Microstructure changes in whiter matter relate to cognitive impairment in Wilson's disease

Machine learning for detecting Wilson's disease by amplitude of low-frequency fluctuation

Wilson's disease (WD) is a genetic disorder with the A7P7B gene mutations. It is difficult to diagnose in clinic. The purpose of this study was to confirm whether amplitude of low-frequency fluctuations (ALFF) is one of the potential biomarkers for the diagnosis of WD. The study enrolled 30 healthy controls (HCs) and 37 WD patients (WDs) to obtain their resting-state functional magnetic resonance imaging (rs-fMRI) data. ALFF was obtained through preprocessing of the rs-fMRI data. To distinguish between patients with WDs and HCs, four clusters with abnormal ALFF-z values were identified through between-group comparisons. Based on these clusters, three machine learning models were developed, including Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Abnormal ALFF z-values were also combined with volume information, clinical variables, and imaging features to develop machine learning models. There were 4 clusters where the ALFF z-values of the WDs were significantly higher than that of the HCs. Cluster1 was in the cerebellar region, Cluster2 was in the left caudate nucleus, Cluster3 was in the bilateral thalamus, and Cluster4 was in the right caudate nucleus. In the training set and test set, the models trained with Cluster2, Cluster3, and Cluster4 achieved area of curve (AUC) greater than 0.80. In the Delong test, only the AUC values of models trained with Cluster4 exhibited statistical significance. The AUC values of the Logit model (P = 0.04) and RF model (P = 0.04) were significantly higher than those of the SVM model. In the test set, the LR model and RF model trained with Cluster3 had high specificity, sensitivity, and accuracy. By conducting the Delong test, we discovered that there was no statistically significant inter-group difference in AUC values between the model that integrates multi-modal information and the model before fusion. The LR models trained with multimodal information and Cluster 4, as well as the LR and RF models trained with multimodal information and Cluster 3, have demonstrated high accuracy, specificity, and sensitivity. Overall, these findings suggest that using ALFF based on the thalamus or caudate nucleus as markers can effectively differentiate between WDs and HCs. The fusion of multimodal information did not significantly improve the classification performance of the models before fusion.

Altered microstructural pattern of the cortex and basal forebrain cholinergic system in wilson's disease: an automated fiber quantification tractography study

Basal forebrain (BF) cholinergic projection neurons form a highly extensive input to the cortex. Failure of BF cholinergic circuits is responsible for the cognitive impairment associated with Wilson's disease (WD), but whether and how the microstructural changes in fiber projections between the BF and cerebral cortex influence prospective memory (PM) remain poorly understood. We collected diffusion tensor imaging (DTI) data from 21 neurological WD individuals and 26 healthy controls (HCs). The experiment reconstructed the probabilistic streamlined tractography of 18 white matter tracts using an automated fiber quantification (AFQ) toolkit. Tract properties (FA, MD, RD, and AD) were computed for 100 points along each tract for each participant, and the differences between the groups were examined. Subsequently, correlation analysis was performed to evaluate whether abnormal microstructural white matter integrity measures correlate with PM performance. Additional investigations used a tract-based spatial statistics (TBSS) approach to identify regions with altered white matter structure between groups and verify the reliability of the AFQ results. The highest nonoverlapping DTI-related differences were detected in the anterior thalamic radiation (ATR), corticospinal tract (CST), corpus callosum, association fibers, and limbic system fibers. Additionally, PM parameters of the patient group were highly correlated with white matter microstructure changes in the inferior longitudinal fasciculus. Our study highlights that the performance of projections between cholinergic input and output areas-the cerebral cortex and BF-may serve as neural biomarkers of PM and disease prognosis.

Free water imaging as a novel biomarker in Wilson's disease: A cross-sectional study

BACKGROUND

The bi-tensor free water imaging may provide more specific information in detecting microstructural brain tissue alterations than conventional single tensor diffusion tensor imaging. The study aimed to investigate microstructural changes in deep gray matter (DGM) nuclei of Wilson's disease (WD) using a bi-tensor free water imaging and whether the findings correlate with the neurological impairment in WD patients.

METHODS

The study included 29 WD patients and 25 controls. Free water and free water corrected fractional anisotropy (FA_T) in DGM nuclei of WD patients were calculated. The correlations of free water and FA_T with the Unified WD Rating Scale (UWDRS) neurological subscale of WD patients were performed.

RESULTS

Free water and FA_T values were significantly increased in multiple DGM nuclei of neurological WD patients compared to controls. WD patients with normal appearing on conventional MRI also had significantly higher free water and FA_T values in multiple DGM nuclei than controls. Positive correlations were noted between the UWDRS neurological subscores and free water values of the putamen and pontine tegmentum as well as FA_T values of the dentate nucleus, red nucleus, and globus pallidus. In addition, the measured free water and FA_T values of specific structures also showed a positive correlation with specific clinical symptoms in neurological WD patients, such as dysarthria, parkinsonian signs, tremor, dystonia, and ataxia.

CONCLUSIONS

Free water imaging detects microstructural changes in both normal and abnormal appearing DGM nuclei of WD patients. Free water imaging indices were correlated with the severity of neurological impairment in WD patients.

Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease

Objective

To investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson's disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.

Methods

Overall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD.

Results

The area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment.

Conclusion

The nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.

Increased Magnetic Susceptibility in the Deep Gray Matter Nuclei of Wilson's Disease: Have We Been Ignoring Atrophy?

Background

Histopathological studies in Wilson's disease (WD) have revealed increased copper and iron concentrations in the deep gray matter nuclei. However, the commonly used mean bulk susceptibility only reflects the regional metal concentration rather than the total metal content, and regional atrophy may affect the assessment of mean bulk susceptibility. Our study aimed to quantitatively assess the changes of metal concentration and total metal content in deep gray matter nuclei by quantitative susceptibility mapping to distinguish patients with neurological and hepatic WD from healthy controls.

Methods

Quantitative susceptibility maps were obtained from 20 patients with neurological WD, 10 patients with hepatic WD, and 25 healthy controls on a 3T magnetic resonance imaging system. Mean bulk susceptibility, volumes, and total susceptibility of deep gray matter nuclei in different groups were compared using a linear regression model. The area under the curve (AUC) was calculated by receiver characteristic curve to analyze the diagnostic capability of mean bulk susceptibility and total susceptibility.

Results

Mean bulk susceptibility and total susceptibility of multiple deep gray matter nuclei in patients with WD were higher than those in healthy controls. Compared with patients with hepatic WD, patients with neurological WD had higher mean bulk susceptibility but similar total susceptibility in the head of the caudate nuclei, globus pallidus, and putamen. Mean bulk susceptibility of putamen demonstrated the best diagnostic capability for patients with neurological WD, the AUC was 1, and the sensitivity and specificity were all equal to 1. Total susceptibility of pontine tegmentum was most significant for the diagnosis of patients with hepatic WD, the AUC was 0.848, and the sensitivity and specificity were 0.7 and 0.96, respectively.

Conclusion

Brain atrophy may affect the assessment of mean bulk susceptibility in the deep gray matter nuclei of patients with WD, and total susceptibility should be an additional metric for total metal content assessment. Mean bulk susceptibility and total susceptibility of deep gray matter nuclei may be helpful for the early diagnosis of WD.

Basal ganglia-orbitofrontal circuits are associated with prospective memory deficits in Wilson's disease

Degenerative changes in the basal ganglia (BG) are thought to contribute to neurological symptoms in Wilson's disease (WD). However, very little is known about whether and how the BG have an influence on prospective memory (PM) by interacting with the cerebral cortex. Here, we employed structural magnetic resonance imaging to systematically examine the effect of volume atrophy of BG on cortical thickness and to evaluate the relationships between cortical thickness of regions associated with BG atrophy and PM performance in WD. Cortical thickness atrophy in the left temporal pole and medial frontal gyrus are not related to degenerative changes in BG. Cortical thickness in the left superior frontal gyrus and right orbitofrontal gyrus (ORB) have stronger correlations with volume atrophy of the left accumbens, pallidum, and putamen in WD when compared with healthy controls. Furthermore, the cortical thickness of the right ORB is not only significantly correlated with PM performance but can also distinguish the severity of PM impairment in WD. Additionally, the middle cingulate cortex was related to volume atrophy of the accumbens, and its cortical thickness has a significant positive correlation with event-based PM. Together, these findings highlight that BG-orbitofrontal circuits may serve as neural biomarkers of PM and provide implications for the neural mechanisms underlying cognitive impairment in WD.

Structural and Functional Changes Are Related to Cognitive Status in Wilson's Disease

Patients with Wilson’s disease (WD) suffer from prospective memory (PM) impairment, and some of patients develop cognitive impairment. However, very little is known about how brain structure and function changes effect PM in WD. Here, we employed multimodal neuroimaging data acquired from 22 WD patients and 26 healthy controls (HC) who underwent three-dimensional T1-weighted, diffusion tensor imaging (DTI), and resting state functional magnetic resonance imaging (RS-fMRI). We investigated gray matter (GM) volumes with voxel-based morphometry, DTI metrics using the fiber tractography method, and RS-fMRI using the seed-based functional connectivity method. Compared with HC, WD patients showed GM volume reductions in the basal ganglia (BG) and occipital fusiform gyrus, as well as volume increase in the visual association cortex. Moreover, whiter matter (WM) tracks of WD were widely impaired in association and limbic fibers. WM tracks in association fibers are significant related to PM in WD patients. Relative to HC, WD patients showed that the visual association cortex functionally connects to the thalamus and hippocampus, which is associated with global cognitive function in patients with WD. Together, these findings suggested that PM impairment in WD may be modulated by aberrant WM in association fibers, and that GM volume changes in the association cortex has no direct effect on cognitive status, but indirectly affect global cognitive function by its aberrant functional connectivity (FC) in patients with WD. Our findings may provide a new window to further study how WD develops into cognitive impairment, and deepen our understanding of the cognitive status and neuropathology of WD.

Paramagnetic Metal Accumulation in the Deep Gray Matter Nuclei Is Associated With Neurodegeneration in Wilson's Disease

Background

Neuropathological studies have revealed copper and iron accumulation in the deep gray matter (DGM) nuclei of patients with Wilson’s disease (WD). However, the association between metal accumulation and neurodegeneration in WD has not been well studied in vivo. The study was aimed to investigate whether metal accumulation in the DGM was associated with the structural and functional changes of DGM in neurological WD patients.

Methods

Seventeen neurological WD patients and 20 healthy controls were recruited for the study. Mean bulk susceptibility values and volumes of DGM were obtained from quantitative susceptibility mapping (QSM). Regions of interest including the head of the caudate nucleus, globus pallidus, putamen, thalamus, substantia nigra, red nucleus, and dentate nucleus were manually segmented. The susceptibility values and volumes of DGM in different groups were compared using a linear regression model. Correlations between susceptibility values and volumes of DGM and Unified Wilson’s Disease Rating Scale (UWDRS) neurological subscores were investigated.

Results

The susceptibility values of all examined DGM in WD patients were higher than those in healthy controls (P < 0.05). Volume reductions were observed in the head of the caudate nucleus, globus pallidus, putamen, thalamus, and substantia nigra of WD patients (P < 0.001). Susceptibility values were negatively correlated with the volumes of the head of the caudate nucleus (r_p = −0.657, P = 0.037), putamen (r_p = −0.667, P = 0.037), and thalamus (r_p = −0.613, P = 0.046) in WD patients. UWDRS neurological subscores were positively correlated with the susceptibility values of all examined DGM. The susceptibility values of putamen, head of the caudate nucleus, and dentate nucleus could well predict UWDRS neurological subscores.

Conclusion

Our study provided in vivo evidence that paramagnetic metal accumulation in the DGM was associated with DGM atrophy and neurological impairment. The susceptibility of DGM could be used as a biomarker to assess the severity of neurodegeneration in WD.