Auditory resting-state network connectivity in tinnitus: a functional MRI study

The underlying functional neuroanatomy of tinnitus remains poorly understood. Few studies have focused on functional cerebral connectivity changes in tinnitus patients. The aim of this study was to test if functional MRI "resting-state" connectivity patterns in auditory network differ between tinnitus patients and normal controls. Thirteen chronic tinnitus subjects and fifteen age-matched healthy controls were studied on a 3 tesla MRI. Connectivity was investigated using independent component analysis and an automated component selection approach taking into account the spatial and temporal properties of each component. Connectivity in extra-auditory regions such as brainstem, basal ganglia/NAc, cerebellum, parahippocampal, right prefrontal, parietal, and sensorimotor areas was found to be increased in tinnitus subjects. The right primary auditory cortex, left prefrontal, left fusiform gyrus, and bilateral occipital regions showed a decreased connectivity in tinnitus. These results show that there is a modification of cortical and subcortical functional connectivity in tinnitus encompassing attentional, mnemonic, and emotional networks. Our data corroborate the hypothesized implication of non-auditory regions in tinnitus physiopathology and suggest that various regions of the brain seem involved in the persistent awareness of the phenomenon as well as in the development of the associated distress leading to disabling chronic tinnitus.     

Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.     

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations.     

Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis

ABSTRACT: BACKGROUND: Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls. METHODS: The whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n=22) and on age- and gender-matched, healthy control subjects (n=22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information. RESULTS: The results showed that 93.2% (p<0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight. CONCLUSIONS: This study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.     

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.     

Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

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Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity

Background

Local network connectivity disruptions in Alzheimer''s disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data.

Methodology/Principal Findings

18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions.

Conclusions/Significance

We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.     

Functional inter-cortical connectivity among motor-related cortices during motor imagery: A magnetoencephalographic study

Neural connectivity was measured during motor imagery (MI) and motor execution (ME) using magnetoencephalography in nine healthy subjects, MI, and at rest. Lower coherence values during ME and MI between sensorimotor areas than at rest, and lower values during MI between the left supplementary motor area and inferior frontal gyrus than ME suggested the sensorimotor network of MI functioned with similar connectivity to ME and that the inhibitory activity functioned continuously during MI, respectively.     

Altered effective connectivity network of the amygdala in social anxiety disorder: a resting-state FMRI study

The amygdala is often found to be abnormally recruited in social anxiety disorder (SAD) patients. The question whether amygdala activation is primarily abnormal and affects other brain systems or whether it responds "normally" to an abnormal pattern of information conveyed by other brain structures remained unanswered. To address this question, we investigated a network of effective connectivity associated with the amygdala using Granger causality analysis on resting-state functional MRI data of 22 SAD patients and 21 healthy controls (HC). Implications of abnormal effective connectivity and clinical severity were investigated using the Liebowitz Social Anxiety Scale (LSAS). Decreased influence from inferior temporal gyrus (ITG) to amygdala was found in SAD, while bidirectional influences between amygdala and visual cortices were increased compared to HCs. Clinical relevance of decreased effective connectivity from ITG to amygdala was suggested by a negative correlation of LSAS avoidance scores and the value of Granger causality. Our study is the first to reveal a network of abnormal effective connectivity of core structures in SAD. This is in support of a disregulation in predescribed modules involved in affect control. The amygdala is placed in a central position of dysfunction characterized both by decreased regulatory influence of orbitofrontal cortex and increased crosstalk with visual cortex. The model which is proposed based on our results lends neurobiological support towards cognitive models considering disinhibition and an attentional bias towards negative stimuli as a core feature of the disorder.     

Task-related effects on the temporal and spatial dynamics of resting-state functional connectivity in the default network

Recent evidence points to two potentially fundamental aspects of the default network (DN), which have been relatively understudied. One is the temporal nature of the functional interactions among nodes of the network in the resting-state, usually assumed to be static. The second is possible influences of previous brain states on the spatial patterns (i.e., the brain regions involved) of functional connectivity (FC) in the DN at rest. The goal of the current study was to investigate modulations in both the spatial and temporal domains. We compared the resting-state FC of the DN in two runs that were separated by a 45 minute interval containing cognitive task execution. We used partial least squares (PLS), which allowed us to identify FC spatiotemporal patterns in the two runs and to determine differences between them. Our results revealed two primary modes of FC, assessed using a posterior cingulate seed--a robust correlation among DN regions that is stable both spatially and temporally, and a second pattern that is reduced in spatial extent and more variable temporally after cognitive tasks, showing switching between connectivity with certain DN regions and connectivity with other areas, including some task-related regions. Therefore, the DN seems to exhibit two simultaneous FC dynamics at rest. The first is spatially invariant and insensitive to previous brain states, suggesting that the DN maintains some temporally stable functional connections. The second dynamic is more variable and is seen more strongly when the resting-state follows a period of task execution, suggesting an after-effect of the cognitive activity engaged during task that carries over into resting-state periods.     

Cognitive dysfunction in early multiple sclerosis: altered centrality derived from resting-state functional connectivity using magneto-encephalography

Background

Cognitive dysfunction in multiple sclerosis (MS) is frequent. Insight into underlying mechanisms would help to develop therapeutic strategies.

Objective

To explore the relationship of cognitive performance to patterns of nodal centrality derived from magneto-encephalography (MEG).

Methods

34 early relapsing-remitting MS patients (median EDSS 2.0) and 28 age- and gender-matched healthy controls (HC) had a MEG, a neuropsychological assessment and structural MRI. Resting-state functional connectivity was determined by the synchronization likelihood. Eigenvector Centrality (EC) was used to quantify for each sensor its connectivity and importance within the network. A cognition-score was calculated, and normalized grey and white matter volumes were determined. EC was compared per sensor and frequency band between groups using permutation testing, and related to cognition.

Results

Patients had lower grey and white matter volumes than HC, male patients lower cognitive performance than female patients. In HC, EC distribution showed highest nodal centrality over bi-parietal sensors (“hubs”). In patients, nodal centrality was even higher bi-parietally (theta-band) but markedly lower left temporally (upper alpha- and beta-band). Lower cognitive performance correlated to decreased nodal centrality over left temporal (lower alpha-band) and right temporal (beta-band) sensors, and to increased nodal centrality over right parieto-temporal sensors (beta-band). Network changes were most pronounced in male patients.

Conclusions

Partial functional disconnection of the temporal regions was associated with cognitive dysfunction in MS; increased centrality in parietal hubs may reflect a shift from temporal to possibly less efficient parietal processing. To better understand patterns and dynamics of these network changes, longitudinal studies are warranted, also addressing the influence of gender.     

The neuronal correlates of digits backward are revealed by voxel-based morphometry and resting-state functional connectivity analyses

Digits backward (DB) is a widely used neuropsychological measure that is believed to be a simple and effective index of the capacity of the verbal working memory. However, its neural correlates remain elusive. The aim of this study is to investigate the neural correlates of DB in 299 healthy young adults by combining voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analyses. The VBM analysis showed positive correlations between the DB scores and the gray matter volumes in the right anterior superior temporal gyrus (STG), the right posterior STG, the left inferior frontal gyrus and the left Rolandic operculum, which are four critical areas in the auditory phonological loop of the verbal working memory. Voxel-based correlation analysis was then performed between the positive rsFCs of these four clusters and the DB scores. We found that the DB scores were positively correlated with the rsFCs within the salience network (SN), that is, between the right anterior STG, the dorsal anterior cingulate cortex and the right fronto-insular cortex. We also found that the DB scores were negatively correlated with the rsFC within an anti-correlation network of the SN, between the right posterior STG and the left posterior insula. Our findings suggest that DB performance is related to the structural and functional organizations of the brain areas that are involved in the auditory phonological loop and the SN.     

Sex-dependent correlations between the personality dimension of harm avoidance and the resting-state functional connectivity of amygdala subregions

Harm avoidance (HA) is a personality dimension involving the tendency to respond intensely to signals of aversive stimuli. Many previous neuroimaging studies have associated HA scores with the structural and functional organization of the amygdala, but none of these studies have evaluated the correlation between HA score and amygdala resting-state functional connectivity (rsFC). Moreover, the amygdala is not a homogeneous structure, and it has been divided into several structurally and functionally distinct subregions. Investigating the associations between HA score and properties of subregions of the amygdala could greatly improve our understanding of HA. In the present study, using a large sample of 291 healthy young adults, we aimed to uncover correlations between HA scores and the rsFCs of each amygdala subregion and to uncover possible sex-based differences in these correlations. We found that subregions of the amygdala showed different rsFC patterns, which contributed differently to individual HA scores. More specifically, HA scores were correlated with rsFCs between the laterobasal amygdala subregion and temporal and occipital cortices related to emotional information input, between the centromedial subregion and the frontal cortices associated with emotional output control, and between the superficial subregion and the frontal and temporal areas involved in both functions. Moreover, significant gender-based differences were uncovered in these correlations. Our findings provide a more detailed model of association between HA scores and amygdala rsFC, extend our understanding of the connectivity of subregions of the amygdala, and confirm sex-based differences in HA associations.     

Increased synthesis rate of fibrinogen as a basis for its elevated plasma levels in obese female adolescents

Increased concentrations of plasma fibrinogen, an independent risk factor for cardiovascular disease (CVD), in obese children have been reported. The underlying mechanism for this, however, remains to be defined. In the current study, we measured the fractional synthesis rates (FSR) of plasma fibrinogen in six healthy postpubertal obese girls [body mass index (BMI) 36.6 +/- 1.8 kg/m(2); age 16.6 +/- 0.5 yr] and six age-matched lean normal control girls (BMI 20.8 +/- 0.7 kg/m(2); age 16.4 +/- 0.4 yr) during a primed, continuous infusion of L-[1-(13)C]leucine in the postabsorptive state. The method involved purification of plasma fibrinogen by use of immunoaffinity chromatography followed by measurement of [(13)C]leucine enrichment using gas chromatography-combustion-isotope ratio mass spectrometry. The FSR of fibrinogen in obese girls (35.06 +/- 2.61%/day) was almost double that in lean girls (17.02 +/- 1.43%/day), and this increase was associated with a relative increase in plasma concentration of fibrinogen as well as BMI in the subjects studied. Obese subjects had high fasting insulin levels (138 +/- 47 pmol/l) compared with lean subjects (54 +/- 11 pmol/l), whereas their glucose concentrations were similar (4.5 +/- 0.3 mmol/l in obese and 4.4 +/- 0.4 mmol/l in lean subjects), suggesting insulin resistance. The doubling of the FSR of fibrinogen provides novel insight into the mechanism of elevated levels of plasma fibrinogen and suggests a primary role for increased synthesis in producing the hyperfibrinogenemia associated with obesity. This finding may have important implications in the design of therapies for modulating plasma fibrinogen levels in obesity and/or CVD in childhood.     

Increased functional connectivity with puberty in the mentalising network involved in social emotion processing

This article is part of a Special Issue “Puberty and Adolescence”.     

Altered regional homogeneity in the development of minimal hepatic encephalopathy: a resting-state functional MRI study

Background

Little is known about how spontaneous brain activity progresses from non-hepatic encephalopathy (non-HE) to minimal HE (MHE). The purpose of this study was to evaluate the evolution pattern of spontaneous brain activities in cirrhotic patients using resting-state fMRI with a regional homogeneity (ReHo) method.

Methodology/Principal Findings

Resting-state fMRI data were acquired in 47 cirrhotic patients (minimal HE [MHE], n = 20, and non-HE, n = 27) and 25 age-and sex-matched healthy controls. The Kendall’s coefficient of concordance (KCC) was used to measure the regional homogeneity. The regional homogeneity maps were compared with ANOVA tests among MHE, non-HE, and healthy control groups and t-tests between each pair in a voxel-wise way. Correlation analyses were performed to explore the relationships between regional ReHo values and Child-Pugh scores, number connection test type A (NCT-A), digit symbol test (DST) scores, venous blood ammonia levels. Compared with healthy controls, both MHE and non-HE patients showed decreased ReHo in the bilateral frontal, parietal and temporal lobes and increased ReHo in the bilateral caudate. Compared with the non-HE, MHE patients showed decreased ReHo in the bilateral precuneus, cuneus and supplementary motor area (SMA). The NCT-A of cirrhotic patients negatively correlated with ReHo values in the precuneus, cuneus and lingual gyrus. DST scores positively correlated with ReHo values in the cuneus, precuneus and lingual gyrus, and negatively correlated with ReHo values in the bilateral caudate (P<0.05, AlphaSim corrected).

Conclusions/Significance

Diffused abnormal homogeneity of baseline brain activity was nonspecific for MHE, and only the progressively decreased ReHo in the SMA and the cuneus, especially for the latter, might be associated with the development of MHE. The ReHo analysis may be potentially valuable for detecting the development from non-HE to MHE.     

The baseline and longitudinal changes of PCC connectivity in mild cognitive impairment: a combined structure and resting-state fMRI study

The baseline and longitudinal changes of the posterior cingulate cortex (PCC) connectivity were assessed in order to clarify the neural mechanism of mild cognitive impairment (MCI). Twenty-eight right-handed subjects (14 MCI patients and 14 healthy elders) participated in this study. Clinical and neuropsychological examinations were performed on all the subjects. PCC functional connectivity was studied by examining the correlation between low frequency fMRI signal fluctuations in the PCC and those in all the other brain regions. Additionally, we traced all the MCI patients and compared their PCC connectivity in the initial stage and that in 3 years later. We also explored the relationship between the PCC functional connectivity strength and cognitive performances. Our results are as follows: Functional connectivity between the PCC and a set of regions is decreased in MCI patients. Most of these regions are within the default mode network (DMN). Three years later, the regions of superior frontal gyrus (SFG) and middle frontal gyrus (MFG) presented further decreased connectivity to the PCC in MCI. In addition, we also find enhanced functional connectivity between PCC and medial prefrontal cortex (MPFC), PCC and anterior cingulate cortex (ACC) in MCI patients. At last, our research also shows that the PCC connectivity with some regions significantly correlates with the cognitive performances of patients as measured by mini-mental state examination (MMSE), and California verbal learning test (CVLT) scores. The baseline and longitudinal changes of the PCC connectivity in our study suggest that impairment and compensation coexist in the disease progress of MCI patients.     

Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.