White Matter Fiber Tracts Research Articles

The corpus callosum in people with congenital adrenal hyperplasia (CAH).

Congenital Adrenal Hyperplasia (CAH) is a group of genetic disorders that affect the adrenal glands. CAH manifests in abnormal levels of cortisol and androgens and is accompanied by white matter alterations. However, no CAH study has specifically targeted the corpus callosum, the brain's largest white matter fiber tract. To bridge that gap in the literature, we investigated callosal morphology in 53 individuals with CAH and 53 matched controls (66 women, 40 men). In addition to calculating areas for seven callosal subsections, we estimated the callosal thickness at 100 equidistant points. All statistical analyses were conducted while co-varying for age and total brain volume and applying corrections for multiple comparisons. There were no significant effects of biological sex and no significant group-by-sex interactions. However, there was a significant effect of group, both for area measures and thickness estimates, indicating smaller dimensions within the callosal splenium and isthmus in people with CAH. Our findings corroborate previous studies highlighting white matter alterations in CAH and may suggest that callosal integrity is compromised due to potentially adverse effects of glucocorticoids, a standard treatment for both men and women with CAH.

Fast and reliable quantitative measures of white matter development with magnetic resonance fingerprinting

Abstract Developmental cognitive neuroscience aims to shed light on evolving relationships between brain structure and cognitive development. To this end, quantitative methods that reliably measure individual differences in brain tissue properties are fundamental. Standard qualitative MRI sequences are influenced by scan parameters and hardware-related biases, and also lack physical units, making the analysis of individual differences problematic. In contrast, quantitative MRI can measure physical properties of the tissue but with the cost of long scan durations and sensitivity to motion. This poses a critical limitation for studying young children. Here, we examine the reliability of an efficient quantitative multiparameter mapping method - Magnetic Resonance Fingerprinting (MRF) - in children scanned longitudinally. We focus on T1 values in white matter, since quantitative T1 values are known to primarily reflect myelin content, a key factor in brain development. Forty-nine children aged 8-13y (mean 10.3y ±1.4) completed two scanning sessions 2-4 months apart. In each session, two 2-minute 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan duration on image quality and scan-rescan reliability. A separate calibration scan was used to measure B0 inhomogeneity and correct for bias. We examined the impact of scan time and B0 inhomogeneity correction on scan-rescan reliability of values in white matter, by comparing single 2-min and combined two 2-min scans, with and without B0-correction. Whole-brain voxel-based reliability analysis showed that combining two 2-min MRF scans improved reliability (pearson’s r=0.87) compared with a single 2-min scan (r=0.84), while B0-correction had no effect on reliability in white matter (r=0.86 and 0.83 4-min vs 2-min). Using diffusion tractography, we segmented major white matter fiber tracts and examined the profiles of MRF-derived T1 values along each tract. We found that T1 values from MRF showed similar or greater reliability compared with diffusion parameters. Lastly, we found that R1 (1/T1) values in multiple white matter tracts were significantly correlated with age. In sum, MRF-derived T1 values were highly reliable in a longitudinal sample of children and replicated known age effects. Reliability in white matter was improved by longer scan duration but was not affected by B0-correction, making it a quick and straightforward scan to collect. We propose that MRF provides a promising avenue for acquiring quantitative brain metrics in children and patient populations where scan time and motion are of particular concern.

Predicting brain tumour growth patterns using a novel MRI-based tumour spread map: application to radiotherapy planning.

The treatment of glioblastomas (GBM) with radiation therapy is extremely challenging due to their invasive nature and high recurrence rate within normal brain tissue. In this work, we present a new metric called the tumour spread (TS) map, which utilizes diffusion tensor imaging (DTI) to predict the probable direction of tumour cells spread along fiber tracts. We hypothesized that the TS map could serve as a predictive tool for identifying patterns of likely recurrence in patients with GBM and, therefore, be used to modify the delivery of radiation treatment to pre-emptively target regions at high risk of tumour spread. In this proof-of-concept study, we visualized the white matter fiber tract pathways within the brain using diffusion tensor tractography and developed an algorithm which mathematically calculates a relative probability index in each voxel, resulting in the generation of the TS map. Based on the information provided by the TS map, the original radiotherapy target volume was then modified to include areas with a higher probability of tumour spread and exclude other areas with a lower probability of spread. A volumetric modulated arc therapy (VMAT) treatment plan was then developed utilizing the modified target volumes and subsequently compared to that using the original target volumes. Follow-up anatomical imaging obtained 8 months post-surgery was assessed to validate our findings. A TS map was generated on a glioblastoma patient demonstrating a relative probability of tumour spread along fiber tracts throughout the brain. The modified planning target volume better covered brain regions with a higher risk of tumour spread while still demonstrating a 21% reduction in volume compared to the original planning target volume, resulting in greater preservation of normal tissue. The modified VMAT plan resulted in an average mean dose to four identified recurrences of 80% of the prescription dose, while the original VMAT plan delivered only 63% of the prescription dose as the average mean dose to the recurrences. The utilization of tractography and the generation of corresponding TS maps offer a promising approach to predicting patterns of tumour recurrence and optimizing treatment delivery. Further research is needed to validate the predictive value of the TS map on a larger cohort of patients and explore its potential in personalized treatment strategies for GBM patients.

White Matter Fiber Bundle Alterations Correlate with Gait and Cognitive Impairments in Parkinson's Disease based on HARDI Data.

The neuroanatomical basis of white matter fiber tracts in gait impairments in individuals suffering from Parkinson's Disease (PD) is unclear. Twenty-four individuals living with PD and 29 Healthy Controls (HCs) were included. For each participant, two-shell High Angular Resolution Diffusion Imaging (HARDI) and high-resolution 3D structural images were acquired using the 3T MRI. Diffusion-weighted data preprocessing was performed using the orientation distribution function to trace the main fiber tracts in PD individuals. Clinical characteristics between the two groups were compared, and the correlation between the FA value and behavioral data was analyzed. Quantitative gait and clinical parameters were recorded in PD at ON and OFF states, respectively. The mean tract-specific FA values of the right Cingulum Cingulate (rCC) were statistically different between the PD group and the HC group (p =0.047). The FA value of 34-58 equidistant nodes in rCC was positively correlated with Mini-Mental State Examination (MMSE) (r=0.527, p=0.024), Berg Balance Scale (BBS)-OFF (r=0.480, p =0.040), and BBS-ON (r=0.528, p =0.024) scores, while it was negatively correlated with the MDS-UPDRS-III-ON score (r=-0.502, p =0.030). Regarding the gait analysis, the FA value was significantly correlated with velocity, cadence, and stride time of the pace and rhythm domains in both 'ON' and 'OFF' states, respectively (p<0.05). This study served as an initial exploration to establish that HARDI sequences could be employed as a robust tool for analyzing microstructural alterations in white matter fiber bundles among PD patients, although the sample size was small. We confirmed microstructural integrity impairment of rCC to be significantly associated with both gait and cognitive deficits in patients with PD. Early detection of microstructural changes in rCC and targeted treatment can help improve behavioral disorders. In the future, we intend to further integrate multimodal data with assessments of patient behavior both prior to and following intervention. We will validate our findings within an independent cohort to monitor disease progression and evaluate the efficacy of therapeutic interventions.

A novel approach in MRI signal processing for unveiling the intricacies of brain axonal organization.

This article introduces an innovative methodology to unveil the intricacies of white matter fiber pathways in the brain using diffusion MRI. Relying on the rationale that traditional methods observe a significant decrease in signal intensity values in the direction of higher diffusivity, our novel approach strategically selects for diffusion-sensitizing gradient directions (dSGDs, representing the directions along which signals are generated) aligned with reduced signal intensities. By treating these selected directions as maximum diffusivity directions, we generate uniformly distributed gradient directions (GDs) around them, which are subsequently employed in the reconstruction process. This approach addresses the shortcomings of existing methods. It improves upon the uniform gradient directions (UGDs) approach, which suffers from gradient direction redundancy, and the adaptive gradient directions (AGDs) approach, which requires solving the linear system twice per voxel. Proposed method simultaneously addresses both limitations, offering a more efficient and streamlined process. The effectiveness of our proposedmethodology is rigorously evaluated through simulations and experiments involving real data, showcasing its superior performance in uncovering the complex white matter fiber pathways in the brain.

Automated White Matter Fiber Tract Segmentation for the Brainstem

ABSTRACTThis study aimed to develop an automatic segmentation method for brainstem fiber bundles. We utilized the brainstem as a seed region for probabilistic tractography based on multishell, multitissue constrained spherical deconvolution in 40 subjects from the Human Connectome Project (HCP). All tractography data were registered into a common space to construct a brainstem fiber cluster atlas. A total of 100 fiber clusters were identified and annotated. Cortical parcellation–based fiber selection was then performed to extract fibers within the annotated clusters that projected to their corresponding cortical regions. This atlas was applied for automatic brainstem fiber bundle segmentation in 10 HCP subjects and 8 patients with brainstem cavernous malformations. The spatial overlap between automatic and manual reconstruction was assessed. Ultimately, eight fiber bundles were identified in the brainstem atlas on the basis of their trajectories: the corticospinal tract (CST), corticobulbar tract, frontopontine tract, parieto‐occipital‐pontine tract, medial lemniscus, and superior, middle, and inferior cerebellar peduncles. The mean and standard deviation of the weighted dice (wDice) scores between the automatic and manual reconstructions were 0.9076 ± 0.0950 for the affected CST, 0.9388 ± 0.0439 for the contralateral CST, 0.9130 ± 0.0588 for the affected medial lemniscus, and 0.9600 ± 0.0243 for the contralateral medial lemniscus. This proposed method effectively distinguishes major brainstem fiber bundles across subjects while reducing labor costs and interoperator variability inherent to manual reconstruction. Additionally, this method is robust in that it allows for the visualization and identification of fiber tracts surrounding brainstem cavernous malformations.

Alterations of White Matter Microstructure in Migraine Patients Vary in the Peri-ictal Phases.

Alterations in white matter (WM) microstructure are commonly found in migraine patients. Here, we employ a longitudinal study of episodic migraine without aura using diffusion magnetic resonance imaging (dMRI) to investigate whether such WM microstructure alterations vary through the different phases of the pain cycle. Fourteen patients with episodic migraine without aura related with menstruation were scanned through four phases of their (spontaneous) migraine cycle (interictal, preictal, ictal, and postictal). Fifteen healthy controls were studied in the corresponding phases of the menstrual cycle. Multishell dMRI data were acquired and preprocessed to obtain maps of diffusion parameters reflecting WM microstructure. After a whole-brain analysis comparing patients with controls, a region-of-interest analysis was performed to determine whether the patients' microstructural changes varied across the migraine cycle in specific WM tracts. Compared with controls, patients showed reduced axial diffusivity (AD) in several WM tracts across the whole brain in the interictal phase and increased fractional anisotropy (FA) in commissural fibers in the ictal phase. Interestingly, AD returned to baseline levels during peri-ictal phases in specific projection and association fibers. In contrast, FA values decreased in the ictal phase away from normal values in a few commissural and projection tracts. Widespread WM fiber tracts suffer structural variations across the migraine cycle, suggesting microstructural changes potentially associated with limbic and salience functional networks and highlighting the importance of the cycle phase in imaging studies of migraine.

Alterations in the white matter fiber tracts of preschool-aged children with autism spectrum disorder: an automated fiber quantification study.

Little is known about the precise impaired patterns of white matter (WM) fiber tracts in preschool-aged children with autism spectrum disorder (ASD). Thus, we used diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to explore the changes in WM fiber tracts in preschool-aged children with ASD and its correlation with the severity of clinical manifestations. A total of 43 pediatric ASD and 42 age- and sex-matched typical developing children were examined with DTI. AFQ was used to quantify 100 nodes of 16 specific WM fiber tracts. The DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), were compared between the two groups. Correlation analysis was performed to evaluate whether abnormal DTI metrics correlated with the Childhood Autism Rating Scale. Compared with typical developing children, at the entire level, the ASD group exhibited a decreased FA [P=0.04 after false discovery rate (FDR) correction] and an increased MD (P=0.048 after FDR correction), and RD (P=0.024 after FDR correction) in the left inferior longitudinal fasciculus (ILF). A decreased FA (P=0.02 after FDR correction) and an increased RD (P=0.024 after FDR correction) were shown in forceps minor. An increased MD (P=0.016 after FDR correction) and RD (P=0.026 after FDR correction) were shown in the left inferior frontal-occipital fasciculus (IFOF). At the point-wise level, significantly decreased FA, and increased MD and RD were observed in the anterior part of forceps minor, left IFOF, and posterior part of left ILF in the ASD group (all P<0.05 after FDR correction). Furthermore, there was a negative correlation between FA of the posterior part of left ILF and the Childhood Autism Rating Scale within the ASD group (r=-0.475, P<0.001). Preschool-aged children with ASD displayed a site-specific propensity for WM fiber tract impairment, mainly in the left cerebral hemisphere. The decreased integrity for the posterior part of the left ILF may reflect the severity of autistic symptoms.

Cortical tau aggregation drives axonal degeneration in white matter fiber tracts in Alzheimer’s disease

AbstractBackgroundIn Alzheimer’s disease (AD), cortical tau aggregation is a strong predictor of cortical brain atrophy as shown by MRI and PET studies, particularly driving the degeneration of neuronal somata in the grey matter. However, tau’s physiological role is to stabilize microtubules within axons in the brain’s white matter (WM) pathways. Therefore, tau’s white‐to‐grey‐matter translocation and aggregation in neurofibrillary tangles close to neuronal somata may induce WM degeneration through destabilization of axonal microtubule integrity. To address this, we determined whether cortical tau predicted faster atrophy of connected WM tracts in AD.MethodWe included from ADNI cohort 37 amyloid‐PET negative (Aβ‐) cognitively normal (CN) participants and 88 amyloid‐PET positive (Aβ+) participants across the AD‐spectrum (i.e. CN/MCI/Dementia = 50/28/10), with baseline amyloid‐PET, longitudinal tau‐PET and longitudinal structural MRI data. For replication, we included baseline amyloid‐PET, tau‐PET and MRI data of 321 CN‐Aβ+ subjects from the A4 cohort. T1‐weighed MRIs were segmented into grey and white matter and non‐linearly normalized to MNI space using CAT12. The cortical Brainnetome Atlas and a diffusion imaging‐based tractography atlas were applied tau‐PET and MRI data to i) determine cortical tau‐PET accumulation rates within Brainnetome ROIs, and ii) assess WM volume changes within fiber tracts connected to each cortical ROI. Statistical regression‐models were adjusted for age, sex, WM hyperintensity volume, global amyloid, intracranial volume, and APOE4‐status.ResultIn ADNI, higher baseline temporo‐parietal tau‐PET predicted faster volume reductions in connected WM tracts (Figure 1A), especially pronounced in Aβ+ subjects (Figure 1B). Similarly, faster tau accumulation was strongly linked to widespread WM volume reductions in connected fiber tracts (Figure 2A; Figure 2B), particularly exacerbated in Aβ+ APOE4 carriers (Figure 3A) compared to non‐carriers (Figure 3B). These results suggest that tau accumulation and WM degeneration are parallel processes in AD, modulated by APOE4. In the A4 validation sample of preclinical AD patients, we detected congruent associations between inferio‐temporal tau‐PET increase and reduced WM volume in connected fiber tracts (Figure 4).ConclusionCortical tau aggregation is associated with progressive WM atrophy in connected fiber tracts throughout the brain, suggesting that tau accumulation triggers axonal degeneration, which may induce neuronal disconnection and dysfunction and thereby contributing to AD progression.

Cortical tau aggregation drives axonal degeneration in white matter fiber tracts in Alzheimer's disease

Cortical tau aggregation drives axonal degeneration in white matter fiber tracts in Alzheimer's disease

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts.

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during invivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the invivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive invivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.

Ultrahigh-resolution 7-Tesla anatomic magnetic resonance imaging and diffusion tensor imaging of ex vivo formalin-fixed human brainstem-cerebellum complex.

Brain cross-sectional images, tractography, and segmentation are valuable resources for neuroanatomical education and research but are also crucial for neurosurgical planning that may improve outcomes in cerebellar and brainstem interventions. Although ultrahigh-resolution 7-Tesla (7T) magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) reveal such structural brain details in living or fresh unpreserved brain tissue, imaging standard formalin-preserved cadaveric brain specimens often used for neurosurgical anatomic studies has proven difficult. This study sought to develop a practical protocol to provide anatomic information and tractography results of an ex vivo human brainstem-cerebellum specimen. A protocol was developed for specimen preparation and 7T MRI with image postprocessing on a combined brainstem-cerebellum specimen obtained from an 85-year-old male cadaver with a postmortem interval of 1 week that was stored in formalin for 6 months. Anatomic image series were acquired for detailed views and diffusion tractography to map neural pathways and segment major anatomic structures within the brainstem and cerebellum. Complex white matter tracts were visualized with high-precision segmentation of crucial brainstem structures, delineating the brainstem-cerebellum and mesencephalic-dentate connectivity, including the Guillain-Mollaret triangle. Tractography and fractional anisotropy mapping revealed the complexities of white matter fiber pathways, including the superior, middle, and inferior cerebellar peduncles and visible decussating fibers. 3-dimensional (3D) reconstruction and quantitative and qualitative analyses verified the anatomical precision of the imaging relative to a standard brain space. This novel imaging protocol successfully captured the intricate 3D architecture of the brainstem-cerebellum network. The protocol, unique in several respects (including tissue preservation and rehydration times, choice of solutions, preferred sequences, voxel sizes, and diffusion directions) aimed to balance high resolution and practical scan times. This approach provided detailed neuroanatomical imaging while avoiding impractically long scan times. The extended postmortem and fixation intervals did not compromise the diffusion imaging quality. Moreover, the combination of time efficiency and ultrahigh-resolution imaging results makes this protocol a strong candidate for optimal use in detailed neuroanatomical studies, particularly in presurgical trajectory planning.

A diffusion tensor imaging-based multidimensional study of brain structural changes after long-term high-altitude exposure and their relationships with cognitive function.

Brain structure changes after long-term adaptation to the high-altitude environment; however, related studies are few, results are in consistent, and long-term effects on cognitive function and pathophysiological mechanisms are unclear. Therefore, diffusion tensor imaging (DTI) was used to investigate the damage to white matter fiber tracts and correlations between brain structural abnormalities and cognitive function. Forty healthy Han people living on the high-altitude and 40 healthy Han people living on the plains were enrolled in this study and underwent magnetic resonance imaging, emotional state assessment, and cognitive function tests. The sex, age, education level, and social status of the two groups were not different. The tract-based spatial statistics (TBSS) method was used to analyze the DTI parameters of the white matter fiber tracts of the two groups. Moreover, the partial correlation method (age and sex as covariates) was used to analyze the correlations between the intergroup differences in the DTI parameters and a series of clinical indicators of emotional state and cognitive function. Two-sample t tests, Mann-Whitney U test, generalized linear model, or chi-square tests were used for statistical analysis. Compared with those individuals in the plain group, the scores on the PSQI, SDS, SAS, PHQ-9, and GAD-7 of individuals in the high-altitude group were higher, while the scores on the DST-Backwards, MoCA, and MMSE in the high-altitude group were lower. The fractional anisotropy (FA) value of the body of the corpus callosum in the high-altitude group was lower than that in the plain group. The FA value of the body of the corpus callosum in the plain group was negatively correlated with the Logical Memory, while no significant correlation was found in the high-altitude group. This study revealed that long-term exposure to a high-altitude environment could lead to a series of changes in sleep, emotion, and cognitive function and irreversible damage to the white matter microstructure of the body of the corpus callosum, which is the related brain region responsible for logical memory. The absence of logical memory impairment in the healthy Han Chinese population living on the high-altitude in this study may be due to the existence of adaptive compensation after long-term high-altitude exposure.

A left-lateralized white matter tract associated with communication in domestic dogs

The ability to communicate with conspecifics is an adaptive behavior important for survival and reproduction, particularly in lineages that evolved enlarged brains and complex social behavior. In humans, language is supported by a robust, left-lateralized white matter fiber tract called the arcuate fasciculus, which links Broca's and Wernicke's areas, the core neocortical language regions located in the frontal and temporal lobes, respectively1. This tract is also present in chimpanzees, less substantial than in humans and either weakly leftwardly-asymmetric or not asymmetric2. Other mammalian lineages have evolved large brains, complex behavior and social communication in parallel with primates, notably including carnivores. In dogs (Canis familiaris), domestication has almost certainly involved additional selective pressures and environmental factors that have shaped the evolution and development of neural circuits for communication. We report that the dog brain possesses a large, left-lateralized white matter tract that links cortical centers for productive and receptive communication, and that this tract is positively associated with individual variation in receptive vocabulary size.

Tidying up white matter: Neuroplastic transformations in sensorimotor tracts following slackline skill acquisition.

This study investigated changes in white matter (WM) morphology following complex motor learning, that is, the learning to walk a slackline. A sample of young adults from the general population underwent brain imaging before the slackline intervention, after successful learning, and after a subsequent follow-up period by applying state-of-the-art measures for the assessment of micro- and macrostructural characteristics of WM fiber tracts (voxel-based and fixel-based). A randomly assigned control group (CG) was scanned at the same time points of assessment but received no intervention over the study period. Learning to walk a slackline resulted in manifold changes in WM morphology: (1) Whole brain fixel-based analyses revealed robust increases in the fiber cross-section in bundles closely associated with sensorimotor functions (e.g., superior longitudinal fasciculi, corticospinal tract); (2) The neurite orientation dispersion and density imaging (NODDI) parameters showed widespread decreases in overlapping fiber bundles. In the CG, no time-related WM changes were apparent at all. This well-controlled longitudinal intervention study provides substantial new evidence that learning a complex motor skill modulates fiber organization and fiber density in sensorimotor tracts.

Segmental abnormalities of white matter microstructure in multiple sclerosis and neuromyelitis optica spectrum disorder identified by automated fiber quantification

BackgroundMultiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are different in pathogenesis, but both could lead to white matter (WM) microstructural damage. The aim of this study was to explore the differences in the patterns of WM fiber tract damage in relapsing-remitting MS (RRMS) and NMOSD by automated fiber quantification (AFQ). Materials and MethodsForty-one RRMS patients, 30 NMOSD patients and 30 healthy controls (HC) underwent MRI examination. AFQ was applied to identify and quantify 100 equally spaced nodes of specific WM fiber tracts for each participant. Measurements of fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD) and radial diffusivity (RD) for each segment of a specific fiber tract were compared between RRMS, NMOSD and HC. ResultsThe decrease in FA was found in 7 fiber tracts in entire tract comparison and 9 fiber tracts in pointwise comparison in RRMS patients. However, the FA in left thalamic radiation (TR) and right uncinate fasciculus showed significant differences between RRMS and HC only in the pointwise comparison, but not in the entire tract comparison. The MD, AD and RD of WM fiber tracts in RRMS patients were extensively increased both in the entire level and in the pointwise level. NMOSD patients showed significant FA decrease in left TR and callosum forceps minor (CF_minor), and significant RD increase in CF_minor in the pointwise level. In the pointwise comparison between RRMS and NMOSD, significant FA decrease was found in right inferior fronto-occipital fasciculus and bilateral inferior longitudinal fasciculus in RRMS patients, focal or widespread MD, AD and RD increase was found in multiple fiber tracts. ConclusionThe AFQ approach is a more sensitive way to reflect WM microstructural abnormalities, revealing extensive WM microstructural damage in RRMS and limited WM fiber tract damage in NMOSD.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Diffusion and functional MRI reveal microstructural and network connectivity impairment in adult-onset neuronal intranuclear inclusion disease.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder lacking reliable neuroimaging biomarkers. This study aimed to evaluate microstructural and functional connectivity alterations using diffusion kurtosis imaging (DKI) and resting-state fMRI (rs-fMRI), and to investigate their diagnostic potential as biomarkers. Twenty-three patients with NIID and 40 matched healthy controls (HCs) were recruited. Firstly, gray matter (GM) and white matter (WM) changes were assessed by voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Then we explored modifications in brain functional networks connectivity by independent component analysis. And the relationship between the altered DKI parameters and neuropsychological evaluation was analyzed. Finally, a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different gray matter and white matter parameters. Compared with the HCs, NIID patients showed reduced mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), and kurtosis fractional anisotropy (KFA) values in deep gray matter regions. Significantly decreased MK, RK, AK, KFA and fractional anisotropy (FA), and increased mean diffusivity (MD) values were observed in extensive white matter fiber tracts. Notable alterations in functional connectivity were also detected. Among all DKI parameters, the diagnostic efficiency of AK in GM and FA in WM regions was the highest. Adult-onset NIID patients exhibited altered microstructure and functional network connectivity. Our findings suggest that DKI parameters may serve as potential imaging biomarkers for diagnosing adult-onset NIID.

Bayesian pathway analysis over brain network mediators for survival data.

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity, and time to disease onset with maximum information extraction, we propose a Bayesian approach to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural model which includes a symmetric matrix-variate accelerated failure time model for disease onset and a symmetric matrix response regression for the network-variate mediator. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Simulations are carried out to confirm the advantages of our proposed method over existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.

White matter microstructure damage measured by automated fiber quantification correlates with pain symptoms in lung cancer patients.

To investigative the white matter (WM) alterations in lung cancer patients with cancer pain (CP+), and explore the correlations between damaged WM fiber tracts and clinical indicators. Twenty-six CP+, 26 lung cancer patients without CP (CP-), and 31 healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) and clinical assessments. Automated fiber quantification (AFQ) technique was performed to identify the 20 WM fiber bundles, and the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were extracted. Intergroup comparisons of these diffusion metrics were conducted based on the entire fiber bundle level and 100 node levels along each tract. The associations between altered diffusion metrics and the numeric rating scale (NRS) scores, as well as the pain duration, were analyzed. At the entire level, the CP + group showed impaired WM structure in the right cingulum hippocampus (CH_R). At the pointwise level, the CP + group exhibited extensive nodal FA reduction or MD, RD, and AD elevation. In addition, the AD of the posterior portion of the right inferior longitudinal fasciculus (ILF_R, nodes 71-75) in the CP + group was positively correlated with the pain duration, and the FA of CH_R (nodes 22-38) was negatively correlated with NRS score. Extensive WM microstructural damage may be a pattern of brain abnormalities in lung cancer patients with CP, and in particular, specific nodal disruption along pain-related fiber tracts may be a sensitive imaging biomarker to characterize the severity and duration of CP.