Prognostic value of imbalanced interhemispheric functional coordination in early therapeutic efficacy in major depressive disorder

Aberrant resting-state voxel-mirrored homotopic connectivity in major depressive disorder with and without anxiety

OBJECTIVE

Prior researchers have identified distinct differences in functional connectivity neuroimaging characteristics among MDD patients. However, the auxiliary diagnosis and subtype differentiation roles of VMHC values in MDD patients have yet to be fully understood. We aim to explore the separating ability of VMHC values in patients with anxious MDD or with non-anxious MDD and HCs.

METHODS

We recruited 90 patients with anxious MDD, 69 patients with non-anxious MDD and 84 HCs. We collected a set of clinical variables included HAMD-17 scores, HAMA scores and rs-fMRI data. The data were analyzed combining difference analysis, SVM, correlation analysis and ROC analysis.

RESULTS

Relative to HCs, non-anxious MDD patients displayed significant lower VMHC values in the insula and PCG, and anxious MDD patients displayed a significant decrease in VMHC values in the cerebellum_crus2, STG, postCG, MFG and IFG. Compared with non-anxious MDD patients, the anxious MDD showed significant enhanced VMHC values in the PCG. The VMHC values in the insula and cerebellum_crus2 regions showed a better ability to discriminate HCs from patients with non-anxious MDD or with anxious MDD. The VMHC values in PCG showed a better ability to discriminate patients with anxious MDD and non-anxious MDD patients.

CONCLUSION

The VMHC values in the insula and cerebellum_crus2 regions could be served as imaging markers to differentiate HCs from patients with non-anxious MDD or with anxious MDD respectively. And the VMHC values in the PCG could be used to discriminate patients with anxious MDD from the non-anxious MDD patients.

Decreased inter-hemispheric cooperation in major depressive disorder and its association with neurotransmitter profiles

BACKGROUND

Inter-hemispheric cooperation is a prominent feature of the human brain, and previous neuroimaging studies have revealed aberrant inter-hemispheric cooperation patterns in patients with major depressive disorder (MDD). Typically, inter-hemispheric cooperation is examined by calculating the functional connectivity (FC) between each voxel in one hemisphere and its anatomical (structurally homotopic) counterpart in the opposite hemisphere. However, bilateral hemispheres are actually asymmetric in anatomy.

METHODS

In the present study, we utilized connectivity between functionally homotopic voxels (CFH) to investigate abnormal inter-hemispheric cooperation in 96 MDD patients compared to 173 age- and sex-matched healthy controls (HCs). In addition, we analyzed the spatial correlations between abnormal CFH and the density maps of 13 neurotransmitter receptors and transporters.

RESULTS

The CFH values in bilateral orbital frontal gyri and bilateral postcentral gyri were abnormally decreased in patients with MDD. Furthermore, these CFH abnormalities were correlated with clinical symptoms. In addition, the abnormal CFH pattern in MDD patients was spatially correlated with the distribution pattern of 5-HT1AR.

LIMITATIONS

drug effect; the cross-sectional research design precludes causal inferences; the neurotransmitter atlases selected were constructed from healthy individuals rather than MDD patients.

CONCLUSION

These findings characterized the abnormal inter-hemispheric cooperation in MDD using a novel method and the underlying neurotransmitter mechanism, which promotes our understanding of the pathophysiology of depression.

Distinct homotopic functional connectivity patterns of the amygdalar sub-regions as biomarkers in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) affects multiple functional neural networks. Neuroimaging studies using resting-state functional connectivity (FC) have focused on the amygdala but did not assess changes in connectivity between the left and right amygdala. The current study aimed to examine the inter-hemispheric functional connectivity (homotopic FC, HoFC) between different amygdalar sub-regions in patients with MDD compared to healthy controls, and to examine whether amygdalar sub-regions' HoFC also predicts response to Serotonin Selective Reuptake Inhibitors (SSRIs).

METHOD

Sixty-seven patients with MDD and 64 matched healthy controls were recruited. An MRI scan focusing on resting state fMRI and clinical and cognitive evaluations were performed. An atlas seed-based approach was used to identify the lateral and medial sub-regions of the amygdala. HoFC of these sub-regions was compared between groups and correlated with severity of depression, and emotional processing performance. Baseline HoFC levels were used to predict response to SSRIs after 2 months of treatment.

RESULTS

Patients with MDD demonstrated decreased inter-hemispheric FC in the medial (F3,120 = 4.11, p = 0.008, η2 = 0.096) but not in the lateral (F3,119 = 0.29, p = 0.82, η2 = 0.008) amygdala compared with healthy controls. The inter-hemispheric FC of the medial sub-region correlated with symptoms severity (r = -0.33, p < 0.001) and emotional processing performance (r = 0.38, p < 0.001). Moreover, it predicted treatment response to SSRIs 65.4 % of the cases.

LIMITATIONS

The current study did not address FC changes in MDD biotypes. In addition, structural connectivity was not examined.

CONCLUSIONS

Using a unique perspective of the amygdalar distinct areas elucidated differential inter-hemispheric FC patterns in MDD patients, emphasizing the role of interhemispheric communication in depression.

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l'efficacité précoce d'un antidépresseur chez des patients souffrant du trouble dépressif majeur d'après les caractéristiques multidimensionnelles de la méthylation de l'ADN du gène P11 et de la IRMf-rs

OBJECTIVE

This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).

METHODS

A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening.

RESULTS

The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models.

CONCLUSION

The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Disrupted interhemispheric coordination of sensory-motor networks and insula in major depressive disorder

Objective

Prior researches have identified distinct differences in neuroimaging characteristics between healthy controls (HCs) and patients with major depressive disorder (MDD). However, the correlations between homotopic connectivity and clinical characteristics in patients with MDD have yet to be fully understood. The present study aimed to investigate common and unique patterns of homotopic connectivity and their relationships with clinical characteristics in patients with MDD.

Methods

We recruited 42 patients diagnosed with MDD and 42 HCs. We collected a range of clinical variables, as well as exploratory eye movement (EEM), event-related potentials (ERPs) and resting-state functional magnetic resonance imaging (rs-fMRI) data. The data were analyzed using correlation analysis, support vector machine (SVM), and voxel-mirrored homotopic connectivity (VMHC).

Results

Compared with HCs, patients with MDD showed decreased VMHC in the insula, and increased VMHC in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus. SVM analysis using VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula, or VMHC values in the superior/middle occipital gyrus and insula as inputs can distinguish HCs and patients with MDD with high accuracy, sensitivity, and specificity.

Conclusion

The study demonstrated that decreased VMHC in the insula and increased VMHC values in the sensory-motor networks may be a distinctive neurobiological feature for patients with MDD, which could potentially serve as imaging markers to discriminate HCs and patients with MDD.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

Impaired robust interhemispheric function integration of depressive brain from REST-meta-MDD database in China

BACKGROUND

Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations.

METHODS

In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns.

RESULTS

As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q < 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs.

CONCLUSIONS

We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response

BACKGROUND

Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time.

METHODS

A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks.

RESULTS

We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets.

LIMITATIONS

Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result.

CONCLUSIONS

Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.

Altered Brain Network Centrality in Patients with Diabetic Optic Neuropathy: A Resting-State FMRI Study

OBJECTIVE

Recent studies have suggested that diabetic optic neuropathy (DON) independently increases the incidence of brain diseases like cerebral infarction and hemorrhage. In this study, voxel-level degree centrality (DC) was used to study potential changes in functional network brain activity in DON patients.

METHODS

The study included 14 DON patients and 14 healthy controls (HCs) matched by age, sex, and weight. All subjects underwent resting functional magnetic resonance imaging. Receiver operating characteristic curves and Pearson correlation analysis were performed.

RESULTS

The DC values of the left frontal mid-orb and right middle frontal gyrus/right frontal sup were significantly lower in DON patients compared to HCs. The DC value of the left temporal lobe was also significantly higher than in HCs.

CONCLUSION

Three different brain regions show DC changes in DON patients, suggesting common optic neuropathy in the context of diabetes and providing new ideas for treating optic nerve disease in patients with long-term diabetes.

ABBREVIATIONS

AUC = area under the curve; BCVA = best corrected visual acuity; DC = degree centrality; DON = diabetic optic neuropathy; fMRI = functional magnetic resonance imaging; HC = healthy control; LFMO = left frontal mid orb; LTL = left temporal lobe; RFS = right frontal sup; RMFG = right middle frontal gyrus; ROC = receiver operating characteristic.

Brain structural changes associated with aberrant functional responses to the Valsalva maneuver in heart failure

Heart failure (HF) patients show inability to regulate autonomic functions in response to autonomic challenges. The autonomic deficits may stem from brain tissue injury in central autonomic regulatory areas, resulting from ischemic and hypoxic processes accompanying the condition. However, the direct evaluation of correlations between brain structural injury and functional timing and magnitude of neural signal patterns within affected areas, which may lead to impaired autonomic outflow, is unclear. In this study, we evaluate neural responses to the Valsalva maneuver with blood oxygen level-dependent functional magnetic resonance imaging in 29 HF patients and 35 control subjects and brain structural changes using diffusion tensor imaging-based mean diffusivity in a subsample of 19 HF and 24 control subjects. HF showed decreased neural activation in multiple autonomic and motor control areas, including cerebellum cortices, vermis, left insular, left putamen, and bilateral postcentral gyrus. Structural brain changes emerged in similar autonomic, as well as cognitive and mood regulation areas. Functional MRI responses in cerebellum and insula in HF subjects are delayed or decreased in magnitude to the challenge. The impaired functional responses of insular and cerebellar sites are correlated with the severity of tissue changes. These results indicate that the functions of insular and cerebellar regions, sites that are involved in autonomic regulation, are compromised, and that autonomic deficits in these areas have brain structural basis for impaired functions. Our study enhanced our understanding of brain structural and functional alterations underlying impaired autonomic regulations in HF subjects.

Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder

Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common and specific alterations between these two types of networks and whether the alterations were differentially involved in the two hemispheres. Multimodal MRI data were collected from 35 MDD patients and 35 healthy controls, whose functional and structural hemispheric networks were constructed, characterized, and compared. We found that functional brain networks were profoundly altered at multiple levels, while structural brain networks were largely intact in patients with MDD. Specifically, the functional alterations included decreases in intra-hemispheric (left and right) and inter-hemispheric (heterotopic) functional connectivity; decreases in local, global and normalized global efficiency for both hemispheric networks; increases in normalized local efficiency for the left hemispheric networks; and decreases in intra-hemispheric integration and inter-hemispheric communication in the dorsolateral superior frontal gyrus, anterior cingulate gyrus and hippocampus. Regarding hemispheric asymmetry, largely similar patterns were observed between the functional and structural networks: the right hemisphere was over-connected and more efficient than the left hemisphere globally; the occipital and partial regions exhibited leftward asymmetry, and the frontal and temporal sites showed rightward lateralization with regard to regional connectivity profiles locally. Finally, the functional-structural coupling of intra-hemispheric connections was significantly decreased and correlated with the disease severity in the patients. Overall, this study demonstrates modality- and hemisphere-dependent and invariant network alterations in MDD, which are helpful for understanding elaborate and characteristic patterns of integrative dysfunction in this disease.

Extreme male developmental trajectories of homotopic brain connectivity in autism

It has been proposed that autism spectrum disorder (ASD) may be characterized by an extreme male brain (EMB) pattern of brain development. Here, we performed the first investigation of how age-related changes in functional brain connectivity may be expressed differently in females and males with ASD. We analyzed resting-state functional magnetic resonance imaging data of 107 typically developing (TD) females, 114 TD males, 104 females, and 115 males with ASD (6-26 years) from the autism brain imaging data exchange repository. We explored how interhemispheric homotopic connectivity and its maturational curvatures change across groups. Differences between ASD and TD and between females and males with ASD were observed for the rate of changes in connectivity in the absence of overall differences in connectivity. The largest portion of variance in age-related changes in connectivity was described through similarities between TD males, ASD males, and ASD females, in contrast to TD females. We found that shape of developmental curvature is associated with symptomatology in both males and females with ASD. We demonstrated that females and males with ASD tended to follow the male pattern of developmental changes in interhemispheric connectivity, supporting the EMB theory of ASD.

Evidence of altered brain network centrality in patients with diabetic nephropathy and retinopathy: an fMRI study using a voxel-wise degree centrality approach

BACKGROUND

Over recent years, some researchers believe that diabetic nephropathy (DN) and diabetic retinopathy (DR) both independently increase the incidence of brain diseases, such as stroke, cerebral infarction, and cerebral hemorrhage. In the present study, we used the voxel-wise degree centrality (DC) method to investigate potential changes of functional network brain activity in patients with DN and retinopathy (DNR).

METHODS

Twenty DNR patients (9 men, 11 women) and 20 healthy controls (HCs; 9 men, 11 women) were recruited; the controls were matched for age, sex, and educational background. All subjects underwent resting-state functional magnetic resonance imaging. Ophthalmoscopy, renal biopsy and single-photon emission computed tomography were used to evaluate microvascular lesions in the eye and kidney. Data were categorized using receiver operating characteristic curves, and correlation analysis was performed using Pearson's correlation analysis.

RESULTS

Compared with HCs, DNR patients showed reduced mean DC values in the right inferior temporal gyrus (RITG) and left subcallosal gyrus regions (LSG) and increased mean DC values in the bilateral precuneus (BP). Moreover, mean DC in the BP was correlated with renal estimated glomerular filtration rate (eGFR; r = 0.762). The area under the curve (AUC) value was 0.829 for BP and 0.839 for RITG and LSG.

CONCLUSION

DNR patients showed dysfunction in three different brain regions. The linear correlation between eGFR and mean brain DC values indicates the presence of common diabetic microangiopathy in the brain and kidney, which may provide new ideas for multiorgan microvascular lesions of diabetics.

Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review

PURPOSE OF REVIEW

Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies.

RECENT FINDINGS

Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder's inherent clinical heterogeneity. These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.

Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder

Several behavioral and neuroimaging markers could be used to predict eventual antidepressant medication (ADM) outcomes in patients with major depressive disorder (MDD). However, these predictors are either subjective or complex, which has limited their clinical use. Thus, we aimed to identify an objective and easy-to-get marker to predict early therapeutic efficacy. Forty-seven drug-naïve patients with MDD and 47 age-, gender- and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scans. We calculated the variable coefficient (VC) of the global signal for each subject. Baseline Hamilton Rating Scale for Depression (HRSD) score and that after 2 weeks of ADM were assessed for patients. Although there was no difference in VC between patients with MDD and healthy controls, we found a significant positive correlation between the VC and the decline rate of HRSD scores in the patients. Compared with the non-responding depression (NRD) group, the treatment-responsive depression (TRD) group had a higher VC. Receiver operator characteristic curve analysis revealed that the VC exhibited a good ability to differentiate TRD from NRD. In addition, the linear and logistic regression analyses showed that the VC was a significant predictor of the decline rate of HRSD scores and the antidepressant treatment response. These findings suggest that variance of the global signal may serve as a useful marker to help clinicians find an appropriate drug for individuals with MDD at the earliest opportunity and then further to facilitate personalized therapy.

Analysis of voxel-mirrored homotopic connectivity in medication-free, current major depressive disorder

BACKGROUND

Recent neuroimaging studies suggest that abnormal function connectivity exists in patients with major depressive disorder (MDD). The aim of this study was to further analyze the underlying neural mechanism of MDD and explore whether clinical characteristics are correlated with the alerted homotopic connectivity in patients with MDD.

METHODS

Using voxel-mirrored homotopic connectivity (VMHC) during resting state, we compared 80 medication-free patients having current episodes of MDD and 124 never-depressed healthy controls (HCs) matched for age and gender.

RESULTS

We found decreased VMHC in patients with MDD in bilateral posterior cingulate cortex (PCC) extending to precuneus (Pre) compared with the HCs, which provided strong support for the potential role of PCC/Pre in recognizing interhemispheric connectivity deficits of MDD. Negative correlation between illness course and VMHC in PCC was observed as well.

LIMITATIONS

First, we just compared the functional connectivity at a rest state but not under a specific task. Second, we did not mitigate the delayed effect on the measurable alterations in homotopic brain activity. Third, we did not make a longitudinal comparison after patients receiving therapeutic drugs.

CONCLUSIONS

These findings that linking illness course with functional brain changes in depression help us understand the neural architecture of MDD.

Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation

BACKGROUND

Although supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory.

NEW METHODS

To address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume.

RESULTS

The performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods.

CONCLUSIONS

The proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.

Altered resting-state hippocampal and caudate functional networks in patients with obstructive sleep apnea

INTRODUCTION

Brain structural injury and metabolic deficits in the hippocampus and caudate nuclei may contribute to cognitive and emotional deficits found in obstructive sleep apnea (OSA) patients. If such contributions exist, resting-state interactions of these subcortical sites with cortical areas mediating affective symptoms and cognition should be disturbed. Our aim was to examine resting-state functional connectivity (FC) of the hippocampus and caudate to other brain areas in OSA relative to control subjects, and to relate these changes to mood and neuropsychological scores.

METHODS

We acquired resting-state functional magnetic resonance imaging (fMRI) data from 70 OSA and 89 healthy controls using a 3.0-Tesla magnetic resonance imaging scanner, and assessed psychological and behavioral functions, as well as sleep issues. After standard fMRI data preprocessing, FC maps were generated for bilateral hippocampi and caudate nuclei, and compared between groups (ANCOVA; covariates, age and gender).

RESULTS

Obstructive sleep apnea subjects showed significantly higher levels of anxiety and depressive symptoms over healthy controls. In OSA subjects, the hippocampus showed disrupted FC with the thalamus, para-hippocampal gyrus, medial and superior temporal gyrus, insula, and posterior cingulate cortex. Left and right caudate nuclei showed impaired FC with the bilateral inferior frontal gyrus and right angular gyrus. In addition, altered limbic-striatal-cortical FC in OSA showed relationships with behavioral and neuropsychological variables.

CONCLUSIONS

The compromised hippocampal-cortical FC in OSA may underlie depression and anxious mood levels in OSA, while impaired caudate-cortical FC may indicate deficits in reward processing and cognition. These findings provide insights into the neural mechanisms underlying the comorbidity of mood and cognitive deficits in OSA.