Literature DB >> 25761006

The inclusion of functional connectivity information into fMRI-based neurofeedback improves its efficacy in the reduction of cigarette cravings.

Dong-Youl Kim1, Seung-Schik Yoo2, Marion Tegethoff3, Gunther Meinlschmidt3,4, Jong-Hwan Lee1.   

Abstract

Real-time fMRI (rtfMRI) neurofeedback (NF) facilitates volitional control over brain activity and the modulation of associated mental functions. The NF signals of traditional rtfMRI-NF studies predominantly reflect neuronal activity within ROIs. In this study, we describe a novel rtfMRI-NF approach that includes a functional connectivity (FC) component in the NF signal (FC-added rtfMRI-NF). We estimated the efficacy of the FC-added rtfMRI-NF method by applying it to nicotine-dependent heavy smokers in an effort to reduce cigarette craving. ACC and medial pFC as well as the posterior cingulate cortex and precuneus are associated with cigarette craving and were chosen as ROIs. Fourteen heavy smokers were randomly assigned to receive one of two types of NF: traditional activity-based rtfMRI-NF or FC-added rtfMRI-NF. Participants received rtfMRI-NF training during two separate visits after overnight smoking cessation, and cigarette craving score was assessed. The FC-added rtfMRI-NF resulted in greater neuronal activity and increased FC between the targeted ROIs than the traditional activity-based rtfMRI-NF and resulted in lower craving score. In the FC-added rtfMRI-NF condition, the average of neuronal activity and FC was tightly associated with craving score (Bonferroni-corrected p = .028). However, in the activity-based rtfMRI-NF condition, no association was detected (uncorrected p > .081). Non-rtfMRI data analysis also showed enhanced neuronal activity and FC with FC-added NF than with activity-based NF. These results demonstrate that FC-added rtfMRI-NF facilitates greater volitional control over brain activity and connectivity and greater modulation of mental function than activity-based rtfMRI-NF.

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Year:  2015        PMID: 25761006     DOI: 10.1162/jocn_a_00802

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  31 in total

Review 1.  Closed-loop brain training: the science of neurofeedback.

Authors:  Ranganatha Sitaram; Tomas Ros; Luke Stoeckel; Sven Haller; Frank Scharnowski; Jarrod Lewis-Peacock; Nikolaus Weiskopf; Maria Laura Blefari; Mohit Rana; Ethan Oblak; Niels Birbaumer; James Sulzer
Journal:  Nat Rev Neurosci       Date:  2016-12-22       Impact factor: 34.870

Review 2.  The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex.

Authors:  Ahmet O Ceceli; Charles W Bradberry; Rita Z Goldstein
Journal:  Neuropsychopharmacology       Date:  2021-08-18       Impact factor: 7.853

3.  The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt.

Authors:  Shannon L Zelle; Kathleen M Gates; Julie A Fiez; Michael A Sayette; Stephen J Wilson
Journal:  Neuroimage       Date:  2016-03-11       Impact factor: 6.556

4.  Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback.

Authors:  Sook-Lei Liew; Mohit Rana; Sonja Cornelsen; Marcos Fortunato de Barros Filho; Niels Birbaumer; Ranganatha Sitaram; Leonardo G Cohen; Surjo R Soekadar
Journal:  Neurorehabil Neural Repair       Date:  2015-12-14       Impact factor: 3.919

Review 5.  Resting-state functional connectivity and nicotine addiction: prospects for biomarker development.

Authors:  John R Fedota; Elliot A Stein
Journal:  Ann N Y Acad Sci       Date:  2015-09       Impact factor: 5.691

6.  Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

Authors:  Junghoe Kim; Vince D Calhoun; Eunsoo Shim; Jong-Hwan Lee
Journal:  Neuroimage       Date:  2015-05-15       Impact factor: 6.556

7.  Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals.

Authors:  Kishore Vakamudi; Cameron Trapp; Khaled Talaat; Kunxiu Gao; Bruno Sa De La Rocque Guimaraes; Stefan Posse
Journal:  Brain Connect       Date:  2020-10-08

Review 8.  Real-Time fMRI in Neuroscience Research and Its Use in Studying the Aging Brain.

Authors:  Mohit Rana; Andrew Q Varan; Anis Davoudi; Ronald A Cohen; Ranganatha Sitaram; Natalie C Ebner
Journal:  Front Aging Neurosci       Date:  2016-10-18       Impact factor: 5.750

9.  Translating Neurocognitive Models of Auditory-Verbal Hallucinations into Therapy: Using Real-time fMRI-Neurofeedback to Treat Voices.

Authors:  Thomas Fovet; Natasza Orlov; Miriam Dyck; Paul Allen; Klaus Mathiak; Renaud Jardri
Journal:  Front Psychiatry       Date:  2016-06-27       Impact factor: 4.157

10.  Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting.

Authors:  Gunther Meinlschmidt; Jong-Hwan Lee; Esther Stalujanis; Angelo Belardi; Minkyung Oh; Eun Kyung Jung; Hyun-Chul Kim; Janine Alfano; Seung-Schik Yoo; Marion Tegethoff
Journal:  Front Psychol       Date:  2016-07-28
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