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

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

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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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.

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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 algorithm

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Alarjani, Maitha, and Badar Almarri. "fMRI-based Alzheimer’s disease detection via functional connectivity analysis: a systematic review." PeerJ Computer Science 10 (October 16, 2024): e2302. http://dx.doi.org/10.7717/peerj-cs.2302.

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Alzheimer’s disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer’s disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer’s in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of h

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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.

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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, tempo

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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 (2022): 3199. http://dx.doi.org/10.3390/rs14133199.

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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 t

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Wu, Xintong. "CustomDancer: Customized Dance Recommendation by Text-Dance Retrieval." Applied and Computational Engineering 174, no. 1 (2025): 293–305. https://doi.org/10.54254/2755-2721/2025.po25157.

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Dance serves as both a cultural cornerstone and a medium for personal expression, yet the exponential growth of online dance content has intensified the need for personalized retrieval systems. While text-based dance retrieval offers a promising avenue for users to articulate preferences through natural language, existing methods face critical challenges: aligning abstract textual semantics with motion kinematics, reconciling cross-modal heterogeneity, and overcoming the scarcity of annotated data. To address these gaps, we presentCustomDancer, a novel framework that bridges music, motion, and

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Ezra, Din, Shai Mastitz, and Irina Rabaev. "Signsability: Enhancing Communication through a Sign Language App." Software 3, no. 3 (2024): 368–79. http://dx.doi.org/10.3390/software3030019.

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The integration of sign language recognition systems into digital platforms has the potential to bridge communication gaps between the deaf community and the broader population. This paper introduces an advanced Israeli Sign Language (ISL) recognition system designed to interpret dynamic motion gestures, addressing a critical need for more sophisticated and fluid communication tools. Unlike conventional systems that focus solely on static signs, our approach incorporates both deep learning and Computer Vision techniques to analyze and translate dynamic gestures captured in real-time video. We

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Baxter, Luke, Sean Fitzgibbon, Fiona Moultrie, et al. "Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants." NeuroImage 186 (February 2019): 286–300. http://dx.doi.org/10.1016/j.neuroimage.2018.11.006.

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Xiao, Shunfu, Honghong Chai, Ke Shao, et al. "Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field." Remote Sensing 12, no. 2 (2020): 269. http://dx.doi.org/10.3390/rs12020269.

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Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segme

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Grootswagers, Tijl, Susan G. Wardle, and Thomas A. Carlson. "Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data." Journal of Cognitive Neuroscience 29, no. 4 (2017): 677–97. http://dx.doi.org/10.1162/jocn_a_01068.

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Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decod

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Wang, Jiale, Nanzhu Liu, Yuxin Xie, Shengmao Que, and Ming Xia. "A Multimodal CNN–Transformer Network for Gait Pattern Recognition with Wearable Sensors in Weak GNSS Scenarios." Electronics 14, no. 8 (2025): 1537. https://doi.org/10.3390/electronics14081537.

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Human motion recognition is crucial for applications like navigation, health monitoring, and smart healthcare, especially in weak GNSS scenarios. Current methods face challenges such as limited sensor diversity and inadequate feature extraction. This study proposes a CNN–Transformer–Attention framework with multimodal enhancement to address these challenges. We first designed a lightweight wearable system integrating synchronized accelerometer, gyroscope, and magnetometer modules at wrist, chest, and foot positions, enabling multi-dimensional biomechanical data acquisition. A hybrid preprocess

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Feng, Jingxiang, Peiran Zhao, Haoran Zheng, Jessada Konpang, Adisorn Sirikham, and Phuri Kalnaowakul. "Enhancing Autonomous Driving Perception: A Practical Approach to Event-Based Object Detection in CARLA and ROS." Vehicles 7, no. 2 (2025): 53. https://doi.org/10.3390/vehicles7020053.

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Robust object detection in autonomous driving is challenged by inherent limitations of conventional frame-based cameras, such as motion blur and limited dynamic range. In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges o

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ElNakieb, Yaser, Mohamed T. Ali, Ahmed Elnakib, et al. "Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study." Bioengineering 10, no. 1 (2023): 56. http://dx.doi.org/10.3390/bioengineering10010056.

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In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classificatio

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Tourville, Jason A., Alfonso Nieto-Castañón, Matthias Heyne, and Frank H. Guenther. "Functional Parcellation of the Speech Production Cortex." Journal of Speech, Language, and Hearing Research 62, no. 8S (2019): 3055–70. http://dx.doi.org/10.1044/2019_jslhr-s-csmc7-18-0442.

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Neuroimaging has revealed a core network of cortical regions that contribute to speech production, but the functional organization of this network remains poorly understood. Purpose We describe efforts to identify reliable boundaries around functionally homogenous regions within the cortical speech motor control network in order to improve the sensitivity of functional magnetic resonance imaging (fMRI) analyses of speech production and thus improve our understanding of the functional organization of speech production in the brain. Method We used a bottom-up, data-driven approach by pooling dat

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Abramov, Nikolay, Yulia Emelyanova, Vitaly Fralenko, et al. "Intelligent Methods for Forest Fire Detection Using Unmanned Aerial Vehicles." Fire 7, no. 3 (2024): 89. http://dx.doi.org/10.3390/fire7030089.

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This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast

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Chen, Duowen, Liqi Zhou, and Chi Guo. "A Low-Latency Dynamic Object Detection Algorithm Fusing Depth and Events." Drones 9, no. 3 (2025): 211. https://doi.org/10.3390/drones9030211.

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Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training and inference on high-performance graphics cards. These cards are not only bulky and power-hungry but also challenging to deploy on compact robotic platforms. Fortunately, the emergence of event cameras, inspired by biological vision, provides a promising solution to these li

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Gnanajeyaraman Rajaram. "Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning." Journal of Electrical Systems 20, no. 5s (2024): 2095–103. http://dx.doi.org/10.52783/jes.2547.

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Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, in

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Gnanajeyaraman Rajaram. "Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning." Journal of Electrical Systems 20, no. 5s (2024): 2051–59. http://dx.doi.org/10.52783/jes.2542.

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Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, in

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Zantopp, Nico, Akif Budak, Cosmin Delea, Herberto Werner, Ching Nok Au, and Johannes Oeffner. "Lidar-Based Obstacle Detection and Path Prediction for Unmanned Surface Vehicles." Journal of Physics: Conference Series 2867, no. 1 (2024): 012027. http://dx.doi.org/10.1088/1742-6596/2867/1/012027.

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Abstract Unmanned Surface Vehicles (USVs) represent an advanced technology with a wide range of applications in maritime contexts, and they offer a promising alternative to manned systems, particularly for the provision of services such as riverbed mapping. Furthermore, the use of USVs in combination with other robotic systems, such as flying drones, remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs), opens up innovative ways to inspect and monitor maritime infrastructure. However, the use of these systems in dynamic environments, such as ports or shipping l

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Kraft, Jessica N., Hanna K. Hausman, Cheshire Hardcastle, et al. "1 Task-Based Functional Connectivity and Network Segregation of the Useful Field of View (UFOV) fMRI task." Journal of the International Neuropsychological Society 29, s1 (2023): 606–7. http://dx.doi.org/10.1017/s1355617723007701.

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Objective:Interventions using a cognitive training paradigm called the Useful Field of View (UFOV) task have shown to be efficacious in slowing cognitive decline. However, no studies have looked at the engagement of functional networks during UFOV task completion. The current study aimed to (a) assess if regions activated during the UFOV fMRI task were functionally connected and related to task performance (henceforth called the UFOV network), (b) compare connectivity of the UFOV network to 7 resting-state functional connectivity networks in predicting proximal (UFOV) and near-transfer (Double

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Jafari, Habib, Shamarina Shohaimi, Nader Salari, et al. "A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study." PLOS ONE 17, no. 1 (2022): e0262701. http://dx.doi.org/10.1371/journal.pone.0262701.

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Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and fin

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Barkovska, Olesia, Oleksandr Holovchenko, Denis Storchai, Anton Kostin, and Nikita Lehezin. "Investigation of computer vision techniques for indoor navigation systems." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 2(32) (June 30, 2025): 5–15. https://doi.org/10.30837/2522-9818.2025.2.005.

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The subject of this article is the development and implementation of computer vision methods that can be integrated into an indoor navigation system designed for individuals with visual impairments. The goal of the study is to enhance such a system with advanced object recognition capabilities in enclosed environments by combining modern technologies, including artificial intelligence, spatial analysis, voice control, and Bluetooth-based localization. To achieve this, a number of tasks were carried out. These included an analysis of the problem domain and justification of the study’s relevance

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Janek, Marian, Ivan Martincek, and Gabriela Tarjanyiova. "Method for Extracting Arterial Pulse Waveforms from Interferometric Signals." Sensors 25, no. 14 (2025): 4389. https://doi.org/10.3390/s25144389.

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This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible

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Harrington, Y., M. Paolini, V. Bettonagli, et al. "Resting State Functional Connectivity is Associated With Treatment Response in Major Depression: A Real World Study." European Psychiatry 66, S1 (2023): S606—S607. http://dx.doi.org/10.1192/j.eurpsy.2023.1266.

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IntroductionMajor depressive disorder (MDD) is largely considered the most prevalent psychiatric disorder worldwide. Despite its domineering presence, effective treatment for many individuals remains elusive. Investigation into relevant biological markers, specifically neuroimaging correlates, of MDD and treatment response have gained traction in recent years; however, findings are still inconsistent.ObjectivesIn this study, we aimed to investigate the resting state functional connectivity patterns associated with treatment response in MDD inpatients in a real world setting.MethodsForty-three

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Kaur, Jasleen, Arlene Oetomo, Vivek Chauhan, and Plinio Morita. "0291 Evaluating Sleep Quality Metrics Using Zero-Effort Technology: Implications for Public Health Dynamics." SLEEP 47, Supplement_1 (2024): A126. http://dx.doi.org/10.1093/sleep/zsae067.0291.

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Abstract Introduction Sleep quality is critical to human health and well-being, with implications for manifold physiological and psychological processes. The quality and reliability of the data due to recall bias and subjective interpretation often limit traditional methods of sleep data collection. This research presents a novel framework that can objectively measure and evaluate sleep quality using smart thermostats equipped with motion sensors, providing non-invasive and effortless sleep monitoring. Methods We leveraged the ecobee 'Donate Your Data' initiative, which collects data from smar

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Dheeraj Tallapragada and Vedant Sagare. "Multichannel EMG-based gesture recognition utilizing advanced machine learning techniques: A random forest classifier for high-precision signal classification." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 323–32. http://dx.doi.org/10.30574/wjarr.2024.24.2.3332.

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This research examines how advanced machine learning algorithms can be used to classify multichannel electromyographic (EMG) signals with a high level of accuracy to assist in recognizing hand gestures. The goal is to create a robust and scalable system for gesture-based virtual control using EMG signals with potential applications in assistive technologies, rehabilitation, and human-computer interaction. Data were gathered using a MYO Thalmic bracelet containing eight EMG sensors on thirty-six subjects, and a Random Forest classifier was trained to identify seven distinct types of hand gestur

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Dheeraj, Tallapragada, and Sagare Vedant. "Multichannel EMG-based gesture recognition utilizing advanced machine learning techniques: A random forest classifier for high-precision signal classification." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 323–32. https://doi.org/10.5281/zenodo.15074834.

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This research examines how advanced machine learning algorithms can be used to classify multichannel electromyographic (EMG) signals with a high level of accuracy to assist in recognizing hand gestures. The goal is to create a robust and scalable system for gesture-based virtual control using EMG signals with potential applications in assistive technologies, rehabilitation, and human-computer interaction. Data were gathered using a MYO Thalmic bracelet containing eight EMG sensors on thirty-six subjects, and a Random Forest classifier was trained to identify seven distinct types of hand gestur

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Raspor, Eva, Peter K. Hahn, Tom Lancaster, et al. "S177. IMPACT OF NOS1AP AND ITS INTERACTION PARTNERS AT THE GLUTAMATERGIC SYNAPSE ON WORKING MEMORY NETWORKS - AN FMRI IMAGING GENETICS STUDY." Schizophrenia Bulletin 46, Supplement_1 (2020): S105. http://dx.doi.org/10.1093/schbul/sbaa031.243.

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Abstract Background N-methyl-D-aspartate receptor (NMDAR) hypofunction is an important pathophysiological mechanism in schizophrenia. At the postsynapse the NMDAR interacts with the post-synaptic density (PSD). Neuronal nitric oxide synthase 1 (NOS1) binds to the PSD scaffolding proteins PSD-93 and PSD-95, enabling NMDAR-mediated release of nitric oxide via NOS1. NOS1AP (adaptor of NOS1) is capable of disrupting the interactions between NOS1, PSD-93, and PSD95. Therefore, NOS1AP is closely involved in both glutamatergic and nitrinergic neurotransmission. NOS1AP has been implicated as a risk ge

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Coloigner †, Julie, Chau Vu †, Matt Borzage, et al. "Analysis of Hemodynamic Changes and Bold Signals of Sickle Cell Disease Patients during Desaturation." Blood 126, no. 23 (2015): 3384. http://dx.doi.org/10.1182/blood.v126.23.3384.3384.

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Abstract Introduction: Using near-infrared spectroscopy (NIRS), previous studies have shown that sickle-cell disease (SCD) patients have low cerebral oxygen saturation values [1]. Moreover, the hemoglobin S in sickle cell disease has impaired oxygen carrying capacity [2]. Here, we propose to investigate the effect of induced desaturation on the SCD brain. In particular, we analyzed the falling functional magnetic resonance imaging (fMRI) response during hypoxia, which results from local concentration changes in paramagnetic deoxy-hemoglobin (DHB). Moreover, we also explore the near-infrared sp

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Homan, Philipp, Anil Malhotra, Todd Lencz, and Pamela De Rosse. "M19. NIGROSTRIATAL CONNECTIVITY AND THE PREDICTION OF THOUGHT DISTURBANCE IN EARLY PSYCHOSIS." Schizophrenia Bulletin 46, Supplement_1 (2020): S140—S141. http://dx.doi.org/10.1093/schbul/sbaa030.331.

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Abstract Background Dopamine neurons are known to fire both tonically and phasically, resulting in tonic dopamine concentrations and spikes in those concentrations (often referred to as transients). Empirical evidence has shown elevated activity in the striatum in response to neutral stimuli which correlated with positive symptoms, in line with the proposed increased prediction errors. The increase of sponataneous phasic dopamine release in early psychosis should also be evident by altered resting state connectivity between the midbrain and its dopaminergic projections to the dorsal striatum.

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Rubio, Jose, Chrisina Fales, Anita Barber, Todd Lencz, Anil Malhotra, and John Kane. "T23. ANTIPSYCHOTIC EXPOSURE AND STRIATAL FUNCTIONAL CONNECTIVITY IN PSYCHOSIS RELAPSE: A HYPOTHESIS GENERATING STUDY." Schizophrenia Bulletin 46, Supplement_1 (2020): S240. http://dx.doi.org/10.1093/schbul/sbaa029.583.

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Abstract Background Most individuals with schizophrenia experience relapse over the course of the illness, yet unfortunately the mechanisms of this phenomenon are poorly understood. This research is often confounded by non-adherence with antipsychotic drugs. We propose to study relapse in individuals treated with long acting injectable antipsychotics (LAIs), for whom treatment adherence is confirmed. Since striatal resting state functional connectivity (RSFC) has been shown to reflect pathophysiological aspects of antipsychotic treatment response, we aim to study striatal RSFC in relapse in in

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Unterholzner, *Jakob, Immanuel Gregory Elbau, Alexander Kautzky, et al. "LOCKDOWN-ASSOCIATED CHANGES IN THE DEFAULT MODE NETWORK IN DEPRESSION AND HEALTHY INDIVIDUALS DURING THE COVID-19 PANDEMIC IN AUSTRIA." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i65. https://doi.org/10.1093/ijnp/pyae059.111.

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Abstract Background Major threatening life events can impact the functionality of brain networks as revealed by neuroimaging studies [1]. The COVID-19 pandemic was an unprecedented global event that was associated with extensive restrictions to daily life. These measures greatly challenged the whole population including specific subgroups such as patients with depression. Here, we investigated the impact of pandemic-associated lockdowns on functional connectivity (FC) of major functional brain networks. Aims and objectives We hypothesized that there would be an effect of restriction measures o

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Enguix, Vicente, Jeanette Kenley, David Luck, Julien Cohen-Adad, and Gregory Anton Lodygensky. "NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline." Frontiers in Neuroinformatics 16 (June 17, 2022). http://dx.doi.org/10.3389/fninf.2022.843114.

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Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast com

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Banerjee, Rohan, Merve Kaptan, Alexandra Tinnermann, et al. "EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data." Imaging Neuroscience, 2025. https://doi.org/10.1162/imag.a.98.

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Abstract Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone

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Zhu, Wei, Guangle Zhang, Xiao-Hong Zhu, and Wei Chen. "A robust approach for analyzing and mapping hierarchical brain connectome towards laminar-specific neural networks." Imaging Neuroscience, 2025. https://doi.org/10.1162/imag_a_00543.

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Abstract Probing neuronal activity and functional connectivity at cortical layer and sub-cortical nucleus level provides opportunities for mapping local and remote neural circuits and resting-state networks (RSN) critical for understanding cognition and behaviors. However, conventional resting-state fMRI (rs-fMRI) has been applied predominantly at relatively low spatial resolution and macroscopic level, unable to obtain laminar-specific information and neural circuits across the cortex at mesoscopic level. In addition, it is lack of sophisticated processing pipeline to deal with small laminar

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Taylor, Paul A., Daniel R. Glen, Gang Chen, et al. "A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more." Imaging Neuroscience, 2024. http://dx.doi.org/10.1162/imag_a_00246.

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Abstract Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preproc

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Morfini, Francesca, Susan Whitfield-Gabrieli, and Alfonso Nieto-Castañón. "Functional connectivity MRI quality control procedures in CONN." Frontiers in Neuroscience 17 (March 23, 2023). http://dx.doi.org/10.3389/fnins.2023.1092125.

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Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN to

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Chen, Huihui, Yining Zhang, Limei Zhang, Lishan Qiao, and Dinggang Shen. "Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification." Frontiers in Aging Neuroscience 12 (January 14, 2021). http://dx.doi.org/10.3389/fnagi.2020.595322.

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Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aim

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Lamouroux, Alix, Julie Coloigner, Pierre Maurel, Nicolas Farrugia, and Giulia Lioi. "Evaluating Lesion-Specific Preprocessing Pipelines For RS-FMRI In Stroke Patients: Impact on Functional Connectivity and Behavioral Prediction." Imaging Neuroscience, 2025. https://doi.org/10.1162/imag.a.6.

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Abstract Functional magnetic resonance imaging (fMRI) is essential for studying brain function and connectivity. Resting-state fMRI, which captures spontaneous brain activity without task requirements, is particularly suited for individuals with post-stroke impairments. However, the inherent noise and artifacts in fMRI signals can compromise analysis accuracy, especially in stroke patients with complex neurological conditions. Currently, there is no consensus on the best preprocessing approach for stroke fMRI data. In this study we design and evaluate three preprocessing pipelines: a standard

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Notter, Michael P., Peer Herholz, Sandra Da Costa, et al. "fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines." Brain Topography, December 27, 2022. http://dx.doi.org/10.1007/s10548-022-00935-8.

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AbstractHow functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened th

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De Rosa, Alessandro Pasquale, Fabrizio Esposito, Paola Valsasina, et al. "Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative." Journal of Neurology, November 9, 2022. http://dx.doi.org/10.1007/s00415-022-11479-z.

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AbstractThe Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothnes

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