Infra-slow EEG fluctuations are correlated with resting-state network dynamics in FMRI.
J Neurosci. 2014 Jan 8;34(2):356-62
Authors: Hiltunen T, Kantola J, Abou Elseoud A, Lepola P, Suominen K, Starck T, Nikkinen J, Remes J, Tervonen O, Palva S, Kiviniemi V, Palva JM
Ongoing neuronal activity in the CNS waxes and wanes continuously across widespread spatial and temporal scales. In the human brain, these spontaneous fluctuations are salient in blood oxygenation level-dependent (BOLD) signals and correlated within specific brain systems or "intrinsic-connectivity networks." In electrophysiological recordings, both the amplitude dynamics of fast (1-100 Hz) oscillations and the scalp potentials per se exhibit fluctuations in the same infra-slow (0.01-0.1 Hz) frequency range where the BOLD fluctuations are conspicuous. While several lines of evidence show that the BOLD fluctuations are correlated with fast-amplitude dynamics, it has remained unclear whether the infra-slow scalp potential fluctuations in full-band electroencephalography (fbEEG) are related to the resting-state BOLD signals. We used concurrent fbEEG and functional magnetic resonance imaging (fMRI) recordings to address the relationship of infra-slow fluctuations (ISFs) in scalp potentials and BOLD signals. We show here that independent components of fbEEG recordings are selectively correlated with subsets of cortical BOLD signals in specific task-positive and task-negative, fMRI-defined resting-state networks. This brain system-specific association indicates that infra-slow scalp potentials are directly associated with the endogenous fluctuations in neuronal activity levels. fbEEG thus yields a noninvasive, high-temporal resolution window into the dynamics of intrinsic connectivity networks. These results support the view that the slow potentials reflect changes in cortical excitability and shed light on neuronal substrates underlying both electrophysiological and behavioral ISFs.
PMID: 24403137 [PubMed - in process]
Linking Inter-individual Differences in the Conflict Adaptation Effect to Spontaneous Brain Activity.
Neuroimage. 2014 Jan 4;
Authors: Wang T, Chen Z, Zhao G, Hitchman G, Liu C, Zhao X, Liu Y, Chen A
Conflict adaptation has been widely researched in normal and clinical populations. There are large individual differences in conflict adaptation, and it has been linked to the schizotypal trait. However, no study to date has examined how individual differences in spontaneous brain activity are related to behavioral conflict adaptation (performance). Resting-state functional magnetic resonance imaging (RS-fMRI) is a promising tool to investigate this issue. The present study evaluated the regional homogeneity (ReHo) of RS-fMRI signals in order to explore the neural basis of individual differences in conflict adaptation across two independent samples comprising a total of 67 normal subjects. A partial correlation analysis was carried out to examine the relationship between ReHo and behavioral conflict adaptation, while controlling for reaction time, standard deviation and flanker interference effects. This analysis was conducted on 39 subjects' data (sample 1); the results showed significant positive correlations in the left dorsolateral prefrontal cortex (DLPFC) and left ventrolateral prefrontal cortex. We then conducted a test-validation procedure on the remaining 28 subjects' data (sample 2) to examine the reliability of the results. Regions of interest were defined based on the correlation results. Regression analysis showed that variability in ReHo values in the DLPFC accounted for 48% of the individual differences in the conflict adaptation effect in sample 2. The present findings provide further support for the importance of the DLPFC in the conflict adaptation process. More importantly, we demonstrated that ReHo of RS-fMRI signals in the DLPFC can predict behavioral performance in conflict adaptation, which provides potential biomarkers for the early detection of cognitive control deterioration.
PMID: 24398332 [PubMed - as supplied by publisher]
Imaging early consolidation of perceptual learning with face stimuli during rest.
Brain Cogn. 2014 Jan 3;85C:170-179
Authors: Vilsten JS, Mundy ME
Studies investigating visual perceptual learning (VPL) have traditionally used simple visual tasks and focused on assessing the active (online) processes of learning and memory: encoding and retrieval. The assessment of complex stimuli and the passive (offline) process of consolidation is, however, necessary for a full understanding of the development of VPL and has received little direct analysis. In the current study, 30 young adults completed a VPL task with face stimuli while undergoing an fMRI scan. Activity was assessed within offline rest breaks both during and after the learning task. Changes in baseline activity within functionally-relevant regions were identified during these rest periods. Furthermore, differences in consolidation-related resting activity were evident between individuals who performed well on the active task, and those who performed less well. These findings provide preliminary evidence that activity during offline rest breaks, which immediately follow the active task, is associated with consolidation and learning, in VPL.
PMID: 24394347 [PubMed - as supplied by publisher]
Dopamine-dependent architecture of cortico-subcortical network connectivity.
Cereb Cortex. 2013 Jul;23(7):1509-16
Authors: Cole DM, Oei NY, Soeter RP, Both S, van Gerven JM, Rombouts SA, Beckmann CF
Maladaptive dopaminergic mediation of reward processing in humans is thought to underlie multiple neuropsychiatric disorders, including addiction, Parkinson's disease, and schizophrenia. Mechanisms responsible for the development of such disorders may depend on individual differences in neural signaling within large-scale cortico-subcortical circuitry. Using a combination of functional neuroimaging and pharmacological challenges in healthy volunteers, we identified opposing dopamine agonistic and antagonistic neuromodulatory effects on distributed functional interactions between specific subcortical regions and corresponding neocortical "resting-state" networks, known to be involved in distinct aspects of cognition and reward processing. We found that, relative to a placebo, levodopa and haloperidol challenges, respectively, increased or decreased the functional connectivity between (1) the midbrain and a "default mode" network, (2) the right caudate and a right-lateralized frontoparietal network, and (3) the ventral striatum and a fronto-insular network. Further, we found drug-specific associations between brain circuitry reactivity to dopamine modulation and individual differences in trait impulsivity, revealing dissociable drug-personality interaction effects across distinct dopamine-dependent cortico-subcortical networks. Our findings identify possible systems underlying pathogenesis and treatment efficacy in disorders of dopamine deficiency.
PMID: 22645252 [PubMed - indexed for MEDLINE]
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers.
Neuroimage. 2014 Jan 2;
Authors: Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM
Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.
PMID: 24389422 [PubMed - as supplied by publisher]
Exploring the network dynamics underlying brain activity during rest.
Prog Neurobiol. 2013 Dec 31;
Authors: Cabral J, Kringelbach ML, Deco G
Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-sclae computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.
PMID: 24389385 [PubMed - as supplied by publisher]
Major Depressive Disorder Is Associated with Abnormal Interoceptive Activity and Functional Connectivity in the Insula.
Biol Psychiatry. 2013 Dec 8;
Authors: Avery JA, Drevets WC, Moseman SE, Bodurka J, Barcalow JC, Simmons WK
BACKGROUND: Somatic complaints and altered interoceptive awareness are common features in the clinical presentation of major depressive disorder (MDD). Recently, neurobiological evidence has accumulated demonstrating that the insula is one of the primary cortical structures underlying interoceptive awareness. Abnormal interoceptive representation within the insula may thus contribute to the pathophysiology and symptomatology of MDD.
METHODS: We compared functional magnetic resonance imaging blood oxygenation level-dependent responses between 20 unmedicated adults with MDD and 20 healthy control participants during a task requiring attention to visceral interoceptive sensations and also assessed the relationship of this blood oxygenation level-dependent response to depression severity, as rated using the Hamilton Depression Rating Scale. Additionally, we examined between-group differences in insula resting-state functional connectivity and its relationship to Hamilton Depression Rating Scale ratings of depression severity.
RESULTS: Relative to the healthy control subjects, unmedicated MDD subjects exhibited decreased activity bilaterally in the dorsal mid-insula cortex (dmIC) during interoception. Activity within the insula during the interoceptive attention task was negatively correlated with both depression severity and somatic symptom severity in depressed subjects. Major depressive disorder also was associated with greater resting-state functional connectivity between the dmIC and limbic brain regions implicated previously in MDD, including the amygdala, subgenual prefrontal cortex, and orbitofrontal cortex. Moreover, functional connectivity between these regions and the dmIC was positively correlated with depression severity.
CONCLUSIONS: Major depressive disorder and the somatic symptoms of depression are associated with abnormal interoceptive representation within the insula.
PMID: 24387823 [PubMed - as supplied by publisher]
Functional connectivity networks with and without global signal correction.
Front Hum Neurosci. 2013;7:880
Authors: Hayasaka S
In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.
PMID: 24385961 [PubMed]
Network diffusion accurately models the relationship between structural and functional brain connectivity networks.
Neuroimage. 2013 Dec 30;
Authors: Abdelnour F, Voss HU, Raj A
The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain's long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways.
PMID: 24384152 [PubMed - as supplied by publisher]
Flexible connectivity in the aging brain revealed by task modulations.
Hum Brain Mapp. 2013 Dec 31;
Authors: Geerligs L, Saliasi E, Renken RJ, Maurits NM, Lorist MM
Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n-back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time-courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age-related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2-back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling. Hum Brain Mapp, 2014. © 2013 Wiley Periodicals, Inc.
PMID: 24382835 [PubMed - as supplied by publisher]
White matter lesion load is associated with resting state functional MRI activity and amyloid pet but not FDG in mild cognitive impairment and early alzheimer's disease patients.
J Magn Reson Imaging. 2013 Dec 31;
Authors: Zhou Y, Yu F, Duong TQ, for the Alzheimer's Disease Neuroimaging Initiative
PURPOSE: To quantify and investigate the interactions between multimodal MRI/positron emission tomography (PET) imaging metrics in elderly patients with early Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls.
MATERIALS AND METHODS: Thirteen early AD, 17 MCI patients, and 14 age-matched healthy aging controls from the Alzheimer's Disease Neuroimaging Initiative database were selected based on availability of data. Default mode network (DMN) functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) were obtained for resting state functional MRI (RS-fMRI). White matter lesion load (WMLL) was quantified from MRI T2-weighted FLAIR images. Amyloid deposition with PET [(18) F]-Florbetapir tracer and metabolism of glucose by means of [(18) F]-fluoro-2-deoxyglucose (FDG) images were quantified using ratio of standard uptake values (rSUV).
RESULTS: Whole-brain WMLL and amyloid deposition were significantly higher (P < 0.005) in MCI and AD patients compared with controls. RS-fMRI results showed significantly reduced (corrected P < 0.05) DMN connectivity and altered fALFF activity in both MCI and AD groups. FDG uptake results showed hypometabolism in AD and MCI patients compared with controls. Correlations (P < 0.05) were found between WMLL and amyloid load, FDG uptake and amyloid load, as well as between amyloid load (rSUV) and fALFF.
CONCLUSION: Our quantitative results of four MRI and PET imaging metrics (fALFF/DMN, WMLL, amyloid, and FDG rSUV values) agree with published values. Significant correlations between MRI metrics, including WMLL/functional activity and PET amyloid load suggest the potential of MRI and PET-based biomarkers for early detection of AD.J. Magn. Reson. Imaging 2013. © 2013 Wiley Periodicals, Inc.
PMID: 24382798 [PubMed - as supplied by publisher]
An investigation into the functional and structural connectivity of the Default Mode Network.
Neuroimage. 2013 Dec 29;
Authors: van Oort ES, van Walsum AM, Norris DG
Connectivity analyses based on both resting-state (rs-)fMRI and diffusion weighted imaging studies suggest that the human brain contains regions that act as hubs for the entire brain, and that elements of the Default Mode Network (DMN) play a pivotal role in this network. In the present study, the detailed functional and structural connectivity of the DMN was investigated. Resting state fMRI (35 minutes duration) and Diffusion Weighted Imaging (DWI) data (256 directions) were acquired from forty-seven healthy subjects at 3 Tesla. Tractography was performed on the DWI data. The resting state data were analysed using a combination of Independent Component Analysis, partial correlation analysis and graph theory. This forms a data driven approach for examining the connectivity of the DMN. ICA defined regions of interest were used as a basis for a partial correlation analysis. The resulting partial correlation coefficients were used to compute graph theoretical measures. This was performed on a single subject basis, and combined to compute group results depicting the spatial distribution of betweenness centrality within the DMN. Hubs with high betweenness centrality were frequently found in association areas of the brain. This approach makes it possible to distinguish the hubs in the DMN as belonging to different anatomical association systems. The start and end points of the fibre tracts coincide with hubs found using the resting state analysis.
PMID: 24382524 [PubMed - as supplied by publisher]
Spontaneous brain activity in adult patients with moyamoya disease: A resting-state fMRI study.
Brain Res. 2013 Dec 28;
Authors: Lei Y, Li Y, Ni W, Jiang H, Yang Z, Guo Q, Gu Y, Mao Y
Adult patients with moyamoya disease (MMD) are reported to suffer from vascular cognitive impairment (VCI), including considerable impairment of executive function/attention. The spatial pattern of functional brain activity in adult MMD patients with VCI has not been studied before and can be measured by examining the amplitude of low-frequency fluctuations (ALFF) of blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) during rest. Twenty-three adult patients with MMD were recruited to participate in this study, including 11 with VCI and 12 without VCI (NonVCI), as well as 22 healthy young adults (normal control, NC). Widespread differences in ALFF were observed between the VCI/NonVCI and NC groups in such regions as the frontal, parietal and temporal gyrus, with parts of the frontal gyrus, such as the anterior cingulate cortex (ACC) and the right supplemental motor area (SMA), showing significant differences in ALFF. Of note, such regions as the parietal gyrus, the right superior frontal gyrus (SFG), the right superior temporal gyrus (STG) and the left caudate nucleus (CN) exhibited significant changes in ALFF during the progressive cognitive decline of MMD. Taken together, our results demonstrate that MMD exhibits a specific intrinsic pattern of ALFF and that this pattern changes with the progression of cognitive decline, providing insight into the pathophysiological nature of this disease.
PMID: 24380677 [PubMed - as supplied by publisher]
Borders, Map Clusters, and Supra-Areal Organization in Visual Cortex.
Neuroimage. 2013 Dec 26;
Authors: Buckner RL, Yeo BT
V1 is a canonical cortical area with clearly delineated architectonic boundaries and a continuous topographic representation of the visual hemifield. It thus serves as a touchstone for understanding what new mapping methods can tell us about cortical organization. By parcellating human cortex using local gradients in functional connectivity, Wig et al. (2013) detected the V1 border with V2. By contrast, previously-published clustering methods that focus on global similarity in connectivity reveal a supra-areal organization that emphasizes eccentricity bands spanning V1 and its neighboring extrastriate areas; i.e. in the latter analysis, the V1 border is not evident. Thus the focus on local connectivity gradients emphasizes qualitatively different features of cortical organization than are captured by global similarity measures. What is intriguing to consider is that each kind of information might be telling us something unique about cortical organization. Global similarity measures may be detecting map clusters and other supra-areal arrangements that reflect a fundamental level of organization.
PMID: 24374078 [PubMed - as supplied by publisher]
Structural and functional underconnectivity as a negative predictor for language in autism.
Hum Brain Mapp. 2013 Dec 21;
Authors: Verly M, Verhoeven J, Zink I, Mantini D, Oudenhove LV, Lagae L, Sunaert S, Rommel N
The development of language, social interaction, and communicative skills are remarkably different in the child with autism spectrum disorder (ASD). Atypical brain connectivity has frequently been reported in this patient population. However, the interplay between their brain connectivity and language performance remains largely understudied. Using diffusion tensor imaging tractography and resting-state fMRI, the authors explored the structural and functional connectivity of the language network and its relation to the language profile in a group of healthy control subjects (N = 25) and a group of children with ASD (N = 17). The authors hypothesized that in children with ASD, a neural connectivity deficit of the language network can be related to the observed abnormal language function. They found an absence of the right-hemispheric arcuate fascicle (AF) in 28% (7/25) of the healthy control children and in 59% (10/17) of the children with ASD. In contrast to healthy control children, the absence of the right-hemispheric AF in children with autism was related to a lower language performance as indicated by a lower verbal IQ, lower scores on the Peabody Picture Vocabulary Test, and lower language scores on the Dutch version of the Clinical Evaluation of Language Fundamentals (CELF-4NL). In addition, through iterative fMRI data analyses, the language impairment of children with ASD could be linked to a marked loss of intrahemispheric functional connectivity between inferior frontal and superior temporal regions, known as the cortical language network. Both structural and functional underconnectivity patterns coincide and are related to an abnormal language function in children with ASD. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
PMID: 24375710 [PubMed - as supplied by publisher]
Brain complex network analysis by means of resting state fMRI and graph analysis: Will it be helpful in clinical epilepsy?
Epilepsy Behav. 2013 Dec 27;
Authors: Onias H, Viol A, Palhano-Fontes F, Andrade KC, Sturzbecher M, Viswanathan G, de Araujo DB
Functional magnetic resonance imaging (fMRI) has just completed 20years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy. This article is part of a Special Issue entitled "NEWroscience 2013".
PMID: 24374054 [PubMed - as supplied by publisher]
Somatotopic reorganization of hand representation in bilateral arm amputees with or without special foot movement skill.
Brain Res. 2013 Dec 26;
Authors: Yu XJ, He HJ, Zhang QW, Zhao F, Zee CS, Zhang S, Gong XY
Bilateral arm amputees usually are excellent foot users. To explore the plasticity of the primary motor cortex in upper-extremities amputees and to determine if the acquisition of special foot movement skill is related with the bilateral hand amputation, we studied the primary motor cortex by using combined task and resting state functional Magnetic Resonance Imaging (fMRI). We investigated 6 bilateral arm amputees with or without special foot movement skill. In the task fMRI study, we found that toe tapping of all the amputees activated the bilateral hand area, including cases without special foot skill. In addition, cases without special foot skill mainly activated the precentral gyrus, which differed from those with more adept foot motor skill who activated both the precentral and postcentral gyri. To further understand the plasticity of the hand area, the resting state functional connectivity was investigated between the foot and hand regions. One-tailed two-sample t-test suggested that the connections between two areas became significantly stronger in the amputee group. Our study demonstrates that hand region of the cortex does not remain 'silent' after bilateral arm amputation, but rather is recruited by other modalities such as adjacent or nonadjacent cortexes to process motor information in a functionally relevant manner. From the data presented, it seems that the bilateral arm amputees have a strong potential to develop new skills in their remaining extremities and practice may further enhance this potential.
PMID: 24373804 [PubMed - as supplied by publisher]
Multiple sclerosis impairs regional functional connectivity in the cerebellum.
Neuroimage Clin. 2013;4:130-8
Authors: Dogonowski AM, Andersen KW, Madsen KH, Sørensen PS, Paulson OB, Blinkenberg M, Siebner HR
Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study changes in long-range functional brain connectivity in multiple sclerosis (MS). Yet little is known about how MS affects functional brain connectivity at the local level. Here we studied 42 patients with MS and 30 matched healthy controls with whole-brain rs-fMRI at 3 T to examine local functional connectivity. Using the Kendall's Coefficient of Concordance, regional homogeneity of blood-oxygen-level-dependent (BOLD)-signal fluctuations was calculated for each voxel and used as a measure of local connectivity. Patients with MS showed a decrease in regional homogeneity in the upper left cerebellar hemisphere in lobules V and VI relative to healthy controls. Similar trend changes in regional homogeneity were present in the right cerebellar hemisphere. The results indicate a disintegration of regional processing in the cerebellum in MS. This might be caused by a functional disruption of cortico-ponto-cerebellar and spino-cerebellar inputs, since patients with higher lesion load in the left cerebellar peduncles showed a stronger reduction in cerebellar homogeneity. In patients, two clusters in the left posterior cerebellum expressed a reduction in regional homogeneity with increasing global disability as reflected by the Expanded Disability Status Scale (EDSS) score or higher ataxia scores. The two clusters were mainly located in Crus I and extended into Crus II and the dentate nucleus but with little spatial overlap. These findings suggest a link between impaired regional integration in the cerebellum and general disability and ataxia.
PMID: 24371795 [PubMed]
[Neuromolecular mechanism of the superiority illusion].
Brain Nerve. 2014 Jan;66(1):49-55
Authors: Yamada M
Abstract The majority of individuals evaluate themselves as above average. This is a cognitive bias called "the superiority illusion". This illusory self-evaluation helps us to have hopes for the future, and has been central to the process of human evolution. Possessing this illusion is also important for mental health, as depressed people appear to have a more realistic perception of themselves, dubbed "depressive realism". Our recent study revealed the spontaneous brain activity and central dopaminergic neurotransmission that generate this illusion, using resting-state fMRI and PET. A functional connectivity between the frontal cortex and striatum, regulated by inhibitory dopaminergic neurotransmission, determines individual levels of the superiority illusion. We further revealed that blocking the dopamine transporter, which enhanced the level of dopamine, increased the degree of the superiority illusion. These findings suggest that dopamine acts on striatal dopamine receptors to suppress fronto-striatal functional connectivity, leading to disinhibited, heuristic, approaches to positive self-evaluation. These findings help us to understand how this key aspect of the human mind is biologically determined, and will suggest treatments for depressive symptoms by targeting specific molecules and neural circuits.
PMID: 24371131 [PubMed - in process]
Functionally altered neurocircuits in a rat model of treatment-resistant depression show prominent role of the habenula.
Eur Neuropsychopharmacol. 2013 Dec 10;
Authors: Gass N, Cleppien D, Zheng L, Schwarz AJ, Meyer-Lindenberg A, Vollmayr B, Weber-Fahr W, Sartorius A
Treatment-resistant depression (TRD) remains a pressing clinical problem. Optimizing treatment requires better definition of the function and specificity of the brain circuits involved. To investigate disease-related alterations of brain function we used a genetic animal model of TRD, congenital learned helplessness (cLH), and functional magnetic resonance imaging as a translational tool. High-resolution regional cerebral blood volume (rCBV) and resting-state functional connectivity measurements were acquired at 9.4T to determine regional dysfunction and interactions that could serve as vulnerability markers for TRD. Effects of cLH on rCBV were determined by statistical parametric mapping using 35 atlas-based regions of interest. Effects of cLH on functional connectivity were assessed by seed region analyses. Significant bilateral rCBV reductions were observed in the lateral habenula, dentate gyrus and subiculum of cLH rats. In contrast, focal bilateral increase in rCBV was observed in the bed nucleus of stria terminalis (BNST), a component of the habenular neurocircuitry. Functional connectivity was primarily enhanced in cLH rats, most notably with respect to serotonergic projections from the dorsal raphe nucleus to the forebrain, within the hippocampal-prefrontal network and between the BNST and lateral frontal regions. Dysregulation of neurocircuitry similar to that observed in depressed patients was detected in cLH rats, supporting the validity of the TRD model and suitability of high-field fMRI as a translational technology to detect and monitor vulnerability markers. Our findings also define neurocircuits that can be studied for TRD treatment in patients, and could be employed for translational research in rodent models.
PMID: 24370074 [PubMed - as supplied by publisher]