Morris Laurel S, Kundu Prantik, Dowell Nicholas, Mechelmans Daisy J, Favre Pauline, Irvine Michael A, Robbins Trevor W, Daw Nathaniel, Bullmore Edward T, Harrison Neil A, Voon Valerie
Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom.
Department of Psychiatry, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA.
Cortex. 2016 Jan;74:118-33. doi: 10.1016/j.cortex.2015.11.004. Epub 2015 Nov 18.
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.
离散但相互重叠的额叶-纹状体回路介导了广泛可分离的认知和行为过程。我们使用一种最近开发的多回波静息态功能磁共振成像(MRI)序列,其信噪比相比传统序列有极大提升,以此绘制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 - SMAs、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-纹状体连接的功能性质,在探索性评估习惯性无模型行为(SMA和壳核)时,展示了既定目标导向行为(涉及内侧眶额皮质 - mOFC和VS)、概率性反转学习(外侧眶额皮质 - lOFC和VS)和注意力转移(背外侧前额叶皮质 - dlPFC和VS)的相关因素。我们进一步使用神经突方向离散度和密度成像(NODDI)来表明,更多基于目标导向的模型学习(MBc)也与更高的mOFC神经突密度相关,而习惯性无模型学习(MFc)与壳核中的神经突复杂性有关。这些数据突出了MFc的计算模型与传统习惯学习测量方法之间的相似性。我们强调了行为控制平行系统的内在功能和结构架构。