Готові списки джерел за темами / FMRI, motion, preprocessing, pipeline

Добірка наукової літератури з теми "FMRI, motion, preprocessing, pipeline"

Автор: Grafiati

Опубліковано: 14 березня 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Зміст

Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "FMRI, motion, preprocessing, pipeline".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "FMRI, motion, preprocessing, pipeline"

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 (September 2, 2020): 5325–40. http://dx.doi.org/10.1002/hbm.25195.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Churchill, Nathan W., Anita Oder, Hervé Abdi, Fred Tam, Wayne Lee, Christopher Thomas, Jon E. Ween, Simon J. Graham, and Stephen C. Strother. "Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods." Human Brain Mapping 33, no. 3 (March 31, 2011): 609–27. http://dx.doi.org/10.1002/hbm.21238.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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 (April 2020): S4. http://dx.doi.org/10.1093/schbul/sbaa028.007.

Повний текст джерела

Анотація:

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 that in HC would show correlations between Glx and rs-fMRI in the salience and default mode network, but these relationships would be altered in FEP. Methods Data from 53 HC (age = 24.70 ±6.23, 34M/19F) and 60 FEP (age = 24.08 ±6.29, 38M/22F) were analyzed. To obtain MRS data, a voxel was placed in the ACC (PRESS, TR/TE = 2000/80ms). Metabolite concentrations were quantified with respect to internal water using the AMARES algorithm in jMRUI. rs-fMRI data were processed using a standard preprocessing pipeline in the CONN toolbox. BOLD signal from a priori brain regions of interest from posterior cingulate cortex (default mode network, DMN), anterior cingulate cortex (salience network, SN), and right posterior parietal cortex (central executive network, CEN) were extracted and correlated with the rest of the brain to measure functional connectivity (FC). Group analyses were performed on Glx, FC, and Glx-FC interactions while controlling for age, gender, and motion when applicable. FC and Glx-FC analyses were performed using small volume correction [(p < 0.01, threshold-free cluster enhancement corrected (TFCE)]. Results No significant between-group differences were found in Glx concentration in the ACC [F(1, 108) = 0.34, p = 0.56], but reduced FC was found on each network in FEP compared to HC (pTFCE corrected). Group Glx-FC interactions were found in the form of positive correlations between Glx and FC in DMN and SN in the HC group, but not in FEP; and negative correlations in CEN in HC, but not in FEP. Discussion While we did not find significant group differences in ACC Glx measurements, ACC Glx modulated FC differentially in FEP and HC. Positive correlations between Glx and FC were found in the SN and DMN, suggesting long range modulation of the two networks in HC, but not in FEP. Additionally, negative correlations between Glx and FC were found in CEN in HC, but not in FEP. Overall, these results suggest that even in the absence of group differences in Glx concentration, the long-range modulation of these 3 networks by ACC Glx is altered in FEP.

Стилі APA, Harvard, Vancouver, ISO та ін.

Jo, Hang Joon, Stephen J. Gotts, Richard C. Reynolds, Peter A. Bandettini, Alex Martin, Robert W. Cox, and Ziad S. Saad. "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.

Повний текст джерела

Анотація:

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 on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMeLOCAL), a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

Стилі APA, Harvard, Vancouver, ISO та ін.

Nørgaard, Martin, Melanie Ganz, Claus Svarer, Vibe G. Frokjaer, Douglas N. Greve, Stephen C. Strother, and Gitte M. Knudsen. "Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study." Journal of Cerebral Blood Flow & Metabolism 40, no. 9 (October 1, 2019): 1902–11. http://dx.doi.org/10.1177/0271678x19880450.

Повний текст джерела

Анотація:

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 384 commonly used preprocessing strategies on a previously reported positive association between the change from baseline in neocortical serotonin transporter binding determined with [11C]DASB-PET, and change in depressive symptoms, following a pharmacological sex hormone manipulation intervention in 30 women. The two preprocessing steps that were most critical for the outcome were motion correction and kinetic modeling of the dynamic PET data. We found that 36% of the applied preprocessing strategies replicated the originally reported finding ( p < 0.05). For preprocessing strategies with motion correction, the replication percentage was 72%, whereas it was 0% for strategies without motion correction. In conclusion, the choice of preprocessing strategy can have a major impact on a study outcome.

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Wang, Xian Lun, Li Li, and Yu Xia Cui. "Detection and Location of Underwater Pipeline Based on Mathematical Morphology for an AUV." Key Engineering Materials 561 (July 2013): 591–96. http://dx.doi.org/10.4028/www.scientific.net/kem.561.591.

Повний текст джерела

Анотація:

Underwater pipelines of oil and gas need periodic inspection to prevent damage due to the biological activity of water, turbulent current and tidal abrasion. Currently, vision-based autonomous underwater vehicle plays an important role in this field. A system has been designed to help an autonomous vehicle in sea-bottom survey operation. Image understanding and object recognition directly affect the accuracy of inspection. An image smoothing method based on mathematical morphology is proposed. The disturbances on acquired images caused by the motion are partially removed. A series of algorithms about image preprocessing, segmentation and recognition are proposed to access pipeline contours from the top-view images effectively. Navigation data based on Hough transformation is presented after the analysis of contours. Finally, the processing effect on a pipeline image demonstrates the effectiveness of the system.

Стилі APA, Harvard, Vancouver, ISO та ін.

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 (April 14, 2022): e0265112. http://dx.doi.org/10.1371/journal.pone.0265112.

Повний текст джерела

Анотація:

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, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest, and 8 out of top 10 FD-ISC windows had FD below the mean. Possible reasons and future implications of these findings are discussed.

Стилі APA, Harvard, Vancouver, ISO та ін.

Eldefrawy, Mahmoud, Scott A. King, and Michael Starek. "Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking." Remote Sensing 14, no. 13 (July 3, 2022): 3199. http://dx.doi.org/10.3390/rs14133199.

Повний текст джерела

Анотація:

3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.

Стилі APA, Harvard, Vancouver, ISO та ін.

Akdeniz, Gülsüm. "Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy." Computational Intelligence and Neuroscience 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/3091815.

Повний текст джерела

Анотація:

Objective. Complexity analysis of functional brain structure data represents a new multidisciplinary approach to examining complex, living structures. I aimed to construct a connectivity map of visual brain activities using resting-state functional magnetic resonance imaging (fMRI) data and to characterize the level of complexity of functional brain activity using these connectivity data. Methods. A total of 25 healthy controls and 20 patients with attention deficit hyperactivity disorder (ADHD) participated. fMRI preprocessing analysis was performed that included head motion correction, temporal filtering, and spatial smoothing process. Brain entropy (BEN) was calculated using the Shannon entropy equation. Results. My findings demonstrated that patients exhibited reduced brain complexity in visual brain areas compared to controls. The mean entropy value of the ADHD group was 0.56±0.14, compared to 0.64±0.11 in the control group. Conclusion. My study adds an important novel result to the growing literature pertaining to abnormal visual processing in ADHD that my ADHD patients had lower BEN values, indicating more-regular functional brain structure and abnormal visual information processing.

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Дисертації з теми "FMRI, motion, preprocessing, pipeline"

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.

Повний текст джерела

Анотація:

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 movement parameters, and the reduction of these artefacts with different approaches, such as linear regression of nuisance signals and scrubbing or censoring procedure, were introduced. However, we identify two main drawbacks: i) the different measures used for the evaluation of motion artefacts were based on user-dependent thresholds, and ii) each study described and applied their own pre-processing pipeline. Few studies analysed the effect of these different pipelines on subsequent analyses methods in task-based fMRI.The first aim of the study is to obtain a tool for motion fMRI data assessment, based on auto-calibrated procedures, to detect outlier subjects and outliers volumes, targeted on each investigated sample to ensure homogeneity of data for motion. The second aim is to compare the impact of different pre-processing pipelines on task-based fMRI using GLM based on recent advances in resting state fMRI preprocessing pipelines. Different output measures based on signal variability and task strength were used for the assessment.

Стилі APA, Harvard, Vancouver, ISO та ін.

Spring, Robyn. "Extracting FMRI Brain Patterns Significantly Related to Behavior via Individual Preprocessing Pipeline Optimization." Thesis, 2012. http://hdl.handle.net/1807/33517.

Повний текст джерела

Анотація:

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. Several sets of fMRI preprocessing pipelines were tested and optimized in a fixed and individual-subject manner, using methods outlined by Churchill et al. (2011). Results: Individual-subject pipeline optimization, compared to fixed, significantly increased reproducibility, significantly increased the detection of positively and negatively activated voxels, and resulted in a brain-pattern with significant correlation to a task behavioral measure. Conclusions: Individual-subject pipeline optimization, compared to fixed, led to stronger and more reliable brain-patterns that are significantly correlated with behavior.

Стилі APA, Harvard, Vancouver, ISO та ін.

Частини книг з теми "FMRI, motion, preprocessing, pipeline"

Ryou, Wonryong, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, and Martin Vechev. "Scalable Polyhedral Verification of Recurrent Neural Networks." In Computer Aided Verification, 225–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_10.

Повний текст джерела

Анотація:

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 achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

Стилі APA, Harvard, Vancouver, ISO та ін.

Nieto-Castanon, Alfonso. "FMRI minimal preprocessing pipeline." In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN, 3–16. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2207.6599.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Nieto-Castanon, Alfonso. "FMRI denoising pipeline." In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN, 17–25. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2207.6600.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "FMRI, motion, preprocessing, pipeline"

Meda, Shashwath, Mike Stevens, Erwin Boer, Catherine Boyle, Greg Book, Nicolas Ward, and Godfrey Pearlson. "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.

Повний текст джерела

Анотація:

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 functional MRI (fMRI). Methods: N=26 frequent cannabis users were administered 0.5 grams of 13% THC or placebo flower cannabis via a Stortz+Bickel ‘Volcano’ vaporizer using paced inhalation, on separate days at least 1 week apart. On each study day, participants drove a virtual driving simulator (steering wheel, brake, gas pedal) inside an MRI scanner approximately 40 minutes post-dosing. Each fMRI driving session presented a naturalistic simulated environment that unobtrusively engaged drivers with scenarios that tested specific driving skills and response. There were three, approximately 10 min epochs where drivers engaged in task of lane keeping/weaving (LK), lead car following (CF), and safe overtaking (OT). fMRI data were prepared for analyses using the Human Connectome Project pipeline, then subjected to group independent component analysis (ICA) to isolate 50 spatially independent networks. 40 ICA networks were deemed valid and non-noisy. Network regions in these components were identified using 387 parcel locations, incorporating a cortical parcellation atlas (Glasser et al 2016) and detailed subcortical labels. A placebo minus high difference connectivity map was generated for each subject. A similar placebo minus high behavioral score was generated for each subject and then subjected to a principal component analysis (PCA) to reduce it to 8 orthogonal behavioral factors. Of the 8 driving behavior factors, two represented CF events (F1 and F5), three LK (F3, F4, and F8), and three OT (F2, F6, and F7). Driving behavior factors were evaluated for linear association with connectivity maps via FSL’s randomize (p<0.01 FWE-corrected significance). Results:Across all components examined, we found connectivity differences between placebo v high THC within right motion-sensitive visual cortex (parcel FST) (visual) and right superior temporal gyrus (social cognition) to positively correlate with LK driving performance. The strongest brain-behavior relationships were found for OT-related behavioral factors. Connectivity in left dorsolateral parcel a9-46v (cognitive flexibility) and right motor cortex parcel 3b (somatosensory) correlated negatively with F6 (OT). A left superior frontal parcel (higher order cognition/working memory) correlated negatively with F7 (OT) and finally R inferior frontal gyrus (response inhibition and reward deduction) correlated positively with F7 (OT). Conclusion: Our preliminary analyses yield a complex yet informative picture of key brain areas sensitive to acute CNB exposure on different driving behaviors using a simulated environment, further underscoring the impact of substance use on driving as a potential public safety issue.

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!