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Dissociation of Regional Activity in Default Mode Network in Medication-Naive, First-Episode Somatization Disorder.

Sun, 07/06/2014 - 17:30

Dissociation of Regional Activity in Default Mode Network in Medication-Naive, First-Episode Somatization Disorder.

PLoS One. 2014;9(7):e99273

Authors: Su Q, Yao D, Jiang M, Liu F, Jiang J, Xu C, Dai Y, Yu M, Long L, Li H, Liu J, Zhang Z, Zhang J, Xiao C, Guo W

Abstract
BACKGROUND: Patients with somatization disorder (SD) have altered neural activity in the brain regions of the default mode network (DMN). However, the regional alteration of the DMN in SD remains unknown. The present study was designed to investigate the regional alterations of the DMN in patients with SD at rest.
METHODS: Twenty-five first-episode, medication-naive patients with SD and 28 age-, sex-, education- matched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI) scan. The fractional amplitude of low-frequency fluctuations (fALFF) was applied to analyze the data.
RESULTS: Patients with SD showed a dissociation pattern of resting-state fALFF in the DMN, with increased fALFF in the bilateral superior medial prefrontal cortex (MPFC, BA8, 9) and decreased fALFF in the left precuneus (PCu, BA7). Furthermore, significantly positive correlation was observed between the z values of the voxels within the bilateral superior MPFC and somatization subscale scores of the Symptom Check List (SCL-90) in patients with SD.
CONCLUSIONS: Our findings indicate that there is a dissociation pattern of the anterior and posterior DMN in first-episode, treatment-naive patients with SD. The results provide new insight for the importance of the DMN in the pathophysiology of SD.

PMID: 24983962 [PubMed - as supplied by publisher]

Machine learning classification of resting state functional connectivity predicts smoking status.

Sun, 07/06/2014 - 17:30

Machine learning classification of resting state functional connectivity predicts smoking status.

Front Hum Neurosci. 2014;8:425

Authors: Pariyadath V, Stein EA, Ross TJ

Abstract
Machine learning-based approaches are now able to examine functional magnetic resonance imaging data in a multivariate manner and extract features predictive of group membership. We applied support vector machine (SVM)-based classification to resting state functional connectivity (rsFC) data from nicotine-dependent smokers and healthy controls to identify brain-based features predictive of nicotine dependence. By employing a network-centered approach, we observed that within-network functional connectivity measures offered maximal information for predicting smoking status, as opposed to between-network connectivity, or the representativeness of each individual node with respect to its parent network. Further, our analysis suggests that connectivity measures within the executive control and frontoparietal networks are particularly informative in predicting smoking status. Our findings suggest that machine learning-based approaches to classifying rsFC data offer a valuable alternative technique to understanding large-scale differences in addiction-related neurobiology.

PMID: 24982629 [PubMed]

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

Sun, 07/06/2014 - 17:30

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

Front Neural Circuits. 2014;8:64

Authors: Dey S, Rao AR, Shah M

Abstract
Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

PMID: 24982615 [PubMed - in process]

Time-resolved resting-state brain networks.

Sun, 07/06/2014 - 17:30

Time-resolved resting-state brain networks.

Proc Natl Acad Sci U S A. 2014 Jun 30;

Authors: Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M

Abstract
Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.

PMID: 24982140 [PubMed - as supplied by publisher]

Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State fMRI Study in Propofol-Induced Loss of Consciousness.

Wed, 07/02/2014 - 17:00

Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State fMRI Study in Propofol-Induced Loss of Consciousness.

PLoS One. 2014;9(6):e100012

Authors: Amico E, Gomez F, Di Perri C, Vanhaudenhuyse A, Lesenfants D, Boveroux P, Bonhomme V, Brichant JF, Marinazzo D, Laureys S

Abstract
BACKGROUND: Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a resting state dataset. In the present study we want to extend this methodology for the investigation of resting state fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations.
METHODS: Resting-state fMRI volumes on 18 healthy subjects were acquired in four clinical states during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC) at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation). Spatial clustering on the PCC time frames extracted was then performed (number of clusters = 8), to obtain 8 different PCC co-activation patterns (CAPs) for each level of consciousness.
RESULTS: The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex), some others new (e.g., reduced co-activation in motor cortex and visual area).
CONCLUSION: In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local functional changes in the brain associated to propofol-induced modulation of consciousness.

PMID: 24979748 [PubMed - as supplied by publisher]

Impaired development of intrinsic connectivity networks in children with medically intractable localization-related epilepsy.

Wed, 07/02/2014 - 17:00

Impaired development of intrinsic connectivity networks in children with medically intractable localization-related epilepsy.

Hum Brain Mapp. 2014 Jun 30;

Authors: Ibrahim GM, Morgan BR, Lee W, Smith ML, Donner EJ, Wang F, Beers CA, Federico P, Taylor MJ, Doesburg SM, Rutka JT, Carter Snead O

Abstract
Typical childhood development is characterized by the emergence of intrinsic connectivity networks (ICNs) by way of internetwork segregation and intranetwork integration. The impact of childhood epilepsy on the maturation of ICNs is, however, poorly understood. The developmental trajectory of ICNs in 26 children (8-17 years) with localization-related epilepsy and 28 propensity-score matched controls was evaluated using graph theoretical analysis of whole brain connectomes from resting-state functional magnetic resonance imaging (fMRI) data. Children with epilepsy demonstrated impaired development of regional hubs in nodes of the salience and default mode networks (DMN). Seed-based connectivity and hierarchical clustering analysis revealed significantly decreased intranetwork connections, and greater internetwork connectivity in children with epilepsy compared to controls. Significant interactions were identified between epilepsy duration and the expected developmental trajectory of ICNs, indicating that prolonged epilepsy may cause progressive alternations in large-scale networks throughout childhood. DMN integration was also associated with better working memory, whereas internetwork segregation was associated with higher full-scale intelligence quotient scores. Furthermore, subgroup analyses revealed the thalamus, hippocampus, and caudate were weaker hubs in children with secondarily generalized seizures, relative to other patient subgroups. Our findings underscore that epilepsy interferes with the developmental trajectory of brain networks underlying cognition, providing evidence supporting the early treatment of affected children. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.

PMID: 24976288 [PubMed - as supplied by publisher]

Dynamic functional connectivity of the default mode network tracks daydreaming.

Tue, 07/01/2014 - 15:30

Dynamic functional connectivity of the default mode network tracks daydreaming.

Neuroimage. 2014 Jun 25;

Authors: Kucyi A, Davis KD

Abstract
Humans spend much of their time engaged in stimulus-independent thoughts, colloquially known as "daydreaming" or "mind-wandering." A fundamental question concerns how awake, spontaneous brain activity represents the ongoing cognition of daydreaming versus unconscious processes characterized as "intrinsic." Since daydreaming involves brief cognitive events that spontaneously fluctuate, we tested the hypothesis that the dynamics of brain network functional connectivity (FC) are linked with daydreaming. We determined the general tendency to daydream in healthy adults based on a daydreaming frequency scale (DDF). Subjects then underwent both resting state functional magnetic resonance imaging (rs-fMRI) and fMRI during sensory stimulation with intermittent thought probes to determine the occurrences of mind-wandering events. Brain regions within the default mode network (DMN), purported to be involved in daydreaming, were assessed for 1) static FC across entire fMRI scans, and 2) dynamic FC based on FC variability (FCV) across 30s progressively sliding windows of 2s increments within each scan. We found that during both resting and sensory stimulation states, individual differences in DDF were negatively correlated with static FC between the posterior cingulate cortex and a ventral DMN subsystem involved in future-oriented thought. Dynamic FC analysis revealed that DDF was positively correlated with FCV within the same DMN subsystem in the resting state but not during stimulation. However, dynamic but not static FC, in this subsystem was positively correlated with an individual's degree of self-reported mind-wandering during sensory stimulation. These findings identify temporal aspects of spontaneous DMN activity that reflect conscious and unconscious processes.

PMID: 24973603 [PubMed - as supplied by publisher]

Altered cortical and subcortical local coherence in PTSD: evidence from resting-state fMRI.

Tue, 07/01/2014 - 15:30

Altered cortical and subcortical local coherence in PTSD: evidence from resting-state fMRI.

Acta Radiol. 2014 Jun 27;

Authors: Zhong Y, Zhang R, Li K, Qi R, Zhang Z, Huang Q, Lu G

Abstract
BACKGROUND: Post-traumatic stress disorder (PTSD) is often characterized by region-specific brain activation/deactivation and functional abnormalities in corticolimbic circuitry, as elucidated by task-dependent functional neuroimaging. However, little is known about the abnormalities in the local coherence of cortical and subcortical activity occurring during the resting state.
PURPOSE: To evaluate the functional discrepancy of local coherence between cortical and subcortical regions in PTSD patients using resting-state functional magnetic resonance imaging (fMRI).
MATERIAL AND METHODS: Resting-state fMRI (RS-fMRI) was performed on 14 outpatients with PTSD, along with 14 age- and sex-matched normal control subjects. Regional homogeneity (ReHo), a measurement of the coherence of spontaneous RS-fMRI signal oscillations within spatially neighboring voxels, was examined.
RESULTS: Compared with the normal controls, PTSD patients showed increased local coherence in subcortical regions, including amygdala, hippocampus, thalamus, and putamen, and decreased local coherence in cortical regions, including medial prefrontal cortex and dorsolateral prefrontal cortex. Moreover, a correlation analysis of the ReHo measurement versus the severity of the disorder was performed, and highly positive correlation were observed in the right amygdala.
CONCLUSION: The present study identified a functional discrepancy of local coherence between cortical and subcortical regions in PTSD patients compared with normal controls. The findings revealed that resting-state abnormalities might lead to further improvement of the understanding of the neural substrates of cognitive impairment and symptoms in PTSD.

PMID: 24973255 [PubMed - as supplied by publisher]

Increased thalamic intrinsic oscillation amplitude in relapsing-remitting multiple sclerosis associated with the slowed cognitive processing.

Tue, 07/01/2014 - 15:30

Increased thalamic intrinsic oscillation amplitude in relapsing-remitting multiple sclerosis associated with the slowed cognitive processing.

Clin Imaging. 2014 May 21;

Authors: Zhou F, Zhuang Y, Wu L, Zhang N, Zeng X, Gong H, Zee CS

Abstract
OBJECTIVE: To investigate the relationship between the amplitude of thalamic intrinsic neuronal activity, structural imaging indices, and the clinical neurological scales in relapsing-remitting multiple sclerosis (RRMS).
METHODS: Twenty-three patients with RRMS and 23 healthy controls were examined by resting-state fMRI (rs-fMRI) scan. Thalamic intrinsic oscillation amplitude was calculated by amplitude of low frequency fluctuation (ALFF) of rs-fMRI, as well as its correlations with clinical and structural imaging indices.
RESULTS: Compared with the healthy controls, RRMS patients showed significantly increased ALFF values in bilateral thalami (P<.05, corrected). In the patient group, positive correlation was found between bilateral ALFF values and paced auditory serial addition test (left: P=.033; right: P=.016). Significant correlation was detected between the ALFF values and fractional anisotropy (FA) values in the left thalamus (r=0.550, P=.007); only tendency increased correlation was detected between the ALFF values and FA values in the right thalamus (P=.114). No correlation was observed between bilateral thalamic ALFF values and disease duration, expanded disability status scale score, brain parenchymal fraction, or total white matter lesion loads (P>.05).
CONCLUSION: The increased thalamic intrinsic oscillation amplitude as an ineffective reorganization was responded to microstructural damage in the RRMS patients, as well as it was associated with the slowed cognitive processing in relatively minimally disabled stage.

PMID: 24973078 [PubMed - as supplied by publisher]

A Functional Polymorphism of the MAOA Gene Modulates Spontaneous Brain Activity in Pons.

Sun, 06/29/2014 - 12:30

A Functional Polymorphism of the MAOA Gene Modulates Spontaneous Brain Activity in Pons.

Biomed Res Int. 2014;2014:243280

Authors: Lei H, Zhang X, Di X, Rao H, Ming Q, Zhang J, Guo X, Jiang Y, Gao Y, Yi J, Zhu X, Yao S

Abstract
Objective. To investigate the effects of a functional polymorphism of the monoamine oxidase A (MAOA) gene on spontaneous brain activity in healthy male adolescents. Methods. Thirty-one healthy male adolescents with the low-activity MAOA genotype (MAOA-L) and 25 healthy male adolescents with the high-activity MAOA genotype (MAOA-H) completed the 11-item Barratt Impulsiveness Scale (BIS-11) questionnaire and were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitude of low-frequency fluctuation (ALFF) of the blood oxygen level-dependent (BOLD) signal was calculated using REST software. ALFF data were related to BIS scores and compared between genotype groups. Results. Compared with the MAOA-H group, the MAOA-L group showed significantly lower ALFFs in the pons. There was a significant correlation between the BIS scores and the ALFF values in the pons for MAOA-L group, but not for the MAOA-H group. Further regression analysis showed a significant genotype by ALFF values interaction effect on BIS scores. Conclusions. Lower spontaneous brain activity in the pons of the MAOA-L male adolescents may provide a neural mechanism by which boys with the MAOA-L genotype confers risk for impulsivity and aggression.

PMID: 24971323 [PubMed - in process]

GABA, Resting-State Connectivity and the Developing Brain.

Sun, 06/29/2014 - 12:30

GABA, Resting-State Connectivity and the Developing Brain.

Neonatology. 2014 Jun 26;106(2):149-155

Authors: Kwon SH, Scheinost D, Lacadie C, Benjamin J, Myers EH, Qiu M, Schneider KC, Rothman DL, Constable RT, Ment LR

Abstract
Background: Preclinical data demonstrate that gamma-aminobutyric acid (GABA) interneurons initiate connectivity in the developing brain. Objectives: The goal of this study was to compare GABA concentration and its relationship to functional connectivity in the brains of term and preterm infants at term-equivalent age. Methods: Infants received both magnetic resonance spectroscopy (MRS) and functional magnetic resonance imaging (fMRI) scans at term-equivalent age. Whole brain functional connectivity MRI data using intrinsic connectivity distribution maps were compared to identify areas with differences in resting-state functional connectivity between the preterm and term control groups. MRS measured concentrations of GABA, glutamate, N-acetyl-aspartate (NAA) and choline; NAA/choline was then calculated for comparison between the 2 groups. Results: Preterm infants had lower right frontal GABA and glutamate concentrations than term controls and showed a significantly different relationship between connectivity and GABA concentration in the right frontal lobe. Preterm infants had a positive correlation between GABA concentration and connectivity, while term controls demonstrated a negative correlation between these two developmentally regulated parameters. Conclusion: These results suggest that regional GABA concentrations are associated with normal and altered neonatal resting-state connectivity. © 2014 S. Karger AG, Basel.

PMID: 24970028 [PubMed - as supplied by publisher]

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction.

Sun, 06/29/2014 - 12:30

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction.

Neuroimage. 2014 Jun 23;

Authors: Beall EB, Lowe MJ

Abstract
Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.

PMID: 24969568 [PubMed - as supplied by publisher]

Exploring the Patterns of Acupuncture on Mild Cognitive Impairment Patients Using Regional Homogeneity.

Sat, 06/28/2014 - 17:00

Exploring the Patterns of Acupuncture on Mild Cognitive Impairment Patients Using Regional Homogeneity.

PLoS One. 2014;9(6):e99335

Authors: Liu Z, Wei W, Bai L, Dai R, You Y, Chen S, Tian J

Abstract
PURPOSE: To investigate the different responses to acupuncture in MCI patients and age-matched healthy subjects reflected by the Regional Homogeneity (ReHo) indices.
METHODS: The experiment was performed at the acupoint KI3 in 12 MCI patients and 12 healthy controls, respectively. A novel non-repeated event-related (NRER) fMRI design paradigm was applied to separately detect neural activities related to different stages of acupuncture (pre-acupuncture resting state, needling manipulation and post-acupuncture resting state). ReHo values were calculated for MCI patients and healthy controls in pre- and post-acupuncture resting state. Then, a two-way ANCOVA with repeated measures with post-hoc two sample t-tests was performed to explore the different responses to acupuncture in the two groups.
RESULTS: The ANCOVA revealed a significant main effect of group, but no significant main effect of acupuncture and interactions between group and acupuncture. During the pre-acupuncture resting state, ReHo values increased in the precentral gyrus (PCG), superior frontal gyrus (SFG), and insula (INS) and decreased mainly in middle temporal gyrus (MTG), parahippocampal (PHIP) and cingulate cortex in MCI patients compared with healthy controls. Furthermore, we found that the regions including precuneus (PCUN), and cingulate cortex showed increased ReHo values for MCI patients following acupuncture. For healthy controls, the medial frontal gyrus, PCG, anterior cingulate cortex (ACC) and INS showed enhanced ReHo values following acupuncture. During the post-acupuncture resting state, MCI patients showed increased ReHo values mainly in the MTG, superior parietal lobule (SPL), middle frontal gyrus (MFG), supramarginal (SMG), and PCG, and decreased ReHo values mainly in the frontal regions, PHIP, and posterior cingulated cortex (PCC) compared to healthy controls.
CONCLUSION: Though we found some ReHo changes between MCI patients and healthy controls, the two-way ANCOVA results showed no significant effects after multiple corrections. Further study is needed to reveal the real acupuncture effects on MCI patients.

PMID: 24968124 [PubMed - as supplied by publisher]

Dysfunction of affective network in post ischemic stroke depression: a resting-state functional magnetic resonance imaging study.

Fri, 06/27/2014 - 15:30

Dysfunction of affective network in post ischemic stroke depression: a resting-state functional magnetic resonance imaging study.

Biomed Res Int. 2014;2014:846830

Authors: Zhang P, Xu Q, Dai J, Wang J, Zhang N, Luo Y

Abstract
Objective. Previous studies have demonstrated that stroke characteristics and social and psychological factors jointly contribute to the development of poststroke depression (PSD). The purpose of this study was to identify altered functional connectivity (FC) of the affective network (AN) in patients with PSD and to explore the correlation between FC and the severity of PSD. Materials and Methods. 26 PSD patients, 24 stroke patients without depression, and 24 age-matched normal controls underwent the resting-state functional MRI (fMRI) scanning. The bilateral anterior cingulated cortices (ACCs) were selected as regions of interest (ROIs). FC was calculated and compared among the three groups. The association between FC and Hamilton Depression Rate Scale (HDRS) scores of PSD group was investigated. Results. The FC of the AN was disrupted in PSD patients compared to stroke patients without depression and normal controls. Moreover, the left orbital part of inferior frontal gyrus which indicated altered FC was significantly correlated with HDRS scores in PSD patients. Conclusions. Dysfunction of the affective network may be one of the reasons of the development of PSD.

PMID: 24963485 [PubMed - in process]

Correlation between the Effects of Acupuncture at Taichong (LR3) and Functional Brain Areas: A Resting-State Functional Magnetic Resonance Imaging Study Using True versus Sham Acupuncture.

Fri, 06/27/2014 - 15:30

Correlation between the Effects of Acupuncture at Taichong (LR3) and Functional Brain Areas: A Resting-State Functional Magnetic Resonance Imaging Study Using True versus Sham Acupuncture.

Evid Based Complement Alternat Med. 2014;2014:729091

Authors: Wu C, Qu S, Zhang J, Chen J, Zhang S, Li Z, Chen J, Ouyang H, Huang Y, Tang C

Abstract
Functional magnetic resonance imaging (fMRI) has been shown to detect the specificity of acupuncture points, as proved by numerous studies. In this study, resting-state fMRI was used to observe brain areas activated by acupuncture at the Taichong (LR3) acupoint. A total of 15 healthy subjects received brain resting-state fMRI before acupuncture and after sham and true acupuncture, respectively, at LR3. Image data processing was performed using Data Processing Assistant for Resting-State fMRI and REST software. The combination of amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) was used to analyze the changes in brain function during sham and true acupuncture. Acupuncture at LR3 can specifically activate or deactivate brain areas related to vision, movement, sensation, emotion, and analgesia. The specific alterations in the anterior cingulate gyrus, thalamus, and cerebellar posterior lobe have a crucial effect and provide a valuable reference. Sham acupuncture has a certain effect on psychological processes and does not affect brain areas related to function.

PMID: 24963329 [PubMed]

Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Fri, 06/27/2014 - 15:30

Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Schizophr Res. 2014 Jun 21;

Authors: Tomescu MI, Rihs TA, Becker R, Britz J, Custo A, Grouiller F, Schneider M, Debbané M, Eliez S, Michel CM

Abstract
Previous studies have repeatedly found altered temporal characteristics of EEG microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG microstates. If this is the case, temporal alterations of EEG microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG-fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS.

PMID: 24962438 [PubMed - as supplied by publisher]

Altered brain functional networks in heavy smokers.

Fri, 06/27/2014 - 15:30

Altered brain functional networks in heavy smokers.

Addict Biol. 2014 Jun 24;

Authors: Lin F, Wu G, Zhu L, Lei H

Abstract
Recent neuroimaging studies have demonstrated that cigarette smoking is associated with changed brain structure and function. However, little is known about alterations of the topological organization of brain functional networks in heavy smokers. Thirty-one heavy smokers and 33 non-smokers underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding the correlation matrices of 90 brain regions and their topological properties were analyzed using graph network analysis. Non-parametric permutation tests were performed to investigate group differences in network topological measures and multiple regression analysis was conducted to determine the relationships between the network metrics and smoking-related variables. Both heavy smokers and non-smokers exhibited small-world architecture in their brain functional networks. Compared with non-smokers, however, heavy smokers showed altered topological measurements characterized by lower global efficiency, higher local efficiency and clustering coefficients and greater path length. Furthermore, heavy smokers demonstrated decreased nodal global efficiency mainly in brain regions within the default mode network, whereas increased nodal local efficiency predominated in the visual-related regions. In addition, heavy smokers exhibited an association between the altered network metrics and the duration of cigarette use or the severity of nicotine dependence. Our results suggest that heavy smokers may have less efficient network architecture in the brain, and chronic cigarette smoking is associated with disruptions in the topological organization of brain networks. Our findings may further the understanding of the effects of chronic cigarette smoking on the brain and the pathophysiological mechanisms underlying nicotine dependence.

PMID: 24962385 [PubMed - as supplied by publisher]

Overlapping and parallel cerebello-cerebral networks contributing to sensorimotor control: an intrinsic functional connectivity study.

Fri, 06/27/2014 - 15:30
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Overlapping and parallel cerebello-cerebral networks contributing to sensorimotor control: an intrinsic functional connectivity study.

Neuroimage. 2013 Dec;83:837-48

Authors: Kipping JA, Grodd W, Kumar V, Taubert M, Villringer A, Margulies DS

Abstract
In concert with sensorimotor control areas of the cerebrum, the cerebellum shows differential activation patterns during a variety of sensorimotor-related tasks. However, the spatial details and extent of the complex and heterogeneous cerebello-cerebral systems involved in action control remain uncertain. In this study, we use intrinsic functional connectivity (iFC) to examine cerebello-cerebral networks of five cerebellar lobules (I-IV, V, VI, and VIIIa/b) that have been empirically identified to form the functional basis of sensorimotor processes. A refined cerebellar seed-region selection allowed us to identify a network of primary sensorimotor and supplementary motor areas (I-V), a network of prefrontal, premotor, occipito-temporal and inferior-parietal regions (VI), and two largely overlapping networks involving premotor and superior parietal regions, the temporo-parietal junction as well as occipito-temporal regions (VIIIa/b). All networks involved the medial prefrontal/cingulate cortex. These cerebral clusters were used in a partial correlation analysis to systematically map cerebral connectivity throughout the entire cerebellum. We discuss these findings in the framework of affective and cognitive control, sensorimotor, multisensory systems, and executive/language systems. Within the cerebellum we found that cerebro-cerebellar systems seem to run in parallel, as indicated by distinct sublobular functional topography of prefrontal, parietal, sensorimotor, cingulate, and occipito-temporal regions. However, all areas showed overlapping connectivity to various degrees in both hemispheres. The results of both analyses demonstrate that different sublobular parts of the cerebellar lobules may dominate in different aspects of primary or higher-order sensorimotor processing. This systems-level cerebellar organization provides a more detailed structure for cerebello-cerebral interaction which contributes to our understanding of complex motor behavior.

PMID: 23872155 [PubMed - indexed for MEDLINE]

Multiphasic modification of intrinsic functional connectivity of the rat brain during increasing levels of propofol.

Fri, 06/27/2014 - 15:30
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Multiphasic modification of intrinsic functional connectivity of the rat brain during increasing levels of propofol.

Neuroimage. 2013 Dec;83:581-92

Authors: Liu X, Pillay S, Li R, Vizuete JA, Pechman KR, Schmainda KM, Hudetz AG

Abstract
The dose-dependent effects of anesthetics on brain functional connectivity are incompletely understood. Resting-state functional magnetic resonance imaging (rsfMRI) is widely used to assess the functional connectivity in humans and animals. Propofol is an anesthetic agent with desirable characteristics for functional neuroimaging in animals but its dose-dependent effects on rsfMRI functional connectivity have not been determined. Here we tested the hypothesis that brain functional connectivity undergoes specific changes in distinct neural networks at anesthetic depths associated with loss of consciousness. We acquired spontaneous blood oxygen level-dependent (BOLD) signals simultaneously with electroencephalographic (EEG) signals from rats under steady-state, intravenously administered propofol at increasing doses from light sedation to deep anesthesia (20, 40, 60, 80, and 100 mg/kg/h IV). Power spectra and burst suppression ratio were calculated from the EEG to verify anesthetic depth. Functional connectivity was determined from the whole brain correlation of BOLD data in regions of interest followed by a segmentation of the correlation maps into anatomically defined regional connectivity. We found that propofol produced multiphasic, dose dependent changes in functional connectivity of various cortical and subcortical networks. Cluster analysis predicted segregation of connectivity into two cortical and two subcortical clusters. In one cortical cluster (somatosensory and parietal), the early reduction in connectivity was followed by transient reversal; in the other cluster (sensory, motor and cingulate/retrosplenial), this rebound was absent. The connectivity of the subcortical cluster (brainstem, hippocampal and caudate) was strongly reduced, whereas that of another (hypothalamus, medial thalamus and n. basalis) did not. Subcortical connectivity increased again in deep anesthesia associated with EEG burst suppression. Regional correlation analysis confirmed the breakdown of connectivity within and between specific cortical and subcortical networks with deepening propofol anesthesia. Cortical connectivity was suppressed before subcortical connectivity at a critical propofol dose associated with loss of consciousness.

PMID: 23851326 [PubMed - indexed for MEDLINE]

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project.

Thu, 06/26/2014 - 14:30

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project.

Front Hum Neurosci. 2014;8:409

Authors: McDonough IM, Nashiro K

Abstract
An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity-a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.

PMID: 24959130 [PubMed]