Dataset Information

Mapping social target detection with functional magnetic resonance imaging.

ABSTRACT: The neural correlates of cognitive control and social processing functions, as well as the characteristic patterns of anomalous brain activation patterns in psychiatric conditions associated with impairment in these functions, have been well characterized. However, these domains have primarily been examined in isolation. The present study used event-related functional magnetic resonance imaging to map brain areas recruited during a target-detection task designed to evaluate responses to both non-social (i.e. shape) and social (i.e. face) target stimuli. Both shape and face targets activated a similar brain network, including the postcentral gyrus, the anterior and posterior cingulate gyri and the right midfrontal gyrus, whereas face targets additionally activated the thalamus, fusiform and temporooccipital cortex, lingual gyrus and paracingulate gyrus. Comparison of activations to social and non-social target events revealed that a small portion of the dorsal anterior cingulate gyrus (Brodmann's area 32) and the supracalcarine cortex were preferentially activated to face targets. These findings indicate that non-social and social stimuli embedded within a cognitive control task activate overlapping and distinct brain regions. Clinical cognitive neuroscience research of disorders characterized by cognitive dysfunction and impaired social processing would benefit from the use of tasks that evaluate the combined effects of deficits in these two domains.

SUBMITTER: Dichter GS

PROVIDER: S-EPMC2656884 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Mapping social target detection with functional magnetic resonance imaging.

Dichter Gabriel S GS Felder Jennifer N JN Bodfish James W JW Sikich Linmarie L Belger Aysenil A

Social cognitive and affective neuroscience 20081116 1

The neural correlates of cognitive control and social processing functions, as well as the characteristic patterns of anomalous brain activation patterns in psychiatric conditions associated with impairment in these functions, have been well characterized. However, these domains have primarily been examined in isolation. The present study used event-related functional magnetic resonance imaging to map brain areas recruited during a target-detection task designed to evaluate responses to both non ...[more]

PMID: 19015088

Similar Datasets

Project description:ContextCurrent behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD.ObjectiveTo measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT).DesignFunctional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli.SettingPatients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology.PatientsThirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD.InterventionsBrain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT.Main outcome measuresWhole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure.ResultsPretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline.ConclusionsThe results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient.

Project description:Background:Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. Purpose:To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. Materials and Methods:Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. Results:T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). Conclusion:Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

Dataset Information

Mapping social target detection with functional magnetic resonance imaging.

Publications

Mapping social target detection with functional magnetic resonance imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets