Correlation between structural and functional connectivity impairment in amyotrophic lateral sclerosis.
Hum Brain Mapp. 2014 Mar 6;
Authors: Schmidt R, Verstraete E, de Reus MA, Veldink JH, van den Berg LH, van den Heuvel MP
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, characterized by progressive loss of motor function. While the pathogenesis of ALS remains largely unknown, imaging studies of the brain should lead to more insight into structural and functional disease effects on the brain network, which may provide valuable information on the underlying disease process. This study investigates the correlation between changes in structural connectivity (SC) and functional connectivity (FC) of the brain network in ALS. Structural reconstructions of the brain network, derived from diffusion weighted imaging (DWI), were obtained from 64 patients and 27 healthy controls. Functional interactions between brain regions were derived from resting-state fMRI. Our results show that (i) the most structurally affected connections considerably overlap with the most functionally impaired connections, (ii) direct connections of the motor cortex are both structurally and functionally more affected than connections at greater topological distance from the motor cortex, and (iii) there is a strong positive correlation between changes in SC and FC averaged per brain region (r = 0.44, P < 0.0001). Our findings indicate that structural and functional network degeneration in ALS is coupled, suggesting the pathogenic process affects both SC and FC of the brain, with the most prominent effects in SC. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 24604691 [PubMed - as supplied by publisher]
Acupuncture modulates resting state hippocampal functional connectivity in Alzheimer disease.
PLoS One. 2014;9(3):e91160
Authors: Wang Z, Liang P, Zhao Z, Han Y, Song H, Xu J, Lu J, Li K
Our objective is to clarify the effects of acupuncture on hippocampal connectivity in patients with Alzheimer disease (AD) using functional magnetic resonance imaging (fMRI). Twenty-eight right-handed subjects (14 AD patients and 14 healthy elders) participated in this study. Clinical and neuropsychological examinations were performed on all subjects. MRI was performed using a SIEMENS verio 3-Tesla scanner. The fMRI study used a single block experimental design. We first acquired baseline resting state data during the initial 3 minutes and then performed acupuncture stimulation on the Tai chong and He gu acupoints for 3 minutes. Last, we acquired fMRI data for another 10 minutes after the needle was withdrawn. The preprocessing and data analysis were performed using statistical parametric mapping (SPM5) software. Two-sample t-tests were performed using data from the two groups in different states. We found that during the resting state, several frontal and temporal regions showed decreased hippocampal connectivity in AD patients relative to control subjects. During the resting state following acupuncture, AD patients showed increased connectivity in most of these hippocampus related regions compared to the first resting state. In conclusion, we investigated the effect of acupuncture on AD patients by combing fMRI and traditional acupuncture. Our fMRI study confirmed that acupuncture at Tai chong and He gu can enhance the hippocampal connectivity in AD patients.
PMID: 24603951 [PubMed - in process]
Reduced intrinsic connectivity of amygdala in adults with major depressive disorder.
Front Psychiatry. 2014;5:17
Authors: Ramasubbu R, Konduru N, Cortese F, Bray S, Gaxiola-Valdez I, Goodyear B
Imaging studies of major depressive disorder (MDD) have demonstrated enhanced resting-state activity of the amygdala as well as exaggerated reactivity to negative emotional stimuli relative to healthy controls (HCs). However, the abnormalities in the intrinsic connectivity of the amygdala in MDD still remain unclear. As the resting-state activity and functional connectivity (RSFC) reflect fundamental brain processes, we compared the RSFC of the amygdala between unmedicated MDD patients and HCs. Seventy-four subjects, 55 adults meeting the DSM-IV criteria for MDD and 19 HCs, underwent a resting-state 3-T functional magnetic resonance imaging scan. An amygdala seed-based low frequency RSFC map for the whole brain was generated for each group. Compared with HCs, MDD patients showed a wide-spread reduction in the intrinsic connectivity of the amygdala with a variety of brain regions involved in emotional processing and regulation, including the ventrolateral prefrontal cortex, insula, caudate, middle and superior temporal regions, occipital cortex, and cerebellum, as well as increased connectivity with the bilateral temporal poles (p < 0.05 corrected). The increase in the intrinsic connectivity of amygdala with the temporal poles was inversely correlated with symptom severity and anxiety scores. Although the directionality of connections between regions cannot be inferred from temporal correlations, the reduced intrinsic connectivity of the amygdala predominantly with regions involved in emotional processing may reflect impaired bottom-up signaling for top-down cortical modulation of limbic regions leading to abnormal affect regulation in MDD.
PMID: 24600410 [PubMed]
Sensitivity of fNIRS to cognitive state and load.
Front Hum Neurosci. 2014;8:76
Authors: Fishburn FA, Norr ME, Medvedev AV, Vaidya CJ
Functional near-infrared spectroscopy (fNIRS) is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-min resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC), bilateral ventrolateral PFC (vlPFC), frontopolar cortex (FP), and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.
PMID: 24600374 [PubMed]
Changes in low-frequency fluctuations in patients with antisocial personality disorder revealed by resting-state functional MRI.
PLoS One. 2014;9(3):e89790
Authors: Liu H, Liao J, Jiang W, Wang W
Antisocial Personality Disorder (APD) is a personality disorder that is most commonly associated with the legal and criminal justice systems. The study of the brain in APD has important implications in legal contexts and in helping ensure social stability. However, the neural contribution to the high prevalence of APD is still unclear. In this study, we used resting-state functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of APD. Thirty-two healthy individuals and thirty-five patients with APD were recruited. The amplitude of low-frequency fluctuations (ALFF) was analyzed for the whole brain of all subjects. Our results showed that APD patients had a significant reduction in the ALFF in the right orbitofrontal cortex, the left temporal pole, the right inferior temporal gyrus, and the left cerebellum posterior lobe compared to normal controls. We observed that the right orbitofrontal cortex had a negative correlation between ALFF values and MMPI psychopathic deviate scores. Alterations in ALFF in these specific brain regions suggest that APD patients may be associated with abnormal activities in the fronto-temporal network. We propose that our results may contribute in a clinical and forensic context to a better understanding of APD.
PMID: 24598769 [PubMed - in process]
Lag structure in resting state fMRI.
J Neurophysiol. 2014 Mar 5;
Authors: Mitra A, Snyder AZ, Hacker CD, Raichle ME
The discovery that spontaneous fluctuations in BOLD (blood oxygen level dependent) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of approximately one second. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open versus closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.
PMID: 24598530 [PubMed - as supplied by publisher]
Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification.
Neuroinformatics. 2014 Mar 5;
Authors: Li Y, Wee CY, Jie B, Peng Z, Shen D
Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach.
PMID: 24595922 [PubMed - as supplied by publisher]
Functional and structural network impairment in childhood frontal lobe epilepsy.
PLoS One. 2014;9(3):e90068
Authors: Vaessen MJ, Jansen JF, Braakman HM, Hofman PA, De Louw A, Aldenkamp AP, Backes WH
In childhood frontal lobe epilepsy (FLE), cognitive impairment and educational underachievement are serious, well-known co-morbidities. The broad scale of affected cognitive domains suggests wide-spread network disturbances that not only involves, but also extends beyond the frontal lobe. In this study we have investigated whole brain connectional properties of children with FLE in relation to their cognitive impairment and compared them with healthy controls. Functional connectivity (FC) of the networks was derived from dynamic fluctuations of resting state fMRI and structural connectivity (SC) was obtained from fiber tractograms of diffusion weighted MRI. The whole brain network was characterized with graph theoretical metrics and decomposed into modules. Subsequently, the graph metrics and the connectivity within and between modules were related to cognitive performance. Functional network disturbances in FLE were related to increased clustering, increased path length, and stronger modularity compared to healthy controls, which was accompanied by stronger within- and weaker between-module functional connectivity. Although structural path length and clustering appeared normal in children with FLE, structural modularity increased with stronger cognitive impairment. It is concluded that decreased coupling between large-scale functional network modules is a hallmark for impaired cognition in childhood FLE.
PMID: 24594874 [PubMed - in process]
Modulation of Resting State Functional Connectivity of the Motor Network by Transcranial Pulsed Current Stimulation (tPCS).
Brain Connect. 2014 Mar 4;
Authors: Sours C, Alon G, Roys S, Gullapalli RP
The effects of transcranial pulsed current stimulation (tPCS) on resting state functional connectivity (rs-FC) within the motor network were investigated. Eleven healthy participants received one MRI session with three resting state fMRI scans, one before stimulation (PRE-STIM) to collect baseline measures, one during stimulation (STIM), and one after 13 minutes of stimulation (POST-STIM). Rs-FC measures during the STIM and POST-STIM conditions were compared to the PRE-STIM baseline. Regions of interest (ROIs) for the rs-FC analysis were extracted from the significantly activated clusters obtained during finger tapping motor paradigm and included the right primary motor cortex (R M1), left primary motor cortex (L M1), supplemental motor area (SMA), and cerebellum (Cer). The main findings were reduced rs-FC between the left M1 and surrounding motor cortex, and increased rs-FC between the left M1 and left thalamus during stimulation, but increased rs-FC between the Cer and right insula after stimulations. Bivariate measures of connectivity demonstrate reduced strength of connectivity for the whole network average (p=0.044) and reduced diversity of connectivity for the network average during stimulation (p=0.024). During the POST-STIM condition, the trend of reduced diversity for the network average was statistically weaker (p=0.071). In conclusion, while many of the findings are comparable to previous reports using simultaneous transcranial direct current stimulation (tDCS) and fMRI acquisition, we also demonstrate additional changes in connectivity patterns that are induced by tPCS.
PMID: 24593667 [PubMed - as supplied by publisher]
Two distinct neural mechanisms underlying indirect reciprocity.
Proc Natl Acad Sci U S A. 2014 Mar 3;
Authors: Watanabe T, Takezawa M, Nakawake Y, Kunimatsu A, Yamasue H, Nakamura M, Miyashita Y, Masuda N
Cooperation is a hallmark of human society. Humans often cooperate with strangers even if they will not meet each other again. This so-called indirect reciprocity enables large-scale cooperation among nonkin and can occur based on a reputation mechanism or as a succession of pay-it-forward behavior. Here, we provide the functional and anatomical neural evidence for two distinct mechanisms governing the two types of indirect reciprocity. Cooperation occurring as reputation-based reciprocity specifically recruited the precuneus, a region associated with self-centered cognition. During such cooperative behavior, the precuneus was functionally connected with the caudate, a region linking rewards to behavior. Furthermore, the precuneus of a cooperative subject had a strong resting-state functional connectivity (rsFC) with the caudate and a large gray matter volume. In contrast, pay-it-forward reciprocity recruited the anterior insula (AI), a brain region associated with affective empathy. The AI was functionally connected with the caudate during cooperation occurring as pay-it-forward reciprocity, and its gray matter volume and rsFC with the caudate predicted the tendency of such cooperation. The revealed difference is consistent with the existing results of evolutionary game theory: although reputation-based indirect reciprocity robustly evolves as a self-interested behavior in theory, pay-it-forward indirect reciprocity does not on its own. The present study provides neural mechanisms underlying indirect reciprocity and suggests that pay-it-forward reciprocity may not occur as myopic profit maximization but elicit emotional rewards.
PMID: 24591599 [PubMed - as supplied by publisher]
Altered spontaneous brain activity in primary open angle glaucoma: a resting-state functional magnetic resonance imaging study.
PLoS One. 2014;9(2):e89493
Authors: Song Y, Mu K, Wang J, Lin F, Chen Z, Yan X, Hao Y, Zhu W, Zhang H
BACKGROUND: Previous studies demonstrated that primary open angle glaucoma (POAG) is associated with abnormal brain structure; however, little is known about the changes in the local synchronization of spontaneous activity. The main objective of this study was to investigate spontaneous brain activity in patients with POAG using regional homogeneity (ReHo) analysis based on resting state functional magnetic resonance imaging (rs-fMRI).
METHODOLOGY/PRINCIPAL FINDINGS: Thirty-nine POAG patients and forty-one age- and gender- matched healthy controls were finally included in the study. ReHo values were used to evaluate spontaneous brain activity and whole brain voxel-wise analysis of ReHo was carried out to detect differences by region in spontaneous brain activity between groups. Compared to controls, POAG patients showed increased ReHo in the right dorsal anterior cingulated cortex, the bilateral medial frontal gyrus and the right cerebellar anterior lobe, and decreased ReHo in the bilateral calcarine, bilateral precuneus gryus, bilateral pre/postcentral gyrus, left inferior parietal lobule and left cerebellum posterior lobe. A multiple linear regression analysis was performed to explore the relationships between clinical measures and ReHo by region showed significant group differences in the POAG group. Negative correlations were found between age and the ReHo values of the superior frontal gyrus (r = -0.323, p = 0.045), left calcarine (r = -0.357, p = 0.026) and inferior parietal lobule (r = -0.362, p = 0.024). A negative correlation was found between the ReHo values of the left precuneus and the cumulative mean defect (r = -0.400, p = 0.012).
CONCLUSIONS: POAG was associated with abnormal brain spontaneous activity in some brain regions and such changed regional activity may be associated with clinical parameters. Spontaneous brain activity may play a role in POAG initiation and progression.
PMID: 24586822 [PubMed - in process]
Efficiency of weak brain connections support general cognitive functioning.
Hum Brain Mapp. 2014 Mar 2;
Authors: Santarnecchi E, Galli G, Polizzotto NR, Rossi A, Rossi S
Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 24585433 [PubMed - as supplied by publisher]
An Multivariate Distance-Based Analytic Framework for Connectome-Wide Association Studies.
Neuroimage. 2014 Feb 27;
Authors: Shehzad Z, Kelly C, Reiss PT, Cameron Craddock R, Emerson JW, McMahon K, Copland DA, Xavier Castellanos F, Milham MP
The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain- phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.
PMID: 24583255 [PubMed - as supplied by publisher]
Modeling dynamic functional information flows on large-scale brain networks.
Med Image Comput Comput Assist Interv. 2013;16(Pt 2):698-705
Authors: Lv P, Guo L, Hu X, Li X, Jin C, Han J, Li L, Liu T
Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls.
PMID: 24579202 [PubMed - in process]
Predictive models of resting state networks for assessment of altered functional connectivity in MCI.
Med Image Comput Comput Assist Interv. 2013;16(Pt 2):674-81
Authors: Jiang X, Zhu D, Li K, Zhang T, Shen D, Guo L, Liu T
Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.
PMID: 24579199 [PubMed - in process]
Identification of MCI using optimal sparse MAR modeled effective connectivity networks.
Med Image Comput Comput Assist Interv. 2013;16(Pt 2):319-27
Authors: Wee CY, Li Y, Jie B, Peng ZW, Shen D
Capability of detecting causal or effective connectivity from resting-state functional magnetic resonance imaging (R-fMRI) is highly desirable for better understanding the cooperative nature of the brain. Effective connectivity provides specific dynamic temporal information of R-fMRI time series and reflects the directional causal influence of one brain region over another. These causal influences among brain regions are normally extracted based on the concept of Granger causality. Conventionally, the effective connectivity is inferred using multivariate autoregressive (MAR) modeling with default model order q = 1, considering low frequency fluctuation of R-fMRI time series. This assumption, although reduces the modeling complexity, does not guarantee the best fitting of R-fMRI time series at different brain regions. Instead of using the default model order, we propose to estimate the optimal model order based upon MAR order distribution to better characterize these causal influences at each brain region. Due to sparse nature of brain connectivity networks, an orthogonal least square (OLS) regression algorithm is incorporated to MAR modeling to minimize spurious effective connectivity. Effective connectivity networks inferred using the proposed optimal sparse MAR modeling are applied to Mild Cognitive Impairment (MCI) identification and obtained promising results, demonstrating the importance of using optimal causal relationships between brain regions for neurodegeneration disorder identification.
PMID: 24579156 [PubMed - in process]
Placebo analgesia and reward processing: Integrating genetics, personality, and intrinsic brain activity.
Hum Brain Mapp. 2014 Feb 27;
Authors: Yu R, Gollub RL, Vangel M, Kaptchuk T, Smoller JW, Kong J
Our expectations about an event can strongly shape our subjective evaluation and actual experience of events. This ability, applied to the modulation of pain, has the potential to affect therapeutic analgesia substantially and constitutes a foundation for non-pharmacological pain relief. A typical example of such modulation is the placebo effect. Studies indicate that placebo may be regarded as a reward, and brain activity in the reward system is involved in this modulation process. In the present study, we combined resting-state functional magnetic resonance imaging (rs-fMRI) measures, genotype at a functional COMT polymorphism (Val158Met), and personality measures in a model to predict the magnitude of placebo conditioning effect indicated by subjective pain rating reduction to calibrated noxious stimuli. We found that the regional homogeneity (ReHo), an index of local neural coherence, in the ventral striatum, was significantly associated with conditioning effects on pain rating changes. We also found that the number of Met alleles at the COMT polymorphism was linearly correlated to the suppression of pain. In a fitted regression model, we found the ReHo in the ventral striatum, COMT genotype, and Openness scores accounted for 59% of the variance in the change in pain ratings. The model was further tested using a separate data set from the same study. Our findings demonstrate the potential of combining resting-state connectivity, genetic information, and personality to predict placebo effect. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 24578196 [PubMed - as supplied by publisher]
Combining spatial independent component analysis with regression to identify the subcortical components of resting-state fMRI functional networks.
Brain Connect. 2014 Feb 27;
Authors: Malherbe C, Messé A, Bardinet E, Pélégrini-Issac M, Perlbarg V, Marrelec G, Worbe Y, Yelnik J, Lehéricy S, Benali H
Functional brain networks are sets of cortical, subcortical and cerebellar regions whose neuronal activities are synchronous over multiple time scales. Spatial independent component analysis (sICA) is a widespread approach to identify functional networks in the human brain from functional magnetic resonance imaging (fMRI) resting-state data, and there is now a general agreement regarding the cortical regions involved in each network. It is well known that these cortical regions are preferentially connected with specific subcortical functional territories, however subcortical components have not been observed whether in a robust or in a reproducible manner using sICA. This article presents a new method to analyze resting-state fMRI data that allows for robust and reproducible association of subcortical regions with well-known patterns of cortical regions. The approach relies on the hypothesis that the time course in subcortical regions is similar to that in cortical regions belonging to the same network. First, sICA followed by hierarchical clustering is performed on cortical time series to extract group functional cortical networks. Secondly, these networks are complemented with related subcortical areas based on the similarity of their time courses, using an individual general linear model and a random-effect group analysis. Two independent resting-state fMRI datasets were processed and the subcortical components of both datasets overlapped by up to 99% depending on the network, showing the reproducibility and the robustness of our approach. The relationship between subcortical components and functional cortical networks was consistent with functional territories (sensorimotor, associative and limbic) from an immunohistochemical atlas of the basal ganglia.
PMID: 24575752 [PubMed - as supplied by publisher]
Altered baseline brain activity in children with bipolar disorder during mania state: a resting-state study.
Neuropsychiatr Dis Treat. 2014;10:317-23
Authors: Lu D, Jiao Q, Zhong Y, Gao W, Xiao Q, Liu X, Lin X, Cheng W, Luo L, Xu C, Lu G, Su L
BACKGROUND: Previous functional magnetic resonance imaging (fMRI) studies have shown abnormal functional connectivity in regions involved in emotion processing and regulation in pediatric bipolar disorder (PBD). Recent studies indicate, however, that task-dependent neural changes only represent a small fraction of the brain's total activity. How the brain allocates the majority of its resources at resting state is still unknown. We used the amplitude of low-frequency fluctuation (ALFF) method of fMRI to explore the spontaneous neuronal activity in resting state in PBD patients.
METHODS: Eighteen PBD patients during the mania phase and 18 sex-, age- and education-matched healthy subjects were enrolled in this study and all patients underwent fMRI scanning. The ALFF method was used to compare the resting-state spontaneous neuronal activity between groups. Correlation analysis was performed between the ALFF values and Young Mania Rating Scale scores.
RESULTS: Compared with healthy controls, PBD patients presented increased ALFF in bilateral caudate and left pallidum as well as decreased ALFF in left precuneus, left superior parietal lobule, and bilateral inferior occipital gyrus. Additionally, ALFF values in left pallidum were positively correlated with Young Mania Rating Scale score in PBD.
CONCLUSION: The abnormal resting-state neuronal activities of the basal ganglia, parietal cortex, and occipital cortex may play an important role in the pathophysiology in PBD patients.
PMID: 24570585 [PubMed]
Altered intra- and inter-regional synchronization of superior temporal cortex in deaf people.
Cereb Cortex. 2013 Aug;23(8):1988-96
Authors: Li Y, Booth JR, Peng D, Zang Y, Li J, Yan C, Ding G
Functional organization of the brain can be fundamentally altered by auditory deprivation. Previous studies found that the superior temporal cortex in deaf people is reorganized to process non-auditory stimuli, as revealed by the extrinsic task-induced brain activities. However, it is unknown how the intrinsic activities of this region are impacted by deafness. This study explored this issue using resting-state functional magnetic resonance imaging. We examined 60 congenitally deaf (CD) individuals, 39 acquired deaf (AD) individuals, and 38 hearing controls (HC), and focused on the effect of deafness on the intra- and inter-regional synchronization of different parts of superior temporal sulcus (STS). We found that intra-regional synchronization or regional homogeneity (ReHo) of the middle STS (mSTS) was decreased in AD compared with HC or CD, while the CD had preserved ReHo in mSTS. Greater connectivity was observed between mSTS and posterior STS in CD and HC than in AD, while both CD and AD had weaker connectivity of mSTS with the anterior STS (aSTS) compared with HC. Moreover, the connectivity of mSTS-aSTS in CD and AD was associated with their language skills. These findings confirmed our hypothesis that the intrinsic function of different parts of STS is distinctly impacted by deafness.
PMID: 22767633 [PubMed - indexed for MEDLINE]