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Brain Networks (brain + network)
Selected AbstractsNeural connectivity as an intermediate phenotype: Brain networks under genetic controlHUMAN BRAIN MAPPING, Issue 7 2009Andreas Meyer-Lindenberg Abstract Recent evidence suggests that default mode connectivity characterizes neural states that account for a sizable proportion of brain activity and energy expenditure, and therefore represent a plausible neural intermediate phenotype. This implies the possibility of genetic control over systems-level connectivity features. Imaging genetics is an approach to combine genetic assessment with multimodal neuroimaging to discover neural systems linked to genetic abnormalities or variation. In the present contribution, we report results obtained from applying this strategy to both structural connectivity and functional connectivity data. Using data for serotonergic (5-HTTLPR, MAO-A) and dopaminergic (DARPP-32) genes as examples, we show that systems-level connectivity networks under genetic control can be identified. Remarkable similarities are observed across modalities and scales of description. Features of connectivity often better account for behavioral effects of genetic variation than regional parameters of activation or structure. These data provide convergent evidence for genetic control in humans over connectivity systems, whose characterization has promise for identifying neural systems mediating genetic risk for complex human behavior and psychiatric disease. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source] Brain networks: Graph theoretical analysis and development modelsINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 2 2010Myoung Won Cho Abstract A trendy method to understand the brain is to make a map representing the structural network of the brain, also known as the connectome, on the scale of a brain region. Indeed analysis based on graph theory provides quantitative insights into general topological principles of brain network organization. In particular, it is disclosed that typical brain networks share the topological properties, such as small-world and scale-free, with many other complex networks encountered in nature. Such topological properties are regarded as characteristics of the optimal neural connectivity to implement efficient computation and communication; brains with disease or abnormality show distinguishable deviations in the graph theoretical analysis. Considering that conventional models in graph theory are, however, not adequate for direct application to the neural system, we also discuss a model for explaining how the neural connectivity is organized. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 108,116, 2010 [source] Step-by-step: The effects of physical practice on the neural correlates of locomotion imagery revealed by fMRIHUMAN BRAIN MAPPING, Issue 5 2010Silvio Ionta Abstract Previous studies have shown that mental imagery is a suitable tool to study the progression of the effect of practice on brain activation. Nevertheless, there is still poor knowledge of changes in brain activation patterns during the very early stages of physical practice. In this study, early and late practice stages of different kinds of locomotion (i.e., balanced and unbalanced) have been investigated using functional magnetic resonance imaging during mental imagery of locomotion and stance. During the task, cardiac activity was also recorded. The cerebral network comprising supplementary motor area, basal ganglia, bilateral thalamus, and right cerebellum showed a stronger activation during the imagery of locomotion with respect to imagery of stance. The heart beat showed a significant increase in frequency during the imagery of locomotion with respect to the imagery of stance. Moreover, early stages of practice determined an increased activation in basal ganglia and thalamus with respect to late stages. In this way, it is proposed the modulation of the brain network involved in the imagery of locomotion as a function of physical practice time. Hum Brain Mapp, 2010. © 2009 Wiley-Liss, Inc. [source] fMRI changes in relapsing-remitting multiple sclerosis patients complaining of fatigue after IFN,-1a injectionHUMAN BRAIN MAPPING, Issue 5 2007Maria A. Rocca Abstract If fatigue in multiple sclerosis (MS) is related to an abnormal activation of the sensorimotor brain network, the activity of such a network should vary with varying fatigue. We studied 22 patients treated with interferon beta 1a (IFN,-1a; Avonex, Biogen, Cambridge, MA) with no fatigue (10) and with reversible fatigue (12). fMRI examinations were performed: 1) the same day of IFN,-1a injection (no fatigue; entry), 2) the day after IFN,-1a injection (fatigue; time 1), and 3) 4 days after IFN,-1a injection (no fatigue; time 2). Patients performed a simple motor task with the right, clinically unaffected hand. At time 1, compared with entry and time 2, MS patients with reversible fatigue showed an increased activation of the thalamus bilaterally. In MS patients without fatigue thalamus was more activated at entry than at time 1. In both groups at entry the primary SMC and the SMA were more activated than at times 1 and 2. At entry and time 1, when compared to patients with reversible fatigue, those without showed increased activations of the SII. Conversely, patients with reversible fatigue had increased activations of the thalamus and of several regions of the frontal lobes. An abnormal recruitment of the fronto-thalamic circuitry is associated with IFN,-1a-induced fatigue in MS patients. Hum Brain Mapp, 2007. © 2006 Wiley-Liss, Inc. [source] Relationship between atrophy and ,-amyloid deposition in Alzheimer diseaseANNALS OF NEUROLOGY, Issue 3 2010Gaël Chételat PhD Objective Elucidating the role of aggregated ,-amyloid in relation to gray matter atrophy is crucial to the understanding of the pathological mechanisms of Alzheimer disease and for the development of therapeutic trials. The present study aims to assess this relationship. Methods Brain magnetic resonance imaging and [11C]Pittsburgh compound B (PiB)-positron emission tomography scans were obtained from 94 healthy elderly subjects (49 with subjective cognitive impairment), 34 patients with mild cognitive impairment, and 35 patients with Alzheimer disease. The correlations between global and regional neocortical PiB retention and atrophy were analyzed in each clinical group. Results Global and regional atrophy were strongly related to ,-amyloid load in participants with subjective cognitive impairment but not in patients with mild cognitive impairment or Alzheimer disease. Global neocortical ,-amyloid deposition correlated to atrophy in a large brain network including the hippocampus, medial frontal and parietal areas, and lateral temporoparietal cortex, whereas regional ,-amyloid load was related to local atrophy in the areas of highest ,-amyloid load only, that is, medial orbitofrontal and anterior and posterior cingulate/precuneus areas. Interpretation There is a strong relationship between ,-amyloid deposition and atrophy very early in the disease process. As the disease progresses to mild cognitive impairment and Alzheimer disease clinical stages, pathological events other than, and probably downstream from, aggregated ,-amyloid deposition might be responsible for the ongoing atrophic process. These findings suggest that antiamyloid therapy should be administered very early in the disease evolution to minimize synaptic and neuronal loss. ANN NEUROL 2010;67:317,324 [source] Abnormal activity in reward brain circuits in human narcolepsy with cataplexyANNALS OF NEUROLOGY, Issue 2 2010Aurélie Ponz PhD Objective Hypothalamic hypocretins (or orexins) regulate energy metabolism and arousal maintenance. Recent animal research suggests that hypocretins may also influence reward-related behaviors. In humans, the loss of hypocretin-containing neurons results in a major sleep-wake disorder called narcolepsy-cataplexy, which is associated with emotional disturbances. Here, we aim to test whether narcoleptic patients show an abnormal pattern of brain activity during reward processing. Methods We used functional magnetic resonance imaging in 12 unmedicated patients with narcolepsy-cataplexy to measure the neural responses to expectancy and experience of monetary gains and losses. We statistically compared the patients' data with those obtained in a group of 12 healthy matched controls. Results and Interpretation Our results reveal that activity in the dopaminergic ventral midbrain (ventral tegmental area) was not modulated in narcolepsy-cataplexy patients during high reward expectancy (unlike controls), and that ventral striatum activity was reduced during winning. By contrast, the patients showed abnormal activity increases in the amygdala and in dorsal striatum for positive outcomes. In addition, we found that activity in the nucleus accumbens and the ventral-medial prefrontal cortex correlated with disease duration, suggesting that an alternate neural circuit could be privileged over the years to control affective responses to emotional challenges and compensate for the lack of influence from ventral midbrain regions. Our study offers a detailed picture of the distributed brain network involved during distinct stages of reward processing and shows for the first time, to our knowledge, how this network is affected in hypocretin-deficient narcoleptic patients. ANN NEUROL 2010;67:190,200 [source] Functional segmentation of the brain cortex using high model order group PICAHUMAN BRAIN MAPPING, Issue 12 2009Vesa Kiviniemi Abstract Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source] Analyzing brain networks with PCA and conditional Granger causalityHUMAN BRAIN MAPPING, Issue 7 2009Zhenyu Zhou Abstract Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source] Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorderHUMAN BRAIN MAPPING, Issue 2 2009Liang Wang Abstract In this study, we investigated the changes in topological architectures of brain functional networks in attention-deficit/hyperactivity disorder (ADHD). Functional magnetic resonance images (fMRI) were obtained from 19 children with ADHD and 20 healthy controls during resting state. Brain functional networks were constructed by thresholding the correlation matrix between 90 cortical and subcortical regions and further analyzed by applying graph theoretical approaches. Experimental results showed that, although brain networks of both groups exhibited economical small-world topology, altered functional networks were demonstrated in the brain of ADHD when compared with the normal controls. In particular, increased local efficiencies combined with a decreasing tendency in global efficiencies found in ADHD suggested a disorder-related shift of the topology toward regular networks. Additionally, significant alterations in nodal efficiency were also found in ADHD, involving prefrontal, temporal, and occipital cortex regions, which were compatible with previous ADHD studies. The present study provided the first evidence for brain dysfunction in ADHD from the viewpoint of global organization of brain functional networks by using resting-state fMRI. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source] Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasksHUMAN BRAIN MAPPING, Issue 7 2008Vince D. Calhoun Abstract Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. Hum Brain Mapp, 2008. © 2008 Wiley-Liss, Inc. [source] Brain networks: Graph theoretical analysis and development modelsINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 2 2010Myoung Won Cho Abstract A trendy method to understand the brain is to make a map representing the structural network of the brain, also known as the connectome, on the scale of a brain region. Indeed analysis based on graph theory provides quantitative insights into general topological principles of brain network organization. In particular, it is disclosed that typical brain networks share the topological properties, such as small-world and scale-free, with many other complex networks encountered in nature. Such topological properties are regarded as characteristics of the optimal neural connectivity to implement efficient computation and communication; brains with disease or abnormality show distinguishable deviations in the graph theoretical analysis. Considering that conventional models in graph theory are, however, not adequate for direct application to the neural system, we also discuss a model for explaining how the neural connectivity is organized. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 108,116, 2010 [source] Septal networks: relevance to theta rhythm, epilepsy and Alzheimer's diseaseJOURNAL OF NEUROCHEMISTRY, Issue 3 2006Luis V. Colom Abstract Information processing and storing by brain networks requires a highly coordinated operation of multiple neuronal groups. The function of septal neurons is to modulate the activity of archicortical (e.g. hippocampal) and neocortical circuits. This modulation is necessary for the development and normal occurrence of rhythmical cortical activities that control the processing of sensory information and memory functions. Damage or degeneration of septal neurons results in abnormal information processing in cortical circuits and consequent brain dysfunction. Septal neurons not only provide the optimal levels of excitatory background to cortical structures, but they may also inhibit the occurrence of abnormal excitability states. [source] BOLD Response During Spatial Working Memory in Youth With Heavy Prenatal Alcohol ExposureALCOHOLISM, Issue 12 2009Andrea D. Spadoni Background:, Prenatal alcohol exposure has been consistently linked to neurocognitive deficits and structural brain abnormalities in affected individuals. Structural brain abnormalities observed in regions supporting spatial working memory (SWM) may contribute to observed deficits in visuospatial functioning in youth with fetal alcohol spectrum disorders (FASDs). Methods:, We used functional magnetic resonance imaging (fMRI) to assess the blood oxygen level dependent (BOLD) response in alcohol-exposed individuals during a SWM task. There were 22 young subjects (aged 10,18 years) with documented histories of heavy prenatal alcohol exposure (ALC, n = 10), and age- and sex-matched controls (CON, n = 12). Subjects performed a SWM task during fMRI that alternated between 2-back location matching (SWM) and simple attention (vigilance) conditions. Results:, Groups did not differ on task accuracy or reaction time to the SWM condition, although CON subjects had faster reaction times during the vigilance condition (617 millisecond vs. 684 millisecond, p = 0.03). Both groups showed similar overall patterns of activation to the SWM condition in expected regions encompassing bilateral dorsolateral prefrontal lobes and parietal areas. However, ALC subjects showed greater BOLD response to the demands of the SWM relative to the vigilance condition in frontal, insular, superior, and middle temporal, occipital, and subcortical regions. CON youth evidenced less increased brain activation to the SWM relative to the vigilance task in these areas (p < 0.05, clusters > 1,664 ,l). These differences remained significant after including Full Scale IQ as a covariate. Similar qualitative results were obtained after subjects taking stimulant medication were excluded from the analysis. Conclusions:, In the context of equivalent performance to a SWM task, the current results suggest that widespread increases in BOLD response in youth with FASDs could either indicate decreased efficiency of relevant brain networks, or serve as a compensatory mechanism for deficiency at neural and/or cognitive levels. In context of existing fMRI evidence of heightened prefrontal activation in response to verbal working memory and inhibition demands, the present findings may indicate that frontal structures are taxed to a greater degree during cognitive demands in individuals with FASDs. [source] Levodopa affects functional brain networks in parkinsonian resting tremor,MOVEMENT DISORDERS, Issue 1 2009Bettina Pollok PhD Abstract Resting tremor in idiopathic Parkinson's disease (PD) is associated with an oscillatory network comprising cortical as well as subcortical brain areas. To shed light on the effect of levodopa on these network interactions, we investigated 10 patients with tremor-dominant PD and reanalyzed data in 11 healthy volunteers mimicking PD resting tremor. To this end, we recorded surface electromyograms of forearm muscles and neuromagnetic activity using a 122-channel whole-head magnetometer (MEG). Measurements were performed after overnight withdrawal of levodopa (OFF) and 30 min after oral application of fast-acting levodopa (ON). During OFF, patients showed the typical antagonistic resting tremor. Using the analysis tool Dynamic Imaging of Coherent Sources, we identified the oscillatory network associated with tremor comprising contralateral primary sensorimotor cortex (S1/M1), supplementary motor area (SMA), contralateral premotor cortex (PMC), thalamus, secondary somatosensory cortex (S2), posterior parietal cortex (PPC), and ipsilateral cerebellum oscillating at 8 to 10 Hz. After intake of levodopa, we found a significant decrease of cerebro-cerebral coupling between thalamus and motor cortical areas. Similarly, in healthy controls mimicking resting tremor, we found a significant decrease of functional interaction within a thalamus,premotor,motor network during rest. However, in patients with PD, decrease of functional interaction between thalamus and PMC was significantly stronger when compared with healthy controls. These data support the hypothesis that (1) in patients with PD the basal ganglia and motor cortical structures become more closely entrained and (2) levodopa is associated with normalization of the functional interaction between thalamus and motor cortical areas. © 2008 Movement Disorder Society [source] Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after strokeANNALS OF NEUROLOGY, Issue 3 2010Alex R. Carter MD Objective Focal brain lesions can have important remote effects on the function of distant brain regions. The resulting network dysfunction may contribute significantly to behavioral deficits observed after stroke. This study investigates the behavioral significance of changes in the coherence of spontaneous activity in distributed networks after stroke by measuring resting state functional connectivity (FC) using functional magnetic resonance imaging. Methods In acute stroke patients, we measured FC in a dorsal attention network and an arm somatomotor network, and determined the correlation of FC with performance obtained in a separate session on tests of attention and motor function. In particular, we compared the behavioral correlation with intrahemispheric FC to the behavioral correlation with interhemispheric FC. Results In the attention network, disruption of interhemispheric FC was significantly correlated with abnormal detection of visual stimuli (Pearson r with field effect = ,0.624, p = 0.002). In the somatomotor network, disruption of interhemispheric FC was significantly correlated with upper extremity impairment (Pearson r with contralesional Action Research Arm Test = 0.527, p = 0.036). In contrast, intrahemispheric FC within the normal or damaged hemispheres was not correlated with performance in either network. Quantitative lesion analysis demonstrated that our results could not be explained by structural damage alone. Interpretation These results suggest that lesions cause state changes in the spontaneous functional architecture of the brain, and constrain behavioral output. Clinically, these results validate using FC for assessing the health of brain networks, with implications for prognosis and recovery from stroke, and underscore the importance of interhemispheric interactions. ANN NEUROL 2010;67:365,375 [source] Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity,ARTHRITIS & RHEUMATISM, Issue 8 2010Vitaly Napadow Objective Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. The objective of this study was to investigate the degree of connectivity between multiple brain networks in patients with FM, as well as how activity in these networks correlates with the level of spontaneous pain. Methods Resting-state functional magnetic resonance imaging (FMRI) data from 18 patients with FM and 18 age-matched healthy control subjects were analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic, or resting-state, connectivity was evaluated in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also analyzed for covariance with intrinsic connectivity. Results Patients with FM had greater connectivity within the DMN and right EAN (corrected P [Pcorr] < 0.05 versus controls), and greater connectivity between the DMN and the insular cortex, which is a brain region known to process evoked pain. Furthermore, greater intensity of spontaneous pain at the time of the FMRI scan correlated with greater intrinsic connectivity between the insula and both the DMN and right EAN (Pcorr < 0.05). Conclusion These findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in patients with FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay among multiple brain networks. [source] | |||||||||||||