Whole-brain White Matter Research Articles

Disrupted white matter structural networks in patients with acute ischemic stroke in the right basal ganglia

Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. Graph theoretical analysis was employed to investigate the topological architecture of whole-brain white matter structural networks in 40 AIS patients in the right BG and 40 healthy controls (HC), and network-based statistics (NBS) were applied to examine structural connectivity alterations. Compared to HC, AIS patients exhibited altered global network properties characterized by increased small-worldness, normalized clustering coefficient, and shortest path length, as well as decreased clustering coefficient, local efficiency, and global efficiency. The nodes with significantly decreased nodal properties in AIS patients were primarily located in the default mode network, limbic system, sensorimotor system, salience network, and central executive network. Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.

Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities

Abstract We examined how the connectivity structure of left thalamus, especially thalamo-cortical connections, are reflected in children’s reading performance. Diffusion-weighted MRI at 3T and a series of reading measures were collected from 64 children ages from 8 to 14 years, 33 girls and 31 boys. The topological properties of the left thalamus were computed based on the whole-brain white matter network and a hub-attached reading network, and correlated with reading scores. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Two efficiency measures were negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of efficiency scores. A validation analysis using an independent dataset replicated most of the findings, except for the correlation between local efficiency of the thalamus and rapid automatized naming. Our result highlights the role of the left thalamus and thalamo-cortical network in understanding the neurocognitive bases of skilled reading and dyslexia in children.

Impact of excessive abdominal obesity on brain microstructural abnormality in schizophrenia

Significant evidence links obesity and schizophrenia (SZ), but the brain associations are still largely unclear. 48 people with SZ were divided into two subgroups: patients with lower waist circumference (SZ-LWC: n = 24) and patients with higher waist circumference (SZ-HWC: n = 24). Healthy controls (HC) were included for comparison (HC: n = 27). Using tract-based spatial statistics, we compared fractional anisotropy (FA) of the whole-brain white matter skeleton between these three groups (SZ-LWC, SZ-HWC, HC). Using Free Surfer, we compared whole-brain cortical thickness and the selected subcortical volumes between the three groups. FA of widespread white matter and the mean cortical thickness in the right temporal lobe and insular cortex were significantly lower in the SZ-HWC group than in the HC group. The FA of regional white matter was significantly lower in the SZ-LWC group than in the HC group. There were no significant differences in mean subcortical volumes between the groups. Additionally, the cognitive performances were worse in the SZ-HWC group, who had more severe triglycerides elevation. This study provides evidence for microstructural abnormalities of white matter, cortical thickness and neurocognitive deficits in SZ patients with excessive abdominal obesity.

Associations between white and grey matter damage and gait impairment in cerebral amyloid angiopathy

BackgroundCerebral amyloid angiopathy (CAA) is associated with white matter damage and neurodegeneration. Gait is impaired in CAA; however, the neural basis of this impairment is unclear. Research questionAre gait impairments in patients with CAA associated with altered cerebral white matter diffusivity and/or atrophy of cortical and subcortical grey matter. MethodsParticipants with CAA (n=29), Alzheimer’s disease (AD; n=16), and normal controls (n=47) were included. Gait was assessed using a 6 m walkway with parameters categorized into rhythm, pace, postural control, and variability domains. The dual-task cost (DTC) of gait speed was calculated for counting backwards, animal fluency, and serial sevens tasks. Whole-brain white matter disruption was quantified using the peak width of skeletonized mean diffusivity (PSMD), and thickness and volume of select cortical, subcortical, and cerebellar regions were quantified using FreeSurfer. ResultsIn CAA participants, associations were found between PSMD and pace (standardized parameter estimate (β), 95 % confidence interval (CI): 0.17, 0.03–0.32), and medial orbital frontal cortical thickness and counting backwards DTC (parameter estimate (PE), 95 % CI: −5.7 %/SD, −0.24 to −11.23). Across all groups, including CAA, associations were found between PSMD and pace, variability, counting backwards DTC, and animal fluency DTC; between frontal cortical thickness and pace, counting backwards DTC, and animal fluency DTC; between cortical regions affected by AD (inferior parietal cortex, inferior and middle temporal gyrus) and counting backwards DTC; and between thalamus volume and postural control. SignificanceReduced white matter structural integrity and grey matter loss is associated with poor overall gait performance in CAA, AD, and normal controls.

Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer’s disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0–81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=−0.12, CI=−0.14,−0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =−0.18, CI=−0.20,−0.15, P<0.00001).

A quantitative MRI comparative study of imaging markers for cerebral small vessel disease in the middle-aged and elderly patients with and without hypertension

Objective: To explore the difference of MRI markers of small cerebral vascular disease in middle-aged and elderly patients with hypertension and non-hypertension. Methods: A cross-sectional study. The clinical data of 316 patients who underwent head MRI with susceptibility weighted imaging scans at the Affiliated Zhongda Hospital of Southeast University from November 2013 to August 2022 were retrospectively analyzed, including 190 males and 126 females, with the age of (71.6±8.9)years. According to the history of hypertension, the patients were divided into hypertension group(n=259) and the non-hypertension group(n=57). The patients in the non-hypertension group were further divided into abnormal blood pressure group on admission (n=19) and normal blood pressure group on admission (n=38). The imaging features of different CSVD dimensions in the patient's images were quantified or graded and compared between hypertensive and non-hypertensive patient groups. Deep learning methods were employed to segment white matter lesions, and voxel-wise analysis was conducted to investigate the differences in whole-brain white matter lesion probability between patients in both groups. Spearman correlation analysis was used to analyze the correlation between hypertension and small cerebral vascular disease. Results: Compared with the non-hypertensive group, the cerebral microhemorrhage count, deep microhemorrhage count, basal ganglia level lacunae count, perivascular space (EPVS) grade of hemioval center level and EPVS grade of basal ganglia level were higher in the hypertensive group (all P<0.05). The cerebral microhemorrhage count [3.0(1.0, 15.0) vs 1.0 (0, 4.2)], deep microhemorrhage count [1.0 (0, 7.0) vs 1.0 (0, 4.2)] and EPVS classification at basal ganglium level [2.0(1.0, 3.0) vs 1.0(1.0, 2.0)] in the group with history of hypertension were higher than those in the group with normal blood pressure at admission (all P<0.05). The EPVS grade at the central level of the semiovale in the hypertension group was higher than that of the group with normal blood pressure at admission [2.0(1.0, 2.0) vs 1.0 (1.0, 2.0)], and also higher than that of the group with abnormal blood pressure at admission [2.0(1.0, 2.0) vs 1.0(1.0, 2.0)](both P<0.05). Voxel-by-voxel analysis showed no significant difference in the probability of white matter lesions in the whole brain between patients with and without a history of hypertension, but patients with a history of hypertension showed more extensive para-ventricular white matter hypersignaling than those without a history of hypertension. Spearman correlation analysis showed that hypertension grade was correlated with the number of microbleeding lesions in depth (r=0.149), the number of lacunae lesions in the center of the hemioval (r=0.209), and the number of lacunae lesions in the basal ganglia (r=0.204) (all P<0.05). Conclusions: Chronic hypertension can affect different dimensions of small vessel disease imaging, primarily manifested in the increases of deep microbleed counts and the EPVS grade.

Diffusion Basis Spectrum Imaging of White Matter in Schizophrenia and Bipolar Disorder.

Multiple studies point to the role of neuroinflammation in the pathophysiology of schizophrenia (SCZ), however, there have been few in vivo tools for imaging brain inflammation. Diffusion basis spectrum imaging (DBSI) is an advanced diffusion-based MRI method developed to quantitatively assess microstructural alternations relating to neuroinflammation, axonal fiber, and other white matter (WM) pathologies. We acquired one-hour-long high-directional diffusion MRI data from young control (CON, n = 27), schizophrenia (SCZ, n = 21), and bipolar disorder (BPD, n = 21) participants aged 18-30. We applied Tract-based Spatial Statistics (TBSS) to allow whole-brain WM analyses and compare DBSI-derived isotropic and anisotropic diffusion measures between groups. Clinical relationships of DBSI metrics with clinical symptoms were assessed across SCZ and control participants. In SCZ participants, we found a generalized increase in DBSI-derived cellularity (a putative marker of neuroinflammation), a decrease in restricted fiber fraction (a putative marker of apparent axonal density), and an increase in extra-axonal water (a putative marker of vasogenic edema) across several WM tracts. There were only minimal WM abnormalities noted in BPD, mainly in regions of the corpus callosum (increase in DTI-derived RD and extra-axonal water). DBSI metrics showed significant partial correlations with psychosis and mood symptoms across groups. Our findings suggest that SCZ involves generalized white matter neuroinflammation, decreased fiber density, and demyelination, which is not seen in bipolar disorder. Larger studies are needed to identify medication-related effects. DBSI metrics could help identify high-risk groups requiring early interventions to prevent the onset of psychosis and improve outcomes.

White matter fiber morphology in persisting postconcussive symptoms and posttraumatic headache after pediatric concussion: a fixel-based analysis.

Posttraumatic headache (PTH) represents the most common acute and persistent postconcussive symptom (PCS) in children after concussion, yet there remains a lack of valid and objective biomarkers to facilitate risk stratification and early intervention in this patient population. Fixel-based analysis of diffusion-weighted imaging, which overcomes constraints of traditional diffusion tensor imaging analyses, can improve the sensitivity and specificity of detecting white matter changes postconcussion. The aim of this study was to investigate whole-brain and tract-based differences in white matter morphology, including fiber density (FD) and fiber bundle cross-section (FC) area in children with PCSs and PTH at 2 weeks after concussion. This prospective longitudinal study recruited children aged 5-18 years who presented to the emergency department of a tertiary pediatric hospital with a concussion sustained within the previous 48 hours. Participants underwent diffusion-weighted MRI at 2 weeks postinjury. Whole-brain white matter statistical analysis was performed at the level of each individual fiber population within an image voxel (fixel) to compute FD, FC, and a combined metric (FD and bundle cross-section [FDC]) using connectivity-based fixel enhancement. Tract-based Bayesian analysis was performed to examine FD in 23 major white matter tracts. Comparisons of 1) recovered (n = 27) and symptomatic (n = 16) children, and those with 2) PTH (n = 13) and non-PTH (n = 30; overall mean age 12.99 ± 2.70 years, 74% male) found no fiber-specific white matter microstructural differences in FD, FC, or FDC at 2 weeks postconcussion, when adjusting for age and sex (family-wise error rate corrected p value > 0.05). Tract-based Bayesian analysis showed evidence of no effect of PTH on FD in 10 major white matter tracts, and evidence of no effect of recovery group on FD in 3 white matter tracts (Bayes factor < 1/3). Using whole-brain fixel-wise and tract-based analyses, these findings indicate that fiber-specific properties of white matter microstructure are not different between children with persisting PCSs compared with recovered children 2 weeks after concussion. These data extend the limited research on white matter fiber-specific morphology while overcoming limitations inherent to traditional diffusion models. Further validation of our findings with a large-scale cohort is warranted.

Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status

The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer’s disease (AD). They project broadly throughout the brain’s white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.

Left corticospinal tract could be a biomarker to identify the dual prodromal LRRK2/GBA mutated Parkinson's disease.

Prodromal Parkinson's disease (PD) carriers of dual leucine-rich repeat kinase 2 (LRRK2) and glucosylceramidase β (GBA) variants are rare, and their biomarkers are less well developed. This study aimed to investigate the biomarkers for diagnosing the prodromal phase of LRRK2-GBA-PD (LRRK2-GBA-prodromal). We assessed the clinical and whole-brain white matter microstructural characteristics of 54 prodromal PD carriers of dual LRRK2 (100% M239T) and GBA (95% N409S) variants, along with 76 healthy controls (HCs) from the Parkinson's Progression Markers Initiative (PPMI) cohort. By analyzing the four values of 100 nodes on 20 fiber bundles, totaling 8000 data points, we identified the smallest p value in the fractional anisotropy (FA) value of the 38th segment of left corticospinal tract (L-CST) with differences between LRRK2-GBA-prodromal and HCs (p = 8.94 × 10-9). The FA value of the 38th node of the L-CST was significantly lower in LRRK2-GBA-prodromal (FA value, 0.65) compared with HCs (FA value, 0.71). The receiver-operating characteristic curve showed a cut-off value of 0.218 for the FA value of L-CST, providing sufficient sensitivity (79.2%) and specificity (72.2%) to distinguish double mutation prodromal PD from the healthy population. L-CST, especially the 38th node, may potentially serve as a biomarker for distinguishing individuals with double mutation prodromal PD from the healthy population.

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study

BackgroundWe aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyperactivity disorder (ADHD) from those without ADHD using a machine-learning approach. MethodsFifty-one ADHD patients and 60 typically developing controls (TDC) underwent diffusion spectrum imaging at two time points. We evaluated three models to classify ADHD and TDC using various machine-learning algorithms. Model 1 employed baseline white matter features of 45 white matter tracts at Time 1; Model 2 incorporated features from both time points; and Model 3 (main analysis) further included the relative rate of change per year of white matter tracts. ResultsThe random forest algorithm demonstrated the best performance for classification. Model 1 achieved an area-under-the-curve (AUC) of 0.67. Model 3, incorporating Time 2 variables and relative rate of change per year, improved the performance (AUC = 0.73). In addition to identifying several white matter features at two time points, we found that the relative rate of change per year in the superior longitudinal fasciculus, frontal aslant tract, stria terminalis, inferior fronto-occipital fasciculus, thalamic and striatal tracts, and other tracts involving sensorimotor regions are important features of ADHD. A higher relative change rate in certain tracts was associated with greater improvement in visual attention, spatial short-term memory, and spatial working memory. ConclusionsOur findings support the significant diagnostic value of white matter microstructure and the developmental change rates of specific tracts, reflecting deviations from typical development trajectories, in identifying ADHD.

Personalizing Deep Brain Stimulation Therapy for Parkinson's Disease With Whole-Brain MRI Radiomics and Machine Learning.

Background Deep brain stimulation (DBS) is a well-recognised treatment for advanced Parkinson's disease (PD) patients. Structural brain alterations of the white matter can correlate with disease progression and act as a biomarker for DBS therapy outcomes. This study aims to develop a machine learning-driven predictive model for DBS patient selection using whole-brain white matter radiomics and common clinical variables. Methodology A total of 120 PD patients underwent DBS of the subthalamic nucleus. Their therapy effect was assessed at the one-year follow-up with the Unified Parkinson's Disease Rating Scale-part III (UPDRSIII) motor component. Radiomics analysis of whole-brain white matter was performed with PyRadiomics. The following machine learning methods were used: logistic regression (LR), support vector machine, naïve Bayes, K-nearest neighbours, and random forest (RF) to allow prediction of clinically meaningful UPRDSIII motor response before and after. Clinical variables were also added to the model to improve accuracy. Results The RF model showed the best performance on the final whole dataset with an area under the curve (AUC) of 0.99, accuracy of 0.95, sensitivity of 0.93, and specificity of 0.97. At the same time, the LR model showed an AUC of 0.93, accuracy of 0.88, sensitivity of 0.84, and specificity of 0.91. Conclusions Machine learning models can be used in clinical decision support tools which can deliver true personalised therapy recommendations for PD patients. Clinicians and engineers should choose between best-performing, less interpretable models vs. most interpretable, lesser-performing models. Larger clinical trials would allow to build trust among clinicians and patients to widely use these AI tools in the future.

Neurite orientation dispersion and density imaging reveals abnormal white matter and glymphatic function in active young boxers.

The neurological effects and underlying pathophysiological mechanisms of sports-related concussion (SRC) in active young boxers remain poorly understood. This study aims to investigate the impairment of white matter microstructure and assess changes in glymphatic function following SRC by utilizing neurite orientation dispersion and density imaging (NODDI) on young boxers who have sustained SRC. A total of 60 young participants were recruited, including 30 boxers diagnosed with SRC and 30 healthy individuals engaging in regular exercise. The assessment of whole-brain white matter damage was conducted using diffusion metrics, while the evaluation of glymphatic function was performed through diffusion tensor imaging (DTI) analysis along the perivascular space (DTI-ALPS) index. A two-sample t-test was utilized to examine group differences in DTI and NODDI metrics. Spearman correlation and generalized linear mixed models were employed to investigate the relationship between clinical assessments of SRC and NODDI measurements. Significant alterations were observed in DTI and NODDI metrics among young boxers with SRC. Additionally, the DTI-ALPS index in the SRC group exhibited a significantly higher value than that of the control group (left side: 1.58 vs. 1.48, PFDR=0.009; right side: 1.61 vs. 1.51, PFDR=0.02). Moreover, it was observed that the DTI-ALPS index correlated with poorer cognitive test results among boxers in this study population. Repetitive SRC in active young boxers is associated with diffuse white matter injury and glymphatic dysfunction, highlighting the detrimental impact on brain health. These findings highlight the importance of long-term monitoring of the neurological health of boxers.

Reduced myelin content in bipolar disorder: A study of inhomogeneous magnetization transfer

BackgroundPrevious neuroimaging and pathological studies have found myelin-related abnormalities in bipolar disorder (BD), which prompted the use of magnetic resonance (MR) imaging technology sensitive to neuropathological changes to explore its neuropathological basis. We holistically investigated alterations in myelin within BD patients by inhomogeneous magnetization transfer (ihMT), which is sensitive and specific to myelin content. MethodsThirty-one BD and 42 healthy controls (HC) were involved. Four MR metrics, i.e., ihMT ratio (ihMTR), pseudo-quantitative ihMT (qihMT), magnetization transfer ratio and pseudo-quantitative magnetization transfer (qMT), were compared between groups using analysis methods based on whole-brain voxel-level and white matter regions of interest (ROI), respectively. ResultsThe voxel-wise analysis showed significantly inter-group differences of ihMTR and qihMT in the corpus callosum. The ROI-wise analysis showed that ihMTR, qihMT, and qMT values in BD group were significantly lower than that in HC group in the genu and body of corpus callosum, left anterior limb of the internal capsule, left anterior corona radiate, and bilateral cingulum (p < 0.001). And the qihMT in genu of corpus callosum and right cingulum were negatively correlated with depressive symptoms in BD group. LimitationsThis study is based on cross-sectional data and the sample size is limited. ConclusionThese findings suggest the reduced myelin content of anterior midline structure in the bipolar patients, which might be a critical pathophysiological feature of BD.

Estimating the prevalence of Non-Verbal Learning Disability (NVLD) from the ABCD sample.

Non-Verbal Learning Disability (NVLD) is a neurodevelopmental disorder characterized by deficits in processing visuospatial information but with age-appropriate verbal skills. This cognitive profile has been hypothesized to be associated with atypical white matter, but at the present there is a lack of evidence for this hypothesis. Currently, the condition is not characterized within the main diagnostic systems, in part because no clear set of criteria for characterizing the disorder exists. This report is the first attempt to estimate NVLD prevalence, using two sets of diagnostic criteria, in a large sample of over 11,000 children who were selected without regards to problems of specific nature, either psychological, neurological, physical and/or social. Furthermore, it examined the association between the profile of cognitive abilities and aspects of whole-brain white matter measures in children with and without symptoms associated with NVLD. Participants were drawn from the Adolescent Brain Cognitive Development (ABCD) study, a 10-year longitudinal study of 11,876 children in the U.S. The data used in the present study were drawn from the initial testing point at which the children were 9-10years old. Prevalence of NVLD based on two distinct sets of criteria, correlations between the measures used to create the criteria, correlations between criteria measures and measures of white matter integrity. The cognitive criteria included measures of visuospatial processing, reading, intelligence and social skills. By varying the cut-offs applied to social skills in conjunction with visuo-spatial difficulties, spared reading skills and intelligence scores, we calculated prevalence for two NVLD groups. White matter characteristics were measures of volume, fractional anisotropy and mean diffusivity. Based on the criteria used, the estimated prevalence of NVLD varied from 1 to 8%. Furthermore, children with NVLD showed a dissociation between measures of visuo-spatial processing not observed in non-NVLD children. At the neurological level, findings provide preliminary evidenceof associations between the cognitive profile of NVLD and abnormalities in white matters tracts. The present study documents that exists, within this large non-selected sample, a proportion of youth who show evidence of NVLD. Given those results, it appears essential to establish the best diagnostic criteria, to improve the treatment options and quality of life for children with this disorder.

101 Glymphatic System Impairment Associated With Neurocognitive Dysfunction and Alzheimers Biomarkers in Moyamoya Disease

INTRODUCTION: Glymphatic system alterations have been proved to be associated with cognitive dysfunction in Alzheimer’s Disease (AD). The glymphatic pathway has not been elucidated in moyamoya disease (MMD), which was recognized as a chronic hypoperfusion model for AD. METHODS: We prospectively recruited 30 MMD patients and 30 matched healthy controls (HC) from October 2022 to March 2023. Participants underwent neurocognition evaluation, and multimodal magnetic resonance imaging (MRI). AD-related serum biomarkers, as well as correlations among these variables were examined. The glymphatic function was assessed by diffusion tensor image analysis along perivascular space (DTI-ALPS). Whole-brain (white matter and grey matter) volumes were calculated by using 3D T1-weighted MRI. Microstructural changes of white matter (intracellular volume fraction, Vic; orientation dispersion index, OD) were evaluated by neurite orientation dispersion and density imaging (NODDI). RESULTS: Serum Alzheimer’s biomarkers (ApoE, Aβ40, Aβ42, and tau) were significantly elevated and associated with neurocognitive dysfunction in MMD. Lower periventricular ALPS-index was identified in patients with MMD (P &lt; 0.001). The decreased ALPS-index correlated with declined neurocognitive performance of intelligence quotient, attention, and memory, increased levels of serum ApoE, Aβ40, Aβ42, as well as higher periventricular white matter volume and lower periventricular grey matter volume (P &lt; 0.001). Moreover, MMD patients had significantly lower Vic in the white matter and a lower OD in the cortex than HC. CONCLUSIONS: We firstly demonstrated the impaired glymphatic system as assessed by lower ALPS-index in patients with MMD. Glymphatic impairment was associated with AD-related biomarkers and cognitive dysfunction in MMD. These findings suggested that glymphatic disorder may interact with MMD-related pathophysiological processes.

White matter structural changes before and after microvascular decompression for hemifacial spasm.

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

Diffusion kurtosis imaging of brain white matter alteration in patients with coronary artery disease based on the TBSS method.

The aim of our study was to examine the alterations in microstructure in patients with coronary artery disease (CAD) and cognitive impairment (CI) using diffusion kurtosis imaging (DKI). Additionally, we aimed to investigate the potential correlation between DKI parameters and cognitive function. A total of 28 CAD patients and 30 healthy controls (HC) were prospectively enrolled in our study. All participants underwent routine and diffusion sequences of head imaging. DKE software was utilized to generate various diffusion kurtosis imaging parameters (DKI), including kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and mean diffusivity (MD). Nonparametric tests were conducted using tract-based spatial statistics (TBSS) to compare the parameter values between the two groups. The parameter values of the significantly different fiber tracts were extracted and correlated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Compared to the HC group, patients with coronary artery disease exhibited significant reductions in FA values in the bilateral Superior corona radiata, bilateral Anterior corona radiata, bilateral Posterior corona radiata, corpus callosum, left Posterior thalamic radiation, right Posterior limb of internal capsule, Anterior limb of internal capsule, and Cerebral peduncle, as well as in the left Superior longitudinal fasciculus. Additionally, KFA values decreased in the bilateral Anterior corona radiata, bilateral Anterior limb of internal capsule, and Genu of the corpus callosum. The MK values decreased in the right Posterior corona radiata, Retrolenticular part of the internal capsule, Posterior thalamic radiation (including optic radiation), Superior longitudinal fasciculus, and left Posterior thalamic radiation (including optic radiation). Moreover, the RK values decreased in the bilateral Retrolenticular part of the internal capsule, right Posterior thalamic radiation (including optic radiation), and Superior longitudinal fasciculus, as well as in the left Superior longitudinal fasciculus and Posterior thalamic radiation (including optic radiation) (p < 0.01, TFCE corrected), while no significant differences were observed in other parameter values (p > 0.01, TFCE corrected). The FA values of the right posterior limb of the internal capsule (r = 0.610, p = 0.001) and the right cerebral peduncle (r = 0.622, p < 0.001) were positively correlated with MMSE scores. Additionally, a significant correlation between kurtosis and diffusion coefficient parameters (FA and KFA) was observed. CAD patients showed radial shrinkage and complexity of brain white matter microstructure. Whole-brain white matter analysis based on TBSS DKI can objectively reflect the characteristics of white matter damage in CAD patients, providing a basis for the auxiliary diagnosis of CAD with CI.

Brain white matter microstructural alterations in patients with systemic lupus erythematosus: an automated fiber quantification study.

This study aimed to identify damaged segments of brain white matter fiber tracts in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ), and analyze their relationship with cognitive impairment. Clinical and imaging data for 39 female patients with SLE and for 44 female healthy controls (HCs) were collected. AFQ was used to track whole-brain white matter tracts in each participant, and each tract was segmented into 100 equally spaced nodes. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated at each node. Correlations were also explored between DTI metrics in the damaged segments of white matter fiber tracts and neuropsychological test scores of patients with SLE. Compared with HCs, SLE patients exhibited significantly lower FA values, and significantly higher MD, AD, RD values in many white matter tracts (all P < 0.05, false discovery rate-corrected). FA values in nodes 97-100 of the left inferior fronto-occipital fasciculus (IFOF) positively correlated with the mini-mental state examination score. AFQ enables precise and accurate identification of damage to white matter fiber tracts in brains of patients with SLE. FA values in the left IFOF correlate with cognitive impairment in SLE.