Akademik Veri Yönetim Sistemi
ECNP (European College of Neuropsychopharmacology) 2025, Amsterdam, Hollanda, 11 - 14 Ekim 2025, (Yayınlanmadı)
Introduction
Understanding brain lateralization is critical for neurosurgical planning and
neuropsychopharmacological applications. Resting-state fMRI (rs-fMRI) provides a powerful
tool for examining hemispheric dominance, particularly in pre-operative patients. However,
global whole-brain analysis may introduce variability, necessitating a more focused regional
analysis. This study aims to assess lateralization indices across different brain networks and
to evaluate the reliability of rs-fMRI for determining dominant hemispheres.
Objective
To quantify and compare laterality indices (LI) across whole-brain, language network, and
frontoparietal network regions, ensuring a clearer understanding of hemispheric dominance
in clinically pre-operative right-dominant patients.
Methods
The study included 25 pre-operative patients, all clinically determined as right dominant. MRI
scans were performed using a 1.5 T MR Intera Achieva scanner (Philips Medical Systems,
Best, The Netherlands) equipped with a SENSE-Head8 coil. Anatomical imaging was
conducted using a T1-weighted inversion-recovery scan with the following parameters: TR =
2494 ms, TE = 15 ms, Flip Angle = 90°, and ETL = 5. The matrix resolution was 512 × 512,
with a slice thickness of 4 mm.
For both task-based and resting-state fMRI, T2-weighted gradient echo-planar imaging was
used. The acquisition parameters were TR = 3000 ms, TE = 50 ms, Flip Angle = 90°, with a
field of view (FOV) of 230 mm and RFOV of 100%. The scans were acquired with a slice
thickness of 4 mm, no gap, a 64 × 64 matrix, and ETL of 48, NA = 1, capturing approximately
28 slices per volume. The resting-state fMRI acquisitions consisted of 80 dynamic series,
ensuring a comprehensive assessment of intrinsic functional connectivity.
Data Processing & Analysis
Preprocessing: FSL was used for brain extraction and motion correction.
Independent Component Analysis (ICA): Performed with single-session ICA
(6mm, 12 DoF) for dimensionality reduction.
LI Calculation: Laterality indices were extracted using FSLmaths and Python.
Thresholding:
o Whole-brain LI threshold > 0.15
o Frontoparietal network LI threshold < 0.15
Results
Table: Summary of Lateralization Indices and Dominant Hemisphere Classification
Brain Region Dominant Group Number of Patients Mean LI
Whole Brain Right Dominant 14 0.0629
Left Dominant 8 -0.0221
Bilateral 2 0.0015
Language Network Right Dominant 11 0.5430
Left Dominant 13 -
Frontoparietal
Network
Right Dominant 25 3718.4695
Left Dominant 0 -
Bilateral 0 -
Discussion
1. Importance of Task-Based Imaging for Hemispheric Determination
o Although rs-fMRI is a robust tool for assessing functional connectivity, task-
based fMRI has been widely used to determine hemispheric dominance,
particularly for motor and language processing.
o The inclusion of task-based paradigms may further enhance the interpretation
of dominance beyond intrinsic connectivity metrics obtained via rs-fMRI.
2. Strengths of Laterality Index (LI) and Prior ROC Analyses
o Previous studies using ROC (Receiver Operating Characteristic) analyses
have demonstrated high discriminatory power of the laterality index in
distinguishing dominant hemispheres.
o Our findings support the utility of LI-based analysis, particularly when
specific ROIs (such as frontoparietal and language networks) are considered
rather than relying solely on whole-brain approaches.
3. Network-Specific Insights:
o The whole-brain analysis displayed more variability in dominance
classification, indicating the potential for confounding effects when analyzing
all regions together.
o The language network exhibited a more balanced distribution of dominance
across patients, highlighting the need for individualized assessments.
o The frontoparietal network demonstrated strong right lateralization in all
patients, suggesting that this region may serve as a reliable marker for
identifying dominant hemispheres.
Conclusions
While whole-brain LI analysis provides a general measure of lateralization,
network-specific analyses (especially the frontoparietal network) offer clearer
hemispheric classification.
Task-based fMRI remains a critical tool in validating and refining rs-fMRI-based
laterality findings.
ROC-based validation of LI values in previous studies highlights its utility as a strong
biomarker for lateralization.
This study supports the integration of both rs-fMRI and task-based imaging for
comprehensive hemispheric dominance assessment in neurosurgical and clinical
settings.
Future Directions
Investigate multi-modal integration of resting-state and task-based fMRI in
dominant hemisphere determination.
Further validation of LI cutoffs via ROC analyses to establish clinically applicable
thresholds for hemisphere dominance.
Consider additional ROI-based LI comparisons across different clinical populations.