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Investigation of Kinetic Parameters of Hesperidin Inhibitor Using Tyrosinase-Based MWCNT Modified Carbon Paste Electrode
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Etkin kaynak yönetimi açısından havalimanı ortak karar alma süreci: Antalya Havalimanı örneği
No full textRegional fMRI-based lateralization in presurgical language-dominant patients
No full textIntroductionUnderstanding brain lateralization is critical for neurosurgical planning andneuropsychopharmacological applications. Resting-state fMRI (rs-fMRI) provides a powerfultool for examining hemispheric dominance, particularly in pre-operative patients. However,global whole-brain analysis may introduce variability, necessitating a more focused regionalanalysis. This study aims to assess lateralization indices across different brain networks andto evaluate the reliability of rs-fMRI for determining dominant hemispheres.ObjectiveTo quantify and compare laterality indices (LI) across whole-brain, language network, andfrontoparietal network regions, ensuring a clearer understanding of hemispheric dominancein clinically pre-operative right-dominant patients.MethodsThe study included 25 pre-operative patients, all clinically determined as right dominant. MRIscans 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 wasconducted 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 wasused. The acquisition parameters were TR = 3000 ms, TE = 50 ms, Flip Angle = 90°, with afield of view (FOV) of 230 mm and RFOV of 100%. The scans were acquired with a slicethickness of 4 mm, no gap, a 64 × 64 matrix, and ETL of 48, NA = 1, capturing approximately28 slices per volume. The resting-state fMRI acquisitions consisted of 80 dynamic series,ensuring a comprehensive assessment of intrinsic functional connectivity.Data Processing &amp; 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 &gt; 0.15o Frontoparietal network LI threshold &lt; 0.15ResultsTable: Summary of Lateralization Indices and Dominant Hemisphere ClassificationBrain Region Dominant Group Number of Patients Mean LIWhole Brain Right Dominant 14 0.0629Left Dominant 8 -0.0221Bilateral 2 0.0015Language Network Right Dominant 11 0.5430Left Dominant 13 -FrontoparietalNetworkRight Dominant 25 3718.4695Left Dominant 0 -Bilateral 0 -Discussion1. Importance of Task-Based Imaging for Hemispheric Determinationo 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 interpretationof dominance beyond intrinsic connectivity metrics obtained via rs-fMRI.2. Strengths of Laterality Index (LI) and Prior ROC Analyseso Previous studies using ROC (Receiver Operating Characteristic) analyseshave demonstrated high discriminatory power of the laterality index indistinguishing dominant hemispheres.o Our findings support the utility of LI-based analysis, particularly whenspecific ROIs (such as frontoparietal and language networks) are consideredrather than relying solely on whole-brain approaches.3. Network-Specific Insights:o The whole-brain analysis displayed more variability in dominanceclassification, indicating the potential for confounding effects when analyzingall regions together.o The language network exhibited a more balanced distribution of dominanceacross patients, highlighting the need for individualized assessments.o The frontoparietal network demonstrated strong right lateralization in allpatients, suggesting that this region may serve as a reliable marker foridentifying dominant hemispheres.Conclusions While whole-brain LI analysis provides a general measure of lateralization,network-specific analyses (especially the frontoparietal network) offer clearerhemispheric classification. Task-based fMRI remains a critical tool in validating and refining rs-fMRI-basedlaterality findings. ROC-based validation of LI values in previous studies highlights its utility as a strongbiomarker for lateralization. This study supports the integration of both rs-fMRI and task-based imaging forcomprehensive hemispheric dominance assessment in neurosurgical and clinicalsettings.Future Directions Investigate multi-modal integration of resting-state and task-based fMRI indominant hemisphere determination. Further validation of LI cutoffs via ROC analyses to establish clinically applicablethresholds for hemisphere dominance. Consider additional ROI-based LI comparisons across different clinical populations.</p