Diffusion Magnetic Resonance Imaging Research Articles

Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies.

This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

Baseline and Longitudinal MRI Markers Associated With 16-Year Mortality in Patients With Cerebral Small Vessel Disease.

Information on whether small vessel disease (SVD) reduces life expectancy is limited. Moreover, the excess mortality risk attributed specifically to SVD compared with controls from the general population has not been evaluated. This study aimed to investigate the baseline and progression of MRI markers of SVD associated with mortality in a 16-year follow-up cohort study and to determine the excess long-term mortality risk of patients with SVD. Participants with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort (RUN DMC) study (with MRI assessments in 2006, 2011, 2015, and 2020) were followed until their death or December 1, 2021. Adjusted Cox regression analyses and linear mixed-effect regression models were used to investigate the association between MRI markers of SVD and mortality. The excess mortality risk of SVD was calculated by comparing mortality data of the RUN DMC study with the general population matched by sex, age, and calendar year. 200 of 503 (39.9%) participants died during a follow-up period of 15.9 years. Cause of death was available for 182 (91%) participants. Baseline white matter hyperintensity volume (HR 1.3 per 1-SD increase [95% CI 1.1-1.5], p = 0.010), presence of lacunes (1.5 [95% CI 1.1-2.0], p = 0.008), mean diffusivity (HR 1.1 per 1-SD increase [95% CI 1.1-1.2], p = 0.001), and total brain volume (HR 1.5 per 1-SD decrease [95% CI 1.3-1.9], p < 0.001) were associated with all-cause mortality after adjusting for age, sex, and vascular risk factors. Total brain volume decrease over time was associated with all-cause mortality after adjusting for age, sex, and vascular risk factors (HR 1.3 per 1-SD decrease [95% CI 1.1-1.7], p = 0.035), and gray matter volume decrease remained significant after additionally adjusting for its baseline volume (1.3 per 1-SD decrease [1.1-1.6], p = 0.019). Participants with a Fazekas score of 3, presence of lacunes, or lower microstructural integrity had an excess long-term mortality risk (21.8, 15.7, 10.1 per 1,000 person-years, respectively) compared with the general population. Excess long-term mortality risk only exists in patients with severe SVD (Fazekas score of 3, presence of lacunes, or lower microstructural integrity). This could help in assisting clinicians to predict the clinical outcomes of patients with SVD by severity.

Topological structural characteristics in patients with systemic lupus erythematosus with and without neuropsychiatric symptoms

PurposeThis study investigated the topological structural characteristics of systemic lupus erythematosus (SLE) with and without neuropsychiatric symptoms (NPSLE and non-NPSLE), and explore their clinical implications.MethodsWe prospectively recruited 50 patients with...

White Matter Microstructure Changes Revealed by Diffusion Kurtosis and Diffusion Tensor Imaging in Mutant Huntingtin Gene Carriers.

Diffusion magnetic resonance imaging (dMRI) has revealed microstructural changes in white matter (WM) in Huntington's disease (HD). To compare the validities of different dMRI, i.e., diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in HD. 22 mutant huntingtin (mHTT) carriers and 14 controls were enrolled. Clinical assessments and dMRI were conducted. Based on CAG-Age Product (CAP) score, mHTT carriers were categorized into high CAP (hCAP) and medium and low CAP (m& lCAP) groups. Spearman analyses were used to explore correlations between imaging parameters in brain regions and clinical assessments. Receiver operating characteristic (ROC) was used to distinguish mHTT carriers from control, and define the HD patients at advanced stage. Compared to controls, mHTT carriers exhibited WM changes in DKI and DTI. There were 22 more regions showing significant differences in HD detected by MK than FA. Only MK in five brain regions showed significantly difference between any two group, and negatively correlated with the disease burden (r = -0.80 to -0.71). ROC analysis revealed that MK was more sensitive and FA was more specific, while Youden index showed that the integration of FA and MK gave rise to higher authenticities, in distinguishing m& lCAP from controls (Youden Index = 0.786), and discerning different phase of HD (Youden Index = 0.804). Microstructural changes in WM occur at early stage of HD and deteriorate over the disease progression. Integrating DKI and DTI would provide the best accuracies for differentiating early HD from control and identifying advanced HD.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Diffusion Imaging of Gray Matter Microstructure in Alzheimer's Disease.

Magnetic resonance imaging (MRI)-based diffusion methods can quantify water molecule diffusion within tissues and are used to measure microstructural changes due to neurodegeneration. In a recent issue of the Journal of Alzheimer's Disease, Nakaya et al. report on the "Assessment of Gray Matter Microstructural Alterations in Alzheimer's Disease by Free Water Imaging". This study and others indicate that MRI diffusion methods, including free water imaging and diffusion tensor imaging, can reveal gray matter microstructural changes present in many AD-affected brain regions. These techniques are a valuable tool for multi-modal and longitudinal imaging studies that can offer insights into AD neurobiology.

Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Generative Pre-trained Transformer (ChatGPT), offer rapid diagnostic advantages. This study assesses ChatGPT's accuracy in interpreting diffusion-weighted imaging (DWI) for acute stroke diagnosis. A retrospective analysis was conducted to identify the presence of stroke using DWI and apparent diffusion coefficient (ADC) map images. Patients aged >18 years who exhibited diffusion restriction and had a clinically explainable condition were included in the study. Patients with artifacts that affected image homogeneity, accuracy, and clarity, as well as those who had undergone previous surgery or had a history of stroke, were excluded from the study. ChatGPT was asked four consecutive questions regarding the identification of the magnetic resonance imaging (MRI) sequence, the demonstration of diffusion restriction on the ADC map after sequence recognition, and the identification of hemispheres and specific lobes. Each question was repeated 10 times to ensure consistency. Senior radiologists subsequently verified the accuracy of ChatGPT's responses, classifying them as either correct or incorrect. We assumed a response to be incorrect if it was partially correct or suggested multiple answers. These responses were systematically recorded. We also recorded non-responses from ChatGPT-4V when it failed to provide an answer to a query. We assessed ChatGPT-4V's performance by calculating the number and percentage of correct responses, incorrect responses, and non-responses across all images and questions, a metric known as "accuracy." ChatGPT-4V was considered successful if it answered ≥80% of the examples correctly. A total of 530 diffusion MRI, of which 266 were stroke images and 264 were normal, were evaluated in the study. For the initial query identifying MRI sequence type, ChatGPT-4V's accuracy was 88.3% for stroke and 90.1% for normal images. For detecting diffusion restriction, ChatGPT-4V had an accuracy of 79.5% for stroke images, with a 15% false positive rate for normal images. Regarding identifying the brain or cerebellar hemisphere involved, ChatGPT-4V correctly identified the hemisphere in 26.2% of stroke images. For identifying the specific brain lobe or cerebellar area affected, ChatGPT-4V had a 20.4% accuracy for stroke images. The diagnostic sensitivity of ChatGPT-4V in acute stroke was found to be 79.57%, with a specificity of 84.87%, a positive predictive value of 83.86%, a negative predictive value of 80.80%, and a diagnostic odds ratio of 21.86. Despite limitations, ChatGPT shows potential as a supportive tool for healthcare professionals in interpreting diffusion examinations in stroke cases, where timely diagnosis is critical. ChatGPT can play an important role in various aspects of stroke cases, such as risk assessment, early diagnosis, and treatment planning.

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images. We developed a deep learning model to synthesize FA/MD from T1. The training data set consisted of 4998 participants with the highest white matter hyperintensity volumes in the UK Biobank. Four external validations data sets with small vessel disease were included: SCANS (St George's Cognition and Neuroimaging in Stroke; n=120), RUN DMC (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort; n=502), PRESERVE (Blood Pressure in Established Cerebral Small Vessel Disease; n=105), and NETWORKS (n=26), along with 1000 normal controls from the UK Biobank. The synthetic maps resembled ground-truth maps (structural similarity index >0.89 for MD maps and >0.80 for FA maps across all external validation data sets except for SCANS). The prediction accuracy of dementia using whole-brain median MD from the synthetic maps is comparable to the ground truth (SCANS ground-truth c-index, 0.822 and synthetic, 0.821; RUN DMC ground truth, 0.816 and synthetic, 0.812) and better than white matter hyperintensity volume (SCANS, 0.534; RUN DMC, 0.710). We have developed a fast and generalizable method to synthesize FA/MD maps from T1 to improve the prediction accuracy of dementia in small vessel disease when diffusion tensor imaging data have not been acquired.

The macroscale routing mechanism of structural brain connectivity related to body mass index.

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management.

White Matter Correlates of Cognition: A Diffusion Magnetic Resonance Imaging Study

BackgroundOur comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition. Methods125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05. ResultsWe identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005). ConclusionOur results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

Abstract The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.

How fiber bundle alterations differ in presumed LATE and amnestic Alzheimer's disease.

Typical Alzheimer's disease (AD) and limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) are two neurodegenerative diseases that present with a similar initial amnestic clinical phenotype but are associated with distinct proteinopathies. We investigated white matter (WM) fiber bundle alterations, using fixel-based analysis, a state-of-the-art diffusion magnetic resonance imaging model, in early AD, presumed LATE, and controls. We also investigated regional cortical atrophy. Both amnestic AD and presumed LATE patients exhibited WM alterations in tracts of the temporal and limbic lobes and in callosal fibers connecting superior frontal gyri. In addition, presumed LATE patients showed alterations in callosal fibers connecting the middle frontal gyri and in the cerebello-thalamo-cortical tract. Cortical thickness was reduced in regions connected by the most altered tracts. These findings, the first to describe WM fiber bundle alterations in presumed LATE, are consistent with results on cortical atrophy and with the staging system of tau or TDP-43 accumulation. Fixel-based analysis revealed white matter (WM) fiber bundle alterations in presumed limbic-predominant age-related TAR DNA-binding protein 43 encephalopathy (LATE) patients identified by isolated episodic/limbic amnesia, the absence of positive Alzheimer's disease (AD) biomarkers, and no other neurological diagnosis after 2 years of follow-up. Presumed LATE and amnestic AD shared similar patterns of WM alterations in fiber bundles of the limbic and temporal lobes, in congruence with their similar limbic cognitive phenotype. Presumed LATE differed from AD by the alteration of the callosal fibers connecting the middle frontal gyri and of the cerebello-thalamo-cortical tract. WM fiber bundle alterations were consistent with results on regional cortical atrophy. The different anatomical patterns of WM degeneration could provide information on the propagation pathways of distinct proteinopathies.

Diffusion Magnetic Resonance Imaging and Human Reward System Research: A Bibliometric Analysis and Visualisation of Current Research Trends.

The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots. The research datasets were acquired from the Scopus database. The search terms used were 'reward' AND 'human' AND 'diffusion imaging' OR 'diffusion tensor imaging' OR 'diffusion MRI' OR 'diffusion-weighted imaging' OR 'tractography' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing's Publish or Perish and VOSviewer software. The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while the top keywords in human reward research were 'diffusion MRI', 'adolescence', 'depression' and 'reward-related brain areas'. These findings may serve as researchers' references to find collaborative authors, relevant journals, cooperative countries/institutions, and hot topics related to dMRI and reward research.

White Matter Microstructural Changes Using Ultra-Strong Diffusion Gradient MRI in Adult-Onset Idiopathic Focal Cervical Dystonia.

Adult-onset idiopathic focal cervical dystonia (AOIFCD) involves abnormal posturing of the cervical musculature and, in some individuals, an associated head tremor. Existing neuroimaging studies have implicated key motor networks. However, measures used to date lack specificity toward underlying pathophysiologic differences. We aim to assess white matter motor pathways for localized, microstructural differences, which may aid in understanding underlying mechanisms. Individuals diagnosed with AOIFCD and an age- and sex-matched control group were prospectively recruited through the Welsh Movement Disorders Research Network. All participants underwent in-depth clinical phenotyping and MRI (structural and diffusion sequences) using ultra-strong diffusion gradients. Tractography (whole-tract median values) and tractometry (along tract profiling) were performed for key white matter motor pathways assessing diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and standard model parameters. Groups were compared using linear model analysis with Bonferroni multiple comparison correction. Fifty participants with AOIFCD and 30 healthy control participants were recruited, with 46 with AOIFCD and 30 healthy controls included for analysis (33 without head tremor, 13 with head tremor). Significant differences were observed in the anterior thalamic radiations (lower mid-tract fractional anisotropy [estimate = -0.046, p = 3.07 × 10-3], radial kurtosis [estimate = -0.165, p = 1.42 × 10-4], f-intra-axonal signal fraction [estimate = -0.044, p = 2.78 × 10-3], p2 orientation coherence [estimate = -0.043, p = 1.64 × 10-3], higher Orientation Dispersion Index [ODI, estimate = 0.023, p = 2.22 × 10-3]) and thalamopremotor tracts (higher mid-tract mean kurtosis [estimate = 0.064, p = 7.56 × 10-4], lower Neurite Density Index [estimate = 0.062, p = 2.1 × 10-3], higher distal tract ODI [estimate = 0.062, p = 3.1 × 10-3], lower f [estimate = -0.1, p = 2.3 × 10-3], and striatopremotor tracts [proximal lower f: estimate = -0.075, p = 1.06 × 10-3]). These measures correlated with clinical measures: dystonia duration (right thalamopremotor distal ODI: r = -0.9, p = 1.29 × 10-14), psychiatric symptoms (obsessive compulsive symptoms: left anterior thalamic radiation p2 r = 0.92, p = 2.797 × 10-11), sleep quality (Sleep Disorders Questionnaire Score: left anterior thalamic radiation ODI: r = -0.84, p = 4.84 × 10-11), pain (left anterior thalamic radiation ODI: r = -0.89, p = 1.4 × 10-13), and cognitive functioning (paired associated learning task p2, r = 0.94, p = 6.68 × 10-20). Overall, localized microstructural differences were identified within tracts linking the prefrontal and premotor cortices with thalamic and basal ganglia regions, suggesting pathophysiologic processes involve microstructural aberrances of motor system modulatory pathways, particularly involving intra-axonal and fiber orientation dispersion measures.

Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities.

The major vascular cause of dementia is cerebral small vessel disease (SVD). Its diagnosis relies on imaging hallmarks, such as white matter hyperintensities (WMH). WMH present a heterogenous pathology, including myelin and axonal loss. Yet, these might be only the "tip of the iceberg." Imaging modalities imply that microstructural alterations underlie still normal-appearing white matter (NAWM), preceding the conversion to WMH. Unfortunately, direct pathological characterization of these microstructural alterations affecting myelinated axonal fibers in WMH, and especially NAWM, is still missing. Given that there are no treatments to significantly reduce WMH progression, it is important to extend our knowledge on pathological processes that might already be occurring within NAWM. Staining of myelin with Luxol Fast Blue, while valuable, fails to assess subtle alterations in white matter microstructure. Therefore, we aimed to quantify myelin surrounding axonal fibers and axonal- and microstructural damage in detail by combining (immuno)histochemistry with polarized light imaging (PLI). To study the extent (of early) microstructural damage from periventricular NAWM to the center of WMH, we refined current analysis techniques by using deep learning to define smaller segments of white matter, capturing increasing fluid-attenuated inversion recovery signal. Integration of (immuno)histochemistry and PLI with post-mortem imaging of the brains of individuals with hypertension and normotensive controls enables voxel-wise assessment of the pathology throughout periventricular WMH and NAWM. Myelin loss, axonal integrity, and white matter microstructural damage are not limited to WMH but already occur within NAWM. Notably, we found that axonal damage is higher in individuals with hypertension, particularly in NAWM. These findings highlight the added value of advanced segmentation techniques to visualize subtle changes occurring already in NAWM preceding WMH. By using quantitative MRI and advanced diffusion MRI, future studies may elucidate these very early mechanisms leading to neurodegeneration, which ultimately contribute to the conversion of NAWM to WMH.

Time-dependent diffusion magnetic resonance imaging: measurement, modeling, and applications

Time-dependent diffusion magnetic resonance imaging: measurement, modeling, and applications

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review.

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Evidence for Altered White Matter Organization After Mild Traumatic Brain Injury: A Scoping Review on the Use of Diffusion Magnetic Resonance Imaging and Blood-Based Biomarkers to Investigate Acute Pathology and Relationship to Persistent Post-Concussion Symptoms.

Mild traumatic brain injury (mTBI) is the most common form of traumatic brain injury. Post-concussive symptoms typically resolve after a few weeks although up to 20% of people experience these symptoms for >3 months, termed persistent post-concussive symptoms (PPCS). Subtle white matter (WM) microstructural damage is thought to underlie neurological and cognitive deficits experienced post-mTBI. Evidence suggests that diffusion magnetic resonance imaging (dMRI) and blood-based biomarkers could be used as surrogate markers of WM organization. We conducted a scoping review according to PRISMA-ScR guidelines, aiming to collate evidence for the use of dMRI and/or blood-based biomarkers of WM organization, in mTBI and PPCS, and document relationships between WM biomarkers and symptoms. We focused specifically on biomarkers of axonal or myelin integrity post-mTBI. Biomarkers excluded from this review therefore included the following: astroglial, perivascular, endothelial, and inflammatory markers. A literature search performed across four databases, EMBASE, Scopus, Google Scholar, and ProQuest, identified 100 records: 68 analyzed dMRI, 28 assessed blood-based biomarkers, and 4 used both. Blood biomarker studies commonly assessed axonal cytoskeleton proteins (i.e., tau); dMRI studies assessed measures of WM organization (i.e., fractional anisotropy). Significant biomarker alterations were frequently associated with heightened symptom burden and prolonged recovery time post-injury. These data suggest that dMRI and blood-based biomarkers may be useful proxies of WM organization, although few studies assessed these complementary measures in parallel, and the relationship between modalities remains unclear. Further studies are warranted to assess the benefit of a combined biomarker approach in evaluating alterations to WM organization after mTBI.