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Previous studies have proposed that altered reward processing and elevated cognitive control underlie the etiology of anorexia nervosa (AN). A newly debated notion suggests altered habit learning and an overreliance on habits may contribute to the persistence of AN. In weight-recovered AN patients, we previously found neuroimaging-based evidence for unaltered reward processing, but elevated cognitive control. In order to differentiate between state versus trait factors, we here contrast the aforementioned hypotheses in a sample of acutely underweight AN (acAN) patients. 37 acAN patients and 37 closely matched healthy controls (HC) underwent a functional MRI while performing an established instrumental motivation task. We found no group differences with respect to neural responses during the anticipation or receipt of reward. However, the behavioral response data showed a bimodal distribution, indicative for a goal-directed (gAN) and a habit-driven (hAN) patient subgroup. Additional analyses revealed decreased mOFC activation during reward anticipation in hAN, which would be in line with a habit-driven response. These findings provide a new perspective on the debate regarding the notion of increased goal-directed versus habitual behavior in AN. If replicable, the observed dissociation between gAN and hAN might help to tailor therapeutic approaches to individual patient characteristics.
Pubmed ID: 31537862
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A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.
View all literature mentionsSoftware package used for interactive, or batched, statistical analysis in social science, health sciences and marketing. Software platform offers advanced statistical analysis, a library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and deployment into applications.Versions that were produced by SPSS Inc. before the IBM acquisition (Versions 18 and earlier) would be given origin or publisher of SPSS Inc. in Chicago.
View all literature mentionsSet of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.
View all literature mentionsSoftware package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.
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