scholarly journals A deconvolution algorithm for multiecho functional MRI: Multiecho Sparse Paradigm Free Mapping

2019 ◽  
Author(s):  
César Caballero-Gaudes ◽  
Stefano Moia ◽  
Puja Panwar ◽  
Peter A. Bandettini ◽  
Javier Gonzalez-Castillo
Keyword(s):  
Brain Activity ◽  
Fmri Data ◽  
Bold Signal ◽  
Dynamic Nature ◽  
Transverse Relaxation ◽  
Percent Signal Change ◽  
Signal Change ◽  
Plausible Values ◽  
The Times ◽  
Model Based Analysis

ABSTRACTThis work introduces a novel algorithm for deconvolution of the BOLD signal in multiecho fMRI data: Multiecho Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the transverse relaxation without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

scholarly journals Brain reactivity using fMRI to insomnia stimuli in insomnia patients with discrepancy between subjective and objective sleep

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young-Bo Kim ◽  
Nambeom Kim ◽  
Jae Jun Lee ◽  
Seo-Eun Cho ◽  
Kyoung-Sae Na ◽  
...  
Keyword(s):  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Cognitive Distortion ◽  
Sleep Diary ◽  
Bold Signal ◽  
Blood Oxygen Level ◽  
General Anxiety ◽  
Sleep Deficit ◽  
Perception Of Sleep

AbstractSubjective–objective discrepancy of sleep (SODS) might be related to the distorted perception of sleep deficit and hypersensitivity to insomnia-related stimuli. We investigated differences in brain activation to insomnia-related stimuli among insomnia patients with SODS (SODS group), insomnia patients without SODS (NOSODS group), and healthy controls (HC). Participants were evaluated for subjective and objective sleep using sleep diary and polysomnography. Functional magnetic resonance imaging was conducted during the presentation of insomnia-related (Ins), general anxiety-inducing (Gen), and neutral (Neu) stimuli. Brain reactivity to the contrast of Ins vs. Neu and Gen vs. Neu was compared among the SODS (n = 13), NOSODS (n = 15), and HC (n = 16) groups. In the SODS group compared to other groups, brain areas including the left fusiform, bilateral precuneus, right superior frontal gyrus, genu of corpus callosum, and bilateral anterior corona radiata showed significantly increased blood oxygen level dependent (BOLD) signal in the contrast of Ins vs. Neu. There was no brain region with significantly increased BOLD signal in the Gen vs. Neu contrast in the group comparisons. Increased brain activity to insomnia-related stimuli in several brain regions of the SODS group is likely due to these individuals being more sensitive to sleep-related threat and negative cognitive distortion toward insomnia.

scholarly journals Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics

2020 ◽  
Author(s):  
Matthew F. Singh ◽  
Anxu Wang ◽  
Michael Cole ◽  
ShiNung Ching ◽  
Todd S. Braver
Keyword(s):  
Statistical Power ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Bold Signal ◽  
Group Level ◽  
Temporal Precision ◽  
Brain Responses ◽  
Evoked Activity ◽  
Activity Dynamics ◽  
Do So

AbstractBrain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out” the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; [1]) models built from individualized resting-state data (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, estimates based upon our technique better generalize between tasks measuring the same construct (cognitive control) and better predict individual differences in behavior. Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural activity.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals How attitudes generated by humanoid robots shape human brain activity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
G. Di Cesare ◽  
F. Vannucci ◽  
F. Rea ◽  
A. Sciutti ◽  
G. Sandini
Keyword(s):  
Human Brain ◽  
Humanoid Robot ◽  
Brain Activity ◽  
Negative Attitude ◽  
Humanoid Robots ◽  
Bold Signal ◽  
Future Perspectives ◽  
Soft Or ◽  
Forms Of Vitality

Abstract During interpersonal interactions, people perform actions with different forms of vitality, communicating their positive or negative attitude toward others. For example, a handshake can be “soft” or “vigorous”, a caress can be ‘kind’ or ‘rushed’. While previous studies have shown that the dorso-central insula is a key area for the processing of human vitality forms, there is no information on the perception of vitality forms generated by a humanoid robot. In this study, two fMRI experiments were conducted in order to investigate whether and how the observation of actions generated by a humanoid robot (iCub) with low and fast velocities (Study 1) or replicating gentle and rude human forms (Study 2) may convey vitality forms eliciting the activation of the dorso-central insula. These studies showed that the observation of robotic actions, generated with low and high velocities, resulted in activation of the parieto-frontal circuit typically involved in the recognition and the execution of human actions but not of the insula (Study 1). Most interestingly, the observation of robotic actions, generated by replicating gentle and rude human vitality forms, produced a BOLD signal increase in the dorso-central insula (Study 2). In conclusion, these data highlight the selective role of dorso-central insula in the processing of vitality forms opening future perspectives on the perception and understanding of actions performed by humanoid robots.

scholarly journals MDMA (ecstasy) use is associated with reduced BOLD signal change during semantic recognition in abstinent human polydrug users: a preliminary fMRI study

2009 ◽  
Vol 24 (2) ◽  
pp. 187-201 ◽  
Author(s):  
V. Raj ◽  
HC Liang ◽  
ND Woodward ◽  
AL Bauernfeind ◽  
J. Lee ◽  
...  
Keyword(s):  
Bold Signal ◽  
Fmri Study ◽  
Signal Change

scholarly journals The relationships between the amount of spared tissue, percent signal change, and accuracy in semantic processing in aphasia

2016 ◽  
Vol 84 ◽  
pp. 113-126 ◽  
Author(s):  
Jordyn A. Sims ◽  
Kushal Kapse ◽  
Peter Glynn ◽  
Chaleece Sandberg ◽  
Yorghos Tripodis ◽  
...  
Keyword(s):  
Semantic Processing ◽  
Percent Signal Change ◽  
Signal Change

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

Measurement of visual sensitivity in migraine: Validation of two scales and correlation with visual cortex activation

Cephalalgia ◽  
2014 ◽  
Vol 35 (7) ◽  
pp. 585-592 ◽  
Author(s):  
Brett Cucchiara ◽  
Ritobrato Datta ◽  
Geoffrey K Aguirre ◽  
Kimberly E Idoko ◽  
John Detre
Keyword(s):  
Visual Cortex ◽  
Visual Stimulus ◽  
Visual Sensitivity ◽  
Strongly Correlated ◽  
Bold Signal ◽  
Visual Discomfort ◽  
Bold Fmri ◽  
Signal Change ◽  
And Control

Objective The objectives of this article are to compare interictal and ictal visual sensitivity between migraine and controls using two published questionnaires, and to correlate responses with a physiologic measure of visual cortex activation. Methods Migraine with (MWA, n = 51) and without (MwoA, n = 45) aura and control individuals ( n = 45) were enrolled and underwent BOLD fMRI with a visual stimulus. The visual discomfort score (VDS) assessed interictal and the migraine photophobia score (MPS) assessed ictal visual sensitivity. Result VDS was significantly higher both in MWA and MwoA vs controls (both p < 0.0001). MPS was greater in MWA vs MwoA ( p = 0.008). Ictal and interictal visual sensitivity strongly correlated in MWA ( p = 0.004) but not MwoA patients ( p = 0.12). BOLD activation in visual cortex was greater in MWA vs controls (2.7% vs 2.3%, p = 0.003) but similar between MwoA and controls. Increasing VDS was associated with greater BOLD signal change in MWA ( p = 0.03) but not MwoA ( p = 0.65) or controls ( p = 0.53). MPS did not correlate with BOLD activation in either group. Conclusion Increased interictal visual sensitivity is present both in MWA and MwoA. However, the correlation with ictal visual sensitivity and with cortical hyper-responsivity varies between MWA and MwoA, suggesting underlying differences between groups.

Visual Analysis of Brain Activity from fMRI Data

2009 ◽  
Vol 28 (3) ◽  
pp. 903-910 ◽  
Author(s):  
Firdaus Janoos ◽  
Boonthanome Nouanesengsy ◽  
Raghu Machiraju ◽  
Han Wei Shen ◽  
Steffen Sammet ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Brain Activity ◽  
Fmri Data
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