Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility.
Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility.
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DOI：
10.1016/j.cortex.2015.11.004
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Voon V
中科院分区：
文献类型：
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作者：
Morris LS;Kundu P;Dowell N;Mechelmans DJ;Favre P;Irvine MA;Robbins TW;Daw N;Bullmore ET;Harrison NA;Voon V
离散但相互重叠的额叶 - 纹状体回路介导着广泛可分离的认知和行为过程。利用一种近期开发的多回波静息态功能磁共振成像（MRI）序列，其信噪比大幅提高，我们绘制了额叶皮质到纹状体的功能投射以及通过直接和间接基底神经节回路的纹状体投射。我们展示了不同的边缘系统（腹内侧前额叶区域、腹侧纹状体 - VS、腹侧被盖区 - VTA）、运动（辅助运动区 - SMA、壳核、黑质）和认知（外侧前额叶和尾状核）功能连接。我们证实了皮质 - 纹状体连接的功能特性，展示了既定的目标导向行为（涉及内侧眶额叶皮质 - mOFC和VS）、概率反转学习（外侧眶额叶皮质 - lOFC和VS）以及注意转移（背外侧前额叶皮质 - dlPFC和VS）的相关性，同时在探索性基础上评估习惯性无模型（SMA和壳核）行为。我们进一步使用神经突方向分散和密度成像（NODDI）来表明，更多基于目标导向的基于模型的学习（MBc）也与更高的mOFC神经突密度相关，而习惯性无模型学习（MFc）涉及壳核中的神经突复杂性。这些数据凸显了MFc的计算描述与习惯学习的常规测量之间的相似性。我们强调了行为控制并行系统的内在功能和结构架构。
Discrete yet overlapping frontal-striatal circuits mediate broadly dissociable cognitive and behavioural processes. Using a recently developed multi-echo resting-state functional MRI (magnetic resonance imaging) sequence with greatly enhanced signal compared to noise ratios, we map frontal cortical functional projections to the striatum and striatal projections through the direct and indirect basal ganglia circuit. We demonstrate distinct limbic (ventromedial prefrontal regions, ventral striatum – VS, ventral tegmental area – VTA), motor (supplementary motor areas – SMAs, putamen, substantia nigra) and cognitive (lateral prefrontal and caudate) functional connectivity. We confirm the functional nature of the cortico-striatal connections, demonstrating correlates of well-established goal-directed behaviour (involving medial orbitofrontal cortex – mOFC and VS), probabilistic reversal learning (lateral orbitofrontal cortex – lOFC and VS) and attentional shifting (dorsolateral prefrontal cortex – dlPFC and VS) while assessing habitual model-free (SMA and putamen) behaviours on an exploratory basis. We further use neurite orientation dispersion and density imaging (NODDI) to show that more goal-directed model-based learning (MBc) is also associated with higher mOFC neurite density and habitual model-free learning (MFc) implicates neurite complexity in the putamen. This data highlights similarities between a computational account of MFc and conventional measures of habit learning. We highlight the intrinsic functional and structural architecture of parallel systems of behavioural control.