fMRI paradigm designing and post-processing tools

James, Jija S; Rajesh, P.; Chandran, Anuvitha; Kesavadas, Chandrasekharan

doi:10.4103/0971-3026.130686

Cited by 17 publications

(13 citation statements)

References 41 publications

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“…Functional MRI data (fMRI, rsfMRI, and phMRI) are typically pre-processed before the actual data analysis. The most commonly used software packages are SPM 1 and FSL 2 Pre-processing includes (1) slice timing correction and realignment over time, (2) spatial normalization to a standardized stereotactic space, and (3) smoothing ( James et al, 2014 ). An important difference between human and rodent fMRI processing is the way group data are handled.…”

Section: Fmri Methodologymentioning

confidence: 99%

The power of using functional fMRI on small rodents to study brain pharmacology and disease

et al. 2015

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Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal’s physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

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Section: Fmri Methodologymentioning

confidence: 99%

The power of using functional fMRI on small rodents to study brain pharmacology and disease

et al. 2015

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“…In order that we develop a robust post-processing approach that would complement current fMRI analysis, all fMRI investigations and preprocessing techniques followed standardized, previously published protocols 25 26 . To avoid any bias in the quality of the fMRI, all healthy volunteers were scanned on clinical scanners and the imaging parameters used followed clinical protocols.…”

Section: Discussionmentioning

confidence: 99%

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

Hassan

Kotrotsou

Bakhtiari

et al. 2016

Sci Rep

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Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

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“…General linear model analysis was performed to calculate activation maps for the 3D-aligned time course dataset for each subject [ 12 ]. Finger tapping paradigm was design as a sequence of activation-deactivation cycles starting with an activation state [ 13 ]. Blood oxygenation level-dependent single-shot T2* with TR 3000ms, TE 150ms, Slice thickness 8, and 64x64, 20 planes were acquired.…”

Section: Methodsmentioning

confidence: 99%

Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

et al. 2020

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Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.

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fMRI paradigm designing and post-processing tools

Cited by 17 publications

References 41 publications

The power of using functional fMRI on small rodents to study brain pharmacology and disease

The power of using functional fMRI on small rodents to study brain pharmacology and disease

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

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