Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: An illustration using fMRI

被引:74
作者
Ng, VWK
Bullmore, ET
de Zubicaray, GI
Cooper, A
Suckling, J
Williams, SCR
机构
[1] Kings Coll London, London WC2R 2LS, England
[2] Guys Kings & St Thomass Med Sch, London, England
[3] Univ Queensland, St Lucia, Qld 4067, Australia
[4] Univ Cambridge, Cambridge CB2 1TN, England
关键词
D O I
10.1162/08989290152001943
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation. Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.
引用
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页码:537 / 545
页数:9
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