title: Frontostriatal anatomical connections predict age- and difficulty-related differences in reinforcement learning.
abstract: Reinforcement learning (RL) is supported by a network of striatal and frontal cortical structures that are connected through white-matter fiber bundles. With age, the integrity of these white-matter connections declines. The role of structural frontostriatal connectivity in individual and age-related differences in RL is unclear, although local white-matter density and diffusivity have been linked to individual differences in RL. Here we show that frontostriatal tract counts in young human adults (aged 18-28), as assessed noninvasively with diffusion-weighted magnetic resonance imaging and probabilistic tractography, positively predicted individual differences in RL when learning was difficult (70% valid feedback). In older adults (aged 63-87), in contrast, learning under both easy (90% valid feedback) and difficult conditions was predicted by tract counts in the same frontostriatal network. Furthermore, network-level analyses showed a double dissociation between the task-relevant networks in young and older adults, suggesting that older adults relied on different frontostriatal networks than young adults to obtain the same task performance. These results highlight the importance of successful information integration across striatal and frontal regions during RL, especially with variable outcomes.
authors.affiliation: Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands. Electronic address: irenevandevijver@gmail.com.
authors.forename: Irene
authors.initials: I
authors.lastname: van de Vijver
authors.affiliation: Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: K Richard
authors.initials: KR
authors.lastname: Ridderinkhof
authors.affiliation: Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; Nieuw Unicum, Zandvoort, The Netherlands.
authors.forename: Helga
authors.initials: H
authors.lastname: Harsay
authors.affiliation: Department of Radiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Reneman
authors.affiliation: Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
authors.forename: James F
authors.initials: JF
authors.lastname: Cavanagh
authors.affiliation: Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Jessika I V
authors.initials: JI
authors.lastname: Buitenweg
authors.affiliation: Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Michael X
authors.initials: MX
authors.lastname: Cohen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology* / Q000523:psychology*; D003342:Corpus Striatum / Q000000981:diagnostic imaging / Q000473:pathology* / Q000502:physiology* / Q000503:physiopathology; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D005625:Frontal Lobe / Q000000981:diagnostic imaging / Q000473:pathology* / Q000502:physiology* / Q000503:physiopathology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D012054:Reinforcement, Psychology; D066127:White Matter / Q000000981:diagnostic imaging / Q000473:pathology / Q000502:physiology / Q000503:physiopathology; D055815:Young Adult
keywords: Aging; Diffusion tensor imaging; Prefrontal cortex; Probabilistic tractography; Reinforcement learning; Striatum