Reversibility of Impaired Large-Scale Functional Brain Networks in Cushing's Disease after Surgery Treatment: A Longitudinal Study.

INTRODUCTION: Chronic exposure to excessive endogenous cortisol leads to brain changes in Cushing's disease (CD). However, it remains unclear how CD affects large-scale functional networks (FNs) and whether these effects are reversible after treatment. This study aimed to investigate functional network changes of CD patients and their reversibility in a longitudinal cohort. METHODS: Active CD patients (N = 37) were treated by transsphenoidal pituitary surgery and reexamined 3 months later. FNs were computed from resting-state fMRI data of the CD patients and matched normal controls (NCs, N = 37). A pattern classifier was built on the FNs to distinguish active CD patients from controls and applied to FNs of the CD patients at the 3-month follow-up. Two subgroups of endocrine-remitted CD patients were identified according to their classification scores, referred to as image-based phenotypically (IBP) recovered and unrecovered CD patients, respectively. The informative FNs identified by the classification model were compared between NCs, active CD patients, and endocrine-remitted patients as well as between IBP recovered and unrecovered CD patients to explore their functional network reversibility. RESULTS: All 37 CD patients reached endocrine remission after treatment. The classification model identified three informative FNs, including cerebellar network (CerebN), fronto-parietal network (FPN), and default mode network. Among them, CerebN and FPN partially recovered toward normal at 3 months after treatment. Moreover, the informative FNs were correlated with 24-h urinary-free cortisol and emotion scales in CD patients. CONCLUSION: These findings suggest that CD patients have aberrant FNs that are partially reversible toward normal after treatment.

Concurrent brain parcellation and connectivity estimation via co-clustering of resting state fMRI data: A novel approach.

Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data-driven manner by leveraging the power of co-clustering-based on resting state fMRI (rs-fMRI) data. We propose the co-clustering-based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co-clustering-based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs-fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs-fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.

Functional parcellation of the hippocampus by semi-supervised clustering of resting state fMRI data.

Many unsupervised methods are widely used for parcellating the brain. However, unsupervised methods aren't able to integrate prior information, obtained from such as exiting functional neuroanatomy studies, to parcellate the brain, whereas the prior information guided semi-supervised method can generate more reliable brain parcellation. In this study, we propose a novel semi-supervised clustering method for parcellating the brain into spatially and functionally consistent parcels based on resting state functional magnetic resonance imaging (fMRI) data. Particularly, the prior supervised and spatial information is integrated into spectral clustering to achieve reliable brain parcellation. The proposed method has been validated in the hippocampus parcellation based on resting state fMRI data of 20 healthy adult subjects. The experimental results have demonstrated that the proposed method could successfully parcellate the hippocampus into head, body and tail parcels. The distinctive functional connectivity patterns of these parcels have further demonstrated the validity of the parcellation results. The effects of aging on the three hippocampus parcels' functional connectivity were also explored across the healthy adult subjects. Compared with state-of-the-art methods, the proposed method had better performance on functional homogeneity. Furthermore, the proposed method had good test-retest reproducibility validated by parcellating the hippocampus based on three repeated resting state fMRI scans from 24 healthy adult subjects.

Alterations in Amygdala Connectivity in Internet Addiction Disorder.

Recent studies have revealed structural and functional abnormalities in amygdala due to Internet addiction (IA) associated with emotional disturbance. However, the role of amygdala connectivity that is responsible for emotion-cognition interactions is largely unknown in IA. This study aims to explore the amygdala connectivity abnormalities in IA. The functional and structural connectivity of bilateral amygdala were examined using seed-based connectivity analysis, and the structural integrity on white mater tracts passing through amygdala was also examined. Additionally, a correlation analysis was performed to investigate the relationship between brain connectivity and duration of IA. We found that IA subjects had decreased negative functional connectivity (FC) between amygdala and dorsolateral prefrontal cortex (DLPFC), and had increased negative FC between amygdala and precuneus and superior occipital gyrus (SOG). While IA subjects had decreased positive FC between amygdala and anterior cingulate cortex (ACC), and had increased positive FC between amygdala and thalamus. The FC between left amygdala and right DLPFC had significant correlation with duration of IA. The structural connectivity and integrity between amygdala and ACC were also decreased in IA subjects. These findings indicate that the amygdala connectivity is altered in IA subjects. The altered FC of amygdala-DLPFC is associated with duration of IA.

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

Wilson's disease patients with neurological symptoms have motor symptoms and cognitive deficits, including frontal executive, visuospatial processing, and memory impairments. Although the brain structural abnormalities associated with Wilson's disease have been documented, it remains largely unknown how Wilson's disease affects large-scale functional brain networks. In this study, we investigated functional brain networks in Wilson's disease. Particularly, we analyzed resting state functional magnetic resonance images of 30 Wilson's disease patients and 26 healthy controls. First, functional brain networks for each participant were extracted using an independent component analysis method. Then, a computationally efficient pattern classification method was developed to identify discriminative brain functional networks associated with Wilson's disease. Experimental results indicated that Wilson's disease patients, compared with healthy controls, had altered large-scale functional brain networks, including the dorsal anterior cingulate cortex and basal ganglia network, the middle frontal gyrus, the dorsal striatum, the inferior parietal lobule, the precuneus, the temporal pole, and the posterior lobe of cerebellum. Classification models built upon these networks distinguished between neurological WD patients and HCs with accuracy up to 86.9% (specificity: 86.7%, sensitivity: 89.7%). The classification scores were correlated with the United Wilson's Disease Rating Scale measures and durations of disease of the patients. These results suggest that Wilson's disease patients have multiple aberrant brain functional networks, and classification scores derived from these networks are associated with severity of clinical symptoms.

Reversibility of cerebral blood flow in patients with Cushing's disease after surgery treatment.

BACKGROUND AND OBJECTIVES: Cushing's disease (CD) patients have metabolic abnormalities in the brain caused by excessive exposure to endogenous cortisol. However, the reversibility of brain metabolism of CD patients after treatment remains largely unknown. METHODS: This study recruited 50 CD patients seeking treatment and 34 matched normal controls (NCs). The patients were treated with Transsphenoidal Adenomectomy (TSA) and reexamined 3 months later. Cerebral blood flow (CBF) of the patients was assessed using 3D pseudo-continuous arterial spin labelling (PCASL) imaging before the treatment and at the 3-month follow-up and were compared with CBF measure of the NCs using a whole-brain voxelwise group comparison method. For remitted patients, their CBF measures and hormone level measures, including adrenocorticotropic hormone (ACTH), 24-hour urinary free cortisol (24hUFC) and serum cortisol, were compared before and after the treatment. Finally, a correlation analysis was carried out to explore the relationship between changes of CBF and hormone level measures of the remitted CD patients. RESULTS: After the treatment, 45 patients reached remission. Compared with the NCs, the CD patients before the treatment exhibited significantly reduced CBF in cortical regions, including occipital lobe, parietal lobe, superior/middle/inferior temporal gyrus, superior/middle/inferior frontal gyrus, orbitofrontal cortex, precentral gyrus, middle/posterior cingulate gyrus, and rolandic operculum, as well as significantly increased CBF in subcortical structures, including caudate, pallidum, putamen, limbic lobe, parahippocampal gyrus, hippocampus, thalamus, and amygdala (p < 0.01, false discovery rate corrected). For the remitted patients, the change in CBF before and after the treatment displayed a spatial pattern similar to the difference between the NCs and the CD patients before the treatment, and no significant difference in CBF was observed between the NCs and the remitted CD patients after the treatment. The changes of 24hUFC were significantly correlated with the changes of averaged CBF within the subcortical region in the remitted patients (p = 0.01). CONCLUSIONS: Our findings demonstrate that the brain metabolic abnormalities of CD patients are reversible when their hormone level changes towards normal after surgery treatment.

Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation.

Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer's Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen's d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer's disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer's disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).

Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study.

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.

Metric Learning for Multi-atlas based Segmentation of Hippocampus.

Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.

Impaired functional default mode network in patients with mild neurological Wilson's disease.

Wilson's disease (WD) is an autosomal recessive metabolic disorder characterized by cognitive, psychiatric and motor signs and symptoms that are associated with structural and pathological brain abnormalities, in addition to liver changes. However, functional brain connectivity pattern of WD patients remains largely unknown. In the present study, we investigated functional brain connectivity pattern of WD patients using resting state functional magnetic resonance imaging. Particularly, we studied default mode network (DMN) using posterior cingulate cortex (PCC) based seed functional connectivity analysis and graph theoretic functional brain network analysis tools, and investigated the relationship between the DMN's functional connectivity pattern of WD patients and their attention functions examined using the attention network test (ANT). Our results demonstrated that WD patients had altered DMN's functional connectivity and lower local and global network efficiency compared with normal controls (NCs). In addition, the functional connectivity between left inferior temporal cortex and right lateral parietal cortex was correlated with altering function, one of the attention functions, across WD and NC subjects. These findings indicated that the DMN's functional connectivity was altered in WD patients, which might be correlated with their attention dysfunction.

Improvement of white matter and functional connectivity abnormalities by repetitive transcranial magnetic stimulation in crossed aphasia in dextral.

As a special aphasia, the occurrence of crossed aphasia in dextral (CAD) is unusual. This study aims to improve the language ability by applying 1 Hz repetitive transcranial magnetic stimulation (rTMS). We studied multiple modality imaging of structural connectivity (diffusion tensor imaging), functional connectivity (resting fMRI), PET, and neurolinguistic analysis on a patient with CAD. Furthermore, we applied rTMS of 1 Hz for 40 times and observed the language function improvement. The results indicated that a significantly reduced structural and function connectivity was found in DTI and fMRI data compared with the control. The PET imaging showed hypo-metabolism in right hemisphere and left cerebellum. In conclusion, one of the mechanisms of CAD is that right hemisphere is the language dominance. Stimulating left Wernicke area could improve auditory comprehension, stimulating left Broca's area could enhance expression, and the results outlasted 6 months by 1 Hz rTMS balancing the excitability inter-hemisphere in CAD.

Optimizing affinity measures for parcellating brain structures based on resting state fMRI data: a validation on medial superior frontal cortex.

BACKGROUND: Parcellating brain structures into functionally homogeneous subregions based on resting state fMRI data could be achieved by grouping image voxels using clustering algorithms, such as normalized cut. The affinity between brain voxels adopted in the clustering algorithms is typically characterized by a combination of the similarity of their functional signals and their spatial distance with parameters empirically specified. However, improper parameter setting of the affinity measure may result in parcellation results biased to spatial smoothness. NEW METHOD: To obtain a functionally homogeneous and spatially contiguous brain parcellation result, we propose to optimize the affinity measure of image voxels using a constrained bi-level programming optimization method. Particularly, we first identify the space of all possible parameters that are able to generate spatially contiguous brain parcellation results. Then, within the constrained parameter space we search those leading to the brain parcellation results with optimal functional homogeneity and spatial smoothness. RESULTS AND COMPARISON WITH EXISTING METHODS: The method has successfully parcellated medial superior frontal cortex into supplementary motor area (SMA) and pre-SMA for 106 subjects based on their resting state fMRI data. These results have been validated through functional connectivity analysis and meta-analysis of existing functional imaging studies and compared with those obtained by state-of-the-art brain parcellation methods. CONCLUSIONS: The validation results have demonstrated that our method could obtain brain parcellation results consistent with the existing functional anatomy knowledge, and the comparison results have further demonstrated that optimizing affinity measure could improve the brain parcellation's robustness and functional homogeneity.

Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns.

Spatial alignment of functional magnetic resonance images (fMRI) of different subjects is a necessary precursor to improve functional consistency across subjects for group analysis in fMRI studies. Traditional structural MRI (sMRI) based registration methods cannot achieve accurate inter-subject functional consistency in that functional units are not necessarily located relative to anatomical structures consistently due to functional variability across subjects. Although spatial smoothing commonly used in fMRI data preprocessing can reduce the inter-subject functional variability, it may blur the functional signals and thus lose the fine-grained information. To overcome the limitations of exiting techniques, in this paper, we propose a novel method for spatial normalization of fMRI data by matching their multi-range functional connectivity patterns progressively. In particular, the image registration of different subjects is achieved by maximizing inter-subject similarity of their functional images' local functional connectivity patterns that characterize functional connectivity information for each voxel of the images to its spatial neighbors. The neighborhood size for computing the local functional connectivity patterns is gradually increased with the progression of image registration to capture the functional connectivity information in larger ranges. We also adopt the congealing groupwise image registration strategy to simultaneously warp a group of subjects to an unbiased template. Experimental comparisons between the proposed method and other fMRI image registration methods have demonstrated that the proposed method could achieve superior registration performance for resting state fMRI data. Experiment results based on real resting-state fMRI data have further demonstrated that the proposed fMRI registration method can achieve a statistically significant improvement in functional consistency across subjects.