Relevant bibliographies by topics / FMRI, motion, preprocessing, pipeline

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'FMRI, motion, preprocessing, pipeline'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "FMRI, motion, preprocessing, pipeline"

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Kopal, Jakub, Anna Pidnebesna, David Tomeček, Jaroslav Tintěra, and Jaroslav Hlinka. "Typicality of functional connectivity robustly captures motion artifacts in rs‐fMRI across datasets, atlases, and preprocessing pipelines." Human Brain Mapping 41, no. 18 (2020): 5325–40. http://dx.doi.org/10.1002/hbm.25195.

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Churchill, Nathan W., Anita Oder, Hervé Abdi, et al. "Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods." Human Brain Mapping 33, no. 3 (2011): 609–27. http://dx.doi.org/10.1002/hbm.21238.

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Maximo, Jose, Frederic Briend, William Armstrong, Nina Kraguljac, and Adrienne Lahti. "O2.2. EVALUATION OF THE RELATIONSHIP BETWEEN GLUTAMATE AND BRAIN CONNECTIVITY IN ANTIPSYCHOTIC-NAïVE FIRST EPISODE PATIENTS – A COMBINED MAGNETIC RESONANCE SPECTROSCOPY AND RESTING STATE FUNCTIONAL CONNECTIVITY MRI STUDY." Schizophrenia Bulletin 46, Supplement_1 (2020): S4. http://dx.doi.org/10.1093/schbul/sbaa028.007.

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Abstract Background Schizophrenia is thought to be a disorder of brain dysconnectivity. An imbalance between cortical excitation/inhibition is also implicated, but the link between these abnormalities remains unclear. The present study used resting state functional connectivity MRI (rs-fcMRI) and magnetic resonance spectroscopy (MRS) to investigate how measurements of glutamate + glutamine (Glx) in the anterior cingulate cortex (ACC) relate to rs-fcMRI in medication-naïve first episode psychosis (FEP) subjects compared to healthy controls (HC). Based on our previous findings, we hypothesized t
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Lamouroux, Alix, Giulia Lioi, Julie Coloigner, Pierre Maurel, and Nicolas Farrugia. "fMRIStroke: A preprocessing pipeline for fMRI Data from Stroke patients." Journal of Open Source Software 9, no. 103 (2024): 6636. http://dx.doi.org/10.21105/joss.06636.

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Jo, Hang Joon, Stephen J. Gotts, Richard C. Reynolds, et al. "Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/935154.

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Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We replicate the previously reported head-motion bias o
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Nørgaard, Martin, Melanie Ganz, Claus Svarer, et al. "Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study." Journal of Cerebral Blood Flow & Metabolism 40, no. 9 (2019): 1902–11. http://dx.doi.org/10.1177/0271678x19880450.

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Positron emission tomography (PET) neuroimaging provides unique possibilities to study biological processes in vivo under basal and interventional conditions. For quantification of PET data, researchers commonly apply different arrays of sequential data analytic methods (“preprocessing pipeline”), but it is often unknown how the choice of preprocessing affects the final outcome. Here, we use an available data set from a double-blind, randomized, placebo-controlled [11C]DASB-PET study as a case to evaluate how the choice of preprocessing affects the outcome of the study. We tested the impact of
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Hocke, Lia Maria, Yunjie Tong, and Blaise deBonneval Frederick. "An Automatic Motion-Based Artifact Reduction Algorithm for fNIRS in Concurrent Functional Magnetic Resonance Imaging Studies (AMARA–fMRI)." Algorithms 16, no. 5 (2023): 230. http://dx.doi.org/10.3390/a16050230.

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Multimodal functional near-infrared spectroscopy–functional magnetic resonance imaging (fNIRS–fMRI) studies have been highly beneficial for both the fNIRS and fMRI field as, for example, they shed light on the underlying mechanism of each method. However, several noise sources exist in both methods. Motion artifact removal is an important preprocessing step in fNIRS analysis. Several manual motion–artifact removal methods have been developed which require time and are highly dependent on expertise. Only a few automatic methods have been proposed. AMARA (acceleration-based movement artifact red
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Jaber, Hussain A., Hadeel K. Aljobouri, Ilyas Cankaya, Orhan M. Kocak, and Oktay Algin. "Preparing fMRI Data for Postprocessing: Conversion Modalities, Preprocessing Pipeline, and Parametric and Nonparametric Approaches." IEEE Access 7 (2019): 122864–77. http://dx.doi.org/10.1109/access.2019.2937482.

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Nath, Deepa, Anil Hiwale, and Nilesh Kurwale. "Optimization of Pipeline through Preprocessing Steps Sequence Alteration using Graph Theory for Resting State fMRI." International Journal of Engineering Trends and Technology 71, no. 3 (2022): 168–74. http://dx.doi.org/10.14445/22315381/ijett-v71i3p217.

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Frew, Simon, Ahmad Samara, Hallee Shearer, Jeffrey Eilbott, and Tamara Vanderwal. "Getting the nod: Pediatric head motion in a transdiagnostic sample during movie- and resting-state fMRI." PLOS ONE 17, no. 4 (2022): e0265112. http://dx.doi.org/10.1371/journal.pone.0265112.

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Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large transdiagnostic public dataset (N = 1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, includi
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Dissertations / Theses on the topic "FMRI, motion, preprocessing, pipeline"

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NIGRI, ANNA. "Quality data assessment and improvement in pre-processing pipeline to minimize impact of spurious signals in functional magnetic imaging (fMRI)." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2911412.

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In the recent years, the field of quality data assessment and signal denoising in functional magnetic resonance imaging (fMRI) is rapidly evolving and the identification and reduction of spurious signal with pre-processing pipeline is one of the most discussed topic. In particular, subject motion or physiological signals, such as respiratory or/and cardiac pulsatility, were showed to introduce false-positive activations in subsequent statistical analyses. Different measures for the evaluation of the impact of motion related artefacts, such as frame-wise displacement and root mean square of mo
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Spring, Robyn. "Extracting FMRI Brain Patterns Significantly Related to Behavior via Individual Preprocessing Pipeline Optimization." Thesis, 2012. http://hdl.handle.net/1807/33517.

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Background: Functional magnetic resonance imaging (fMRI) can require extensive preprocessing to minimize noise and maximize signal. There is evidence suggesting that fixed-subject preprocessing pipelines, the current standard in fMRI preprocessing, are suboptimal compared to individual-subject pipelines. Aim: We sought to test if individual-subject preprocessing pipeline optimization, compared to fixed, resulted in stronger and more reliable brain-patterns in episodic recognition. Methodology: 27 young healthy controls were scanned via fMRI while performing forced-choice episodic recognition
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Book chapters on the topic "FMRI, motion, preprocessing, pipeline"

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Ryou, Wonryong, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, and Martin Vechev. "Scalable Polyhedral Verification of Recurrent Neural Networks." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_10.

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AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To
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Nieto-Castanon, Alfonso. "FMRI minimal preprocessing pipeline." In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2207.6599.

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This chapter describes standard and advanced preprocessing steps in fcMRI. These steps are aimed at correcting or minimizing the influence of well-known factors affecting the quality of functional and anatomical MRI data, including effects arising from subject motion within the scanner, temporal and spatial image distortions due to the sequential nature of the scanning acquisition protocol, and inhomogeneities in the scanner magnetic field, as well as anatomical differences among subjects.
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Nieto-Castanon, Alfonso. "FMRI denoising pipeline." In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2207.6600.

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After the functional data has been preprocessed, the measured blood-oxygen-level-dependent (BOLD) signal often still contains a considerable amount of noise from a combination of physiological effects, outliers, and residual subject-motion factors. If unaccounted for, these factors would introduce very strong and noticeable biases in all functional connectivity measures. This chapter describes standard and advanced denoising procedures in CONN that are used to characterize and remove the effect of these residual non-neural noise sources.
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Conference papers on the topic "FMRI, motion, preprocessing, pipeline"

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Meda, Shashwath, Mike Stevens, Erwin Boer, et al. "Brain-behavior relationships of simulated naturalistic automobile driving under the influence of acute cannabis intoxication: A double-blind, placebo-controlled study." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.32.

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Background: Driving is a complex everyday activity that requires the use and integration of different cognitive and psychomotor functions, many of which are known to be affected when under the influence of cannabis (CNB). Given legal implications of drugged-driving and rapidly increasing use of CNB nationwide, there is an urgent need to better understand the effects of CNB on such functions in the context of driving. This longitudinal, double-blind placebo-controlled study investigated the effects of CNB on driving brain-behavior relationships in a controlled simulated environment using functi
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Journal articles
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