scholarly journals Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors

2017 ◽  
Vol 37 (11) ◽  
pp. 3475-3487 ◽  
Author(s):  
Rebecca W Pak ◽  
Darian H Hadjiabadi ◽  
Janaka Senarathna ◽  
Shruti Agarwal ◽  
Nitish V Thakor ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Clinical Studies ◽  
Neurovascular Unit ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Preclinical And Clinical Studies

Functional magnetic resonance imaging (fMRI) serves as a critical tool for presurgical mapping of eloquent cortex and changes in neurological function in patients diagnosed with brain tumors. However, the blood-oxygen-level-dependent (BOLD) contrast mechanism underlying fMRI assumes that neurovascular coupling remains intact during brain tumor progression, and that measured changes in cerebral blood flow (CBF) are correlated with neuronal function. Recent preclinical and clinical studies have demonstrated that even low-grade brain tumors can exhibit neurovascular uncoupling (NVU), which can confound interpretation of fMRI data. Therefore, to avoid neurosurgical complications, it is crucial to understand the biophysical basis of NVU and its impact on fMRI. Here we review the physiology of the neurovascular unit, how it is remodeled, and functionally altered by brain cancer cells. We first discuss the latest findings about the components of the neurovascular unit. Next, we synthesize results from preclinical and clinical studies to illustrate how brain tumor induced NVU affects fMRI data interpretation. We examine advances in functional imaging methods that permit the clinical evaluation of brain tumors with NVU. Finally, we discuss how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can “normalize” the brain tumor vasculature, and potentially restore neurovascular coupling.

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.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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.

Functional magnetic resonance imaging (fMRI) clinical studies in ALS - paradigms, problems and promises

2000 ◽  
Vol 1 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Benjamin Rix Brooks ◽  
Khlalef Bushara ◽  
Al Khan ◽  
Jason Hershberger ◽  
John O Wheat ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Clinical Studies ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

Language Functional Magnetic Resonance Imaging in Preoperative Assessment of Language Areas: Correlation with Direct Cortical Stimulation

Neurosurgery ◽  
2003 ◽  
Vol 52 (6) ◽  
pp. 1335-1347 ◽  
Author(s):  
Franck-Emmanuel Roux ◽  
Kader Boulanouar ◽  
Jean-Albert Lotterie ◽  
Mehdi Mejdoubi ◽  
James P. LeSage ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Left Hemisphere ◽  
Brain Mapping ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Verb Generation ◽  
Language Areas

Abstract OBJECTIVE The aim of this study was to analyze the usefulness of preoperative language functional magnetic resonance imaging (fMRI), by correlating fMRI data with intraoperative cortical stimulation results for patients with brain tumors. METHODS Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere. fMRI data obtained were analyzed with SPM software, with two standard analysis thresholds (P < 0.005 and then P < 0.05). The fMRI data were then registered in a frameless stereotactic neuronavigational device and correlated with direct brain mapping results. We used a statistical model with the fMRI information as a predictor, spatially correlating each intraoperatively mapped cortical site with fMRI data integrated in the neuronavigational system (site-by-site correlation). Eight patients were also studied with language fMRI postoperatively, with the same acquisition protocol. RESULTS We observed high variability in signal extents and locations among patients with both tasks. The activated areas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supramarginal and angular gyri. A total of 426 cortical sites were tested for each task among the 14 patients. In frontal and temporoparietal areas, poor sensitivity of the fMRI technique was observed for the naming and verb generation tasks (22 and 36%, respectively) with P < 0.005 as the analysis threshold. Although not perfect, the specificity of the fMRI technique was good in all conditions (97% for the naming task and 98% for the verb generation task). Better correlation (sensitivity, 59%; specificity, 97%) was achieved by combining the two fMRI tasks. Variation of the analysis threshold to P < 0.05 increased the sensitivity to 66% while decreasing the specificity to 91%. Postoperative fMRI data (for the cortical brain areas studied intraoperatively) were in accordance with brain mapping results for six of eight patients. Complete agreement between pre- and postoperative fMRI studies and direct brain mapping results was observed for only three of eight patients. CONCLUSION With the paradigms and analysis thresholds used in this study, language fMRI data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intraoperative brain mapping results. A better correlation could be obtained by combining the fMRI tasks. The overall results of this study demonstrated that language fMRI could not be used to make critical surgical decisions in the absence of direct brain mapping. Other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.

scholarly journals Ictal brain hemodynamics in the epileptic focus caused by a brain tumor using functional magnetic resonance imaging (fMRI)

Seizure ◽  
2000 ◽  
Vol 9 (8) ◽  
pp. 585-589 ◽  
Author(s):  
Fumio Kubota ◽  
Senichiro Kikuchi ◽  
Makoto Ito ◽  
Nobuyoshi Shibata ◽  
Takushiro Akata ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Epileptic Focus ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Hemodynamics

scholarly journals A Hilbert-based method for processing respiratory timeseries

2020 ◽  
Author(s):  
Samuel J. Harrison ◽  
Samuel Bianchi ◽  
Jakob Heinzle ◽  
Klaas Enno Stephan ◽  
Sandra Iglesias ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Time Resolution ◽  
Technical Note ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Physiological Noise ◽  
Noise Correction ◽  
The Hilbert Transform

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

scholarly journals Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

2019 ◽  
Author(s):  
Ru-Yuan Zhang ◽  
Xue-Xin Wei ◽  
Kendrick Kay
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Activity ◽  
Neuronal Population ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Correlated Activity ◽  
Population Codes

ABSTRACTPrevious studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

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