Detection of Cerebral Reorganization Induced by Real-Time fMRI Feedback Training of Insula Activation

Background. Studies with real-time functional magnetic resonance imaging (fMRI) demonstrate that humans volitionally regulate hemodynamic signals from circumscribed regions of the brain, leading to area-specific behavioral consequences. Methods to better determine the nature of dynamic functional interactions between different brain regions and plasticity due to self-regulation training are still in development. Objective. The authors investigated changes in brain states while training 6 healthy participants to self-regulate insular cortex by real-time fMRI feedback. Methods. The authors used multivariate pattern analysis to observe spatial pattern changes and a multivariate Granger causality model to show changes in temporal interactions in multiple brain areas over the course of 5 repeated scans per subject during positive and negative emotional imagery with feedback about the level of insular activation. Results. Feedback training leads to more spatially focused recruitment of areas relevant for learning and emotion. Effective connectivity analysis reveals that initial training is associated with an increase in network density; further training “prunes” presumably redundant connections and “strengthens” relevant connections. Conclusions. The authors demonstrate the application of multivariate methods for assessing cerebral reorganization during the learning of volitional control of local brain activity. The findings provide insight into mechanisms of training-induced learning techniques for rehabilitation. The authors anticipate that future studies, specifically designed with this hypothesis in mind, may be able to construct a universal index of cerebral reorganization during skill learning based on multiple similar criteria across various skilled tasks. These techniques may be able to discern recovery from compensation, dose–response curves related to training, and ways to determine whether rehabilitation training is actively engaging necessary networks.

[1]  Kevin N. Ochsner,et al.  For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion , 2004, NeuroImage.

[2]  A. Damasio,et al.  Subcortical and cortical brain activity during the feeling of self-generated emotions , 2000, Nature Neuroscience.

[3]  Rosa Cao,et al.  The hemo-neural hypothesis: on the role of blood flow in information processing. , 2008, Journal of neurophysiology.

[4]  R. Deichmann,et al.  Real-time functional magnetic resonance imaging: methods and applications. , 2007, Magnetic resonance imaging.

[5]  M. Posner,et al.  On the genesis of abstract ideas. , 1968, Journal of experimental psychology.

[6]  J. Gross,et al.  The cognitive control of emotion , 2005, Trends in Cognitive Sciences.

[7]  C. Babiloni,et al.  Influence of the supplementary motor area on primary motor cortex excitability during movements triggered by neutral or emotionally unpleasant visual cues , 2003, Experimental Brain Research.

[8]  Michael Erb,et al.  fMRI Brain-Computer Interfaces: A tutorial on methods and applications , 2008 .

[9]  S. Scott,et al.  Positive Emotions Preferentially Engage an Auditory–Motor “Mirror” System , 2006, The Journal of Neuroscience.

[10]  M. Bradley,et al.  The neural basis of narrative imagery: emotion and action. , 2006, Progress in brain research.

[11]  A. Kelly,et al.  Human functional neuroimaging of brain changes associated with practice. , 2005, Cerebral cortex.

[12]  John D E Gabrieli,et al.  Control over brain activation and pain learned by using real-time functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[13]  O. Hikosaka,et al.  Transition of Brain Activation from Frontal to Parietal Areas in Visuomotor Sequence Learning , 1998, The Journal of Neuroscience.

[14]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[15]  Niels Birbaumer,et al.  Effective functional mapping of fMRI data with support‐vector machines , 2010, Human brain mapping.

[16]  David A. Rosenbaum,et al.  Hierarchical organization of motor programs. , 1987 .

[17]  Karl J. Friston,et al.  Neuroanatomical correlates of externally and internally generated human emotion. , 1997, The American journal of psychiatry.

[18]  Karl J. Friston,et al.  Dynamic causal modeling , 2010, Scholarpedia.

[19]  Frank Schneider,et al.  Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness , 2003, NeuroImage.

[20]  S. Petersen,et al.  The effects of practice on the functional anatomy of task performance. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[22]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[23]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[24]  Leslie G. Ungerleider,et al.  Experience-dependent changes in cerebellar contributions to motor sequence learning , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[25]  K. Yau,et al.  Interoception: the sense of the physiological condition of the body , 2003, Current Opinion in Neurobiology.

[26]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[27]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[28]  Paul M. Fitts,et al.  Perceptual-Motor Skill Learning1 , 1964 .

[29]  M. Kawato,et al.  Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning. , 2006, Journal of neurophysiology.

[30]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[31]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[32]  Kuniyoshi L Sakai,et al.  Language Acquisition and Brain Development , 2005, Science.

[33]  Gary H. Glover,et al.  Learned regulation of spatially localized brain activation using real-time fMRI , 2004, NeuroImage.

[34]  A. Seth Causal connectivity of evolved neural networks during behavior. , 2005, Network.

[35]  Cheryl L. Grady,et al.  Common and Unique Neural Activations in Autobiographical, Episodic, and Semantic Retrieval , 2007, Journal of Cognitive Neuroscience.

[36]  A. Craig How do you feel? Interoception: the sense of the physiological condition of the body , 2002, Nature Reviews Neuroscience.

[37]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[38]  Scott T Grafton,et al.  Functional imaging of face and hand imitation: towards a motor theory of empathy , 2004, NeuroImage.

[39]  Wolfgang Grodd,et al.  Regulation of anterior insular cortex activity using real-time fMRI , 2007, NeuroImage.

[40]  Soo-Young Lee,et al.  Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.

[41]  K. Luan Phan,et al.  Functional Neuroanatomy of Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fMRI , 2002, NeuroImage.

[42]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[43]  T. Klingberg,et al.  Increased prefrontal and parietal activity after training of working memory , 2004, Nature Neuroscience.

[44]  R. Veit,et al.  Self‐regulation of regional cortical activity using real‐time fMRI: The right inferior frontal gyrus and linguistic processing , 2009, Human brain mapping.

[45]  J. O'Doherty,et al.  Reward representations and reward-related learning in the human brain: insights from neuroimaging , 2004, Current Opinion in Neurobiology.

[46]  S. Nieuwenhuis,et al.  Mental Training Affects Distribution of Limited Brain Resources , 2007, PLoS biology.

[47]  N. Birbaumer,et al.  fMRI Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.

[48]  G. Fink,et al.  Neural activation during selective attention to subjective emotional responses , 1997, Neuroreport.