Caria, A. et al. Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35, 1238-1246

Institute of Medical Psychology and Behavioral Neurobiology, Eberhard-Karls-University of Tübingen, Tübingen, Germany. <>
NeuroImage (Impact Factor: 6.36). 05/2007; 35(3):1238-46. DOI: 10.1016/j.neuroimage.2007.01.018
Source: PubMed


Recent advances in functional magnetic resonance imaging (fMRI) data acquisition and processing techniques have made real-time fMRI (rtfMRI) of localized brain areas feasible, reliable and less susceptible to artefacts. Previous studies have shown that healthy subjects learn to control local brain activity with operant training by using rtfMRI-based neurofeedback. In the present study, we investigated whether healthy subjects could voluntarily gain control over right anterior insular activity. Subjects were provided with continuously updated information of the target ROI's level of activation by visual feedback. All participants were able to successfully regulate BOLD-magnitude in the right anterior insular cortex within three sessions of 4 min each. Training resulted in a significantly increased activation cluster in the anterior portion of the right insula across sessions. An increased activity was also found in the left anterior insula but the percent signal change was lower than in the target ROI. Two different control conditions intended to assess the effects of non-specific feedback and mental imagery demonstrated that the training effect was not due to unspecific activations or non feedback-related cognitive strategies. Both control groups showed no enhanced activation across the sessions, which confirmed our main hypothesis that rtfMRI feedback is area-specific. The increased activity in the right anterior insula during training demonstrates that the effects observed are anatomically specific and self-regulation of right anterior insula only is achievable. This is the first group study investigating the volitional control of emotionally relevant brain region by using rtfMRI training and confirms that self-regulation of local brain activity with rtfMRI is possible.

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    • "The localization criterion for the region serving as region-of-interest (ROI) for neurofeedback has been widely variable. Some studies focused on specific anatomically defined regions such as the somatomotor cortex (deCharms et al 2004), anterior cingulate cortex (Weiskopf et al 2003), amygdala (Posse et al 2003) and the insula (Posse et al 2003, Weiskopf et al 2003, deCharms et al 2004, 2005, Caria et al 2007, Hamilton et al 2011, Subramanian et al 2011, Ruiz et al 2013). Others preferred to use functionally defined ROIs as target for neuromodulation (deCharms et al 2005, Hamilton et al 2011, Subramanian et al 2011, Ruiz et al 2013, Greer et al 2014). "
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    ABSTRACT: Objective: Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach: We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results: We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance: Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.
    Journal of Neural Engineering 09/2015; 12(6):066003. DOI:10.1088/1741-2560/12/6/066003 · 3.30 Impact Factor
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    • "This has been suggested as a method to modulate specific functions of neural networks (Yoo et al., 2006). Several studies have demonstrated that healthy participants can learn the control Abbreviations: ACC, anterior cingulate cortex; BCI, brain-computer interface; EEG, electroencephalography; EPI, echo planar image; NF, neurofeedback; ROI, region of interest; rt-fMRI, real-time functional magnetic resonance imaging; SPM, Statistical Parametric Mapping. of circumscribed brain regions using fMRI-based NF (Yoo et al., 2006; Caria et al., 2007; Rota et al., 2009; Hamilton et al., 2011; Scharnowski et al., 2012; Lawrence et al., 2013). Moreover, first attempts of using fMRI-NF as a therapeutic intervention have been made (e.g., Subramanian et al., 2011). "
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    Frontiers in Behavioral Neuroscience 06/2015; 9:169. DOI:10.3389/fnbeh.2015.00169 · 3.27 Impact Factor
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    • "These designs include brain–computer interface (BCI) [18–20,21] or the provision of neurofeedback. In the latter application, the user receives a measure of their own brain activity in real-time, often for the purposes of neurorehabilitation [22] [23] [24]. "
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    ABSTRACT: Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.•Data acquisition•Head position estimation•Source localization•Real-time source estimationThis work explains the technical details and validates each of these steps.
    MethodsX 11/2014; 1. DOI:10.1016/j.mex.2014.10.008
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