Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness

Posse, Stefan; Fitzgerald, Daniel A.; Gao, Kunxiu; Habel, Ute; Rosenberg, David; Moore, Gregory J.; Schneider, Frank

doi:10.1016/s1053-8119(03)00004-1

Cited by 241 publications

(178 citation statements)

References 48 publications

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“…Since rtfMRI has been employed to reinforce the induction of mood states by feedback of amygdala activation [Posse et al, 2003], it is reasonable to believe that neuro-psychiatric conditions associated with (but not limited to) the pathology of the amygdala-hippocampus circuitry could be treated using an rtfMRI approach. The group trend of learned level of BOLD signal contrast (as a percentage compared to baseline) during the rtfMRI trials (sampled last four trial sessions) with standard deviation as error bars.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to its excellent spatial resolution and its non-invasive nature, real-time (rtfMRI) or near real-time processing of fMRI data [Cox et al, 1995;Yoo et al, 1999] has provided a means for individuals to receive information relating to the state of their own brain activity [Posse et al, 2003;Weiskopf et al, 2003]. For example, fMRI was applied for the regulation of painrelated activity in rostral-ventral/dorsal parts of the anterior cingulate cortex [deCharms et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

“…For example, fMRI was applied for the regulation of painrelated activity in rostral-ventral/dorsal parts of the anterior cingulate cortex [deCharms et al, 2005]. Posse et al [2003] studied the feasibility of inducing human emotion by measuring the MR signal changes associated with amygdala activation. fMRI-mediated regulation of activity from the auditory areas was also demonstrated through a selective auditory attention task [Yoo et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

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Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery

Yoo

Lee

O’Leary

et al. 2008

Int J Imaging Syst Tech

103

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We report the long-term effect of real-time functional MRI (rtfMRI) training on voluntary regulation of the level of activation from a hand motor area. During the performance of a motor imagery task of a right hand, blood-oxygenation-level-dependent (BOLD) signal originating from a primary motor area was presented back to the subject in real-time. Demographically matched individuals also received the same procedure without valid feedback information. Followed by the initial rtfMRI sessions, both groups underwent two-week long, daily-practice of the task. Off-line data analysis revealed that the individuals in the experimental group were able to increase the level of BOLD signal from the regulatory target to a greater degree compared to the control group. Furthermore, the learned level of activation was maintained after the two-week period, with the recruitment of additional neural circuitries such as the hippocampus and the limbo-thalamocortical pathway. The activation obtained from the control group, in the absence of proper feedback, was indifferent across the training conditions. The level of BOLD activity from the target regulatory region was positively correlated with a self evaluative score within the experimental group, while the majority of control subjects had difficulty adopting a strategy to attain the desired level of functional regulation. Our results suggest that rtfMRI helped individuals learn how to increase region-specific cortical activity associated with a motor imagery task, and the level of increased activation in motor areas was consolidated after the two-week self-practice period, with the involvement of neural circuitries implicated in motor skill learning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery

Yoo

Lee

O’Leary

et al. 2008

Int J Imaging Syst Tech

103

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“…It is well suited for realtime applications, as it has a fast classification response and a fast training. We have recently interfaced the classifier to our real-time fMRI analysis software (TurboFIRE) (Gembris et al, 2000;Gao and Posse, 2004;Posse et al, 2001Posse et al, , 2003) (see also http://www.mic.health.unm.edu/turbofire) and are currently evaluating training and testing performance with cognitive paradigms. The limitations of the method are related to the sparsity of the information in the brain.…”

Section: Discussionmentioning

confidence: 99%

fMRI pattern classification using neuroanatomically constrained boosting

et al. 2006

Self Cite

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Pattern classification in functional MRI (fMRI) is a novel methodology to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. Thus, most previous fMRI classification methods were applied in individual subjects. In this study, we developed a novel approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. Spatially normalized activation maps were segmented into functional areas using a neuroanatomical atlas and each map was classified separately using local classifiers. A single multiclass output was applied using a weighted aggregation of the classifier's outputs. An Adaboost technique was applied, modified to find the optimal aggregation of a set of spatially distributed classifiers. This Adaboost combined the regionspecific classifiers to achieve improved classification accuracy with respect to conventional techniques. Multiclass classification accuracy was assessed in an fMRI group study with interleaved motor, visual, auditory, and cognitive task design. Data were acquired across 18 subjects at different field strengths (1.5 T, 4 T), with different pulse sequence parameters (voxel size and readout bandwidth). Misclassification rates of the boosted classifier were between 3.5% and 10%, whereas for the single classifier, these were between 15% and 23%, suggesting that the boosted classifier provides a better generalization ability together with better robustness. The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patterns.Keywords: Functional magnetic resonance imaging; Pattern classification; Support vector machines; Adaboost IntroductionBrain activation changes in response to even simple sensory input and motor tasks encompass a widely distributed network of functional brain areas. Information embedded in the spatial shape and extent of these activation patterns, and differences in voxel-tovoxel time course, are not easily quantified with conventional analysis tools, such as statistical parametric mapping (SPM) (Kiebel and Friston, 2004a,b). Pattern classification in functional MRI (fMRI) is a novel approach, which promises to characterize subtle differences in activation patterns between different tasks. However, automatic and reliable classification of patterns is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences in activation patterns, and dependence on the image acquisition methodology. Recent work by Cox and Savoy (2003) demonstrated that linear discriminant analysis and support vector machines (SVM) allow 10-way discrimination of visual activation patterns evoked by the visual presentation of various categories of ...

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“…Bipolar patients follow this trend, particularly in unmedicated patients (YurgelunTodd et al, 2000;Lawrence et al, 2004;Blumberg et al, 2005) and in mania specifically (Altshuler et al, 2005). The amygdala indexes current mood state in healthy adults as well, with increased activation during sad mood, especially when the mood is reinforced by moodcongruent sensory stimuli (Posse et al, 2003;Wang et al, 2006). While the importance of the amygdala in mood disorders is undisputed, volumetric studies of the amygdala in bipolar disorder have yielded contradictory findings (summarized in Table 1).…”

Section: Introductionmentioning

confidence: 99%

Age-dependent reduction of amygdala volume in bipolar disorder

Doty

Payne

Steffens

et al. 2008

Psychiatry Research: Neuroimaging

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The amygdala is hypothesized to play a critical role in mood regulation, yet its involvement in bipolar disorder remains unclear. The aim of the present study was to compare measurements of amygdala volumes in a relatively large sample of bipolar disorder patients and healthy controls ranging in age from 18 to 49 yrs. Fifty-four adult patients meeting DSM-IV criteria for bipolar disorder and 41 healthy controls matched for age, sex, and education underwent structural 1.5 Tesla MRI scanning. Volumetric measurements of the amygdala were obtained using a manual region-of-interest tracing method with software that allowed simultaneous visualization of the amygdala in three orthogonal planes. The anterior head of the hippocampus was removed in the sagittal plane prior to amygdala volumetry measurement. Multiple regression analysis was computed on amygdala volume measurements as a function of diagnosis, age, sex, and cerebral volume. Bipolar patients showed an age-related reduction of amygdala volume but controls did not. Among bipolar subjects, amygdala volume was unrelated to medication history. There were no significant hemispheric or sex interactions with the main effects. Results support a role for amygdala dysfunction in bipolar disorder which appears most robustly in older relative to younger adult patients. Differential aging effects in bipolar disorder may compromise amygdala integrity and contribute to mood dysregulation.

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Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness

Cited by 241 publications

References 48 publications

Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery

Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery

fMRI pattern classification using neuroanatomically constrained boosting

Age-dependent reduction of amygdala volume in bipolar disorder

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