New approaches for exploring anatomical and functional connectivity in the human brain. Biological Psychiatry, 56(9), 613-619

Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, Headley Way, Oxford OX3 9DU, United Kingdom.
Biological Psychiatry (Impact Factor: 10.25). 12/2004; 56(9):613-9. DOI: 10.1016/j.biopsych.2004.02.004
Source: PubMed

ABSTRACT Information processing in the primate brain is based on the complementary principles of modular and distributed information processing. The former emphasizes the specialization of functions within different brain areas. The latter emphasizes the massively parallel nature of brain networks and the fact that function also emerges from the flow of information between brain areas. The localization of function to specific brain areas ("functional segregation") is the commonest approach to investigating function; however, an emerging, complementary approach ("functional integration") describes function in terms of the information flow across networks of areas. Here, we highlight recent advances in neuroimaging methodology that have made it possible to investigate the anatomical architecture of networks in the living human brain with diffusion tensor imaging (DTI). We also highlight recent thinking on the ways in which functional imaging can be used to characterize information transmission across networks in the human brain (functional and effective connectivity).

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Available from: Narender Ramnani, Aug 29, 2015
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    • "Существует несколько способов моделироваа ния или оценки возникающих эффективных связей, такие, как моделирование с помощью структурных уравнений (SEM), причинность по Грейнджеру (Granger Causality), перенос энтропии (Transfer Entropy) или динамичее ское моделирование причинности (Dynamic Causal Modelling, DCM). DCM – это моделирование взаимодейй ствий между областями коры больших полуу шарий, позволяющее делать выводы как о паа раметрах этих взаимодействий, так и о влияя нии факторов эксперимента на параметры [Ramnani, 2004]. В рамках DCM мозг расс сматривается как детерминированная нелии нейная динамическая система, которая полуу чает входные данные и производит выходные [Friston, 2003]. "
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    ABSTRACT: 2 Институт высшей нервной деятельности и нейрофизиологии РАН, Москва, 3 Московский научнооисследовательский институт психиатрии Минздрава России, eemail: Поступила в редакцию 31.03.2014 г. Принята в печать 01.09.2014 г. Динамическое моделирование причинности (DCM) – это метод определения эффективных связей в мозге, т.е. влияния, оказываемого одной нейрональной системой на другую. В рамках DCM мозг рассматривается как детерминированная нелинейная динамическая система, получающая входной сигнал и производящая выходной. В основе DCM для ЭЭГ лежит модель нейронной массы, с помоо щью которой можно построить т.н. прямую модель, предсказывающую вызванный ответ, наблюдаа емый на скальпе. В дальнейшем конкурирующие модели (гипотезы) сравниваются с помощью Байй есова подхода для определения лучшей, т.е. наиболее хорошо описывающей экспериментальные данные. Мы применили DCM для нахождения вероятных моделей генерации вызванной активноо сти в ответ на стандартные и девиантные стимулы в зрительной задаче, а также для оценки устойчии вости модели в группе испытуемых. Модель, предполагающая изменения сил прямых связей в завии симости от стимула, оказалась лучшей и более устойчивой в группе. Полученные результаты нахоо дятся в согласии с DCM для слуховой оддболлпрадигмы, построенной ранее другими авторами. Ключевые слова: ЭЭГ, ВП, динамическое моделирование причинности, зрительные стимулы, Байй есово моделирование, оддболлпарадигма, эффективные связи. Dynamic Causal Modeling (DCM) is a technique designed to assess the effective connectivity in the brain, i.e. the influence one neuronal system exerts over another. The central idea behind DCM is to treat the brain as a deterministic nonlinear dynamical system that is subject to inputs, and produces outputs. DCM for EEG uses neural mass model to explain source activity and to build a forward model that predicts scalpprecorded ree sponse, based on a particular underlying network structure. Further analysis is done by selecting, using the Bayesian inference, among the competing hypotheses (models) the one that is best to explain the data. We used DCM approach to find a plausible model for ERPs recorded for standard and deviant stimuli in visual oddball task, and to evaluate the reproducibility of this model over a set of individual recordings. The model that best explained the data and gave reproducible results was the one that allowed the changes in strength of forward connections. These results are compatible with the DCM for auditory oddball experiment by other authors.
    Pavlov journal of higher nervous activity 01/2014; 64(6):627-638. DOI:10.7868/S0044467714060100
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    • "The comparison of temporal profiles recorded from different regions of interest might yield information on functional synchronicity of different brain areas in relation to the stimulus (Ramnani et al., 2004; Rogers et al., 2007). Connectivity analyses have been performed in both human and animal studies by use of electroencephalogram (EEG) recordings as well as by positron emission tomography (PET) techniques (Gerstein and Perkel, 1969; Horwitz et al., 1984). "
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    ABSTRACT: Imaging methods that enable the investigation of functional networks both in human and animal brain provide important insights into mechanisms underlying pathologies including psychiatric disorders. Since the serotonergic receptor 1A (5-HT1A-R) has been strongly implicated in the pathophysiology of depressive and anxiety disorders, as well as in the action of antidepressant drugs, we investigated brain connectivity related to the 5-HT1A-R system by use of pharmacological functional magnetic resonance imaging in mice. We characterized functional connectivity elicited by activation of 5-HT1A-R and investigated how pharmacological and genetic manipulations of its function may modulate the evoked connectivity. Functional connectivity elicited by administration of the 5-HT1A-R agonist 8-OH-DPAT can be described by networks characterized by small-world attributes with nodes displaying highly concerted response patterns. Circuits identified comprised the brain structures known to be involved in stress-related disorders (e.g. prefrontal cortex, amygdala and hippocampus). The results also highlight the dorsomedial thalamus, a structure associated with fear processing, as a hub of the 5-HT1A-R functional network. Administration of a specific 5-HT1A-R antagonist or use of heterozygous 5-HT1A-R knockout mice significantly reduced functional connectivity elicited by 8-OH-DPAT. Whole brain functional connectivity analysis constitutes an attractive tool to characterize impairments in neurotransmission and the efficacy of pharmacological treatment in a comprehensive manner.
    NeuroImage 02/2013; DOI:10.1016/j.neuroimage.2013.02.031 · 6.36 Impact Factor
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    • "On the other hand, it is very important to master the anatomy of the white matter fiber, especially the sectional anatomy of the fiber, with the purpose of identifying the fiber on FA and color FA maps, and placing the ROI accurately. Another remaining problem for fibre tracking is the limited resolution of the imaging scanner and the incapacity of a tensor to model properly multiple fibre tracts in one voxel [13] [18]. Current deterministic streamlining algorithms follow the main eigenvector of the diffusion tensor, reducing the available information. "
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    ABSTRACT: The aim of this study is to investigate the white matter by the diffusion tensor imaging and the Chinese visible human dataset and to provide the 3D anatomical data of the corticospinal tract for the neurosurgical planning by studying the probabilistic maps and the reproducibility of the corticospinal tract. Diffusion tensor images and high-resolution T1-weighted images of 15 healthy volunteers were acquired; the DTI data were processed using DtiStudio and FSL software. The FA and color FA maps were compared with the sectional images of the Chinese visible human dataset. The probability maps of the corticospinal tract were generated as a quantitative measure of reproducibility for each voxel of the stereotaxic space. The fibers displayed by the diffusion tensor imaging were well consistent with the sectional images of the Chinese visible human dataset and the existing anatomical knowledge. The three-dimensional architecture of the white matter fibers could be clearly visualized on the diffusion tensor tractography. The diffusion tensor tractography can establish the 3D probability maps of the corticospinal tract, in which the degree of intersubject reproducibility of the corticospinal tract is consistent with the previous architectonic report. DTI is a reliable method of studying the fiber connectivity in human brain, but it is difficult to identify the tiny fibers. The probability maps are useful for evaluating and identifying the corticospinal tract in the DTI, providing anatomical information for the preoperative planning and improving the accuracy of surgical risk assessments preoperatively.
    The Scientific World Journal 11/2012; 2012(3):530432. DOI:10.1100/2012/530432 · 1.73 Impact Factor
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