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Effective Connectivity (effective + connectivity)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

Sensorimotor network rewiring in mild cognitive impairment and Alzheimer's disease

HUMAN BRAIN MAPPING, Issue 4 2010
Federica Agosta
Abstract This study aimed at elucidating whether (a) brain areas associated with motor function show a change in functional magnetic resonance imaging (fMRI) signal in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), (b) such change is linear over the course of the disease, and (c) fMRI changes in aMCI and AD are driven by hippocampal atrophy, or, conversely, reflect a nonspecific neuronal network rewiring generically associated to brain tissue damage. FMRI during the performance of a simple motor task with the dominant right-hand, and structural MRI (i.e., dual-echo, 3D T1-weighted, and diffusion tensor [DT] MRI sequences) were acquired from 10 AD patients, 15 aMCI patients, and 11 healthy controls. During the simple-motor task, aMCI patients had decreased recruitment of the left (L) inferior frontal gyrus compared to controls, while they showed increased recruitment of L postcentral gyrus and head of L caudate nucleus, and decreased activation of the cingulum compared with AD patients. Effective connectivity was altered between primary sensorimotor cortices (SMC) in aMCI patients vs. controls, and between L SMC, head of L caudate nucleus, and cingulum in AD vs. aMCI patients. Altered fMRI activations and connections were correlated with the hippocampal atrophy in aMCI and with the overall GM microstructural damage in AD. Motor-associated functional cortical changes in aMCI and AD mirror fMRI changes of the cognitive network, suggesting the occurrence of a widespread brain rewiring with increasing structural damage rather than a specific response of cognitive network. Hum Brain Mapp, 2010. © 2009 Wiley-Liss, Inc. [source]

Changes of effective connectivity between the lateral and medial parts of the prefrontal cortex during a visual task

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 3 2003
Thierry Chaminade
Abstract Structural equation modelling was used to study the change of connectivity during a visual task with continuous variation of the attention load. The model was based on areas defined by the haemodynamic responses described elsewhere [Mazoyer, P., Wicker, B. & Fonlupt, P. (2002) A neural network elicited by parametric manipulation of the attention load. Neuroreport, 13, 2331,2334], including occipitotemporal, parietal, temporal and prefrontal (lateral and medial areas) cortices. We have studied stationary- (which does not depend on the attentional load) and attention-related coupling between areas. This allowed the segregation of two subsystems. The first could reflect a system performing the integration step of the visual signal and the second a system participating in response selection. The major finding is the mutual negative influence between the lateral and medial parts of the prefrontal cortex. This negative influence between these two brain regions increased with the attention load. This is interpreted as a modification of the balance between integration and decision processes that are needed for the task to be efficiently completed. [source]

Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis

HUMAN BRAIN MAPPING, Issue 12 2009
Nicolas A. Crossley
Abstract Background: Superior temporal lobe dysfunction is a robust finding in functional neuroimaging studies of schizophrenia and is thought to be related to a disruption of fronto-temporal functional connectivity. However, the stage of the disorder at which these functional alterations occur is unclear. We addressed this issue by using functional MRI (fMRI) to study subjects in the prodromal and first episode phases of schizophrenia. Methods: Subjects with an at risk mental state (ARMS) for psychosis, a first psychotic episode (FEP), and controls were studied using fMRI while performing a working memory task. Activation in the superior temporal gyrus (STG) was assessed using statistical parametric mapping, and its relationship to frontal activation was examined using dynamic causal modeling. Results: The STG was differentially engaged across the three groups. There was deactivation of this region during the task in controls, whereas subjects with FEP showed activation and the response in subjects with ARMS was intermediately relative to the two other groups. There were corresponding differences in the effective connectivity between the STG and the middle frontal gyrus across the three groups, with a negative coupling between these areas in controls, a positive coupling in the FEP group, and an intermediate value in the ARMS group. Conclusions: A failure to deactivate the superior temporal lobe during tasks that engage prefrontal cortex is evident at the onset of schizophrenia and may reflect a disruption of fronto-temporal connectivity. Qualitatively similar alterations are evident in people with prodromal symptoms of the disorder. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [source]

Testing effective connectivity changes with structural equation modeling: What does a bad model tell us?

HUMAN BRAIN MAPPING, Issue 12 2006
Andrea B. Protzner
Abstract Structural equation modeling (SEM) is a statistical method that can assess changes in effective connectivity across tasks or between groups. In its initial application to neuroimaging data, anatomical connectivity provided the constraints to decompose interregional covariances to estimate effective connections. There have been concerns expressed, however, with the validity of interpreting effective connections for a model that does not adequately fit the data. We sought to address this concern by creating two population networks with different patterns of effective connectivity, extracting three samples sizes (N = 100, 60, 20), and then assessing whether the ability to detect effective connectivity differences depended on absolute model fit. Four scenarios were assessed: (1) elimination of a region showing no task differences; (2) elimination of connections with no task differences; (3) elimination of connections that carried task differences, but could be expressed through alternative indirect routes; (4) elimination of connections that carried task differences, and could not be expressed through indirect routes. We were able to detect task differences in all four cases, despite poor absolute model fit. In scenario 3, total effects captured the overall task differences even though the direct effect was no longer present. In scenario 4, task differences that were included in the model remained, but the missing effect was not expressed. In conclusion, it seems that when independent information (e.g., anatomical connectivity) is used to define the causal structure in SEM, inferences about task- or group-dependent changes are valid regardless of absolute model fit. Hum Brain Mapp, 2006. © 2006 Wiley-Liss, Inc. [source]