scholarly journals PC3 - 190 Impact of Functional Magnetic Resonance Imaging on Clinical Decision Making and Outcomes in Patients with Low Grade Gliomas

2016 ◽  
Vol 43 (S4) ◽  
pp. S19-S20
Author(s):  
E. Kosteniuk ◽  
J.C. Lau ◽  
J.F. Megyesi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Decision Making ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Fmri Data ◽  
Low Grade ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Impact

This study aims to evaluate the impact of preoperative functional magnetic resonance imaging (fMRI) on low grade glioma (LGG) patients’ outcomes and surgical planning. Methods In this retrospective matched cohort study of a single surgeon’s patients, we are comparing two groups of LGG patients (WHO grade II) based on exposure to fMRI. Sixteen LGG patients who underwent fMRI were selected, and 32 control (non-fMRI exposed) patients are being selected through propensity score matching from a pool of 764 brain tumour patients. To assess the impact of fMRI data on clinicians’ decision making process, neurosurgeons within a single centre are completing questionnaires regarding treatment options for each LGG fMRI patient based on clinical data and structural imaging before and after fMRI. Results Within the group of 16 LGG patients who have undergone fMRI studies over a 12-year period, most patients presented with seizures (81 percent), and most lesions were left-sided (81 percent) and frontal (75 percent). Patients underwent either craniotomy (50 percent), stereotactic biopsy (25 percent) or nonsurgically management (25 percent). In surgical patients, between presurgical assessment and eight week post-surgical follow-up, mean modified Rankin scale improved from 1.80 (sd 0.79) to 1.50 (sd 0.97). In our cohort, 5-year mortality was 12.5 percent (mean follow-up duration 5.46 years). Conclusions Data analysis is ongoing with plans to compare relevant patient demographics and outcomes, and to analyse questionnaires to elucidate how surgeons incorporate fMRI data into their therapeutic approach.

scholarly journals P.105 Clinical impact of functional magnetic resonance imaging for pre-operative planning in patients with low grade gliomas

2016 ◽  
Vol 43 (S2) ◽  
pp. S44-S44
Author(s):  
SE Kosteniuk ◽  
JC Lau ◽  
JF Megyesi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Propensity Score ◽  
Functional Magnetic Resonance Imaging ◽  
Low Grade ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Who Grade ◽  
Operative Planning ◽  
The Impact

Background: This study aims to evaluate the impact of pre-operative functional magnetic resonance imaging (fMRI) on low grade glioma (LGG) patients’ outcomes. Methods: In this retrospective matched cohort study (N =48) of a single surgeon’s patients, we are comparing two groups of LGG patients (WHO grade II) based on exposure to fMRI. A 1:2 propensity score match from a pool of 764 brain tumour patients was performed. Results: Within the group of 16 LGG patients who have undergone fMRI studies over a 12-year period, mean age was 40 years, and most presented with seizures (81%). Most lesions were left-sided (81%), and the lobes most commonly involved were frontal (75%) and temporal (31%). Patients underwent either craniotomy (50%), stereotactic biopsy (25%) or nonsurgically management (25%). In surgical patients, between presurgical assessment and eight week post-surgical follow-up, mean modified Rankin scale improved from 1.80±0.79 to 1.50±0.97. In our cohort, 5-year mortality was 12.5% (patients followed for a mean duration of 5.46 years). Conclusions: Data analysis is ongoing with plans to compare relevant demographics and outcomes via 1:2 propensity score matching of LGG patients who underwent fMRI against a control cohort.

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

Statistical Analysis of Functional Magnetic Resonance Imaging Data

2017 ◽  
pp. 103-114
Author(s):  
Nicole A. Lazar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Temporal Correlation ◽  
Fmri Data ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

The analysis of functional magnetic resonance imaging (fMRI) data poses many statistical challenges. The data are massive, noisy, and have a complicated spatial and temporal correlation structure. This chapter introduces the basics of fMRI data collection and surveys common approaches for data analysis.

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