Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility

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• 作者：Laurel S. Morris^a ; ^b ; Prantik Kundu^c ; ^d ; Nicholas Dowell^e ; Daisy J. Mechelmans^c ; Pauline Favre^f ; Michael A. Irvine^c ; Trevor W. Robbins^a ; ^b ; Nathaniel Daw^g ; Edward T. Bullmore^b ; ^c ; ^h ; ⁱ ; Neil A. Harrison^e ; Valerie Voon^b ; ^c ; ^h ; ⁱ ; ^{vv247@cam.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fronto-striatal loops ; Goal-directed ; Habit ; Microstructure ; Neurite density
• 刊名：Cortex
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：74
• 期：Complete
• 页码：118-133
• 全文大小：3704 K

文摘

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 (MB_c) is also associated with higher mOFC neurite density and habitual model-free learning (MF_c) implicates neurite complexity in the putamen. This data highlights similarities between a computational account of MF_c and conventional measures of habit learning. We highlight the intrinsic functional and structural architecture of parallel systems of behavioural control.