Food related processes in the insular cortex.
Front Hum Neurosci. 2013;7:499
Authors: Frank S, Kullmann S, Veit R
Abstract
The insular cortex is a multimodal brain region with regional cytoarchitectonic differences indicating various functional specializations. As a multisensory neural node, the insular cortex integrates perception, emotion, interoceptive awareness, cognition, and gustation. Regarding the latter, predominantly the anterior part of the insular cortex is regarded as the primary taste cortex. In this review, we will specifically focus on the involvement of the insula in food processing and on multimodal integration of food-related items. Influencing factors of insular activation elicited by various foods range from calorie-content to the internal physiologic state, body mass index or eating behavior. Sensory perception of food-related stimuli including seeing, smelling, and tasting elicits increased activation in the anterior and mid-dorsal part of the insular cortex. Apart from the pure sensory gustatory processing, there is also a strong association with the rewarding/hedonic aspects of food items, which is reflected in higher insular activity and stronger connections to other reward-related areas. Interestingly, the processing of food items has been found to elicit different insular activation in lean compared to obese subjects and in patients suffering from an eating disorder (anorexia nervosa (AN), bulimia nervosa (BN)). The knowledge of functional differences in the insular cortex opens up the opportunity for possible noninvasive treatment approaches for obesity and eating disorders. To target brain functions directly, real-time functional magnetic resonance imaging neurofeedback offers a state-of-the-art tool to learn to control the anterior insular cortex activity voluntarily. First evidence indicates that obese adults have an enhanced ability to regulate the anterior insular cortex.
PMID: 23986683 [PubMed]
]]> Frank S, Kullmann S, Veit R Front Hum Neurosci PubMed:23986683 <title>Sustained Reduction of Nicotine Craving With Real-Time Neurofeedback: Exploring the Role of Severity of Dependence.</title> http://www.ncbi.nlm.nih.gov/pubmed/23935182?dopt=Abstract Related ArticlesSustained Reduction of Nicotine Craving With Real-Time Neurofeedback: Exploring the Role of Severity of Dependence.
Nicotine Tob Res. 2013 Aug 9;
Authors: Canterberry M, Hanlon CA, Hartwell KJ, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Saladin ME, Brady KT, George MS
Abstract
BACKGROUND: Neurofeedback delivered via real-time functional magnetic resonance imaging (rtfMRI) is a promising therapeutic technique being explored to facilitate self-regulation of craving in nicotine-dependent cigarette smokers. The current study examined the role of nicotine-dependence severity and the efficacy of multiple visits of neurofeedback from a single region of interest (ROI) in the anterior cingulate cortex (ACC) on craving reduction.
METHODS: Nine nicotine-dependent cigarette smokers participated in three rtfMRI visits examining cue-induced craving and brain activation. Severity of nicotine dependence was assessed using the Fagerström Test for Nicotine Dependence. When viewing smoking-related images with instructions to "crave," patient-tailored ROIs were generated in the vicinity of the ACC. Activity levels from the ROI were fed back while participants viewed smoking cues with the instruction to reduce craving.
RESULTS: Neurofeedback from a single ROI in the ACC led to consistent decreases in self-reported craving and activation in the ACC across the three visits. Dependence severity predicted response to neurofeedback at Visit 3.
CONCLUSIONS: This study builds upon previous rtfMRI studies on the regulation of nicotine craving in demonstrating that feedback from the ACC can reduce activation to smoking cues across three separate visits. Individuals with lower nicotine-dependence severity were more successful in reducing ACC activation over time. These data highlight the need to consider dependence severity in developing more individualized neurofeedback methods.
PMID: 23935182 [PubMed - as supplied by publisher]
]]> Canterberry M, Hanlon CA, Hartwell KJ, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Saladin ME, Brady KT, George MS Nicotine Tob Res PubMed:23935182 <title>Neurofeedback-mediated self-regulation of the dopaminergic midbrain.</title> http://www.ncbi.nlm.nih.gov/pubmed/23791838?dopt=AbstractNeurofeedback-mediated self-regulation of the dopaminergic midbrain.
Neuroimage. 2013 Jun 19;83C:817-825
Authors: Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R
Abstract
The dopaminergic system is involved in reward encoding and reinforcement learning. Dopaminergic neurons from this system in the substantia nigra/ventral tegmental area complex (SN/VTA) fire in response to unexpected reinforcing cues. The goal of this study was to investigate whether individuals can gain voluntary control of SN/VTA activity, thereby potentially enhancing dopamine release to target brain regions. Neurofeedback and mental imagery were used to self-regulate the SN/VTA. Real-time functional magnetic resonance imaging (rtfMRI) provided abstract visual feedback of the SN/VTA activity while the subject imagined rewarding scenes. Skin conductance response (SCR) was recorded as a measure of emotional arousal. To examine the effect of neurofeedback, subjects were assigned to either receiving feedback directly proportional (n=15, veridical feedback) or inversely proportional (n=17, inverted feedback) to SN/VTA activity. Both groups of subjects were able to up-regulate SN/VTA activity initially without feedback. Veridical feedback improved the ability to up-regulate SN/VTA compared to baseline while inverted feedback did not. Additional dopaminergic regions were activated in both groups. The ability to self-regulate SN/VTA was differentially correlated with SCR depending on the group, suggesting an association between emotional arousal and neurofeedback performance. These findings indicate that SN/VTA can be voluntarily activated by imagery and voluntary activation is further enhanced by neurofeedback. The findings may lead the way towards a non-invasive strategy for endogenous control of dopamine.
PMID: 23791838 [PubMed - as supplied by publisher]
]]> Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R Neuroimage PubMed:23791838 <title>Real-time fMRI links subjective experience with brain activity during focused attention.</title> http://www.ncbi.nlm.nih.gov/pubmed/23684866?dopt=AbstractReal-time fMRI links subjective experience with brain activity during focused attention.
Neuroimage. 2013 Nov 1;81:110-8
Authors: Garrison KA, Scheinost D, Worhunsky PD, Elwafi HM, Thornhill TA, Thompson E, Saron C, Desbordes G, Kober H, Hampson M, Gray JR, Constable RT, Papademetris X, Brewer JA
Abstract
Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant's experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditators and non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i.e., focused attention versus mind-wandering). These findings support the feasibility of using rt-fMRI to link objective measures of brain activity with reports of ongoing subjective experience in cognitive neuroscience research, and demonstrate the generalization of expertise in introspective awareness to novel contexts.
PMID: 23684866 [PubMed - in process]
]]> Garrison KA, Scheinost D, Worhunsky PD, Elwafi HM, Thornhill TA, Thompson E, Saron C, Desbordes G, Kober H, Hampson M, Gray JR, Constable RT, Papademetris X, Brewer JA Neuroimage PubMed:23684866 <title>Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits.</title> http://www.ncbi.nlm.nih.gov/pubmed/23683344?dopt=AbstractReduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits.
Psychiatry Res. 2013 Jul 30;213(1):79-81
Authors: Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Brady KT, George MS
Abstract
This multi-visit, real-time functional magnetic resonance imaging feedback study demonstrates that treatment-seeking smokers can effectively modulate their behavioral and brain responses to smoking cues. They are more effective at decreasing activity in functionally defined regions involved in "craving" (e.g. ventral anterior cingulate cortex (vACC)) rather than increasing activity in regions involved in "resisting" (e.g. dorsal medial prefrontal cortex (dmPFC)).
PMID: 23683344 [PubMed - in process]
]]> Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Brady KT, George MS Psychiatry Res PubMed:23683344 <title>Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback.</title> http://www.ncbi.nlm.nih.gov/pubmed/23668969?dopt=AbstractSelf-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback.
Neuroimage. 2013 May 11;
Authors: Zotev V, Phillips R, Yuan H, Misaki M, Bodurka J
Abstract
Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI-EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.
PMID: 23668969 [PubMed - as supplied by publisher]
]]> Zotev V, Phillips R, Yuan H, Misaki M, Bodurka J Neuroimage PubMed:23668969 <title>Learned regulation of brain metabolism.</title> http://www.ncbi.nlm.nih.gov/pubmed/23664452?dopt=AbstractLearned regulation of brain metabolism.
Trends Cogn Sci. 2013 Jun;17(6):295-302
Authors: Birbaumer N, Ruiz S, Sitaram R
Abstract
Self-regulation and voluntary control of circumscribed brain regions using real-time functional MRI (rt-fMRI) allows the establishment of a causal functional link between localized brain activity and behavior and cognition. A long tradition of research has clearly shown the brain's ability to learn volitional control of its own activity and effects on behavior. Yet, the underlying neural mechanism of self-regulation is still not fully understood. Here, we propose that self-regulation of brain activity is akin to skill learning and thus may depend on an intact subcortical motor system. We elaborate on the critical role of the basal ganglia in skill learning and neurofeedback, and clarify that brain-self-regulation need not be an explicit and conscious process as often mistakenly held.
PMID: 23664452 [PubMed - in process]
]]> Birbaumer N, Ruiz S, Sitaram R Trends Cogn Sci PubMed:23664452 <title>Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks.</title> http://www.ncbi.nlm.nih.gov/pubmed/23643926?dopt=AbstractReal-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks.
Biol Psychol. 2013 May 1;
Authors: Ruiz S, Buyukturkoglu K, Rana M, Birbaumer N, Sitaram R
Abstract
With the advent of brain computer interfaces based on real-time fMRI (rtfMRI-BCI), the possibility of performing neurofeedback based on brain hemodynamics has become a reality. In the early stage of the development of this field, studies have focused on the volitional control of activity in circumscribed brain regions. However, based on the understanding that the brain functions by coordinated activity of spatially distributed regions, there have recently been further developments to incorporate real-time feedback of functional connectivity and spatio-temporal patterns of brain activity. The present article reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations. A special emphasis is given to the discussion of novel developments that have enabled the use of this methodology to achieve self-regulation of the functional connectivity between different brain areas and of distributed brain networks, anticipating new and exciting applications for cognitive neuroscience and for the potential alleviation of neuropsychiatric disorders.
PMID: 23643926 [PubMed - as supplied by publisher]
]]> Ruiz S, Buyukturkoglu K, Rana M, Birbaumer N, Sitaram R Biol Psychol PubMed:23643926 <title>Real-time fMRI neurofeedback: progress and challenges.</title> http://www.ncbi.nlm.nih.gov/pubmed/23541800?dopt=AbstractReal-time fMRI neurofeedback: progress and challenges.
Neuroimage. 2013 Aug 1;76:386-99
Authors: Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N, Blefari ML, Bruehl AB, Cohen LG, DeCharms RC, Gassert R, Goebel R, Herwig U, LaConte S, Linden D, Luft A, Seifritz E, Sitaram R
Abstract
In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.
PMID: 23541800 [PubMed - in process]
]]> Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N, Blefari ML, Bruehl AB, Cohen LG, DeCharms RC, Gassert R, Goebel R, Herwig U, LaConte S, Linden D, Luft A, Seifritz E, Sitaram R Neuroimage PubMed:23541800 <title>Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach.</title> http://www.ncbi.nlm.nih.gov/pubmed/23525496?dopt=AbstractAbnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach.
Front Psychiatry. 2013;4:17
Authors: Ruiz S, Birbaumer N, Sitaram R
Abstract
CONSIDERING THAT SINGLE LOCATIONS OF STRUCTURAL AND FUNCTIONAL ABNORMALITIES ARE INSUFFICIENT TO EXPLAIN THE DIVERSE PSYCHOPATHOLOGY OF SCHIZOPHRENIA, NEW MODELS HAVE POSTULATED THAT THE IMPAIRMENTS ASSOCIATED WITH THE DISEASE ARISE FROM A FAILURE TO INTEGRATE THE ACTIVITY OF LOCAL AND DISTRIBUTED NEURAL CIRCUITS: the "abnormal neural connectivity hypothesis." In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI) are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia. The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.
PMID: 23525496 [PubMed]
]]> Ruiz S, Birbaumer N, Sitaram R Front Psychiatry PubMed:23525496 <title>The Use of a priori Information in ICA-Based Techniques for Real-Time fMRI: An Evaluation of Static/Dynamic and Spatial/Temporal Characteristics.</title> http://www.ncbi.nlm.nih.gov/pubmed/23483841?dopt=AbstractThe Use of a priori Information in ICA-Based Techniques for Real-Time fMRI: An Evaluation of Static/Dynamic and Spatial/Temporal Characteristics.
Front Hum Neurosci. 2013;7:64
Authors: Soldati N, Calhoun VD, Bruzzone L, Jovicich J
Abstract
Real-time brain functional MRI (rt-fMRI) allows in vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitor a target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off-line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time, and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.
PMID: 23483841 [PubMed]
]]> Soldati N, Calhoun VD, Bruzzone L, Jovicich J Front Hum Neurosci PubMed:23483841 <title>Neurofeedback-mediated self-regulation of the dopaminergic midbrain.</title> http://www.ncbi.nlm.nih.gov/pubmed/23466940?dopt=AbstractNeurofeedback-mediated self-regulation of the dopaminergic midbrain.
Neuroimage. 2013 Mar 1;75C:176-184
Authors: Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R
Abstract
The dopaminergic system is involved in reward encoding and reinforcement learning. Dopaminergic neurons from this system in the substantia nigra/ventral tegmental area complex (SN/VTA) fire in response to unexpected reinforcing cues. The goal of this study was to investigate whether individuals can gain voluntary control of SN/VTA activity, thereby potentially enhancing dopamine release to target brain regions. Neurofeedback and mental imagery were used to self-regulate the SN/VTA. Real-time functional magnetic resonance imaging (rtfMRI) provided abstract visual feedback of the SN/VTA activity while the subject imagined rewarding scenes. Skin conductance response (SCR) was recorded as a measure of emotional arousal. To examine the effect of neurofeedback, subjects were assigned to either receiving feedback directly proportional (n=15, veridical feedback) or inversely proportional (n=17, inverted feedback) to SN/VTA activity. Both groups of subjects were able to up-regulate SN/VTA activity initially without feedback. Veridical feedback improved the ability to up-regulate SN/VTA compared to baseline while inverted feedback did not. Additional dopaminergic regions were activated in both groups. The ability to self-regulate SN/VTA was differentially correlated with SCR depending on the group, suggesting an association between emotional arousal and neurofeedback performance. These findings indicate that SN/VTA can be voluntarily activated by imagery and voluntary activation is further enhanced by neurofeedback. The findings may lead the way towards a non-invasive strategy for endogenous control of dopamine.
PMID: 23466940 [PubMed - as supplied by publisher]
]]> Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R Neuroimage PubMed:23466940 <title>ICA analysis of fMRI with real-time constraints: an evaluation of fast detection performance as function of algorithms, parameters and a priori conditions.</title> http://www.ncbi.nlm.nih.gov/pubmed/23378835?dopt=AbstractICA analysis of fMRI with real-time constraints: an evaluation of fast detection performance as function of algorithms, parameters and a priori conditions.
Front Hum Neurosci. 2013;7:19
Authors: Soldati N, Calhoun VD, Bruzzone L, Jovicich J
Abstract
Independent component analysis (ICA) techniques offer a data-driven possibility to analyze brain functional MRI data in real-time. Typical ICA methods used in functional magnetic resonance imaging (fMRI), however, have been until now mostly developed and optimized for the off-line case in which all data is available. Real-time experiments are ill-posed for ICA in that several constraints are added: limited data, limited analysis time and dynamic changes in the data and computational speed. Previous studies have shown that particular choices of ICA parameters can be used to monitor real-time fMRI (rt-fMRI) brain activation, but it is unknown how other choices would perform. In this rt-fMRI simulation study we investigate and compare the performance of 14 different publicly available ICA algorithms systematically sampling different growing window lengths (WLs), model order (MO) as well as a priori conditions (none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component as well as computation time. Four algorithms are identified as best performing (constrained ICA, fastICA, amuse, and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to provide equal or improved performances in similarity to the target compared with their off-line counterpart, with greatly reduced computation costs. This study suggests parameter choices that can be further investigated in a sliding-window approach for a rt-fMRI experiment.
PMID: 23378835 [PubMed]
]]> Soldati N, Calhoun VD, Bruzzone L, Jovicich J Front Hum Neurosci PubMed:23378835 <title>A Graphics Processing Unit Accelerated Motion Correction Algorithm and Modular System for Real-time fMRI.</title> http://www.ncbi.nlm.nih.gov/pubmed/23319241?dopt=AbstractA Graphics Processing Unit Accelerated Motion Correction Algorithm and Modular System for Real-time fMRI.
Neuroinformatics. 2013 Jul;11(3):291-300
Authors: Scheinost D, Hampson M, Qiu M, Bhawnani J, Constable RT, Papademetris X
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project ( www.bioimagesuite.org ). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.
PMID: 23319241 [PubMed - in process]
]]> Scheinost D, Hampson M, Qiu M, Bhawnani J, Constable RT, Papademetris X Neuroinformatics PubMed:23319241 <title>Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI.</title> http://www.ncbi.nlm.nih.gov/pubmed/23236006?dopt=AbstractRoles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI.
J Neurophysiol. 2013 Mar;109(5):1250-8
Authors: Hinds O, Thompson TW, Ghosh S, Yoo JJ, Whitfield-Gabrieli S, Triantafyllou C, Gabrieli JD
Abstract
We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.
PMID: 23236006 [PubMed - indexed for MEDLINE]
]]> Hinds O, Thompson TW, Ghosh S, Yoo JJ, Whitfield-Gabrieli S, Triantafyllou C, Gabrieli JD J Neurophysiol PubMed:23236006