Diffusion Magnetic Resonance Imaging Research Articles

Early adversity causes sex-specific deficits in perforant pathway connectivity and contextual memory in adolescent mice

BackgroundEarly life adversity impairs hippocampal development and function across diverse species. While initial evidence indicated potential variations between males and females, further research is required to validate these observations and better understand the underlying mechanisms contributing to these sex differences. Furthermore, most of the preclinical work in rodents was performed in adult males, with only few studies examining sex differences during adolescence when such differences appear more pronounced. To address these concerns, we investigated the impact of limited bedding (LB), a mouse model of early adversity, on hippocampal development in prepubescent and adolescent male and female mice.MethodsRNA sequencing, confocal microscopy, and electron microscopy were used to evaluate the impact of LB and sex on hippocampal development in prepubescent postnatal day 17 (P17) mice. Additional studies were conducted on adolescent mice aged P29-36, which included contextual fear conditioning, retrograde tracing, and ex vivo diffusion magnetic resonance imaging (dMRI).ResultsMore severe deficits in axonal innervation and myelination were found in the perforant pathway of prepubescent and adolescent LB males compared to LB female littermates. These sex differences were due to a failure of reelin-positive neurons located in the lateral entorhinal cortex (LEC) to innervate the dorsal hippocampus via the perforant pathway in males, but not LB females, and were strongly correlated with deficits in contextual fear conditioning.ConclusionsLB impairs the capacity of reelin-positive cells located in the LEC to project and innervate the dorsal hippocampus in LB males but not female LB littermates. Given the critical role that these projections play in supporting normal hippocampal function, a failure to establish proper connectivity between the LEC and the dorsal hippocampus provides a compelling and novel mechanism to explain the more severe deficits in myelination and contextual freezing found in adolescent LB males.

White matter alterations predict outcomes of comprehensive behavioral intervention for tics in children with Tourette syndrome: A diffusion MRI study

AimTourette syndrome (TS) is a neurodevelopmental disorder that cause sudden uncontrolled rapid and repeated vocal sounds or movements called tics. Herein, diffusion magnetic resonance imaging (dMRI) connectometry was implemented to evaluate the white matter connectivity differences among TS patients. MethodsA total of 63 TS and 77 typically developed (TD) individuals were enrolled in the present study. dMRI connectometry was utilized to identify differences in connectivity patterns of white matter tracts in TS patients based on quantitative anisotropy (QA). QA was compared between TS and TD patients and correlated with severity scores such as Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). ResultsHigher white matter connectivity of corpus callosum and bilateral cingulum as well as lower connectivity of corticothalamic and corticostriatal pathways were evident in TS relative to TD. The baseline YGTSS motor, YGTSS total, and PUTS were negatively correlated with corticostriatal pathway, corticothalamic pathway, and bilateral cingulum integrity, respectively. The changes in tic severity scores were also positively correlated with alterations in the white matter integrity of these brain regions following behavioral therapy. ConclusionPatients with TS have several abnormalities in their white matter microstructure particularly in the cortico-striato-thalamo-cortical (CSTC) circuit, correlated with the severity of the disease. Besides, the post-behavioral therapy changes in the white matter integrity of these regions are demonstrated as response predictors.

Widespread White Matter Abnormalities in Concussed Athletes Detected by 7T Diffusion Magnetic Resonance Imaging.

Sports-related concussions may cause white matter injuries and persistent post-concussive symptoms (PPCS). We hypothesized that athletes with PPCS would have neurocognitive impairments and white matter abnormalities that could be revealed by advanced neuroimaging using ultra-high field strength diffusion tensor (DTI) and diffusion kurtosis (DKI) imaging metrics and cerebrospinal fluid (CSF) biomarkers. A cohort of athletes with PPCS severity limiting the ability to work/study and participate in sport school and/or social activities for ≥6 months completed 7T magnetic resonance imaging (MRI) (morphological T1-weighed volumetry, DTI and DKI), extensive neuropsychological testing, symptom rating, and CSF biomarker sampling. Twenty-two athletes with PPCS and 22 controls were included. Concussed athletes performed below norms and significantly lower than controls on all but one of the psychometric neuropsychology tests. Supratentorial white and gray matter, as well as hippocampal volumes did not differ between concussed athletes and controls. However, of the 72 examined white matter tracts, 16% of DTI and 35% of DKI metrics (in total 28%) were significantly different between concussed athletes and controls. DKI fractional anisotropy and axial kurtosis were increased, and DKI radial diffusivity and radial kurtosis decreased in concussed athletes when compared with controls. CSF neurofilament light (NfL; an axonal injury marker), although not glial fibrillary acidic protein, correlated with several diffusion metrics. In this first 7T DTI and DKI study investigating PPCS, widespread microstructural alterations were observed in the white matter, correlating with CSF markers of axonal injury. More white matter changes were observed using DKI than using DTI. These white matter alterations may indicate persistent pathophysiological processes following concussion in sport.

In vivo neuropil density from anatomical MRI and machine learning.

Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from a person's individualized neuropil density would be ideal. We hypothesize that in vivo neuropil density can be derived from magnetic resonance imaging (MRI) data, consisting of longitudinal relaxation (T1) MRI for gray/white matter distinction and diffusion MRI for tissue cellularity (apparent diffusion coefficient, ADC) and axon directionality (fractional anisotropy, FA). We present a machine learning algorithm that predicts neuropil density from in vivo MRI scans, where ex vivo Merker staining and in vivo synaptic vesicle glycoprotein 2A Positron Emission Tomography (SV2A-PET) images were reference standards for cellular and synaptic density, respectively. We used Gaussian-smoothed T1/ADC/FA data from 10 healthy subjects to train an artificial neural network, subsequently used to predict cellular and synaptic density for 54 test subjects. While excellent histogram overlaps were observed both for synaptic density (0.93) and cellular density (0.85) maps across all subjects, the lower spatial correlations both for synaptic density (0.89) and cellular density (0.58) maps are suggestive of individualized predictions. This proof-of-concept artificial neural network may pave the way for individualized energy atlas prediction, enabling microscopic interpretations of functional neuroimaging data.

Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness.

Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.

Evaluation of neuroimaging findings in children who admitted to pediatric emergency clinic with acute neurologic complaints

Aim: Acute neurological complaints are one of the reasons why children present to pediatric emergency departments. Neuro-imaging techniques gain more importance in children since physical examination and anamnesis do not provide sufficient information due to insufficient cooperation. We aimed to determine the distribution of patients who applied to the pediatric emergency department with non-traumatic acute neurological complaints and underwent neu-roimaging, and the clinical benefit of neuroimaging in these patients. Methods: The information and records of the patients who applied to the Konya City Hospital Pediatric Emergency Clinic between January 1 and October 1, 2022, due to acute neurological complaints and underwent neuroimaging were retrieved and analyzed retrospectively. Acute neurological complaints of the patients were classified according to the International Classifi-cation of Diseases-10 diagnostic coding. Results: This study included 180 (50.5% male) patients. The median age of the patients was 120 (interquartile range: 45-180) months. Afebrile convulsion was the most common reason for admission in 69 patients (38.3%). Cranial computed tomography (CCT) was performed in all 180 patients. Of the patients, 68 (37.8%) only underwent CCT scan, while 90 (50%) had diffusion magnetic resonance imaging (MRI), 20 (11.1%) had brain+diffusion MRI, and 2 (1.1%) had brain+diffusion+spinal MRI. Neuroimaging abnormalities were statistically higher in patients with abnormal physical examination findings than in patients with normal physical examination findings (p<0.001). Conclusion: Neuroimaging results are mostly normal even in the presence of symptoms such as seizures, headaches, and impaired consciousness. Therefore, neuroimaging should be planned by considering not only the acute neurological complaint on admission but also the physical examination findings.

Predictive value of magnetic resonance imaging diffusion parameters using artificial intelligence in low-and intermediate-risk prostate cancer patients treated with stereotactic ablative radiotherapy: A pilot study

IntroductionTo investigate the predictive value of the pre-treatment diffusion parameters of diffusion-weighted magnetic resonance imaging (DW-MRI) using artificial intelligence (AI) for prostate-specific antigen (PSA) response in patients with low- and intermediate-risk prostate cancer (PCa) treated with stereotactic ablative radiotherapy (SABR). MethodsRetrospective evaluation was performed for 30 patients using pre-treatment multi-parametric MR image datasets between 2017 and 2021. MR-based mean- and minimum apparent diffusion coefficients (ADCmean, ADCmin) were calculated for the intraprostatic dominant lesion. Therapeutic response was assessed using PSA levels. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis. Statistics performed with a significance level of p ≤ 0.05. ResultsNo biochemical relapse was detected after a median follow-up of twenty-three months (range: 3–50), with a median PSA of 0.01 ng/ml (range: 0.006–2.8) at the last examination. Significant differences were observed between the pre-treatment ADCmean, ADCmin parameters, and the group averages of patients with low and high 1-year-PSA measurements (p < 0.0001, p < 0.0001). In prediction, the random forest (RF) model outperformed the decision tree (DT) and support vector machine (SVM) models by yielding area under the curves (AUC), with 0.722, 0.685, and 0.5, respectively. ConclusionOur findings suggest that pre-treatment MR diffusion data may predict therapeutic response using the novel approach of machine learning in PCa patients treated with SABR. Implications for practiceClinicians shall measure and implement the evaluation of the suggested parameters (ADCmin, ADCmean) to provide the most accurate therapy for the patient.

Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer’s Disease in the UK Biobank

BackgroundDuring the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. MethodsWe used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer’s disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. ResultsOur findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. ConclusionsWe extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.

White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease.

Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood. The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI). A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD). The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus. MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Structural Brain Connectivity and Treatment Improvement in Mood Disorder.

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

DIMOND: DIffusion Model OptimizatioN with Deep Learning.

Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.

Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder.

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Improved inter-subject alignment of the lumbosacral cord for group-level in vivo gray and white matter assessments: A scan-rescan MRI study at 3T.

Magnetic resonance imaging (MRI) enables the investigation of pathological changes in gray and white matter at the lumbosacral enlargement (LSE) and conus medullaris (CM). However, conducting group-level analyses of MRI metrics in the lumbosacral spinal cord is challenging due to variability in CM length, lack of established image-based landmarks, and unknown scan-rescan reliability. This study aimed to improve inter-subject alignment of the lumbosacral cord to facilitate group-level analyses of MRI metrics. Additionally, we evaluated the scan-rescan reliability of MRI-based cross-sectional area (CSA) measurements and diffusion tensor imaging (DTI) metrics. Fifteen participants (10 healthy volunteers and 5 patients with spinal cord injury) underwent axial T2*-weighted and diffusion MRI at 3T. We assessed the reliability of spinal cord and gray matter-based landmarks for inter-subject alignment of the lumbosacral cord, the inter-subject variability of MRI metrics before and after adjusting for the CM length, the intra- and inter-rater reliability of CSA measurements, and the scan-rescan reliability of CSA measurements and DTI metrics. The slice with the largest gray matter CSA as an LSE landmark exhibited the highest reliability, both within and across raters. Adjusting for the CM length greatly reduced the inter-subject variability of MRI metrics. The intra-rater, inter-rater, and scan-rescan reliability of MRI metrics were the highest at and around the LSE (scan-rescan coefficient of variation <3% for CSA measurements and <7% for DTI metrics within the white matter) and decreased considerably caudal to it. To facilitate group-level analyses, we recommend using the slice with the largest gray matter CSA as a reliable LSE landmark, along with an adjustment for the CM length. We also stress the significance of the anatomical location within the lumbosacral cord in relation to the reliability of MRI metrics. The scan-rescan reliability values serve as valuable guides for power and sample size calculations in future longitudinal studies.

Ischemic Stroke After Bee Sting: A Case Report

Introduction The global impact of a bee sting extends beyond common perceptions, showing various indications that may range from local reactions to more severe complication. This report details a compelling case involving a 63-year-old male who developed neurological deficits merely six hours after a bee sting. Case Report A 63 -year-old male patient was brought to the emergency department because of widespread redness, pain and itching at the sting area after a bee sting while pruning roses in the garden. During the follow-up in the emergency room, the patient developed weakness in his right lower/upper extremities. However, the diffusion magnetic resonance imaging of the patient showed an infarction detected in the area adjacent to the left lateral ventricle (Figure1). Discussion Various clinical presentations after a bee sting have been described in the literature. Bee sting cases are generally associated with mild and transient local symptoms. Sometimes life-threatening allergic reactions may occur. Conclusion The scarcity of the reported after-sting strokes emphasizes the need for further exploration into this uncharted territory to better comprehend and manage these exceptional indications. This case is presented for its rarity, underscoring the imperative for increased attention and research on these complications. Keywords: Bee sting, ischemic, stroke.

PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain

Currently, an increasing number of macaque brain MRI datasets are being made publicly accessible. Unlike human, publicly accessible macaque brain datasets suffer from data quality in diffusion magnetic resonance imaging (dMRI) data. Typically, dMRI data require a minimum ratio of 1:10 between low b-value (b &lt; 10) volumes and high b-value (b &gt; 300) volumes. However, the currently accessible macaque datasets do not meet this ratio. Due to site differences in macaque brain images, traditional human brain image-to-image translation models struggle to perform well on macaque brain images. Our work introduces a novel end-to-end primary-auxiliary dual generative adversarial network (PadGAN) for generating low b-value images. The auxiliary generator in the PadGAN is responsible for extracting the latent space features from peak information maps and transmitting them to the primary generator, enabling the primary generator to generate images with rich details. Experimental results demonstrate that PadGAN outperforms existing methods both qualitatively and quantitatively (mean SSIM increased by 0.1139). Diffusion probabilistic tractography using dMRI data augmented by our method yields superior results.

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis.

Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor. This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters. This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated. A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC <sub<mean</sub<, ADC<sub<10%</sub<, ADC<sub<25%</sub<, ADC<sub<50%</sub<, ADC<sub<75%</sub<, ADC<sub<90%</sub<, ADC <sub<95%</sub< and ADC<sub<max</sub> values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p &#60; 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC<sub<5%</sub< and measurements of the lesions (p = 0.041; p < 0.05). Our study found no significant correlation between other prognostic factors, such as histological grade, Ki-67 indices, and ADC values. Our study found a significant difference between tumor volume, ER- and, PR status, HER2, and lymph node involvement, and some ADC values among prognostic factors for breast cancer. Furthermore, ADC histogram analysis can provide additional value in predicting some prognostic factors.

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.

Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations.

While 7T diffusion magnetic resonance imaging (dMRI) has high spatial resolution, its diffusion imaging quality is usually affected by signal loss due to B1 inhomogeneity, T2 decay, susceptibility, and chemical shift. In contrast, 3T dMRI has relative higher diffusion angular resolution, but lower spatial resolution. Combination of 3T and 7T dMRI, thus, may provide more detailed and accurate information about the voxel-wise fiber orientations to better understand the structural brain connectivity. However, this topic has not yet been thoroughly explored until now. In this study, we explored the feasibility of fusing 3T and 7T dMRI data to extract voxel-wise quantitative parameters at higher spatial resolution. After 3T and 7T dMRI data was preprocessed, respectively, 3T dMRI volumes were coregistered into 7T dMRI space. Then, 7T dMRI data was harmonized to the coregistered 3T dMRI B0 (b = 0) images. Last, harmonized 7T dMRI data was fused with 3T dMRI data according to four fusion rules proposed in this study. We employed high-quality 3T and 7T dMRI datasets (N = 24) from the Human Connectome Project to test our algorithms. The diffusion tensors (DTs) and orientation distribution functions (ODFs) estimated from the 3T-7T fused dMRI volumes were statistically analyzed. More voxels containing multiple fiber populations were found from the fused dMRI data than from 7T dMRI data set. Moreover, extra fiber directions were extracted in temporal brain regions from the fused dMRI data at Otsu's thresholds of quantitative anisotropy, but could not be extracted from 7T dMRI dataset. This study provides novel algorithms to fuse intra-subject 3T and 7T dMRI data for extracting more detailed information of voxel-wise quantitative parameters, and a new perspective to build more accurate structural brain networks.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.