Altered large-scale functional brain networks in neurological Wilson's disease

Combining static and dynamic functional connectivity analyses to identify male patients with obstructive sleep apnea and predict clinical symptoms

BACKGROUND AND PURPOSE

Patients with obstructive sleep apnea (OSA) experience chronic intermittent hypoxia and sleep fragmentation, leading to brain ischemia and neurological dysfunction. Therefore, it is important to identify features that can differentiate patients with OSA from healthy controls (HC) and provide insights into the underlying brain alterations associated with OSA. This study aimed to distinguish patients with OSA from healthy individuals and predict clinical symptom alterations using cerebellum-whole-brain static and dynamic functional connectivity (sFC and dFC, respectively), with the cerebellum as the seed region.

METHODS

Sixty male patients with OSA and 60 male HC matched for age, education level, and sex were included. Using 27 cerebellar seeds, sliding-window analysis was performed to calculate sFC and dFC between the cerebellum and the whole brain. The sFC and dFC values were then combined and used in multiple machine-learning models to distinguish patients with OSA from HC and predict the clinical symptoms of patients with OSA.

RESULTS

Patients with OSA showed increased dFC between cerebellar subregions and the superior and middle temporal gyri and decreased dFC with the middle frontal gyrus. Conversely, increased sFC was observed between cerebellar subregions and the cerebellar lobule VI, cingulate gyrus, middle frontal gyrus, inferior parietal lobules, insula, and superior temporal gyrus. Combined dynamic-static FC features demonstrated superior classification performance with a support vector machine in discriminating OSA from HC. In clinical symptom prediction, FC alterations contributed up to 30.11 % to cognitive impairment, 55.96 % to excessive sleepiness, and 27.94 % to anxiety and depression.

CONCLUSIONS

Combining cerebrocerebellar sFC and dFC analyses enables high-precision classification and prediction of OSA. Aberrant FC patterns reflect compensatory brain reorganization and disrupted cognitive network integration, highlighting potential neuroimaging markers for OSA.

Altered cortical functional networks in Wilson's Disease: A resting-state electroencephalogram study

The neuropsychiatric symptoms are common in Wilson's disease (WD) patients. However, it remains unclear about the associated functional brain networks. In this study, source localization-based functional connectivity analysis of close-eye resting-state electroencephalography (EEG) were implemented to assess the characteristics of functional networks in 17 WD patients with neurological involvements and 17 healthy controls (HCs). The weighted phase-lag index (wPLI) was subsequently calculated in source space across five different frequency bands and the resulting connectivity matrix was transformed into a weighted graph whose structure was measured by five graphical analysis indicators, which were finally correlated with clinical scores. Compared to HCs, WD patients revealed disconnected sub-networks in delta, theta and alpha bands. Moreover, WD patients exhibited significantly reduced global clustering coefficients and small-worldness in all five frequency bands. In WD group, the severity of neurological symptoms and structural brain abnormalities were significantly correlated with disrupted functional networks. In conclusion, our study demonstrated that functional network deficits in WD can reflect the severity of their neurological symptoms and structural brain abnormalities. Resting-state EEG may be used as a marker of brain injury in WD.

Large-scale networks changes in Wilson's disease associated with neuropsychiatric impairments: a resting-state functional magnetic resonance imaging study

BACKGROUND

In Wilson's disease (WD) patients, network connections across the brain are disrupted, affecting multidomain function. However, the details of this neuropathophysiological mechanism remain unclear due to the rarity of WD. In this study, we aimed to investigate alterations in brain network connectivity at the whole-brain level (both intra- and inter-network) in WD patients through independent component analysis (ICA) and the relationship between alterations in these brain network functional connections (FCs) and clinical neuropsychiatric features to understand the underlying pathophysiological and central compensatory mechanisms.

METHODS

Eighty-five patients with WD and age- and sex-matched 85 healthy control (HC) were recruited for resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We extracted the resting-state networks (RSNs) using the ICA method, analyzed the changes of FC in these networks and the correlation between alterations in FCs and clinical neuropsychiatric features.

RESULTS

Compared with HC, WD showed widespread lower connectivity within RSNs, involving default mode network (DMN), frontoparietal network (FPN), somatomotor network (SMN), dorsal attention network (DAN), especially in patients with abnormal UWDRS scores. Furthermore, the decreased FCs in the left medial prefrontal cortex (L_ MPFC), left anterior cingulate gyrus (L_ACC), precuneus (PCUN)within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-neurological characteristic examination (UWDRS-N), and the decreased FCs in the L_MPFC, PCUN within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-psychiatric symptoms examination (UWDRS-P). We additionally discovered that the patients with WD exhibited significantly stronger FC between the FPN and DMN, between the DAN and DMN, and between the FPN and DAN compared to HC.

CONCLUSIONS

We have provided evidence that WD is a disease with widespread dysfunctional connectivity in resting networks in brain, leading to neurological features and psychiatric symptoms (e.g. higher-order cognitive control and motor control impairments). The alter intra- and inter-network in the brain may be the neural underpinnings for the neuropathological symptoms and the process of injury compensation in WD patients.

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 large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Dysfunction of the Lenticular Nucleus Is Associated with Dystonia in Wilson's Disease

Dysfunction of the lenticular nucleus is thought to contribute to neurological symptoms in Wilson's disease (WD). However, very little is known about whether and how the lenticular nucleus influences dystonia by interacting with the cerebral cortex and cerebellum. To solve this problem, we recruited 37 WD patients (20 men; age, 23.95 ± 6.95 years; age range, 12-37 years) and 37 age- and sex-matched healthy controls (HCs) (25 men; age, 25.19 ± 1.88 years; age range, 20-30 years), and each subject underwent resting-state functional magnetic resonance imaging (RS-fMRI) scans. The muscle biomechanical parameters and Unified Wilson Disease Rating Scale (UWDRS) were used to evaluate the level of dystonia and clinical representations, respectively. The lenticular nucleus, including the putamen and globus pallidus, was divided into 12 subregions according to dorsal, ventral, anterior and posterior localization and seed-based functional connectivity (FC) was calculated for each subregion. The relationships between FC changes in the lenticular nucleus with muscle tension levels and clinical representations were further investigated by correlation analysis. Dystonia was diagnosed by comparing all WD muscle biomechanical parameters with healthy controls (HCs). Compared with HCs, FC decreased from all subregions in the putamen except the right ventral posterior part to the middle cingulate cortex (MCC) and decreased FC of all subregions in the putamen except the left ventral anterior part to the cerebellum was observed in patients with WD. Patients with WD also showed decreased FC of the left globus pallidus primarily distributed in the MCC and cerebellum and illustrated decreased FC from the right globus pallidus to the cerebellum. FC from the putamen to the MCC was significantly correlated with psychiatric symptoms. FC from the putamen to the cerebellum was significantly correlated with muscle tension and neurological symptoms. Additionally, the FC from the globus pallidus to the cerebellum was also associated with muscle tension. Together, these findings highlight that lenticular nucleus-cerebellum circuits may serve as neural biomarkers of dystonia and provide implications for the neural mechanisms underlying dystonia in WD.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Disrupted topological organization of the motor execution network in Wilson's disease

OBJECTIVE

There are a number of symptoms associated with Wilson's disease (WD), including motor function damage. The neuropathological mechanisms underlying motor impairments in WD are, however, little understood. In this study, we explored changes in the motor execution network topology in WD.

METHODS

We conducted resting-state functional magnetic resonance imaging (fMRI) on 38 right-handed individuals, including 23 WD patients and 15 healthy controls of the same age. Based on graph theory, a motor execution network was constructed and analyzed. In this study, global, nodal, and edge topological properties of motor execution networks were compared.

RESULTS

The global topological organization of the motor execution network in the two groups did not differ significantly across groups. In the cerebellum, WD patients had a higher nodal degree. At the edge level, a cerebello-thalamo-striato-cortical circuit with altered functional connectivity strength in WD patients was observed. Specifically, the strength of the functional connections between the cerebellum and thalamus increased, whereas the cortical-thalamic, cortical-striatum and cortical-cerebellar connections exhibited a decrease in the strength of the functional connection.

CONCLUSION

There is a disruption of the topology of the motor execution network in WD patients, which may be the potential basis for WD motor dysfunction and may provide important insights into neurobiological research related to WD motor dysfunction.

The degeneration changes of basal forebrain are associated with prospective memory impairment in patients with Wilson's disease

INTRODUCTION

Degeneration changes of the basal forebrain (BF) are suggested to play an important role in cognitive impairment and memory loss in patients with Alzheimer's disease and Parkinson's disease. However, little is known about if and how the structure and function of BF are abnormal in Wilson's disease (WD).

METHODS

Here, we employed the structural and resting-state functional magnetic resonance imaging (fMRI) data from 19 WD individuals and 24 healthy controls (HC). Voxel-based morphometry (VBM) and functional connectivity analysis were applied to investigate the structural and functional degeneration changes of BF in WD. Moreover, the linear regression analyses were performed in the patient group to depict the correlations between the aberrant gray volume and functional connectivity of the BF and clinical performances, such as the prospective memory (PM) and mini-mental state examination (MMSE).

RESULTS

VBM analysis showed that compared with HC, the volume of overlapping cell groups of BF termed CH1-3 and CH4 was significantly reduced in WD. Additionally, the decreased functional connectivity of the CH4 was distributed in the bilateral temporal-parietal junction (TPJ), right thalamus, orbitofrontal gyrus (ORB), and left middle cingulate cortex (MCC). The performances of the time-based prospective memory (TBPM) and event-based prospective memory (EBPM) were related to reduced functional connectivity between CH4 and right ORB. Besides, the functional connectivity of left TPJ was also significantly correlated with EBPM in WD.

CONCLUSION

These findings indicated that the degenerative changes of BF may affect PM through the innervation brain function and may help to understand the neural mechanisms underlying cognitive impairment in WD.

Changes in brain functional connectivity and cognition related to white matter lesion burden in hypertensive patients from SPRINT

PURPOSE

Hypertension is a risk factor for cognitive impairment; however, the mechanisms leading to cognitive changes remain unclear. In this cross-sectional study, we evaluate the impact of white matter lesion (WML) burden on brain functional connectivity (FC) and cognition in a large cohort of hypertensive patients from the Systolic Blood Pressure Intervention Trial (SPRINT) at baseline.

METHODS

Functional networks were identified from baseline resting state functional MRI scans of 660 SPRINT participants using independent component analysis. WML volumes were calculated from structural MRI. Correlation analyses were carried out between mean FC of each functional network and global WML as well as WML within atlas-defined white matter regions. For networks of interest, voxel-wise-adjusted correlation analyses between FC and regional WML volume were performed. Multiple variable linear regression models were built for cognitive test performance as a function of network FC, followed by mediation analysis.

RESULTS

Mean FC of the default mode network (DMN) was negatively correlated with global WML volume, and regional WML volume within the precuneus. Voxel-wise correlation analyses revealed that regional WML was negatively correlated with FC of the DMN's left lateral temporal region. FC in this region of the DMN was positively correlated to performance on the Montreal Cognitive Assessment and demonstrated significant mediation effects. Additional networks also demonstrated global and regional WML correlations; however, they did not demonstrate an association with cognition.

CONCLUSION

In hypertensive patients, greater WML volume is associated with lower FC of the DMN, which in turn is related to poorer cognitive test performance.

TRIAL REGISTRATION

NCT01206062.

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.

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.