Magnetic Resonance Diffusion Tensor Imaging Research Articles

Structural Changes in Brain White Matter Tracts Associated with Overactive Bladder Revealed by Diffusion Tensor MRI: Findings from a LURN Cross-Sectional Case-Control Study.

To investigate structural changes in brain white matter tracts using diffusion tensor magnetic resonance imaging (DTI) in patients with overactive bladder (OAB). Treatment-seeking OAB patients and matched controls enrolled in the cross-sectional case-control Symptoms of Lower Urinary Tract Dysfunction Research Network Neuroimaging Study received a brain DTI scan. Microstructural integrity of brain white matter was assessed using fractional anisotropy (FA) and mean diffusivity (MD). OAB and urgency urinary incontinence (UUI) symptoms were assessed using the OAB Questionnaire Short-Form (OAB-q) and International Consultation on Incontinence Questionnaire-UI. The Lower Urinary Tract Symptoms (LUTS) Tool UUI questions and responses were correlated with FA values. Among 221 participants with evaluable DTI data, 146 had OAB (66 urinary urgency [UU]-only without UUI, 80 with UUI); 75 were controls. Compared with controls, participants with OAB showed decreased FA and increased MD, representing greater microstructural abnormalities of brain white matter tracts among OAB participants. These abnormalities occurred in the corpus callosum, bilateral anterior thalamic radiation and superior longitudinal fasciculus tracts, and bilateral insula and para-hippocampal region. Among participants with OAB, higher OAB-q scores were associated with decreased FA in the left inferior fronto-occipital fasciculus, P < .0001. DTI differences between OAB and controls were driven by the UU-only (OAB-dry) but not the UUI (OAB-wet) subgroup. Abnormalities in microstructural integrity in specific brain white matter tracts were more frequent in OAB patients. More severe OAB symptoms were correlated with greater degree of microstructural abnormalities in brain white matter tracts in patients with OAB.

Impaired Glymphatic Flow on Diffusion Tensor MRI as a Marker of Neurodegeneration in Alzheimer's Disease: Correlation with Gray Matter Volume Loss and Cognitive Decline Independent of Cerebral Amyloid Deposition.

Impaired glymphatic flow on the Alzheimer's disease (AD) spectrum may be evaluated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). We aimed to validate impaired glymphatic flow and explore its association with gray matter volume, cognitive status, and cerebral amyloid deposition on the AD spectrum. 80 participants (mean age, 76.9±8.5 years; 57 women) with AD (n = 65) and cognitively normal (CN) (n = 15) who underwent 3T brain MRI including DTI and/or amyloid PET were included. After adjusting for age, sex, apolipoprotein E status, and burden of white matter hyperintensities, the ALPS-index was compared according to the AD spectrum. The association between the ALPS-index and gray matter volume, cognitive status, and quantitative amyloid from PET was assessed. The ALPS-index in the AD was significantly lower (mean, 1.476; 95% CI, 1.395-1.556) than in the CN (1.784;1.615-1.952; p = 0.026). Volumes of the entorhinal cortex, hippocampus, temporal pole, and primary motor cortex showed significant associations with the ALPS-index (all, p < 0.05). There was a positive correlation between the ALPS-index and MMSE score (partial r = 0.435; p < 0.001), but there was no significant correlation between the ALPS-index and amyloid SUVRs (all, p > 0.05). Decreased glymphatic flow measured by DTI-ALPS in AD may serve as a marker of neurodegeneration correlating with structural atrophy and cognitive decline.

Brain functional magnetic resonance imaging in ICU patients who developed delirium

ObjectiveTo detect brain alterations in intensive care unit (ICU) patients who develop delirium using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) and to determine their predictive value.MethodsFifty-two patients who were admitted to the ICU of the Affiliated Hospital of Zunyi Medical University between June 2021 and June 2022 were enrolled. Fifteen patients who were diagnosed with delirium by the Intensive Care Delirium Screening Checklist (ICDSC) after MRI were selected as the delirium group, and 15 healthy volunteers who were examined during the same period served as the control group. Both groups underwent fMRI and DTI. Quantitative fMRI and DTI data were compared between the two groups to detect abnormal structural and functional brain damage. The relationships between MRI outliers and clinical indicators in the delirium group were also assessed.ResultsDemographic characteristics and imaging indicators before delirium were not correlated with ICDSC scores after delirium. Compared with the healthy control group, the delirium group had significantly lower regional homogeneity (ReHo) values in the left caudate nucleus and frontal lobe on fMRI. The amplitude of the low-frequency fluctuations (ALFF) values of the delirium group were significantly increased in the hippocampus but significantly decreased in the frontal lobe. Compared with the healthy control group, the delirium group showed reduced mean diffusivity (MD) values, mainly in the right cerebellum and right middle temporal gyrus; reduced radial diffusivity (RD) values, mainly in the anterior cerebellum and right middle temporal gyrus; reduced fractional anisotropy (FA) values, only in the corpus callosum; and reduced axial diffusivity (AD) values, mainly in the anterior cerebellar lobe, right middle temporal gyrus, and left middle frontal gyrus on DTI. The statistical thresholds for quantitative DTI measurements were p &amp;lt; 0.005 at the voxel level and a cluster size &amp;gt; 5.ConclusionAbnormal resting-state brain activity in the left superior frontal gyrus and structural changes in the frontal lobe, temporal lobe, corpus callosum, hippocampus, and cerebellum were observed in ICU patients who developed delirium during hospitalization. Early-brain fMRI and DTI examinations are recommended for the prediction of delirium according to unique quantitative indicators to facilitate early intervention for critically ill patients, reduce the length of hospital stay, and improve patient prognosis.

Altered topological structure of the brain white matter in maltreated children through topological data analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Diffusion tensor imaging reveals subclinical alterations in muscles of patients with Becker muscular dystrophy.

Becker muscular dystrophy (BMD) is a relatively less investigated neuromuscular disease, partially overlapping the phenotype of Duchenne dystrophy (DMD). Physiopathological and anatomical patterns are still not comprehensively known, despite recent effort in the search of early biomarkers. Aim of this study was to selectively compare normal appearing muscles of BMD with healthy controls. Among a pool of 40 BMD patients and 20 healthy controls, Sartorius and gracilis muscles were selected on the basis of a blinded clinical quantitative/qualitative evaluation, if classified as normal (0 or 1 on Mercuri scale) and subsequently segmented on diffusion tensor MRI scans with a tractographic approach. Diffusion derived parameters were extracted. Non-parametric testing revealed significant differences between normal and normal appearing BMD derived parameters in both muscles, the difference being more evident in sartorius. Bonferroni-corrected P-values (<.05) of Mann-Whitney test could discriminate between BMD and controls for standard deviation of all diffusion parameters (mean diffusivity, fractional anisotropy, axial and radial diffusivity) in both sartorius and gracilis, while in sartorius the significant difference was found also in the average values of the same parameters (with exception of RD). This method could identify microstructural alterations in BMD normal appearing sartorius and gracilis. Diffusion based MRI could be able to identify possible early or subclinical microstructural alterations in dystrophic patients with BMD.

Microstructure white matter changes and serum GFAP among different phenotypes of multiple sclerosis

Background. Multiple sclerosis (MS) is an autoimmune disease that targets the central nervous system (CNS). A patient’s degree of demyelination and axonal degeneration can only be roughly estimated based on clinical symptoms, neurochemical analysis or standard clinical MRI. Magnetic resonance diffusion tensor imaging (DTI) may provide more information on MS pathology than T1- and T2-weighted MRI alone. Glial fibrillary acidic protein (GFAP) uniquely found in astrocytes in the (CNS), non-myelinating Schwann cells in the peripheral nervous system (PNS), and enteric glial cells. GFAP is postulated to be a biomarker of astrocytic damage and reactive astrogliosis. Methods. A total of 60 patients with MS was categorized into three equal groups according to The Multiple Sclerosis Progression Discussion Tool (MSProDiscuss tool): RRMS, SPMS, RRMS with high risk to become SPMS and 20 healthy controls. Baseline clinical characteristics and detailed medical and neurological history were taken into consideration, as well as time of onset of MS, delay in diagnosis, initial symptoms, relapses features and behavior, EDSS and disease modifying therapy. They were subjected to DTI-MRI and blood sampling for GFAP. Results. DTI was able to differentiate between different MS phenotypes and was able to detect progression when we evaluated DTI changes in NAWM in different brain areas as low FA and high MD were associated with progression and increasing disability (p value =0.001). Serum GFAP differs significantly between patients with SPMS or patients in transition, also, it was higher in patients in transition than RRMS or control group (P value &lt;0.001). There was a significant correlation between serum GFAP and DTI changes in NAWM as higher titres of GFAP were associated with lower FA and higher MD values in NAWM of frontal, temporal lobes, and CC body. Conclusion. Serum GFAP in addition to DTI measurable microdamage in NAWM can give us a wide scope of view about potential progression in MS pathology and related astrocytopathy.

The white matter characteristic of the genu of corpus callosum coupled with pain intensity and negative emotion scores in patients with trigeminal neuralgia: a multivariate analysis.

Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder that not only causes intense pain but also affects the psychological health of patients. Since TN pain intensity and negative emotion may be grounded in our own pain experiences, they exhibit huge inter-individual differences. This study investigates the effect of inter-individual differences in pain intensity and negative emotion on brain structure in patients with TN and the possible pathophysiology mechanism underlying this disease. T1 weighted magnetic resonance imaging and diffusion tensor imaging scans were obtained in 46 patients with TN and 35 healthy controls. All patients with TN underwent pain-related and emotion-related questionnaires. Voxel-based morphometry and regional white matter diffusion property analysis were used to investigate whole brain grey and white matter quantitatively. Innovatively employing partial least squares correlation analysis to explore the relationship among pain intensity, negative emotion and brain microstructure in patients with TN. Significant difference in white matter integrity were identified in patients with TN compared to the healthy controls group; The most correlation brain region in the partial least squares correlation analysis was the genus of the corpus callosum, which was negatively associated with both pain intensity and negative emotion. The genu of corpus callosum plays an important role in the cognition of pain perception, the generation and conduction of negative emotions in patients with TN. These findings may deepen our understanding of the pathophysiology of TN.

Association of Blood Pressure With Brain White Matter Microstructural Integrity Assessed With MRI Diffusion Tensor Imaging in Healthy Young Adults.

High blood pressure (BP) in middle-aged and older adults is associated with a brain white matter (WM) microstructural abnormality. However, little evidence is available in healthy young adults. We investigated the associations between high BP and WM microstructural integrity in young adults. This study included 1015 healthy young adults (542 female, 22-37 years) from the Human Connectome Project. Brachial systolic and diastolic BP were measured using a semiautomatic or manual sphygmomanometer. Diffusion-weighted magnetic resonance imaging was acquired to obtain diffusion tensor imaging metrics of free water (FW) content, FW-corrected WM fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity. Using whole-brain voxel-wise linear regression models and ANCOVA, we examined associations of BP and hypertension stage with diffusion tensor imaging metrics after adjusting for age, sex, education, body mass index, smoking status, alcohol consumption history, and differences in the b value used for diffusion magnetic resonance imaging. Systolic and diastolic BP of the sample (mean±SD) were 122.8±13.0 and 76.0±9.9 mm Hg, respectively. Associations of BP with diffusion tensor imaging metrics revealed regional heterogeneity for FW-corrected fractional anisotropy. High BP and high hypertension stage were associated with higher FW and lower FW-corrected axial diffusivity, FW-corrected radial diffusivity, and FW-corrected mean diffusivity. Moreover, associations of high diastolic BP and hypertension stage with high FW were found only in men not in women. High BP in young adults is associated with altered brain WM microstructural integrity, suggesting that high BP may have damaging effects on brain WM microstructural integrity in early adulthood, particularly in men.

Characterization of anthracycline-induced cardiotoxicity by diffusion tensor magnetic resonance imaging.

Anthracyclines are highly potent anti-cancer drugs, but their clinical use is limited by severe cardiotoxic side effects. The impact of anthracycline-induced cardiotoxicity (AIC) on left ventricular (LV) microarchitecture and diffusion properties remains unknown. This study sought to characterize AIC by cardiovascular magnetic resonance diffusion tensor imaging (DTI). Mice were treated with Doxorubicin (DOX; n = 16) for induction of AIC or saline as corresponding control (n = 15). Cardiac function was assessed via echocardiography at the end of the study period. Whole hearts (n = 8 per group) were scanned ex vivo by high-resolution DTI at 7T. Results were correlated with histopathology and mass spectrometry imaging. Mice with AIC demonstrated systolic dysfunction (LVEF 52 ± 3% vs. 43 ± 6%, P < 0.001), impaired global longitudinal strain (-19.6 ± 2.0% vs. -16.6 ± 3.0%, P < 0.01), and cardiac atrophy (LV mass index [mg/mm], 4.3 ± 0.1 vs. 3.6 ± 0.2, P < 0.01). Regional sheetlet angles were significantly lower in AIC, whereas helix angle and relative helicity remained unchanged. In AIC, fractional anisotropy was increased (0.12 ± 0.01 vs. 0.14 ± 0.02, P < 0.05). DOX-treated mice displayed higher planar and less spherical anisotropy (CPlanar 0.07 ± 0.01 vs. 0.09 ± 0.01, P < 0.01; CSpherical 0.89 ± 0.01 vs. 0.87 ± 0.02, P < 0.05). CPlanar and CSpherical yielded good discriminatory power to distinguish between mice with and without AIC (c-index 0.91 and 0.84, respectively, P for both <0.05). AIC is associated with regional changes in sheetlet angle but no major abnormalities of global LV microarchitecture. The geometric shape of the diffusion tensor is altered in AIC. DTI may provide a new tool for myocardial characterization in patients with AIC, which warrants future clinical studies to evaluate its diagnostic utility.

Cognitive Impairment Is Related to Glymphatic System Dysfunction in Pediatric Multiple Sclerosis.

The aim of this study was to investigate whether, compared to pediatric healthy controls (HCs), the glymphatic system is impaired in pediatric multiple sclerosis (MS) patients according to their cognitive status, and to assess its association with clinical disability and MRI measures of brain structural damage. Sixty-five pediatric MS patients (females = 62%; median age = 15.5 [interquartile range, IQR = 14.5;17.0] years) and 23 age- and sex-matched HCs (females = 44%; median age = 14.1 [IQR = 11.8;16.2] years) underwent neurological, neuropsychological and 3.0 Tesla MRI assessment, including conventional and diffusion tensor imaging (DTI). We calculated the diffusion along the perivascular space (DTI-ALPS) index, a proxy of glymphatic function. Cognitive impairment (Co-I) was defined as impairment in at least 2 cognitive domains. No significant differences in DTI-ALPS index were found between HCs and cognitively preserved (Co-P) pediatric MS patients (estimated mean difference [EMD] = -0.002 [95% confidence interval = -0.069; 0.065], FDR-p = 0.956). Compared to HCs and Co-P patients, Co-I pediatric MS patients (n = 20) showed significantly lower DTI-ALPS index (EMD = -0.136 [95% confidence interval = -0.214; -0.058], FDR-p ≤ 0.004). In HCs, no associations were observed between DTI-ALPS index and normalized brain, cortical and thalamic volumes, and normal-appearing white matter (NAWM) fractional anisotropy (FA) and mean diffusivity (MD) (FDR-p ≥ 0.348). In pediatric MS patients, higher brain WM lesion volume (LV), higher NAWM MD, lower normalized thalamic volume, and lower NAWM FA were associated with lower DTI-ALPS index (FDR-p ≤ 0.016). Random Forest selected lower DTI-ALPS index (relative importance [RI] = 100%), higher brain WM LV (RI = 59.5%) NAWM MD (RI = 57.1%) and intelligence quotient (RI = 51.3%) as informative predictors of cognitive impairment (out-of-bag area under the curve = 0.762). Glymphatic system dysfunction occurs in pediatric MS, is associated with brain focal lesions, irreversible tissue loss accumulation and cognitive impairment. ANN NEUROL 2024.

Quantitative MRI assessment of peripheral nerves

Quantitative MRI assessment of peripheral nerves

Structural and functional correlates of disability, motor and cognitive performances in multiple sclerosis: Focus on the globus pallidus

ObjectivesTo explore structural and functional alterations of external (GPe) and internal (GPi) globus pallidus in people with multiple sclerosis (pwMS) compared to healthy controls (HC) and analyze their relationship with measures of clinical disability, motor and cognitive impairment. MethodsSixty pwMS and 30 HC comparable for age and sex underwent 3.0T MRI, including conventional, diffusion tensor MRI and resting state (RS) functional MRI. Expanded Disability Status Scale (EDSS) scores were rated and timed 25-foot walk (T25FW) test, nine-hole peg test (9HPT), and paced auditory serial addition test (PASAT) were administered. Two operators segmented the GP into GPe and GPi. Volumes, T1/T2 ratio, diffusivity indices and seed-based RS functional connectivity (FC) of the GP and its components were assessed. ResultsPwMS had no atrophy or altered diffusivity measures of the GP. Compared to HC, pwMS had higher T1/T2 ratio in both GP regions, which correlated with EDSS score (r = 0.26–0.39, p = 0.01–0.05). RS FC analysis highlighted component-specific functional alterations in pwMS: the GPe had decreased RS FC with fronto-parietal cortices, whereas the GPi had decreased intra-GP RS FC and increased RS FC with the thalamus. Worse EDSS, 9HPT, T25FW and PASAT scores were associated with GP RS FC modifications (r=-0.51‒0.51, p < 0.001). ConclusionsStructural GP involvement in MS was homogeneous across its portions. Increased T1/T2 ratio values, possibly representing iron accumulation, were related to more severe disability. RS FC alterations of the GPe and GPi were consistent with their roles within the basal ganglia network and correlated with worse functional status, suggesting less efficient communication between structures.

Exploiting macro- and micro-structural brain changes for improved Parkinson’s disease classification from MRI data

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Machine learning based on functional and structural connectivity in mild cognitive impairment

ObjectiveAlzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cognitive decline and mental behavioral abnormalities. Mild cognitive impairment (MCI) is regarded as a transitional stage in the progression from normal elderly individuals to patients with AD. While studies have identified abnormalities in brain connectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challenging. We hypothesized that utilizing machine learning (ML) based on both functional and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valuable information for early diagnosis and precise evaluation of patients with MCI. MethodsFollowing clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects underwent examinations for resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support vector machine for classification of the patient and control groups. ResultsWe observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional connectivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connectivity, we found that decreased fractional anisotropy in patients with MCI was significantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results using the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivity features resulted in a classification accuracy and AUC value of 93.75% and 0.99, respectively. ConclusionsIn this study, we demonstrated a high classification performance, underscoring the potential of both brain functional and structural connectivity in distinguishing patients with MCI from normal elderly individuals. Furthermore, the integration of functional connectivity and structural connectivity features indicated that utilizing rs-fMRI and DTI could enhance the accuracy and specificity of identifying patients with MCI compared with relying on a single neuroimaging technique.

Structural Alterations of the Corpus Callosum in Children With Infantile Hydrocephalus.

This study investigates structural alterations of the corpus callosum in children diagnosed with infantile hydrocephalus. We aim to assess both macrostructural (volume) and microstructural (diffusion tensor imaging metrics) facets of the corpus callosum, providing insights into the nature and extent of alterations associated with this condition. Eighteen patients with infantile hydrocephalus (mean age = 9 years) and 18 age- and sex-matched typically developing healthy children participated in the study. Structural magnetic resonance imaging and diffusion tensor imaging were used to assess corpus callosum volume and microstructure, respectively. Our findings reveal significant alterations in corpus callosum volume, particularly in the posterior area, as well as distinct microstructural disparities, notably pronounced in these same segments. These results highlight the intricate interplay between macrostructural and microstructural aspects in understanding the impact of infantile hydrocephalus. Examining these structural alterations provides an understanding into the mechanisms underlying the effects of infantile hydrocephalus on corpus callosum integrity, given its pivotal role in interhemispheric communication. This knowledge offers a more nuanced perspective on neurologic disorders and underscores the significance of investigating the corpus callosum's health in such contexts.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

Human transcriptome array analysis and diffusion tensor imaging in attention-deficit/hyperactivity disorder

The mRNA markers identified using microarray assay and diffusion tensor magnetic resonance imaging (DTI) were applied to elucidate the pathophysiology of attention-deficit hyperactivity disorder (ADHD). First, we obtained total RNA from leukocytes from three children with ADHD and three healthy controls for analysis with microarray assays. Subsequently, we applied real-time quantitative polymerase chain reaction (qRT‒PCR) assays to validate the differential expression of 7 genes (COX7B, CYCS, TFAM, UTP14A, ZNF280C, IFT57 and NDUFB5) between 130 ADHD patients and 70 controls, and we built an ADHD prediction model based on the ΔCt values of aforementioned seven genes (AUROC = 0.98). Finally, in a validation group (28 patients with ADHD and 27 healthy controls), mRNA expression of the above seven genes also significantly differentiated ADHD patients from controls (AUROC value = 0.91). The DTI analysis showed increased fractional anisotropy (FA) of the forceps minor, superior corona radiata, posterior corona radiata and anterior corona radiata in ADHD patients. Moreover, the FA of the right superior corona radiata tract was positively correlated with ΔCt levels of the COX7B gene and the IFT57 gene. The results shed a new light on a genetic profile of ADHD that may help in deciphering the white matter microstructural features in disease pathogenesis.

Investigation of the effects of Gamma Knife radiosurgery on optic pathways using diffusion tensor MRI within the first year after treatment.

To investigate the radiation-induced effects of Gamma Knife radiosurgery (GKRS) for sellar-parasellar tumors on optic pathways using DTI parameters within the first year after treatment. Twenty-five patients with sellar-parasellar tumors underwent MRI before and 3months after GKRS, including T1WI, DTI, T2WI. Moreover, 21 patients underwent follow-up DTI 6-8months after radiosurgery. ROIs were set on optic nerves, optic radiations, and control localizations; DTI parameters for each were calculated. Pre- and post-radiosurgery differences in DTI values were statistically compared and assessed with respect to tumor size changes. Following GKRS, DTI parameters, notably ADC, FA, and RD, showed statistically significant changes in optic nerves and anterior optic radiations. DTI changes were more significant in the group of cases with tumor shrinkage. In this group, DTI of the anterior optic radiations further deteriorated 3months post-GKRS, whereas acute changes in DTI parameters of the optic nerves resolved within 6-8months. DTI of central and posterior optic radiations did not differ significantly following radiosurgery; 6-8months after radiosurgery, visual function was stable in 14 (56%) patients and improved in 11 (44%), showing no correlation with tumor size changes or DTI parameters. White Matter (WM) injury in the optic pathways can be induced by Gamma Knife radiosurgery targeted to sellar and parasellar tumors. Following GKRS, microstructural abnormalities occurred in the optic radiations as well as the optic nerves within the first post-treatment year. Our findings could support modifications to radiosurgical treatment strategies to minimize the risk of permanent WM injury.

Hidden Markov Model based Predicting of Alzheimer’s Disease with graph cut segmentation using MR Diffusion Tensor Imaging (DTI)

Alzheimer’s disease (AD) is the predominant aetiology of dementia among the elderly population, accounting for about 60–70% of all instances of cognitive decline. Diffusion tensor imaging (DTI) is a contemporary methodology that enables the cartography of alterations in the microstructure of white matter (WM) in neurological diseases. Nevertheless, the effort of analysing substantial amounts of medical pictures poses significant challenges, prompting researchers to shift their focus towards machine learning. This approach encompasses a collection of computer algorithms that possess the ability to autonomously adjust their output to align with the desired goal. This work proposed the use of a combined approach using Hidden Markov Model (HMM) and MR-DTI, where Diffusion Tensor Imaging (DTI) is employed as a magnetic resonance imaging technique. The purpose of this method is to forecast the occurrence of AD. Furthermore, the statistical analysis demonstrated a significant correlation between microstructural WM changes with both output in the patient groups and cognitive functioning. This finding suggests that these abnormalities in WM might potentially serve as a biomarker for AD. The proposed method is named as Graphcut Hidden MorkovModel (Graph_HMM) is evaluated on ADNI database with statistical analysis and found that it achieves 99.8% of accuracy, 96.4% of sensitivity, 97.4% of specificity and 12.3% of MSE.

Diagnostic value of Video Electroencephalography combined with Magnetic Resonance Imaging-diffusion tensor imaging in epilepsy.

To explore the diagnostic value of video electroencephalography (VEEG) combined with magnetic resonance imaging diffusion tensor imaging (MRI-DTI) in epilepsy. In this retrospective observational study, clinical data of 60 patients who underwent both VEEG and MRI examinations at the Neurosurgery Department of Quzhou People's Hospital from December 2020 to March 2023 were retrospectively reviewed, and a total of 55 patients were finally included. We evaluated the diagnostic value of combining VEEG and MRI to determine seizure type, location of epileptic focus, and structural abnormalities of brain tissue, using surgical and pathological results as the gold standard. The accuracy of the combined approach for determining the seizure type was 98.18%, which was higher than the accuracy of MRI (85.45%, P<0.05) or VEEG (81.82%, P<0.05) alone. The accuracy of joint examination for lesion location was 100.00%, which was higher than those of MRI (85.45%, P<0.05) or VEEG (83.64%, P<0.05) alone. Similar abnormal brain tissue structure detection rates was found for both MRI and VEEG (P>0.05). The application of MRI-DTI combined with VEEG to diagnose patients with epilepsy allows for the identification of abnormal structural changes in brain and the location of lesions. Combining both approaches can improve the diagnostic accuracy of each technique alone and provide a reference for the formulation and adjustment of disease management plans.