prediction results of the proposed method were more accurate and closer to the real segmentation results. The Dice, P, and R values were 0.8529, 0.8560, and 0.8632, respectively, indicating that the prediction results of Aunet were the closest to the real one. For the dose results, the differences between the maximum dose of the real hippocampus and three different methods were 75.2 (Aunet), 354.0 (Unet), and 462.1 (Pix2pix), and the differences between the average doses of the real hippocampus and three different methods were 30.3 (Aunet), 31.0 (Unet), and 66.6 (Pix2pix). ConclusionThe Aunet model can achieve efficient and accurate automatic delineation of the hippocampus on CT images. The dosimetric difference between the segmented and real hippocampi is extremely small, which is convenient to protect the hippocampus during brain radiotherapy.

Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study.

Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King's College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study.

Machine-Based Learning Shifting to Prediction Model of Deteriorative MCI Due to Alzheimer's Disease - A Two-Year Follow-Up Investigation.

The aim of the present work was to investigate the features of the elderly population aged &#8805;65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to establish a prediction model. A total of 105 patients aged &#8805;65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aβ1-40, Aβ1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI. Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity. Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.

A Role for Data Science in Precision Nutrition and Early Brain Development.

Multimodal brain magnetic resonance imaging (MRI) can provide biomarkers of early influences on neurodevelopment such as nutrition, environmental and genetic factors. As the exposure to early influences can be separated from neurodevelopmental outcomes by many months or years, MRI markers can serve as an important intermediate outcome in multivariate analyses of neurodevelopmental determinants. Key to the success of such work are recent advances in data science as well as the growth of relevant data resources. Multimodal MRI assessment of neurodevelopment can be supplemented with other biomarkers of neurodevelopment such as electroencephalograms, magnetoencephalogram, and non-imaging biomarkers. This review focuses on how maternal nutrition impacts infant brain development, with three purposes: (1) to summarize the current knowledge about how nutrition in stages of pregnancy and breastfeeding impact infant brain development; (2) to discuss multimodal MRI and other measures of early neurodevelopment; and (3) to discuss potential opportunities for data science and artificial intelligence to advance precision nutrition. We hope this review can facilitate the collaborative march toward precision nutrition during pregnancy and the first year of life.

Elevated Aortic Pulse Wave Velocity Relates to Longitudinal Gray and White Matter Changes.

To determine whether baseline aortic stiffness, measured by aortic pulse wave velocity (PWV), relates to longitudinal cerebral gray or white matter changes among older adults. Baseline cardiac magnetic resonance imaging will be used to assess aortic PWV while brain magnetic resonance imaging will be used to assess gray matter and white matter hyperintensity (WMH) volumes at baseline, 18 months, 3 years, 5 years, and 7 years. Approach and Results: Aortic PWV (m/s) was quantified from cardiac magnetic resonance. Multimodal 3T brain magnetic resonance imaging included T1-weighted imaging for quantifying gray matter volumes and T2-weighted fluid-attenuated inversion recovery imaging for quantifying WMHs. Mixed-effects regression models related baseline aortic PWV to longitudinal gray matter volumes (total, frontal, parietal, temporal, occipital, hippocampal, and inferior lateral ventricle) and WMH volumes (total, frontal, parietal, temporal, and occipital) adjusting for age, sex, race/ethnicity, education, cognitive diagnosis, Framingham stroke risk profile, APOE (apolipoprotein E)-ε4 carrier status, and intracranial volume. Two hundred seventy-eight participants (73±7 years, 58% male, 87% self-identified as non-Hispanic White, 159 with normal cognition, and 119 with mild cognitive impairment) from the Vanderbilt Memory & Aging Project (n=335) were followed on average for 4.9±1.6 years with PWV measurements occurring from September 2012 to November 2014 and longitudinal brain magnetic resonance imaging measurements occurring from September 2012 to June 2021. Higher baseline aortic PWV was related to greater decrease in hippocampal (β=-3.6 [mm3/y]/[m/s]; [95% CI, -7.2 to -0.02] P=0.049) and occipital lobe (β=-34.2 [mm3/y]/[m/s]; [95% CI, -67.8 to -0.55] P=0.046) gray matter volume over time. Higher baseline aortic PWV was related to greater increase in WMH volume over time in the temporal lobe (β=17.0 [mm3/y]/[m/s]; [95% CI, 7.2-26.9] P<0.001). All associations may be driven by outliers. In older adults, higher baseline aortic PWV related to greater decrease in gray matter volume and greater increase in WMHs over time. Because of unmet cerebral metabolic demands and microvascular remodeling, arterial stiffening may preferentially affect certain highly active brain regions like the temporal lobes. These same regions are affected early in the course of Alzheimer disease.

Fifteen years of urea cycle disorders brain research: Looking back, looking forward

Urea cycle disorders (UCD) are inherited diseases resulting from deficiency in one of six enzymes or two carriers that are required to remove ammonia from the body. UCD may be associated with neurological damage encompassing a spectrum from asymptomatic/mild to severe encephalopathy, which results in most cases from Hyperammonemia (HA) and elevation of other neurotoxic intermediates of metabolism. Electroencephalography (EEG), Magnetic resonance imaging (MRI) and Proton Magnetic resonance spectroscopy (MRS) are noninvasive measures of brain function and structure that can be used during HA to guide management and provide prognostic information, in addition to being research tools to understand the pathophysiology of UCD associated brain injury. The Urea Cycle Rare disorders Consortium (UCDC) has been invested in research to understand the immediate and downstream effects of hyperammonemia (HA) on brain using electroencephalogram (EEG) and multimodal brain MRI to establish early patterns of brain injury and to track recovery and prognosis. This review highlights the evolving knowledge about the impact of UCD and HA in particular on neurological injury and recovery and use of EEG and MRI to study and evaluate prognostic factors for risk and recovery. It recognizes the work of others and discusses the UCDC's prior work and future research priorities.

A review on the application of UK Biobank in neuroimaging

UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging. Firstly, neuroimage collection and imaging-derived phenotypes are summarized. Secondly, typical studies of UKB in neuroimaging areas are introduced, which include cardiovascular risk factors, regulatory factors, brain age prediction, normality, successful and morbid brain aging, environmental and genetic factors, cognitive ability and gender. Lastly, the open challenges and future directions of UKB are discussed. This article has the potential to open up a new research field for the prevention and treatment of neurological diseases.