Graph lesion-deficit mapping of fluid intelligence.
Graph lesion-deficit mapping of fluid intelligence.
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DOI：
10.1093/brain/awac304
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发表时间：
2023-01-05
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流体智能可以说是人类认知的定义特征。然而，它与大脑的关系的性质仍然是一个有争议的话题。有影响力的建议主要是功能成像数据涉及“多需求”额顶叶和更广泛分布的皮质网络，但现存的病变缺陷的研究具有更大的因果关系的权力几乎都是小，方法上的限制，和不确定的。这项任务需要大量的患者样本，全面的表现调查，精细的解剖映射，以及强大的损伤缺陷推理，尚未被带到承担它。我们评估了165名健康对照和227名额叶或非额叶单侧脑损伤患者的最佳液体智力测试，瑞文先进的进步矩阵，采用一系列病变缺陷推理模型，以响应流体智力的潜在分布性质。非参数贝叶斯随机块模型被用来揭示损伤缺陷网络的社区结构，从混杂的病理分布效应中分离功能性。功能受损仅限于额叶病变患者[F（2，387）= 18.491; P < 0.001;额叶比非额叶和健康参与者差P < 0.01，P <0.001]，右侧比左侧更明显[F（4，385）= 12.237; P < 0.001;右侧明显高于左侧和健康对照组（P < 0.01，P < 0.001）。非额叶病变的患者与对照组无明显区别，并且没有表现出偏侧性调制。多需求网络参与的存在和程度都不会影响性能。传统的基于网络的统计和非参数贝叶斯随机块建模严重牵连右额叶。至关重要的是，这种定位在分层随机块模型中明确地将功能与病理驱动的效应分离后得到了证实，突出显示了涉及额中回和额下回、中央前回和中央后回的右额叶网络，右上级顶叶小叶的贡献较弱。标准病变缺陷分析也得到了类似的结果。我们的研究代表了第一次大规模的调查分布在局部受伤的大脑中的流体智力的神经基板。将新的基于图形的损伤缺陷映射与大样本患者的认知表现的详细调查相结合，提供了关于智力神经基础的关键信息。我们的研究结果表明，一组主要是右额叶区域，而不是一个更广泛分布的网络，是至关重要的高层次功能涉及流体智力。此外，他们认为瑞文高级推理测验是一个有用的液体智力的临床指标和右额叶功能障碍的敏感标志物。Cipolotti等人使用一系列损伤缺陷模型研究了局灶性损伤患者的液体智力。在Raven's Advanced Progressive Matrices上的表现在额叶而非非额叶病变的患者中受损，右额叶病变与比左额叶更大的损伤相关。
Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated ‘multiple demand’ frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven’s Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven’s Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction. Cipolotti et al. investigate fluid intelligence in focally lesioned patients using an array of lesion-deficit models. Performance on Raven’s Advanced Progressive Matrices is impaired in patients with frontal but not non-frontal lesions, with right frontal lesions associated with greater impairments than left.
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影响因子：
2.6
作者：
Cipolotti, Lisa;MacPherson, Sarah E.;Nachev, Parashkev
通讯作者：
Nachev, Parashkev
影响因子：
14.5
作者：
BOLLER, F;VIGNOLO, LA
通讯作者：
VIGNOLO, LA
影响因子：
3.1
作者：
Barbey, Aron K.;Colom, Roberto;Grafman, Jordan
通讯作者：
Grafman, Jordan
影响因子：
14.5
作者：
BASSO, A;DERENZI, E;SPINNLER, H
通讯作者：
SPINNLER, H
影响因子：
14.5
作者：
BASSO, A;CAPITANI, E;SPINNLER, H
通讯作者：
SPINNLER, H