Статті в журналах з теми "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 (September 2, 2020): 5325–40. http://dx.doi.org/10.1002/hbm.25195.
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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.
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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 (April 2020): S4. http://dx.doi.org/10.1093/schbul/sbaa028.007.
Повний текст джерелаАнотація:
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.
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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 джерелаАнотація:
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.
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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.
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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 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.
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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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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 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.
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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 (April 14, 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, 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.
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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 (July 3, 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 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.
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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, 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.
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Baxter, Luke, Sean Fitzgibbon, Fiona Moultrie, Sezgi Goksan, Mark Jenkinson, Stephen Smith, Jesper Andersson, Eugene Duff, and Rebeccah Slater. "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, Mengyuan Shen, Qing Wang, Ruili Wang, Yang Sui, and Yuntao Ma. "Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field." Remote Sensing 12, no. 2 (January 14, 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 segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.
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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 (April 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 decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.
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ElNakieb, Yaser, Mohamed T. Ali, Ahmed Elnakib, Ahmed Shalaby, Ali Mahmoud, Ahmed Soliman, Gregory Neal Barnes, and Ayman El-Baz. "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 (January 2, 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 classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.
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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 (August 29, 2019): 3055–70. http://dx.doi.org/10.1044/2019_jslhr-s-csmc7-18-0442.
Повний текст джерелаАнотація:
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 data from 12 previously conducted fMRI studies of speech production involving the production of monosyllabic and bisyllabic words and pseudowords that ranged from single vowels and consonant–vowel pairs to short sentences (163 scanning sessions, 136 unique participants, 39 different speech conditions). After preprocessing all data through the same pipeline and registering individual contrast maps to a common surface space, hierarchical clustering was applied to contrast maps randomly sampled from the pooled data set in order to identify consistent functional boundaries across subjects and tasks. Boundary completion was achieved by applying adaptive smoothing and watershed segmentation to the thresholded population-level boundary map. Hierarchical clustering was applied to the mean within–functional region of interest (fROI) response to identify networks of fROIs that respond similarly during speech. Results We identified highly reliable functional boundaries across the cortical areas involved in speech production. Boundary completion resulted in 117 fROIs in the left hemisphere and 109 in the right hemisphere. Clustering of the mean within-fROI response revealed a core sensorimotor network flanked by a speech motor planning network. The majority of the left inferior frontal gyrus clustered with the visual word form area and brain regions (e.g., anterior insula, dorsal anterior cingulate) associated with detecting salient sensory inputs and choosing the appropriate action. Conclusion The fROIs provide insight into the organization of the speech production network and a valuable tool for studying speech production in the brain by improving within-group and between-groups comparisons of speech-related brain activity. Supplemental Material https://doi.org/10.23641/asha.9402674
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Jafari, Habib, Shamarina Shohaimi, Nader Salari, Ali Akbar Kiaei, Farid Najafi, Soleiman Khazaei, Mehrdad Niaparast, Anita Abdollahi, and Masoud Mohammadi. "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 (January 20, 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 find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.
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Raspor, Eva, Peter K. Hahn, Tom Lancaster, David E. J. Linden, Florian Freudenberg, Andreas Reif, and Robert A. Bittner. "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 (April 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 gene for schizophrenia and cognitive dysfunction. Its increased expression has been observed in dorsolateral prefrontal post-mortem brain tissue of patients with schizophrenia, and NOS1AP SNPs have been associated with established schizophrenia endophenotypes. These findings suggest that the influence of NOS1AP variants should be observable in neural systems implicated in schizophrenia. In the present study, we investigate the impact of NOS1AP and its interaction partners at the glutamatergic synapse on the cortical working memory (WM) networks using fMRI and a gene set analysis approach. Methods 97 right-handed individuals with no personal or family history of psychiatric disorders underwent fMRI in a 3T Siemens Trio scanner during the performance of a visuospatial change detection WM task. Data analysis in Brain Voyager QX 2.8 included standard data preprocessing. Additionally, a multiscale curvature driven cortex based alignment procedure was used to minimize macro-anatomical variability between subjects. Subsequently, data were analyzed using a random-effects multi-subject general linear model. We investigated 19 regions of interest (ROIs) within the core fronto-parietal WM network. We studied all phases of our WM paradigm (encoding, maintenance, retrieval), which were modeled by a total of 5 regressors (encoding, delays 1–3, retrieval). Genetic data was quality controlled and imputed using the RICOPILI pipeline. Gene-set analyses of the 19 ROIs were performed using MAGMA. Two gene sets were selected: 1) NOS1AP/NOS1; 2) NOS1AP/glutamatergic synapse. We applied a Bonferroni correction for the total of 19 ROIs and 5 regressors (95 tests) to both analyses. Results Both gene set analyses revealed multiple associations between brain activation in core fronto-parietal WM areas. For the NOS1/NOS1AP set, most associations were observed during the late maintenance phase (Delay 3) of our WM paradigm. One association was significant Bonferroni correction: a cluster in the left intraparietal sulcus during the late maintenance phase (Delay 3; β=2.2459, SD=0.0239, SE=0.6451, p=0,00025). For NOS1AP / glutamatergic synapse interaction partners, two associations were significant after Bonferroni correction: a cluster in the right IPS during the early maintenance phase (Delay 1; β=0.8525, SD=0.0257, SE=0.2127, p=0.0000308) and a cluster in a different part of the right IPS during the late maintenance phase (Delay 3; β=0.7186, SD=0.0216, SE=0.2119, p=0,000348). Discussion In our gene set analyses we observed multiple associations between brain activation during WM and NOS1AP and its interaction partners, which were most pronounced during the late maintenance phase of our WM task in bilateral areas within the IPS. Both the more constrained NOS1AP / NOS1 gene set and the NOS1AP / glutamatergic synapse gene set showed similar association patterns. Our results implicate the NOS1AP interactome and the glutamatergic system in information processing and brain function in a cognitive domain strongly impaired in schizophrenia. They also indicate that altered activation of parietal WM areas during the maintenance phase is most strongly affected.
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Coloigner †, Julie, Chau Vu †, Matt Borzage, Adam M. Bush, Natasha Lepore, Thomas D. Coates, and John C. Wood. "Analysis of Hemodynamic Changes and Bold Signals of Sickle Cell Disease Patients during Desaturation." Blood 126, no. 23 (December 3, 2015): 3384. http://dx.doi.org/10.1182/blood.v126.23.3384.3384.
Повний текст джерелаАнотація:
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 spectroscopy (NIRS) changes in oxygenated and deoxygenated hemoglobin from the human prefrontal cortex. Methods: All patients were recruited through informed consent or assent; this study obtained approval from the IRB at Children's hospital Los Angeles. Exclusion criteria included pregnancy, previous stroke, and acute chest pain and crisis hospitalization within one month. Eight patients with SCD and 20 healthy age and ethnicity-matched controls (CTL) were studied. All patients wore oxygen masks in order to control the oxygen level during the experiment in the fMRI. During the scan, the subjects breathed 1) room air for 50 s, 2) 100% N2 for 5 breaths and 3) room air for 6 minutes. The typical length of hypoxia is 5 breaths, but for patients with irregular breathing patterns the maximum duration is 20 seconds. NIRS was used to measure the hemoglobin (OHB, DHB and THB) and a tissue oxygen index (TOI), from the human prefrontal cortex. Differences oh hemoglobin measure, between the normoxia and hypoxia states, were computed and called ΔOHB, ΔDHB and ΔTOI. During resting-state fMRI scanning, subjects were instructed to close their eyes, keep still as much as possible and not to think of anything systematically. FMRI images were acquired during 8 minutes and a total of 240 volumes were collected. Imaging data was first preprocessed with the FMRIB Software Library (FSL), using standard spatial preprocessing steps: Images were (1) slice-time corrected, (2) realigned to remove physiological motion and co-registered to the MNI template space. The percent signal change from the fMRI, % BOLD, was computed based on an average signal in the GM mask. Results: 9 SCD patients (age=23±8) and 20 CTL subjects (age=21±1) were recruited. Fig 1 presents the relationship between %BOLD versus ΔOHB and % BOLD versus ΔDHB, respectively. Due to the lower oxygen affinity of hemoglobin S [1], the decrease in % BOLD (-0.093 ± 0.034) and the ΔOHB (-1.425 ± 0.588) in SCD patients are larger than the decrease in CTL individuals (% BOLD: -0.050 ± 0.021 and ΔOHB: -0.877 ± 0.539); the increase in ΔDHB (1.661 ± 0.805) in SCD patients is also greater than in the control group (ΔDHB: 0.959 ± 0.505), as shown on Figure 2. Based on the THB signal, we can detect hyperemia for most of the subjects during desaturation. Moreover, for some of the SCD patients, the reactivity is slower even at the end of the hypoxic state. We observe a slightly lower increase of ΔTHB for SCD patients, but this was found to be insignificant. This may be the result of a combination of the CBF variability in the SCD group, and of the small sample size. In our study, a decrease of ΔTOI was observed during hypoxia in both groups. The ΔTOI amplitude of the SCD group (-0.080 ± 0.023) is higher than that of the CTL group (-0.047 ± 0.024), which may be due to the SCD brain's diminished physiological adaptive capability to a hypoxic state. Conclusion: Based on a conjoint acquisition of fMRI and NIRS, this study is the first investigation of the effect of the desaturation for SCD patients. As expected, based on these preliminary results, the SCD patients are more affected by hypoxic conditions, such as a larger changes of ΔOHB, ΔDHB, ΔTOI and % BOLD and a slower reactivity, likely due to anemia, high CBF and CBV, and decreased vessel elasticity. †=equal first author contribution References: [1] M. Nahavandi et.al, "Near-infrared spectra absorbance of blood from sickle cell patients and normal individuals," Hematology, vol. 14, no. 1, pp. 46-48, 2009 [2] N. K. Logothetis, "The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal Philos Trans R Soc Lond B Biol Sci, vol. 357, no. 1424, pp. 1003-1037, 2002 Figure 1. Minimum %BOLD versus minimum ΔOHB in the left side and minimum % BOLD versus maximum ΔDHB in the right side. Figure 1. Minimum %BOLD versus minimum ΔOHB in the left side and minimum % BOLD versus maximum ΔDHB in the right side. Figure 2. Bar graph of % BOLD and NIRS signals in CTL and SCD groups Figure 2. Bar graph of % BOLD and NIRS signals in CTL and SCD groups Disclosures No relevant conflicts of interest to declare.
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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 (April 2020): S140—S141. http://dx.doi.org/10.1093/schbul/sbaa030.331.
Повний текст джерелаАнотація:
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. Given that all antipsychotics bind to striatal dopamine D2 receptors, we assumed that this altered functional connectivity would also relate to treatment efficacy of those antipsychotic agents. Using striatal seeds, and extending our own group’s prior work exploring striatal connectivity, we computed an index of nigrostriatal connectivity and estimated its relationship with improvement of positive symptoms under antipsychotic monotherapy. Methods We conducted a parcellation of subcortical midbrain structures including the substantia nigra and ventral tegmental area in two independent early phase psychosis cohorts. Early phase psychosis was defined as having an antipsychotic life time exposure of less than 52 weeks. Patients were between 15 and 40 years old and with a DSM-V-defined diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder with psychotic features and provided informed consent to participate in the study. Exclusion criteria were active alcohol or substance abuse disorders, serious medical conditions, and pregnancy. Preprocessing involved the state-of-the art Human Connectome Project pipeline and extensive correction for motion artifacts. After parcellation, we extracted the connectivity values of the striatal seeds with the substantia nigra and calculated an improved version of the striatal connectivity index with the R-package sci developed by the presenter and freely available online at http://github.com/philipphoman/sci. Results We found that nigrostriatal connectivity was predictive of treatment efficacy in a sample of 41 patients with early phase psychosis. Indeed, individual differences in this connectivity index explained 18% of the variance of positive symptom improvement under antipsychotic treatment. Importantly, we replicated this effect in an independent replication cohort of 40 early phase psychosis patients, where we found that the identical model explained 11% of the variance in symptom improvement. Discussion There is an urgent need for diagnostic and prognostic biomarkers in psychotic disorders, and our results suggest that nigrostriatal connectivity is a promising candidate for a prognostic biomarker for antipsychotic treatment.
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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 (April 2020): S240. http://dx.doi.org/10.1093/schbul/sbaa029.583.
Повний текст джерелаАнотація:
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 individuals treated with LAIs to identify potential mechanisms. In particular, we will compare striatal RSFC between individuals who relapse while treated with LAIs, individuals who are not on LAIs and are non-adherent with antipsychotics at the time of relapse, and healthy controls, to generate a hypothesis about the role of striatal functioning in psychosis relapse. Methods Subjects with a psychotic disorder treated with LAI antipsychotics and history of clinical response to that trial confirmed by collateral, presenting with acute psychotic symptoms at the time of the scan (defined as ≥4 in BPRS in at least one of the psychotic items) (n=16) were compared with subjects also with a psychotic disorder presenting with acute psychotic symptoms who were non-adherent with antipsychotic drugs demonstrated by negative plasma level (n=16), and healthy controls (n=18). Participants were scanned using fMRI and data was pre-processed using the HCP pipeline with the ICA-FIX procedure, removing motion artifacts and nuisance variables. Connectivity maps were generated for 6 bilateral (12 total) striatal regions of interest as in Di Martino et al. 2007, which were compared between groups (cluster threshold p< .05, voxel threshold p<.001 uncorrected). In addition, we calculated striatal connectivity indices (SCI) as in Sarpal et al. 2016, as this metric reflecting RSFC between the striatum and 91 other regions of interest has been shown to have high precision in predicting response to antipsychotics in patients with first episode psychosis. Results We found no significant differences in sex or age between any of the 2 patient groups or the healthy controls, nor of psychopathology between the patient groups. For patients treated with LAIs upon relapse, striatal RSFC was significantly lower in an area in posterior cingulate, whereas it was higher in an area in the middle temporal gyrus, inferior temporal gyrus, and precentral gyrus, compared with healthy controls. When the LAI-treated individuals’ striatal RSFC was compared with that of individuals who were non-adherent with antipsychotic drugs at the time of relapse, it was significantly higher in the posterior parietal cortex, whereas it was lower in the pulvinar (thalamus) and primary and associative cortex. The SCI values for individuals who relapsed despite assured antipsychotic exposure were significantly lower than for non-LAI individuals who had relapsed due to non-adherence (p=0.049), and than healthy controls (p=0.01). Discussion Despite a relatively small sample, these results indicate differences in striatal functional connectivity depending on antipsychotic exposure at the time of relapse. The finding of significantly lower SCI values for LAI treated individuals at the time of relapse compared with non-adherent individuals with relapse and healthy controls suggests the hypothesis that relapse occurring despite assured antipsychotic exposure may result from aberrant striatal functional connectivity which is insufficiently engaged by antipsychotic drugs.
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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.
Повний текст джерелаАнотація:
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 compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
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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.
Повний текст джерелаАнотація:
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 aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods.
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De Rosa, Alessandro Pasquale, Fabrizio Esposito, Paola Valsasina, Alessandro d’Ambrosio, Alvino Bisecco, Maria A. Rocca, Silvia Tommasin, 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 smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
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Notter, Michael P., Peer Herholz, Sandra Da Costa, Omer F. Gulban, Ayse Ilkay Isik, Anna Gaglianese, and Micah M. Murray. "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 the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows’ structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.
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Sammartino, Francesco, Paul Taylor, Gang Chen, Richard C. Reynolds, Daniel Glen, and Vibhor Krishna. "Functional Neuroimaging During Asleep DBS Surgery: A Proof of Concept Study." Frontiers in Neurology 12 (June 28, 2021). http://dx.doi.org/10.3389/fneur.2021.659002.
Повний текст джерелаАнотація:
Object: A real-time functional magnetic resonance imaging (fMRI) feedback during ventral intermediate nucleus (VIM) deep brain stimulation (DBS) under general anesthesia (or “asleep” DBS) does not exist. We hypothesized that it was feasible to acquire a reliable and responsive fMRI during asleep VIM DBS surgery.Methods: We prospectively enrolled 10 consecutive patients who underwent asleep DBS for the treatment of medication-refractory essential tremor. Under general anesthesia, we acquired resting-state functional MRI immediately before and after the cannula insertion. Reliability was determined by a temporal signal-to-noise-ratio >100. Responsiveness was determined based on the fMRI signal change upon insertion of the cannula to the VIM.Results: It was feasible to acquire reliable fMRI during asleep DBS surgery. The fMRI signal was responsive to the brain cannula insertion, revealing a reduction in the tremor network's functional connectivity, which did not reach statistical significance in the group analysis.Conclusions: It is feasible to acquire a reliable and responsive fMRI signal during asleep DBS. The acquisition steps and the preprocessing pipeline developed in these experiments will be useful for future investigations to develop fMRI-based feedback for asleep DBS surgery.
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Pinsard, Basile, Arnaud Boutin, Julien Doyon, and Habib Benali. "Integrated fMRI Preprocessing Framework Using Extended Kalman Filter for Estimation of Slice-Wise Motion." Frontiers in Neuroscience 12 (April 26, 2018). http://dx.doi.org/10.3389/fnins.2018.00268.
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Li, Qiang, Dinghong Gong, Jie Shen, Chang Rao, Lei Ni, and Hongyi Zhang. "SF-MVPA: A from raw data to statistical results and surface space-based MVPA toolbox." Frontiers in Neuroscience 16 (November 21, 2022). http://dx.doi.org/10.3389/fnins.2022.1046752.
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Compared with traditional volume space-based multivariate pattern analysis (MVPA), surface space-based MVPA has many advantages and has received increasing attention. However, surface space-based MVPA requires considerable programming and is therefore difficult for people without a programming foundation. To address this, we developed a MATLAB toolbox based on a graphical interactive interface (GUI) called surface space-based multivariate pattern analysis (SF-MVPA) in this manuscript. Unlike the traditional MVPA toolboxes, which often only include MVPA calculation processes after data preprocessing, SF-MVPA covers the complete pipeline of surface space-based MVPA, including raw data format conversion, surface reconstruction, functional magnetic resonance (fMRI) data preprocessing, comparative analysis, surface space-based MVPA, leave one-run out cross validation, and family-wise error correction. With SF-MVPA, users can complete the complete pipeline of surface space-based MVPA without programming. In addition, SF-MVPA is designed for parallel computing and hence has high computational efficiency. After introducing SF-MVPA, we analyzed a sample dataset of tonal working memory load. By comparison with another surface space-based MVPA toolbox named CoSMoMVPA, we found that the two toolboxes obtained consistent results. We hope that through this toolbox, users can more easily implement surface space-based MVPA.
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Xifra-Porxas, Alba, Michalis Kassinopoulos, and Georgios D. Mitsis. "Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability." eLife 10 (August 3, 2021). http://dx.doi.org/10.7554/elife.62324.
Повний текст джерелаАнотація:
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
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Borkar, Kushal, Anusha Chaturvedi, P. K. Vinod, and Raju Surampudi Bapi. "Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI." Frontiers in Computational Neuroscience 16 (September 12, 2022). http://dx.doi.org/10.3389/fncom.2022.940922.
Повний текст джерелаАнотація:
Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.
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Tarchi, Livio, Stefano Damiani, Teresa Fantoni, Tiziana Pisano, Giovanni Castellini, Pierluigi Politi, and Valdo Ricca. "Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study." Brain Imaging and Behavior, July 21, 2022. http://dx.doi.org/10.1007/s11682-022-00708-8.
Повний текст джерелаАнотація:
Abstract Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor’s Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.
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Pereira-Sanchez, Victor, Alexandre R. Franco, Pilar de Castro-Manglano, Maria A. Fernandez-Seara, Maria Vallejo-Valdivielso, Azucena Díez-Suárez, Miguel Fernandez-Martinez, et al. "Resting-State fMRI to Identify the Brain Correlates of Treatment Response to Medications in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Lessons From the CUNMET Study." Frontiers in Psychiatry 12 (November 16, 2021). http://dx.doi.org/10.3389/fpsyt.2021.759696.
Повний текст джерелаАнотація:
Neuroimaging research seeks to identify biomarkers to improve the diagnosis, prognosis, and treatment of attention-deficit/hyperactivity disorder (ADHD), although clinical translation of findings remains distant. Resting-state functional magnetic resonance imaging (R-fMRI) is increasingly being used to characterize functional connectivity in the brain. Despite mixed results to date and multiple methodological challenges, dominant hypotheses implicate hyperconnectivity across brain networks in patients with ADHD, which could be the target of pharmacological treatments. We describe the experience and results of the Clínica Universidad de Navarra (Spain) Metilfenidato (CUNMET) pilot study. CUNMET tested the feasibility of identifying R-fMRI markers of clinical response in children with ADHD undergoing naturalistical pharmacological treatments. We analyzed cross-sectional data from 56 patients with ADHD (18 treated with methylphenidate, 18 treated with lisdexamfetamine, and 20 treatment-naive patients). Standard preprocessing and statistical analyses with attention to control for head motion and correction for multiple comparisons were performed. The only results that survived correction were noted in contrasts of children who responded clinically to lisdexamfetamine after long-term treatment vs. treatment-naive patients. In these children, we observed stronger negative correlations (anticorrelations) across nodes in six brain networks, which is consistent with higher across-network functional segregation in patients treated with lisdexamfetamine, i.e., less inter-network interference than in treatment-naive patients. We also note the lessons learned, which could help those pursuing clinically relevant multidisciplinary research in ADHD en route to eventual personalized medicine. To advance reproducible open science, our report is accompanied with links providing access to our data and analytic scripts.
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Park, Junyung, Hyeon Seok Seok, Sang-Su Kim, and Hangsik Shin. "Photoplethysmogram Analysis and Applications: An Integrative Review." Frontiers in Physiology 12 (March 1, 2022). http://dx.doi.org/10.3389/fphys.2021.808451.
Повний текст джерелаАнотація:
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Sathe, Anish V., Michael Kogan, KiChang Kang, Jingya Miao, Mashaal Syed, Isaiah Ailes, Caio M. Matias, et al. "Amplitude synchronization of spontaneous activity of medial and lateral temporal gyri reveals altered thalamic connectivity in patients with temporal lobe epilepsy." Scientific Reports 12, no. 1 (November 1, 2022). http://dx.doi.org/10.1038/s41598-022-23297-4.
Повний текст джерелаАнотація:
AbstractIn this study, we examined whether amplitude synchronization of medial (MTL) and lateral (LTL) temporal lobes can detect unique alterations in patients with MTL epilepsy (mTLE) with mesial temporal sclerosis (MTS). This was a retrospective study of preoperative resting-state fMRI (rsfMRI) data from 31 patients with mTLE with MTS (age 23–69) and 16 controls (age 21–35). fMRI data were preprocessed based on a multistep preprocessing pipeline and registered to a standard space. Using each subject’s T1-weighted scan, the MTL and LTL were automatically segmented, manually revised and then fit to a standard space using a symmetric normalization registration algorithm. Dual regression analysis was applied on preprocessed rsfMRI data to detect amplitude synchronization of medial and lateral temporal segments with the rest of the brain. We calculated the overlapped volume ratio of synchronized voxels within specific target regions including the thalamus (total and bilateral). A general linear model was used with Bonferroni correction for covariates of epilepsy duration and age of patient at scan to statistically compare synchronization in patients with mTLE with MTS and controls, as well as with respect to whether patients remained seizure-free (SF) or not (NSF) after receiving epilepsy surgery. We found increased ipsilateral positive connectivity between the LTLs and the thalamus and contralateral negative connectivity between the MTLs and the thalamus in patients with mTLE with MTS compared to controls. We also found increased asymmetry of functional connectivity between temporal lobe subregions and the thalamus in patients with mTLE with MTS, with increased positive connectivity between the LTL and the lesional-side thalamus as well as increased negative connectivity between the MTL and the nonlesional-side thalamus. This asymmetry was also seen in NSF patients but was not seen in SF patients and controls. Amplitude synchronization was an effective method to detect functional connectivity alterations in patients with mTLE with MTS. Patients with mTLE with MTS overall showed increased temporal-thalamic connectivity. There was increased functional involvement of the thalamus in MTS, underscoring its role in seizure spread. Increased functional thalamic asymmetry patterns in NSF patients may have a potential role in prognosticating patient response to surgery. Elucidating regions with altered functional connectivity to temporal regions can improve understanding of the involvement of different regions in the disease to potentially target for intervention or use for prognosis for surgery. Future studies are needed to examine the effectiveness of using patient-specific abnormalities in patterns to predict surgical outcome.
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Li, Shaoyi, Xiaotian Wang, Xi Yang, Kai Zhang, and Saisai Niu. "Investigation of infrared dim and small target detection algorithm based on the visual saliency feature." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, December 22, 2020, 095441002098095. http://dx.doi.org/10.1177/0954410020980955.
Повний текст джерелаАнотація:
Infrared dim and small target detection has an important role in the infrared thermal imaging seeker, infrared search and tracking system, space-based infrared system and other applications. Inspired by human visual system (HVS), based on the fusion of significant features of targets, the present study proposes an infrared dim and small target detection algorithm for complex backgrounds. Firstly, in order to calculate the target saliency map, the proposed algorithm initially uses the difference of Gaussian (DoG) and the contourlet filters for the preprocessing and fusion, respectively. Then the multi-scale improved local contrast measure (ILCM) method is applied to obtain the interested target area, effectively suppress the background clutter and improve the target signal-to-clutter ratio. Secondly, the optical flow method is used to estimate motion regions in the saliency map, which matches with the interested target region to achieve the initial target screening. Finally, in order to reduce the false alarm rate, forward pipeline filtering and optical flow estimation information are applied to achieve the multi-frame target recognition and achieve continuous detection of dim and small targets in image sequences. Experimental results show that compared with the conventional Tophat (TOP-HAT) and ILCM algorithms, the proposed algorithm can achieve stable, continuous and adaptive target detection for multiple backgrounds. The area under curve (AUC) and the harmonic average measure F1 are used to measure the overall performance and optimal performance of the target detection effect. For sky, sea and ground backgrounds, the test results of proposed algorithm for most sequences are 1. It is concluded that the proposed algorithm significantly improves the detection effect.
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Dunn, Julia Passyn, Bidhan Lamicchane, Todd Braver, Tamara Hershey, and Samuel Klein. "SAT-LB59 Functional MRI Study: Weight Loss Induced Changes in Taste Receipt-Induced Activation in the Striatum and Hypothalamus." Journal of the Endocrine Society 4, Supplement_1 (April 2020). http://dx.doi.org/10.1210/jendso/bvaa046.2262.
Повний текст джерелаАнотація:
Abstract Background: Reward behaviors including those related to eating are influenced by output from the ventral striatum (VS), dorsal striatal [caudate(Cau) and putamen(Put)]and hypothalamus (HTH). We hypothesized that weight loss would induce modifications in activation in these regions of interest (ROI) during a consummatory reward task. Methods: We recruited metabolic abnormal obese (MAO) from the VA St.Louis Health Care System and Washington University in St.Louis (WUSTL). MAO was screened for by fasting insulin and plasma glucose, 2 hour 75 gram OGTT, and hemoglobin A1c. MAO was defined as prediabetes by ADA criteria and/ or elevated HOMA-IR. Functional magnetic resonance imaging (fMRI) scanning sessions were completed at the WUSTL Center for Clinical Imaging Research. A rapid event-related design was used to randomly deliver taste of chocolate milk (choc) or tasteless water (wat). Each taste receipt was proceeded by a cue of corresponding image of chocolate milk or a glass of water. A total of 5 runs, each with 24 trials were completed. Imaging analyses included preprocessing with fMRIprep including censoring excessive motion ≥ 0.5mm. Single subject GLM analyses were completed in AFNI. ROIs were designated bilaterally (lt and rt) except for HTH. A canonical HRF was applied to the food cue event and the AFNI tent function over 9 TRs was applied to the taste receipt event. To evaluate for an effect of weight loss (WL) on food cue and taste receipt-induced activation, repeated measures ANOVA for each region was completed with condition (choc or wat) as a covariate. Also in the model for taste receipt, repetition time (TR) was included as a covariate. Results reported as F(sign.). Results: Ten participants achieved at least 7% WL, (range 7-15%), 44±8 years, BMI 38±4kg/m2, f/m 4/6, fasting pg 105±11, 2 hour OGTT pg 132 ±49 mg/dL, HOMA-IR 3.9±1.8. One participant fulfilled criteria for T2D. For taste receipt several significant effects were found for WL: Cau_lt WL 5.9(0.02) and WL*TR 4.9(0.03), Cau_rt WL 8.6(0.004) and WL*TR 8(0.005), Put_lt WL 8.5(0.004), HTH WL*condition 5.4(0.02) and a trend for WL 3.3(0.07). All other comparisons were non-significant including all in the VS and all for food cue. Conclusions: Moderate weight loss in MAO modified taste receipt-induced activation in the Cau, Put, and HTH but not in the VS.