Diffusion Magnetic Resonance Imaging Research Articles

Fiber-specific age-related differences in the white matter of healthy adults uncovered by fixel-based analysis

Diffusion magnetic resonance imaging studies often investigate white matter (WM) microstructural degeneration in aging by probing WM regions that exhibit negative age associations of fractional anisotropy (FA). However, WM regions in which FA is unassociated with age are not necessarily “spared” in aging. Besides the confound of inter-participant heterogeneity, FA conflates all intravoxel fiber populations and does not allow the detection of individual fiber-specific age associations. In this study of 541 healthy adults aged 36–100 years, we use fixel-based analysis to investigate age associations among each “fixel” within a voxel, representing individual fiber populations. We find age associations of fixel-based measures that indicate age-related differences in individual fiber populations amid complex fiber architectures. Different crossing fiber populations exhibit different slopes of age associations. Our findings may provide evidence of selective degeneration of intravoxel WM fibers in aging, which does not necessarily manifest as a change in FA and therefore escapes notice if conventional voxel-based analyses are relied upon alone.

Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants.

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.

Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review.

White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.

Prevalence of Venous Infarction in Patients With Cerebral Venous Thrombosis: Baseline Diffusion-Weighted MRI and Follow-Up MRI.

A venous pattern of infarction on neuroimaging is used as a clue to undiagnosed cerebral venous thrombosis (CVT); prevention of venous infarction is a goal of CVT management; and venous infarction is a factor used for clinical prognostication. Despite widespread use of the term venous infarct, the prevalence of true venous infarction is unclear. Our primary aim was to determine the prevalence of venous infarction in patients with CVT. We also measured the prevalence of diffusion abnormality without infarction, vasogenic edema, and intracranial hemorrhage. Single-center, retrospective cohort study using a registry of 110 consecutive patients admitted to hospital with cerebral venous thrombosis between 2004 and 2014. Inclusion criteria were brain magnetic resonance imaging (MRI) and contrast-enhanced venography at presentation, and repeat brain MRI ≥1 month later. Exclusion criteria were dural arteriovenous fistula, arteriovenous malformation, cavernous sinus thrombosis, or previous neurosurgical procedure. Main outcome was proportion of patients with venous infarction (irreversible ischemic injury) diagnosed using diffusion-weighted MRI at presentation, confirmed using T2-weighted fluid-attenuated inversion recovery MRI ≥1 month later, and reported with 95% CI using the Wilson score interval method. We also report the prevalence of transient diffusion MRI abnormality without infarction, vasogenic edema, and intracranial hemorrhage. Seventy-three patients met the inclusion criteria, and after exclusions, the final study population was 59 patients with median age 41 years (interquartile range, 32-57). Venous infarction occurred in 12% (7/59 [95% CI, 6%-23%]) of patients, and final infarct volume was >1 mL in only 5.1% (3/59) of patients. An additional 8% (5/59 [95% CI, 4%-18%]) of patients had a transient diffusion MRI abnormality without infarction. Prevalence of cerebral vasogenic edema and intracranial hemorrhage were 66% (39/59 [95% CI, 53%-77%]) and 54% (32/59 [95% CI, 41%-66%]), respectively. In patients with CVT, venous infarction is uncommon and venous infarcts are typically very small. Vasogenic edema and hemorrhage are more common consequences of CVT.

Glymphatic system impairment in corticobasal syndrome: diffusion tensor image analysis along the perivascular space (DTI-ALPS).

This study aimed to evaluate the along the perivascular space (ALPS) index based on the diffusion tensor image ALPS (DTI-ALPS) in corticobasal degeneration with corticobasal syndrome (CBD-CBS) and investigate its correlation with motor and cognitive functions. The data of 21 patients with CBD-CBS and 17 healthy controls (HCs) were obtained from the 4-Repeat Tauopathy Neuroimaging Initiative and the Frontotemporal Lobar Degeneration Neuroimaging Initiative databases. Diffusion magnetic resonance imaging was performed using a 3-Tesla MRI scanner. The ALPS index based on DTI-ALPS was automatically calculated after preprocessing. The ALPS index was compared between the CBD-CBS and HC groups via a general linear model analysis, with covariates such as age, sex, years of education, and intracranial volume (ICV). Furthermore, to confirm the relation between the ALPS index and the motor and cognitive score in CBD-CBS, the partial Spearman's rank correlation coefficient was calculated with covariates such as age, sex, years of education, and ICV. A p value of < 0.05 was considered as statistically significant in all statistical analyses. The ALPS index of CBD-CBS was significantly lower than that of HC (Cohen's d = - 1.53, p < 0.005). Moreover, the ALPS index had a significant positive correlation with the mini mental state evaluation score (rs = 0.65, p < 0.005) and a significant negative correlation with the unified Parkinson's Disease Rating Scale III score (rs = - 0.75, p < 0.001). The ALPS index of patients with CBD-CBS, which is significantly lower than that of HCs, is significantly associated with motor and cognitive functions.

Cortical Thickness and Brain Connectivity Mediate the Relation Between White Matter Hyperintensity and Information Processing Speed in Cerebral Small Vessel Disease.

White matter hyperintensities of presumed vascular origin (WMH) are the most common imaging feature of cerebral small vessel disease (cSVD) and are associated with cognitive impairment, especially information processing speed (IPS) deficits. However, it is unclear how WMH can directly impact IPS or whether the cortical thickness and brain connectivity mediate such association. In this study, it was evaluated the possible mediating roles of cortical thickness and brain (structural and functional) connectivity on the relationship between WMH (also considering its topography distribution) and IPS in 389 patients with cSVD from the RUN-DMC (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort) database. Significant (p < 0.05 after multiple comparisons correction) associations of WMH volume and topography with cortical thickness, brain connectivity, and IPS performance in cSVD individuals were found. Additionally, cortical thickness and brain structural and functional connectivity were shown to mediate the association of WMH volume and location with IPS scores. More specifically, frontal cortical thickness, functional sensorimotor network, and posterior thalamic radiation tract were the essential mediators of WMH and IPS in this clinical group. This study provided insight into the mechanisms underlying the clinical relevance of white matter hyperintensities in information processing speed deficits in cSVD through cortical thinning and network disruptions.

Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging.

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.

The genetic architecture of fornix white matter microstructure and their involvement in neuropsychiatric disorders

The fornix is a white matter bundle located in the center of the hippocampaldiencephalic limbic circuit that controls memory and executive functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out a genome-wide association analysis of 30,832 UK Biobank individuals of the six fornix diffusion magnetic resonance imaging (dMRI) traits. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the single nucleotide polymorphisms (SNP), locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in adolescent brain cognitive development (ABCD) cohort. The GWAS identified 63 independent significant variants within 20 genomic loci associated (P < 8.33 × 10−9) with the six fornix dMRI traits. Geminin coiled-coil domain containing (GMNC) and NUAK family SNF1-like kinase 1 (NUAK1) gene were highlighted, which were found in UKB and replicated in ABCD. The heritability of the six traits ranged from 10% to 27%. Gene mapping strategies identified 213 genes, where 11 were supported by all of four methods. Gene-based analyses revealed pathways relating to cell development and differentiation, with astrocytes found to be significantly enriched. Pleiotropy analyses with eight neurological and psychiatric disorders revealed shared variants, especially with schizophrenia under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of fornix and their relevance in neurological and psychiatric disorders.

Magnetic resonance imaging of white matter in Alzheimer's disease: a global bibliometric analysis from 1990 to 2022.

Alzheimer's disease (AD) is a common, progressive, irreversible, and fatal neurodegenerative disorder with rapidly increasing worldwide incidence. Although much research on magnetic resonance imaging (MRI) of the white matter (WM) in AD has been published, no bibliometric analysis study has investigated this issue. Thus, this study aimed to provide an overview of the current status, hotspots, and trends in MRI of WM in AD. We searched for records related to MRI studies of WM in AD from 1990 to 2022 in the Web of Science Core Collection (WOSCC) database. CiteSpace (version 5.1.R8) and VOSviewer (version 1.6.19) software were used for bibliometric analyses. A total of 2,199 articles were obtained from this study. From 1990 to 2022, the number of published articles showed exponential growth of y = 4.1374e0.1294x, with an average of 17.9 articles per year. The top country and institutions were the United States and the University of California Davis, accounting for 44.52 and 5.32% of the total studies, respectively. The most productive journal was Neurology, and the most co-cited journal was Lancet Neurology. Decarli C was the most productive author. The current research frontier trend focuses on the association between small vessel disease and AD, the clinical application and exploration of diffusion MRI, and related markers. This study provides an in-depth overview of publications on MRI of WM in AD, identifying the current research status, hotspots, and frontier trends in the field.

Microstructural white matter biomarkers of symptom severity and therapy outcome in catatonia: Rationale, study design and preliminary clinical data of the whiteCAT study

The number of magnetic resonance imaging (MRI) studies on neuronal correlates of catatonia has dramatically increased in the last 10 years, but conclusive findings on white matter (WM) tracts alterations underlying catatonic symptoms are still lacking. Therefore, we conduct an interdisciplinary longitudinal MRI study (whiteCAT) with two main objectives: First, we aim to enroll 100 psychiatric patients with and 50 psychiatric patients without catatonia according to ICD-11 who will undergo a deep phenotyping approach with an extensive battery of demographic, psychopathological, psychometric, neuropsychological, instrumental and diffusion MRI assessments at baseline and 12 weeks follow-up. So far, 28 catatonia patients and 40 patients with schizophrenia or other primary psychotic disorders or mood disorders without catatonia have been studied cross-sectionally. 49 out of 68 patients have completed longitudinal assessment, so far. Second, we seek to develop and implement a new method for semi-automatic fiber tract delineation using active learning. By training supportive machine learning algorithms on the fly that are custom tailored to the respective analysis pipeline used to obtain the tractogram as well as the WM tract of interest, we plan to streamline and speed up this tedious and error-prone task while at the same time increasing reproducibility and robustness of the extraction process. The goal is to develop robust neuroimaging biomarkers of symptom severity and therapy outcome based on WM tracts underlying catatonia. If our MRI study is successful, it will be the largest longitudinal study to date that has investigated WM tracts in catatonia patients.

Structural connectivity mediates the relationship between blood oxygenation and cognitive function in sickle cell anemia.

In sickle cell disease, the relative importance of reduced hemoglobin (Hb) and peripheral oxygen saturation on brain structure remains uncertain. We applied graph-theoretical analysis to diffusion magnetic resonance imaging data to investigate the effect of structural brain connectivity on cognitive function, alongside the presence or absence, number, and volume of silent cerebral infarction. In patients, we investigated the relationships between network properties, blood oxygenation, and cognition (working memory and processing speed indices). Based on streamline counts and fractional anisotropy, we identified a subnetwork with weakened connectivity in 92 patients with sickle cell disease (91 homozygous for HbS [HbSS], 1 heterozygote with HbSβ0 thalassemia; 49 males; aged 8.0 to 38.8 y), compared with 54 control subjects (22 males; aged 6.7 to 30.6 y). Multiple regression analyses showed a significant effect of Hb on full-network edge density (P< .05) and of peripheral oxygen saturation on streamline-weighted subnetwork efficiency (P< .01). There were effects of fractional anisotropy-weighted full-network and subnetwork efficiency on working memory index (both P< .05), and of streamline-weighted subnetwork efficiency on processing speed index (P= .05). However, there were no effects of presence, number or volume of silent cerebral infarcts. Streamline-weighted efficiency was progressively lower with lower oxygen saturation, with a downstream effect on the processing speed index. In path analysis, indirect relationships between blood oxygenation and cognition, mediated by network properties, were better supported than direct alternatives, with an indirect relationship between low oxygen saturation and processing speed index in patients, mediated by structural connectivity efficiency in a subnetwork of the brain differing from control subjects. Our findings are consistent with the notion that cognitive impairment is primarily mediated by hypoxic-ischemic effects on normal-appearing white matter and highlight the utility of network-based methods in providing biomarkers of cognitive dysfunction in patients with sickle cell disease.

Super-resolved q-space learning of diffusion MRI.

Diffusion magnetic resonance imaging (dMRI) provides a powerful tool to non-invasively investigate neural structures in the living human brain. Nevertheless, its reconstruction performance on neural structures relies on the number of diffusion gradients in the q-space. High-angular (HA) dMRI requires a long scan time, limiting its use in clinical practice, whereas directly reducing the number of diffusion gradients would lead to the underestimation of neuralstructures. We propose a deep compressive sensing-based q-space learning (DCS-qL) approach to estimate HA dMRI from low-angulardMRI. In DCS-qL, we design the deep network architecture by unfolding the proximal gradient descent procedure that addresses the compressive sense problem. In addition, we exploit a lifting scheme to design a network structure with reversible transform properties. For implementation, we apply a self-supervised regression to enhance the signal-to-noise ratio of diffusion data. Then, we utilize a semantic information-guided patch-based mapping strategy for feature extraction, which introduces multiple network branches to handle patches with different tissuelabels. Experimental results show that the proposed approach can yield a promising performance on the tasks of reconstructed HA dMRI images, microstructural indices of neurite orientation dispersion and density imaging, fiber orientation distribution, and fiber bundleestimation. The proposed method achieves more accurate neural structures than competingapproaches.

Age- and time-of-day dependence of glymphatic function in the human brain measured via two diffusion MRI methods.

Advanced age, accompanied by impaired glymphatic function, is a key risk factor for many neurodegenerative diseases. To study age-related differences in the human glymphatic system, we measured the influx and efflux activities of the glymphatic system via two non-invasive diffusion magnetic resonance imaging (MRI) methods, ultra-long echo time and low-b diffusion tensor imaging (DTIlow-b) measuring the subarachnoid space (SAS) flow along the middle cerebral artery and DTI analysis along the perivascular space (DTI-ALPS) along medullary veins in 22 healthy volunteers (aged 21-75 years). We first evaluated the circadian rhythm dependence of the glymphatic activity by repeating the MRI measurements at five time points from 8:00 to 23:00 and found no time-of-day dependence in the awake state under the current sensitivity of MRI measurements. Further test-retest analysis demonstrated high repeatability of both diffusion MRI measurements, suggesting their reliability. Additionally, the influx rate of the glymphatic system was significantly higher in participants aged >45 years than in participants aged 21-38, while the efflux rate was significantly lower in those aged >45 years. The mismatched influx and efflux activities in the glymphatic system might be due to age-related changes in arterial pulsation and aquaporin-4 polarization.

Cerebellar structural, astrocytic, and neuronal abnormalities in the SMNΔ7 mouse model of spinal muscular atrophy.

Spinalmuscular atrophy (SMA) is a neuromuscular disease that affects as many as 1 in 6000 individuals at birth, making it the leading genetic cause of infant mortality. A growing number of studies indicate that SMA is a multi-system disease. The cerebellum has received little attention even though it plays an important role in motor function and widespread pathology has been reported in the cerebella of SMA patients. In this study, we assessed SMA pathology in the cerebellum using structural and diffusion magnetic resonance imaging, immunohistochemistry, and electrophysiology with the SMNΔ7 mouse model. We found a significant disproportionate loss in cerebellar volume, decrease in afferent cerebellar tracts, selective lobule-specific degeneration of Purkinje cells, abnormal lobule foliation and astrocyte integrity, and a decrease in spontaneous firing of cerebellar output neurons in the SMA mice compared to controls. Our data suggest that defects in cerebellar structure and function due to decreased survival motor neuron (SMN) levels impair the functional cerebellar output affecting motor control, and that cerebellar pathology should be addressed to achieve comprehensive treatment and therapy for SMA patients.

Dynamic contrast-enhanced MRI and Apparent diffusion coefficient mapping in the characterization of Palpable breast lesions: A prospective observational study

BackgroundBreast MRI is the imaging modality of choice in patients with palpable breast lesions unequivocal on mammography and ultrasonography. This study aims to evaluate the role of dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient mapping in the characterization and differentiation of various palpable breast lesions. This prospective observational study was conducted in a tertiary care hospital between July 2019 and June 2021. Sixty-six patients with palpable breast lesions were undergone MRI scans of the breasts. The various palpable breast lesions were categorized according to the 5th edition BI-RADS lexicon. The sensitivity of ADC mapping and DCE-MRI was determined for differentiation of various palpable breast lesions according to the BI-RADS category and gold standard histopathological findings.ResultsOf 66 patients with palpable breast lesions, 36 patients (54.5%) were benign and 30 patients (45.5%) were malignant lesions. Malignant palpable breast lesions had a mean ADC value of 0.939 ± 0.166[SD] × 10−3 mm2/s, and benign lesions had 1.891 ± 0.524[SD] × 10−3 mm2/s where unpaired Student t-test showed statistically significant difference of P value 0.0005. BI-RADS 2 lesions had a mean ADC value of 2.056 ± 0.471[SD] × 10−3 mm2/s, BI-RADS 3 had 1.314 ± 0.151[SD] × 10−3 mm2/s, BI-RADS 4 had 0.935 ± 0.119[SD] × 10−3 mm2/s, and BI-RADS 5 had 0.930 ± 0.943[SD] × 10−3 mm2/s. BI-RADS 2 category showed optimal cutoff mean ADC of 1.508 × 10−3 mm2/s with a sensitivity of 85.7% and specificity of 94.7%, BI-RADS 3 lesions had 1.208 × 10−3 mm2/s with a sensitivity of 75% and specificity of 55.2%, BI-RADS 4 lesions had 1.064 × 10−3 mm2/s with a sensitivity 80% and specificity of 67.9%, and BI-RADS 5 lesions had 1.013 × 10−3 mm2/s with a sensitivity of 80% and specificity of 82.6%.ConclusionsBreast MRI is superior to the other imaging modalities for the characterization and differentiation of various palpable breast lesions. The combined use of ADC mapping and DCE-MRI had more sensitivity than conventional MRI, ADC mapping or DCE-MRI alone.

Effects of acute alcohol exposure and chronic alcohol use on neurite orientation dispersion and density imaging (NODDI) parameters.

Little is known about how acute and chronic alcohol exposure may alter the in vivo membrane properties of neurons. We employed neurite orientation dispersion and density imaging (NODDI) to examine acute and chronic effects of alcohol exposure on neurite density. Twenty-one healthy social drinkers (CON) and thirteen nontreatment-seeking individuals with alcohol use disorder (AUD) underwent a baseline multi-shell diffusion magnetic resonance imaging (dMRI) scan. A subset (10 CON, 5 AUD) received dMRI during intravenous infusions of saline and alcohol during dMRI. NODDI parametric images included orientation dispersion (OD), isotropic volume fraction (ISOVF), and corrected intracellular volume fraction (cICVF). Diffusion tensor imaging metrics of fractional anisotropy and mean, axial, and radial diffusivity (FA, MD, AD, RD) were also computed. Average parameter values were extracted from white matter (WM) tracts defined by the Johns Hopkins University atlas. There were group differences in FA, RD, MD, OD, and cICVF, primarily in the corpus callosum. Both saline and alcohol had effects on AD and cICVF in WM tracts proximal to the striatum, cingulate, and thalamus. This is the first work to indicate that acute fluid infusions may alter WM properties, which are conventionally believed to be insensitive to acute pharmacological challenges. It also suggests that the NODDI approach may be sensitive to transient changes in WM. The next steps should include determining if the effect on neurite density differs with solute or osmolality, or both, and translational studies to assess how alcohol and osmolality affect the efficiency of neurotransmission.

Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study.

The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. Prospective. Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. 1 TECHNICAL EFFICACY: Stage 2.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing.

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Assessing within-subject rates of change of placental MRI diffusion metrics in normal pregnancy.

Studying placental development informs when development is abnormal. Most placental MRI studies are cross-sectional and do not study the extent of individual variability throughout pregnancy. We aimed to explore how diffusion MRI measures of placental function and microstructure vary in individual healthy pregnancies throughout gestation. Seventy-nine pregnant, low-risk participants (17 scanned twice and 62 scanned once) were included. T2 -weighted anatomical imaging and a combined multi-echo spin-echo diffusion-weighted sequence were acquired at 3 T. Combined diffusion-relaxometry models were performed using both a -ADC and a bicompartmental -intravoxel-incoherent-motion ( ) model fit. There was a significant decline in placental and ADC (both P < 0.01) over gestation. These declines are consistent in individuals for (covariance = -0.47), but not ADC (covariance = -1.04). The model identified a consistent decline in individuals over gestation in from both the perfusing and diffusing placental compartments, but not in ADC values from either. The placental perfusing compartment fraction increased over gestation (P = 0.0017), but this increase was not consistent in individuals (covariance = 2.57). Whole placental and ADC values decrease over gestation, although only values showed consistent trends within subjects. There was minimal individual variation in rates of change of values from perfusing and diffusing placental compartments, whereas trends in ADC values from these compartments were less consistent. These findings probably relate to the increased complexity of the bicompartmental model, and differences in how different placental regions evolve at a microstructural level. These placental MRI metrics from low-risk pregnancies provide a useful benchmark for clinical cohorts.