I Reinhard1, T Leménager2, M Fauth-Bühler2, D Hermann2, S Hoffmann2, A Heinz3, F Kiefer2, M N Smolka4, S Wellek1, K Mann2, S Vollstädt-Klein5. 1. Department of Biostatistics, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159 Mannheim, Germany. 2. Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159 Mannheim, Germany. 3. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Mitte, 10117 Berlin, Germany. 4. Section of Systems Neuroscience, Department of Psychiatry and Psychotherapy, Technische Universität Dresden, 01187 Dresden, Germany. 5. Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, 68159 Mannheim, Germany. Electronic address: s.vollstaedt-klein@zi-mannheim.de.
Abstract
BACKGROUND: Aggregation of functional magnetic resonance imaging (fMRI) data in regions-of-interest (ROIs) is required for complex statistical analyses not implemented in standard fMRI software. Different data-aggregation measures assess various aspects of neural activation, including spatial extent and intensity. NEW METHOD: In this study, conducted within the framework of the PREDICT study, we compared different aggregation measures for voxel-wise fMRI activations to be used as prognostic factors for relapse in 49 abstinent alcohol-dependent individuals in an outpatient setting using a cue-reactivity task. We compared the importance of the data-aggregation measures as prognostic factors for treatment outcomes by calculating the proportion of explained variation. RESULTS AND COMPARISON WITH EXISTING METHOD(S): Relapse risk was associated with cue-induced brain activation during abstinence in the ventral striatum (VS) and in the orbitofrontal cortex (OFC). While various ROI measures proved appropriate for using fMRI cue-reactivity to predict relapse, on the descriptive level the most "important" prognostic factor was a measure defined as the sum of t-values exceeding an individually defined threshold. Data collected in the VS was superior to that from other regions. CONCLUSIONS: In conclusion, it seems that fMRI cue-reactivity, especially in the VS, can be used as prognostic factor for relapse in abstinent alcohol-dependent patients. Our findings suggest that data-aggregation measures that take both spatial extent and intensity of cue-induced brain activation into account make better biomarkers for predicting relapse than measures that consider an activation's spatial extent or intensity alone.
BACKGROUND: Aggregation of functional magnetic resonance imaging (fMRI) data in regions-of-interest (ROIs) is required for complex statistical analyses not implemented in standard fMRI software. Different data-aggregation measures assess various aspects of neural activation, including spatial extent and intensity. NEW METHOD: In this study, conducted within the framework of the PREDICT study, we compared different aggregation measures for voxel-wise fMRI activations to be used as prognostic factors for relapse in 49 abstinent alcohol-dependent individuals in an outpatient setting using a cue-reactivity task. We compared the importance of the data-aggregation measures as prognostic factors for treatment outcomes by calculating the proportion of explained variation. RESULTS AND COMPARISON WITH EXISTING METHOD(S): Relapse risk was associated with cue-induced brain activation during abstinence in the ventral striatum (VS) and in the orbitofrontal cortex (OFC). While various ROI measures proved appropriate for using fMRI cue-reactivity to predict relapse, on the descriptive level the most "important" prognostic factor was a measure defined as the sum of t-values exceeding an individually defined threshold. Data collected in the VS was superior to that from other regions. CONCLUSIONS: In conclusion, it seems that fMRI cue-reactivity, especially in the VS, can be used as prognostic factor for relapse in abstinent alcohol-dependent patients. Our findings suggest that data-aggregation measures that take both spatial extent and intensity of cue-induced brain activation into account make better biomarkers for predicting relapse than measures that consider an activation's spatial extent or intensity alone.
Authors: Rafael Renteria; Christian Cazares; Emily T Baltz; Drew C Schreiner; Ege A Yalcinbas; Thomas Steinkellner; Thomas S Hnasko; Christina M Gremel Journal: Elife Date: 2021-03-17 Impact factor: 8.140
Authors: Joseph P Schacht; Patrick K Randall; Patricia K Latham; Konstantin E Voronin; Sarah W Book; Hugh Myrick; Raymond F Anton Journal: Neuropsychopharmacology Date: 2017-04-14 Impact factor: 7.853
Authors: Erica N Grodin; Lara A Ray; James MacKillop; Aaron C Lim; Mitchell P Karno Journal: Alcohol Clin Exp Res Date: 2019-01-20 Impact factor: 3.455
Authors: Anita C Hansson; Anne Koopmann; Stefanie Uhrig; Sina Bühler; Esi Domi; Eva Kiessling; Roberto Ciccocioppo; Robert C Froemke; Valery Grinevich; Falk Kiefer; Wolfgang H Sommer; Sabine Vollstädt-Klein; Rainer Spanagel Journal: Neuropsychopharmacology Date: 2017-11-01 Impact factor: 7.853
Authors: A Bifone; A Gozzi; A Cippitelli; A Matzeu; E Domi; H Li; G Scuppa; N Cannella; M Ubaldi; F Weiss; R Ciccocioppo Journal: Addict Biol Date: 2018-10-17 Impact factor: 4.280
Authors: Kathleen A Garrison; Kelly S DeMartini; Philip R Corlett; Patrick D Worhunsky; John H Krystal; Stephanie S O'Malley Journal: Addict Biol Date: 2020-02-18 Impact factor: 4.280