Article

New approaches for exploring anatomical and functional connectivity in the human brain.

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: 9.47). 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).

1 Bookmark
 · 
101 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 2 Институт высшей нервной деятельности и нейрофизиологии РАН, Москва, 3 Московский научнооисследовательский институт психиатрии Минздрава России, eemail: msharaev@mail.ru 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
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Traumatic brain injury (TBI) triggers many secondary changes in tissue biology, which ultimately determine the extent of injury and clinical outcome. Hyaluronan [hyaluronic acid (HA)] is a protective cementing gel present in the intercellular spaces whose degradation has been reported as a causative factor in tissue damage. Yet little is known about the expression and activities of genes involved in HA catabolism after TBI. Young adult male Sprague-Dawley rats were assigned to three groups: naïve control, craniotomy, and controlled-cortical impact-induced TBI (CCI-TBI). Four animals per group were sacrificed at 4 h, 1, 3, and 7 days post-CCI. The mRNA expression of hyaluronan synthases (HAS1-3), hyaluronidases (enzymes for HA degradation, HYAL 1-4, and PH20), and CD44 and RHAMM (membrane receptors for HA signaling and removal) were determined using real-time PCR. Compared to the naïve controls, expression of HAS1 and HAS2 mRNA, but not HAS3 mRNA increased significantly following craniotomy alone and following CCI with differential kinetics. Expression of HAS2 mRNA increased significantly in the ipsilateral brain at 1 and 3 days post-CCI. HYAL1 mRNA expression also increased significantly in the craniotomy group and in the contralateral CCI at 1 and 3 days post-CCI. CD44 mRNA expression increased significantly in the ipsilateral CCI at 4 h, 1, 3, and 7 days post-CCI (up to 25-fold increase). These data suggest a dynamic regulation and role for HA metabolism in secondary responses to TBI.
    Frontiers in Neurology 09/2014; 5:173. DOI:10.3389/fneur.2014.00173
  • Source

Full-text (2 Sources)

Download
62 Downloads
Available from
May 20, 2014

Narender Ramnani