Diffusion Tensor Imaging Data Research Articles

Peak width of skeletonized mean diffusivity: a novel biomarker for white matter damage in spinocerebellar ataxia type 2.

Peak width of skeletonized mean diffusivity (PSMD) is a robust and fully automated imaging marker employed to detect microstructural damage in white matter. This study aimed to evaluate whether PSMD reflects the severity of white matter damage and tracks disease progression in patients with spinocerebellar ataxia type 2 (SCA2). Nine patients with SCA2 and sixteen age- and gender-matched healthy controls were enrolled. Clinical and imaging data were collected at baseline and after 3.5years. Each participant underwent MRI scans twice to obtain diffusion tensor imaging data, from which PSMD were automatically calculated. Differences in PSMD between SCA2 patients and healthy controls were analyzed using a linear mixed model. Additionally, Spearman's rank correlations were employed to assess associations between PSMD values and clinical variables. Patients with SCA2 exhibited higher PSMD values at baseline and follow-up compared to HCs, indicating more severe white matter damage. Longitudinal data revealed a continual increase in PSMD values in SCA2 patients over time. The mixed-effects model confirmed significant differences in PSMD values between the two groups, as well as an interaction effect suggesting different progression rates. These findings suggest that SCA2 associates with progressive deterioration of white matter. No significant correlations were observed between PSMD values and clinical variables in this study. This study underscores the potential of PSMD as a neuroimaging biomarker for detecting microstructural white matter damage and monitoring disease progression in patients with SCA2.

Corpus callosum structure and auditory interhemispheric transfer in spina bifida myelomeningocele.

Maldevelopment of the posterior corpus callosum is common in spina bifida myelomeningocele (SBM) due to hydrocephalus-related hypoplasia and congenital partial hypogenesis. This study examined the relations of macro- and microstructural integrity of the interhemispheric temporal tract in SBM and auditory interhemispheric transfer using consonant-vowel dichotic listening. We collected T₁-weighted and diffusion tensor imaging data from 46 individuals with SBM and 15 typically developing individuals. Probabilistic tractography was used to isolate the interhemispheric white matter connecting auditory processing regions in both hemispheres. Interhemispheric transfer was assessed with a dichotic listening task. Although the typically developing group and the group with SBM showed the normative right-ear advantage, fewer participants showed a right-ear advantage in the group with SBM. The absence of the right-ear advantage was largely in the subgroup with hypogenesis of the splenium or severe posterior hypoplasia. Sex, anterior commissure cross-sectional area, and number of shunt pathways visible on magnetic resonance imaging predicted right-ear superiority. Interhemispheric transfer is disrupted in individuals with SBM and hypogenesis or severe hypoplasia of the posterior corpus callosum. Preservation of interhemispheric transfer is related to expected connections through the posterior corpus callosum and possibly compensatory pathways in the anterior commissure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Association between allostatic load and accelerated white matter brain aging: findings from the UK biobank.

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29years in association analysis and by 0.33years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

Early Improvement in Interstitial Fluid Flow in Patients With Severe Carotid Stenosis After Angioplasty and Stenting.

This study aimed to investigate early changes in interstitial fluid (ISF) flow in patients with severe carotid stenosis after carotid angioplasty and stenting (CAS). We prospectively recruited participants with carotid stenosis ≥80% undergoing CAS at our institute between October 2019 and March 2023. Magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), and the Mini-Mental State Examination (MMSE) were performed 3 days before CAS. MRI with DTI and MMSE were conducted within 24 hours and 2 months after CAS, respectively. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated from the DTI data to determine the ISF status. Increments were defined as the ratio of the difference between post- and preprocedural values to preprocedural values. In total, 102 participants (age: 67.1±8.9 years; stenosis: 89.5%±5.7%) with longitudinal data were evaluated. The DTI-ALPS index increased after CAS (0.85±0.15; 0.85 [0.22] vs. 0.86±0.14; 0.86 [0.21]; P=0.022), as did the MMSE score (25.9±3.7; 24.0 [4.0] vs. 26.9±3.4; 26.0 [3.0]; P<0.001). Positive correlations between increments in the DTI-ALPS index and MMSE score were found in all patients (rs=0.468; P<0.001). An increased 24-hour post-CAS DTI-ALPS index suggests early improvement in ISF flow efficiency. The positive correlation between the 24-hour DTI-ALPS index and 2-month MMSE score increments suggests that early ISF flow improvement may contribute to long-term cognitive improvement after CAS.

Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study.

Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals. In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors. We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging. The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases.

Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach.

Although the involvement of progressive brain alterations in epilepsy was recently suggested, individual patients' trajectories of white matter (WM) disruption are not known. We investigated the disease progression patterns of WM damage and its associations with clinical metrics. We examined the cross-sectional diffusion tensor imaging (DTI) data of 155 patients with unilateral temporal lobe epilepsy (TLE) and 270 age/gender-matched healthy controls, and we then calculated the average fractional anisotropy (FA) values within 20 WM tracts of the whole brain. We used the Subtype and Stage Inference (SuStaIn) program to detect the progression trajectory of FA changes and investigated its association with clinical parameters including onset age, disease duration, drug-responsiveness, and the number of anti-seizure medications (ASMs). The SuStaIn algorithm identified a single subtype model in which the initial damage occurs in the ipsilateral uncinate fasciculus (UF), followed by damage in the forceps, superior longitudinal fasciculus (SLF), and anterior thalamic radiation (ATR). This pattern was replicated when analyzing TLE with hippocampal sclerosis (n = 50) and TLE with no lesions (n = 105) separately. Further-progressed stages were associated with longer disease duration (p < 0.001) and a greater number of ASMs (p = 0.001). the disease progression model based on WM tracts may be useful as a novel individual-level biomarker.

Structural-functional connectomics in major depressive disorder following aiTBS treatment

Major depressive disorder (MDD) has been associated with changes in the structural (SC) and functional connectivity (FC) of the brain. This study investigated the effects of accelerated intermittent theta burst stimulation (aiTBS) on SC-FC coupling and graph theory measures, focusing on the association between baseline SC-FC coupling of the dorsolateral prefrontal cortex (dlPFC) and clinical improvement. In a randomized, sham-controlled, quadruple-blind, crossover study, aiTBS was delivered to the left dlPFC of depressed patients with MDD, and diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were acquired. In 77 MDD patients, significantly increased whole-brain SC-FC coupling was observed, primarily driven by default mode network (DMN) SC-FC coupling, along with increased somatomotor network FC, and decreased FC between the DMN hubs and limbic regions after active aiTBS. Furthermore, significant increases were observed in structural global and local efficiency measures that were not specific to the stimulation condition (active/sham aiTBS). However, these changes did not significantly correlate with clinical improvement. Notably, baseline SC-FC coupling of the left dlPFC was a significant predictor of clinical improvement. Our findings highlight the potential of left dlPFC SC-FC coupling as a predictor of aiTBS treatment outcomes, as well as the effect of aiTBS in enhancing SC-FC coupling.

Cognitive impairment mediates the white matter injury load and gait disorders in subcortical ischemic vascular disease.

Gait disorders are common in patients with subcortical ischemic vascular disease (SIVD). We aim to explore the impact of white matter (WM) damage on gait disorders in SIVD. 21 SIVD patients and 20 normal controls (NC) were included in the study. Montreal Cognitive Assessment (MoCA) was used to evaluate general cognition, while Speed-Accuracy Trade-Off (SAT) was used to assess executive function. Gait velocity, cadence, and stride length were measured. Diffusion Tensor Imaging (DTI) data were analyzed using Tract-Based Spatial Statistics (TBSS) and Peak Width of Skeletonized Mean Diffusivity (PSMD). The relationships among WM damage, gait disorders, and cognitive function were examined through mediation analysis. SIVD scored lower than NC in MoCA and SAT tests (P < 0.001). Gait velocity and stride length were decreased in SIVD. SIVD had lower PSMD (P < 0.001). PSMD correlated with gait parameters, which were totally mediated by MoCA and partially mediated by SAT. The fractional anisotropy (FA) and mean diffusivity (MD) of the genu of the corpus callosum (GCC) and body of CC (BCC) were correlated with gait parameters. The FA of the bilateral anterior corona radiata (ACR) was positively correlated with gait parameters, while the MD of the bilateral superior corona radiata (SCR), bilateral superior longitudinal fasciculus (SLF), and left external capsule (EC) were negatively correlated with them (P < 0.05). Gait impairments in SIVD were associated with cognitive deficits. Cognitive impairment mediated the WM damage and gait disorders. The microstructural alterations of CC, SLF, EC, and CR may be related to changes in gait.

Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks

【Purpose】Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation. 【Methods】We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated. 【Results】Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (p < 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory). 【Conclusions】Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.

Diffusion Tensor Imaging in Boys With Adrenoleukodystrophy: Identification of Cerebral Disease and Association With Neurocognitive Outcomes.

Childhood cerebral adrenoleukodystrophy (C-ALD) is a severe inflammatory demyelinating disease that must be treated at an early stage to prevent permanent brain injury and neurocognitive decline. In standard clinical practice, C-ALD lesions are detected and characterized by a neuroradiologist reviewing anatomical MRI scans. We aimed to assess whether diffusion tensor imaging (DTI) is sensitive to the presence and severity of C-ALD lesions and to investigate associations with neurocognitive outcomes after hematopoietic cell therapy (HCT). In this retrospective cohort study, we analyzed high-resolution anatomical MRI, DTI, and neurocognitive assessments from boys with C-ALD undergoing HCT at the University of Minnesota between 2011 and 2021. Longitudinal DTI data were compared with an age-matched group of boys with ALD and no lesion (NL-ALD). DTI metrics were obtained for atlas-based regions of interest (ROIs) within 3 subdivisions of the corpus callosum (CC), corticospinal tract (CST), and total white matter (WM). Between-group baseline and slope differences in fractional anisotropy (FA) and axial (AD), radial (RD), and mean (MD) diffusivities were compared using analysis of covariance accounting for age, MRI severity (Loes score), and lesion location. Among patients with NL-ALD (n = 14), stable or increasing FA, stable AD, and stable or decreasing RD and MD were generally observed during the 1-year study period across all ROIs. In comparison, patients with mild posterior lesions (Loes 1-2; n = 13) demonstrated lower baseline FA in the CC splenium (C-ALD 0.50 ± 0.08 vs NL-ALD 0.58 ± 0.04; pBH = 0.022 adjusted Benjamini-Hochberg p-value), lower baseline AD across ROIs (e.g., C-ALD 1.34 ± 0.03 ×10-9 m2/s in total WM vs NL-ALD 1.38 ± 0.04 ×10-9 m2/s; pBH = 0.005), lower baseline RD in CC body and CST, and lower baseline MD across ROIs except CC splenium. Longitudinal slopes in CC splenium showed high sensitivity and specificity in differentiating early C-ALD from NL-ALD. Among all patients with C-ALD (n = 38), baseline Loes scores and DTI metrics were associated with post-HCT neurocognitive functions, including processing speed (e.g., FA WM Spearman correlation coefficient R = 0.64) and visual-motor integration (e.g., FA WM R = 0.71). DTI was sensitive to lesion presence and severity as well as clinical neurocognitive effects of C-ALD. DTI metrics quantify C-ALD even at an early stage.

Characterizing patterns of diffusion tensor imaging variance in aging brains.

As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( ) to FA variance in the cuneus and occipital gyrus, but negatively ( ) in the caudate nucleus. Males show significantly ( ) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( ) with a decrease in FA variance. Head motion increases during the rescan of DTI ( millimeters per volume). The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

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

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

Cortical microstructure and hemispheric specialization-A diffusion-imaging analysis in younger and older adults.

Characterizing cortical plasticity becomes increasingly important for identifying compensatory mechanisms and structural reserve in the ageing population. While cortical thickness (CT) largely contributed to systems neuroscience, it incompletely informs about the underlying neuroplastic pathophysiology. In turn, microstructural characteristics may correspond to atrophy mechanisms in a more sensitive way. Fractional anisotropy, a diffusion tensor imaging (DTI) measure, is inversely related to cortical histologic complexity. Axial diffusivity and radial diffusivity are assumed to be linked to the density of structures oriented perpendicular and parallel to the cortical surface, respectively. We hypothesized (1) that cortical DTI will reveal microstructural correlates for hemispheric specialization, particularly in the language and motor systems, and (2) that lateralization of cortical DTI parameters will show an age effect, paralleling age-related changes in activation, especially in the prefrontal cortex. We analysed data from healthy younger and older adult participants (N = 91). DTI and CT data were extracted from regions of the Destrieux atlas. Diffusion measures showed lateralization in specialized motor, language, visual, auditory and inferior parietal cortices. Age-dependent increased lateralization for DTI measures was observed in the prefrontal, angular, superior temporal and lateral occipital cortex. CT did not show any age-dependent alterations in lateralization. Our observations argue that cortical DTI can capture microstructural properties associated with functional specialization, resembling findings from histology. Age effects on diffusion measures in the integrative prefrontal and parietal areas may shed novel light on the atrophy-related plasticity in healthy ageing.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

White matter brain-age in diverse forms of epilepsy and interictal psychosis

Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of special importance. We focused on white matter brain aging in diverse forms of epilepsy and comorbid psychosis. We obtained brain diffusion tensor imaging (DTI) data at 3 T-MRI in 257 patients with epilepsy and 429 healthy subjects. The tract-based fractional anisotropy values of the healthy subjects were used to build a brain-age prediction model, and we calculated the brain-predicted age difference (brain-PAD: predicted age—chronological age) of all subjects. As a result, almost all epilepsy categories showed significantly increased brain-PAD (p < 0.001), including temporal lobe epilepsy (TLE) with no MRI-lesion (+ 4.2 yr), TLE with hippocampal sclerosis (+ 9.1 yr), extratemporal focal epilepsy (+ 5.1 yr), epileptic encephalopathy or progressive myoclonus epilepsy (+ 18.4 yr), except for idiopathic generalized epilepsy (IGE). Patients with psychogenic non-epileptic seizures also presented increased brain-PAD. In TLE, interictal psychosis significantly raised brain-PAD by 8.7 years. In conclusion, we observed increased brain aging in most types of epilepsy, which was generally consistent with brain morphological aging results in previous studies. Psychosis may accelerate brain aging in TLE. These findings may suggest abnormal aging mechanisms in epilepsy and comorbid psychotic symptoms.

An integration of neuroimaging and serum proteomics analysis suggests immune and inflammation are associated with white matter microstructure changes in cerebral small vessel disease with depressive symptoms

IntroductionDepressive symptoms are a common concomitant of cerebral small vessel disease (CSVD), of which pathogenesis requires more study. White matter microstructural abnormalities and proteomic alternation have been widely reported regarding depression in the elderly with CSVD. Exploring the relationship between cerebral white matter microstructural alterations and serum proteins may complete the explanation of molecular mechanisms for the findings from neuroimaging research of CSVD combined with depressive symptoms. MethodsAn untargeted proteomics approach based on mass spectrometry was used to obtain serum proteomic profiles, which were clustered into co-expression protein modules. White matter microstructural integrity was measured using the FMRIB Software Library (FSL) and MATLAB to analyze diffusion tensor imaging (DTI) data and calculate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for 50 regions of interest (ROI). Integrating the proteome with the DTI results, weighted gene co-expression analysis (WGCNA) was used to identify protein modules related to white matter microstructural alterations, and the proteins of the corresponding modules were analyzed for functional enrichment through bioinformatics techniques. ResultsDTI measurements were analCerebral small vessel disease (CSVD); Depression; Diffusion tensor imaging (DTI); Proteomics; Inflammationyzed between individuals with CSVD and depressive symptoms (CSVD+D) (n = 24) and those without depressive symptoms (CSVD-D) (n = 35). Results showed an overall increase in MD, AD, and RD within the left hemisphere of the CSVD+D group, suggesting widespread loss of white matter integrity and axonal demyelination, including left superior longitudinal fasciculus (SLF), left posterior corona radiata (PCR) and right external capsule (EC). We identified two protein modules associated with DTI diffusivity, and functional enrichment analyses revealed that complement and coagulation cascades and immune responses participate in the alternation of white matter microstructure in the CSVD+D group. ConclusionThe results suggested immune- and inflammation-related mechanism was associated with white matter microstructure changes in CSVD with depressive symptoms

Probing diffusion of water and metabolites to assess white matter microstructure in Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder caused by the absence of functional dystrophin protein. In addition to muscle, dystrophin is expressed in the brain in both neurons and glial cells. Previous studies have shown altered white matter microstructure in patients with DMD using diffusion tensor imaging (DTI). However, DTI measures the diffusion properties of water, a ubiquitous molecule, making it difficult to unravel the underlying pathology. Diffusion-weighted spectroscopy (DWS) is a complementary technique which measures diffusion properties of cell-specific intracellular metabolites. Here we performed both DWS and DTI measurements to disentangle intra- and extracellular contributions to white matter changes in patients with DMD. Scans were conducted in patients with DMD (15.5 ± 4.6 y/o) and age- and sex-matched healthy controls (16.3 ± 3.3 y/o). DWS measurements were obtained in a volume of interest (VOI) positioned in the left parietal white matter. Apparent diffusion coefficients (ADCs) were calculated for total N-acetylaspartate (tNAA), choline compounds (tCho), and total creatine (tCr). The tNAA/tCr and tCho/tCr ratios were calculated from the non-diffusion-weighted spectrum. Mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and fractional anisotropy of water within the VOI were extracted from DTI measurements. DWS and DTI data from patients with DMD (respectively n= 20 and n= 18) and n= 10 healthy controls were included. No differences in metabolite ADC or in concentration ratios were found between patients with DMD and controls. In contrast, water diffusion (MD, t= -2.727, p= 0.011; RD, t= -2.720, p= 0.011; AD, t= -2.715, p= 0.012) within the VOI was significantly higher in patients compared with healthy controls. Taken together, our study illustrates the potential of combining DTI and DWS to gain a better understanding of microstructural changes and their association with disease mechanisms in a clinical setting.

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging.

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.

Abnormal white matter integrity of anterior-inferior hypothalamus in mild cognitive impairment patients with depression symptoms

BackgroundThe hypothalamus is a key brain structure involved in the pathogenesis of depression, and its abnormal activity is considered an important pathological mechanism for the formation of depression. The presence of abnormalities in the white matter integrity of hypothalamic subregions in mild cognitive impairment with depressive symptoms (D-MCI) remains unknown. MethodsIn this study, we used diffusion tensor imaging (DTI) to explore the white matter integrity of hypothalamic subregions in D-MCI. On a 3 T magnetic resonance imaging scanner, we collected DTI data from 63 subjects. The subjects included 20 healthy controls (HC), 23 MCI patients without depression (nD-MCI), and 20 patients with D-MCI. The differences in DTI metrics of hypothalamic subregions of the three groups were compared using analysis of variance and post hoc t-tests. We looked at the relationship between clinical variables and DTI metrics in hypothalamus subregions using Pearson correlation analysis. ResultsCompared with nD-MCI and HC groups, D-MCI group showed increased fractional anisotropy (FA) in anterior-inferior hypothalamus. There was a weak negative correlation between FA values in the anterior-inferior hypothalamus and depression scores in D-MCI patients. ConclusionsOur findings suggest that depressive symptoms in MCI patients are associated with abnormal white matter integrity in the anterior-inferior hypothalamus.