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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

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Published on: June 20, 2012

A network-based approach to assess task-compliance in fMRI.

Radheshyam Stepponat¹, Marc Berger², Laurin Schäfer³

¹Developmental and Interventional Neuroimaging Lab (DINLAB), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; Comprehensive Center of Clinical Neurosciences and Mental Health (C3NMH), Austria.

Epilepsy & Behavior : E&B

|February 28, 2026

Summary

Functional MRI (fMRI) language mapping reliability is improved by assessing patient task engagement. Using both language network (LN) and default mode network (DMN) activity objectively measures compliance, enhancing presurgical planning accuracy.

Keywords:

Compliance Default Mode Network Functional Magnetic Resonance Imaging Language Network Task-negative Temporal Lobe Epilepsy

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Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

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Published on: December 5, 2014

Area of Science:

Neuroimaging
Cognitive Neuroscience
Clinical Neurology

Background:

Task-based functional MRI (fMRI) is crucial for mapping language networks and guiding presurgical planning.
Patient engagement and task performance directly impact the validity of fMRI activation maps.
Differentiating true language organization from poor task execution is essential for reliable clinical fMRI.

Purpose of the Study:

To develop and validate objective measures of task engagement during clinical fMRI.
To assess the relationship between functional engagement indices and cognitive performance.
To improve the reliability of language network mapping in epilepsy patients.

Main Methods:

Retrospective analysis of fMRI data from 43 epilepsy patients and 25 healthy controls.
Defined engagement indices based on language network (LN) and default mode network (DMN) activation/suppression.
Classified participants by compliance levels and compared attention performance across groups.

Main Results:

Healthy controls exhibited higher compliance than TLE patients on a demanding comprehension task.
LN engagement robustly predicted better attentional performance across multiple tests.
A significant linear trend showed decreased attention scores with lower compliance.

Conclusions:

Network-based engagement indices (LN and DMN) offer a practical way to approximate task compliance in fMRI.
LN engagement is a strong predictor of attention, while DMN suppression acts as a complementary safeguard.
Integrating task-positive and task-negative markers can enhance fMRI reliability for presurgical planning.