Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

In this study, the functional magnetic resonance imaging data from Alzheimer's patients and nondemented older adults are investigated using graph theory. Voxel-based graphs were built on the data obtained from an international data center and their clustering coefficients and characteristic path lengths were calculated. No significant differences were found between the two groups in these two parameters. Afterwards, the voxels acting as hubs and their corresponding brain regions were determined. Some differences were observed with respect to the hub locations. These results point to the fact that rather than a change in the global organization of the brain, Alzheimer's disease causes a functional change in some brain regions.

PurposeTo investigate the concurrent brain structural and functional alterations related to cognition in patients with cerebral small vessel disease (CSVD).MethodsThirty normal controls and 65 CSVD patients, including 33 patients with mild cognitive impairment and 32 patients with no cognitive impairment were included. Structural and resting-state functional MRI measures, including gray matter volume (GMV) and white matter volume (WMV) using voxel-based morphometry (VBM) analysis and amplitude of low-frequency fluctuation (ALFF), were obtained and compared among the three groups. Associations between cognitive scores and ALFF/VBM coupling in the co-altered regions were investigated in CSVD groups.ResultsMultiple brain regions showed significant differences in GMV and WMV among the three groups (P < 0.01). Abnormal ALFF among the three groups was identified in the left putamen, Rolandic operculum, fusiform gyrus, caudate, parahippocampal gyrus, insula, middle cingulum, bilateral lingual gyrus, and right frontal lobe (P < 0.01). Importantly, a decrease in VBM and increase in ALFF in the left parahippocampal gyrus, caudate and Rolandic operculum, a reduction of the WMV and ALFF in the right superior frontal lobe, and a united rise of GMV and ALFF in the left caudate were detected in CSVD groups. In addition, abnormal ALFF/VBM coupling was significantly related to multiple cognitive assessments.ConclusionThe study indicated a reversed pattern of the brain structural deficits and functional activation in the left parahippocampal gyrus, caudate, and Rolandic operculum, suggesting structure–function decoupling in CSVD groups. These might help further understand the pathophysiological mechanism of CSVD.

IntroductionThe analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage. Previous studies usually estimate FBNs using full band Blood Oxygen Level Dependent (BOLD) signal. However, a single band is not sufficient to capture the diagnostic and prognostic information contained in multiple frequency bands.MethodTo address this issue, we propose a novel multi-band network fusion framework (MBNF) to combine the various information (e.g., the diversification of structural features) of multi-band FBNs. We first decompose the BOLD signal adaptively into two frequency bands named high-frequency band and low-frequency band by the ensemble empirical mode decomposition (EEMD). Then the similarity network fusion (SNF) is performed to blend two networks constructed by two frequency bands together into a multi-band fusion network. In addition, we extract the features of the fused network towards a better classification performance.ResultTo verify the validity of the scheme, we conduct our MBNF method on the public ADNI database for identifying subjects with AD/MCI from normal controls.DiscussionExperimental results demonstrate that the proposed scheme extracts rich multi-band network features and biomarker information, and also achieves better classification accuracy.

Patients with traumatic brain injury show clear impairments in behavioural flexibility and inhibition that often persist beyond the time of injury, affecting independent living and psychosocial functioning. Functional magnetic resonance imaging studies have shown that patients with traumatic brain injury typically show increased and more broadly dispersed frontal and parietal activity during performance of cognitive control tasks. We constructed binary and weighted functional networks and calculated their topological properties using a graph theoretical approach. Twenty-three adults with traumatic brain injury and 26 age-matched controls were instructed to switch between coordination modes while making spatially and temporally coupled circular motions with joysticks during event-related functional magnetic resonance imaging. Results demonstrated that switching performance was significantly lower in patients with traumatic brain injury compared with control subjects. Furthermore, although brain networks of both groups exhibited economical small-world topology, altered functional connectivity was demonstrated in patients with traumatic brain injury. In particular, compared with controls, patients with traumatic brain injury showed increased connectivity degree and strength, and higher values of local efficiency, suggesting adaptive mechanisms in this group. Finally, the degree of increased connectivity was significantly correlated with poorer switching task performance and more severe brain injury. We conclude that analysing the functional brain network connectivity provides new insights into understanding cognitive control changes following brain injury.

This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.

To explore the spatial patterns of the amplitude of low-frequency fluctuation (ALFF) in patients with hepatic encephalopathy (HE) of varying severity and to correlate these fluctuations with clinical markers of HE. This study was approved by the local ethics committee, and written informed consent was obtained from all participants. Twenty-nine patients with HE (15 with overt and 14 with minimal HE) and 17 healthy control subjects underwent resting-state functional magnetic resonance (MR) imaging. The ALFF, an index reflecting the amplitudes of spontaneous brain activity, was compared among patients with overt HE, patients with minimal HE, and control subjects with analysis of variance tests and t tests between each pair. Pearson correlation analysis was performed between the ALFF and the venous blood ammonia level and Child-Pugh score of all patients with HE. Compared with control subjects, patients with overt and minimal HE showed decreased ALFF mainly in regions within the default-mode network (DMN) and increased ALFF in the cerebellum and middle temporal gyrus. Compared with patients with minimal HE, those with overt HE showed decreased ALFF in DMN regions and increased ALFF in the posterior insular cortex (P<.05, corrected for multiple comparisons). Both the venous blood ammonia levels and Child-Pugh scores of individual patients with HE showed negative correlation with ALFF within some DMN regions, whereas they showed positive correlation with ALFF in the posterior insular cortex (P<.05, corrected for multiple comparisons). Patients with HE have diffuse abnormalities in intrinsic brain activity. The levels of decreased ALFF in the DMN and increased ALFF in the posterior insular cortex are dependent on the severity of HE, suggesting continuous impairment of the DMN and a compensatory role of the insula during the progression of HE. Resting-state functional MR imaging with ALFF analysis may be a noninvasive modality with which to detect the progression of HE.

Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem, where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal, and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality, and conditional causality analysis. In particular, our solution provides a more accurate estimation of the frequency-domain causality compared with the classical Geweke causality measure. Using synthetic examples and in vivo resting-state functional magnetic resonance imaging data from the human connectome project, we show that the proposed solution is more accurate at revealing frequency-domain linear dependence among high-dimensional signals.

Facial palsy therapies based on cortical plasticity are in development, but facial synkinesis progress is limited. Studying neural plasticity characteristics, especially network organization and its constitutive elements (nodes/edges), is the key to overcome the bottleneck. We studied 55 participants (33 facial synkinesis patients, 22 healthy controls) with clinical assessments, functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI). We analyzed rich-club organization and metrics of structural brain networks (rich-club coefficients, strength, degree, density, and efficiency). Functional brain network metrics, including functional connectivity and its coupling with the structural network, were also computed. Patients displayed reduced strength and density of rich-club nodes and edges, as well as decreased global efficiency. All nodes exhibited decreased nodal efficiency in patients. Patients had significantly increased functional connectivity and decreased structural-functional coupling strength in rich-club nodes, rich-club edges, and feeder edges. Our study indicates that facial synkinesis patients have weakened structural connections but enhanced functional transmission from rich-club nodes. The loss of connections and efficiency in structural network may trigger compensatory increases in functional connectivity of rich-club nodes. Two potential biomarkers, rich-club edge density and structural-functional coupling strength, may serve as indicators of disease outcome. These findings provide valuable insights into synkinesis mechanisms and offer potential targets for cortical intervention.

Purpose: To investigate spontaneous brain activity amplitude alterations in single and relapsing optic neuritis (sON and rON, respectively) and their relationships with clinical variables.Methods: In total, 42 patients with sON, 35 patients with rON and 50 healthy volunteers were recruited. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans were acquired for all participants and compared to investigate the changes in the amplitude of low-frequency fluctuations (ALFFs) among the three groups. The relationships between the ALFFs in regions with significant differences in the groups and clinical variables, including the logarithm of minimal angle of resolution (LogMAR), Expanded Disability Status Scale (EDSS) score and disease duration, were further explored.Results: Compared with healthy volunteers, the sON and rON patients showed significantly decreased ALFFs in several regions of the occipital and temporal lobes (i.e., inferior occipital gyrus and superior temporal gyrus; corrected p < 0.01 using AlphaSim). The sON patients showed significantly increased ALFFs in the left caudate and certain regions in the frontal lobes (i.e., medial frontal gyrus), whereas the rON patients showed increased ALFFs in the bilateral inferior temporal gyrus and left medial frontal gyrus (corrected p < 0.01 using AlphaSim). Significantly decreased ALFFs were observed in the right inferior parietal lobule (IPL), left posterior cingulate and precuneus in the rON patients compared with those in the sON patients (corrected p < 0.01 using AlphaSim). Significant correlations were observed between the disease duration and ALFF in the left middle temporal gyrus, left inferior occipital gyrus, right lingual gyrus and right IPL (p < 0.05).Conclusion: Functional impairment and adaptation occurred in both the sON and rON patients. Impairment mainly involved the occipital cortex, and functional adaptions predominantly occurred in the frontal lobe. Functional damage was more severe in the rON patients than in the sON patients and correlated with the disease duration.

Objective To explore the changes of rest-state functional MRI (fMRI) features in patients with Parkinson's disease. Methods Twelve patients with Parkinson's disease, collected in our hospital from September 2013 to March 2014, and 12 volunteers (controls) were recruited. The fMRI based on blood oxygen level dependent (BOLD) was performed at resting state; the differences of amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo) were statistically analyzed; and the correlations of values of ALFF and ReHo with uniformed Parkinsonism rating scale (UPDRS) scores and Hoehn-Yahr (H-Y) grading were analyzed. Results PD patients exhibited increased ALFF values in the limbic lobe, the right parahippocampal gyrus and the right middle temporal pole, and reduced ALFF values in the left thalamus as compared with the controls. PD patients exhibited increased ReHo values in the superior medial frontal gyrus, the left middle frontal gyrus, brodmann area (BA) 9 and medial frontal gyrus, and reduced ReHo values in the left rolandic, the left superior temporal gyrus and insula lobe as compared with the controls. ALFF values were positively correlated with the UPDRS III scores in the right posterior cerebellum lobe and cerebellar tonsil. ALFF values and H-Y grading were negatively correlated on the left superior frontal gyrus. Negative correlation was noted between the ReHo values and UPDRS III scores in the right superior temporal gyrus and insular lobe. Positive correlation was noted between the ReHo values and H-Y grading in medial frontal gyrus, limbic lobe, anterior cingulate, medial orbitofrontal area, middle superior frontal gyrus, BA32, left superior frontal gyrus and BA10. Conclusion By resting-state fMRI, abnormal brain function in Parkinson's disease was noted; ReHo and ALFF showed abnormal changes in the connection of neuronal networks, and the compensatory response may lead to the increased brain function. Key words: Parkinson’s disease; Functional MR imaging; Regional homogeneity; Amplitude of low frequency fluctuation

Backgrounds: Schizophrenia (SCZ) and obsessive-compulsive disorder (OCD) are classified as two chronic psychiatric disorders with high comorbidity rate and shared clinical symptoms. Abnormal spontaneous brain activity within the cortical–striatal neural circuits has been observed in both disorders. However, it is unclear if the common or distinct neural abnormalities underlie the neurobiological substrates in the resting state.Methods: Resting-state fMRI data were collected from 88 patients with SCZ, 58 patients with OCD, and 72 healthy control subjects. First, we examined differences in amplitude of low-frequency fluctuations (ALFF) among three groups. Resting-state functional connectivity (rsFC) analysis with the brain region that showed different ALFF as the seed was then conducted to identify the changes in brain networks. Finally, we examined the correlation between the altered activities and clinical symptoms.Results: Both the patients with SCZ and OCD showed increased ALFF in the right hippocampus and decreased ALFF in the left posterior cingulate cortex (PCC). SCZ patients exhibited increased ALFF in the left caudate [voxel-level family-wise error (FWE) P < 0.05] and decreased rsFC between the left caudate and right cerebellum, which correlated with positive symptoms. The left caudate showed increased rsFC with the right thalamus and bilateral supplementary motor complex (SMC) in OCD patients (cluster-level FWE P < 0.05).Conclusions: The hippocampus and PCC are common regions presenting abnormal local spontaneous neuronal activities in both SCZ and OCD, while the abnormality of the striatum can reflect the differences. Increased ALFF in the striatum and symptom-related weakened rsFC between the caudate and cerebellum showed SCZ specificity. Enhanced rsFC between the caudate and SMC may be a key characteristic in OCD. Our research shows the similarities and differences between the two diseases from the perspective of resting-state fMRI, which provides clues to understand the disease and find methods for treatment.

ObjectivesThis study aimed to examine whether the CACNA1C gene rs11832738 polymorphism and major depressive disorder (MDD) have an interactive effect on the untreated regional amplitude of low-frequency fluctuation (ALFF) and to determine whether regional ALFF mediates the association between CACNA1C rs11832738 and MDD.MethodsA total of 116 patients with MDD and 66 normal controls (NCs) were recruited. The MDD and NC groups were further divided into two groups according to genotype: carriers of the G allele (G-carrier group, GG/GA genotypes; MDD, n = 61; NC, n = 26) and AA homozygous group (MDD, n = 55; NC, n = 40). MDD was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Depression severity was assessed using the Hamilton Depression Scale-24 (HAMD-24) at baseline and follow-up (after 2 and 8 weeks of treatment). All subjects underwent functional MRI (fMRI) scans at baseline, and the ALFF was calculated to reflect spontaneous brain activity. The interactions between MDD and CACNA1C single nucleotide polymorphism rs11832738 were determined using two-way factorial analysis of covariance, with age, sex, education, and head motion as covariates. We performed mediation analysis to further determine whether regional ALFF strength could mediate the associations between rs11832738 and depression severity, MDD treatment efficacy.ResultsMDD had a main effect on regional ALFF distribution in three brain areas: the right medial frontal gyrus (MFG_R), the left anterior cingulate cortex (ACC_L), and the right cerebellum posterior lobe (CPL_R); CACNA1C showed a significant interactive effect with MDD on the ALFF of MFG_R. For CACNA1C G allele carriers, the ALFF of MFG_R had a significant positive correlation with the baseline HAMD-24 score. Exploratory mediation analysis revealed that the intrinsic ALFF in MFG_R significantly mediated the association between the CACNA1C rs11832738 polymorphism and baseline HAMD-24 score.ConclusionsA genetic variant in CACNA1C rs11832738 may influence depression severity in MDD patients by moderating spontaneous MFG_R activity.

Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal magnetic resonance (MR) images. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, the application of modified FCM algorithm for MR brain tumor detection is explored. Abnormal brain images from four tumor classes namely metastase, meningioma, glioma and astrocytoma are used in this work. A comprehensive feature vector space is used for the segmentation technique. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures.

Objective To investigate the changes of brain activity in adolescents with first-episode major depressive disorder (MDD) and its significance. Methods Totally 42 first-episode MDD patients with suicidal ideation (SU group), 17 MDD patients without suicidal ideation (NSU group) and 29 healthy controls (HC group) were examined by psychological assessment and resting-state functional magnetic resonance imaging (fMRI). The differences of brain functional activities between the three groups were compared by amplitude of low frequency fluctuation(ALFF). The relationship between the difference of zALFF value in brain regions and the severity of depression, intensity of suicidal ideation were further analyzed. Results The zALFF values in the left hippocampus(MNI: x=-21, y=-33, z=0) and left Frontal_Sup_Medial(MNI: x=3, y=33, z=63) of the three groups were significantly abnormal (P 20, uncorrected). The results of Post-hoc test showed that the zALFF value of left hippocampus increased significantly (F=28.931) and decreased significantly in left Frontal_Sup_Medial(F=16.648)in SU group , compared with NSU group and HC group(P 0.05). In SU group, the zALFF values of left hippocampus and left Frontal_Sup_Medial were not correlated with total scores of HAMD-24(r=-0.214, -0.103) and suicide ideation intensity factor scores(r=0.115, -0.055)(P>0.05). Conclusion Abnormal autonomic nervous activity exists in left hippocampus and left Frontal_Sup_Medial in adolescent MDD patients with suicidal ideation, which may be the neurological basis and biological index of suicidal ideation. Key words: Major depressive disorder; Suicidal ideation; Adolescents; Amplitude of low frequency fluctuation

In this paper, a clustering algorithm based on the fuzzy c-means algorithm (FCM) and the gradient descent method is presented. In the FCM, the minimization process of the objective function is proceeded by solving two equations alternatively in an iterative fashion. Each iteration requires the use of all the data at once. In our proposed approach one datum at a time is presented to the network, and the minimization is proceeded using the gradient descent method. Compared to FCM, the experimental results show that our algorithm is very competitive in terms of speed and stability of convergence for large number of data. >