Segmenting BOLD Signal at Rest Through EEG Microstates. A novel analysis approach to explore Spontaneous Fluctuation of Brain Activity using combined EEG/fMRI.

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.

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

10.1101/2020.12.10.420273 ◽

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.

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How attitudes generated by humanoid robots shape human brain activity

Scientific Reports ◽

10.1038/s41598-020-73728-3 ◽

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.

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Altered resting-state network dynamics in Schizophrenia

10.1101/2020.07.21.20157347 ◽

2020 ◽

Author(s):

Ana Rita Lopes ◽

Anna Sardinha Letournel ◽

Joana Cabral

Keyword(s):

Resting State ◽

Brain Activity ◽

Phase Locking ◽

Bold Signal ◽

Functional Magnetic Resonance ◽

Probability Of Occurrence ◽

Magnetic Resonance Data ◽

Resonance Data ◽

The Brain ◽

Time Point

Schizophrenia remains a poorly understood disease, hence the interest in assessing and indirectly characterizing brain activity and connectivity. This paper aims to search for potential biomarkers in schizophrenia with functional magnetic resonance data, between subjects in the resting state. Firstly, we used fMRI from an open database, SchizConnect, of 48 subjects, in which 27 were control subjects, with no apparent disease and the others 21 were patients with schizophrenia. With the SPM tool, we proceeded to manually pre-process the images obtained, at the risk of having influenced the final results. Then, with the AAL atlas as a reference, we divided the brain into 116 areas. Then, brain activity in these areas were analysed, using the LEiDA method, which aims to characterize brain activity at each time point t by phase locking patterns of the BOLD signal. After the application of LEiDA, brain activity was evaluated based on trajectories and bar graphs of functional connectivity states in which the probability of occurrence and their dwell time were calculated for each state. It was also found that the visual cortex was the subsystem that showed significantly more probability of occurrence in schizophrenia patients to be assessed, and may correspond to symptoms of hallucinations by the patients with schizophrenia.

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

10.1101/558288 ◽

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.

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The Neural Basis of Successful Word Reading in Aphasia

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01214 ◽

2018 ◽

Vol 30 (4) ◽

pp. 514-525 ◽

Cited By ~ 2

Author(s):

Sara B. Pillay ◽

William L. Gross ◽

William W. Graves ◽

Colin Humphries ◽

Diane S. Book ◽

...

Keyword(s):

Word Reading ◽

Functional Neuroimaging ◽

Semantic Network ◽

Brain Activity ◽

Brain Regions ◽

Anterior Cingulate ◽

Angular Gyrus ◽

Bold Signal ◽

Neural Basis ◽

Successful Performance

Understanding the neural basis of recovery from stroke is a major research goal. Many functional neuroimaging studies have identified changes in brain activity in people with aphasia, but it is unclear whether these changes truly support successful performance or merely reflect increased task difficulty. We addressed this problem by examining differences in brain activity associated with correct and incorrect responses on an overt reading task. On the basis of previous proposals that semantic retrieval can assist pronunciation of written words, we hypothesized that recruitment of semantic areas would be greater on successful trials. Participants were 21 patients with left-hemisphere stroke with phonologic retrieval deficits. They read words aloud during an event-related fMRI paradigm. BOLD signals obtained during correct and incorrect trials were contrasted to highlight brain activity specific to successful trials. Successful word reading was associated with higher BOLD signal in the left angular gyrus. In contrast, BOLD signal in bilateral posterior inferior frontal cortex, SMA, and anterior cingulate cortex was greater on incorrect trials. These data show for the first time the brain regions where neural activity is correlated specifically with successful performance in people with aphasia. The angular gyrus is a key node in the semantic network, consistent with the hypothesis that additional recruitment of the semantic system contributes to successful word production when phonologic retrieval is impaired. Higher activity in other brain regions during incorrect trials likely reflects secondary engagement of attention, working memory, and error monitoring processes when phonologic retrieval is unsuccessful.

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Aberrant brain activation following motor skill learning in schizophrenic patients as shown by functional magnetic resonance imaging

Psychological Medicine ◽

10.1017/s0033291701004196 ◽

2001 ◽

Vol 31 (6) ◽

pp. 1079-1088 ◽

Cited By ~ 27

Author(s):

S. KODAMA ◽

H. FUKUZAKO ◽

T. FUKUZAKO ◽

T. KIURA ◽

S. NOZOE ◽

...

Keyword(s):

Motor Skill ◽

Brain Activity ◽

Motor Task ◽

Skill Learning ◽

Motor Skill Learning ◽

Bold Signal ◽

Functional Magnetic Resonance ◽

Complex Motor ◽

Schizophrenic Patients ◽

Signal Response

Background. Motor skill learning may be impaired in schizophrenia. While functional brain imaging studies have shown reduced activation during motor task performance in schizophrenic patients, brain activity changes with motor skill learning in these patients have not been studied by functional imaging.Methods. A sequential complex motor task involving the right hand was performed by nine medicated schizophrenic patients and 10 age-matched healthy controls. Functional magnetic resonance images were obtained using a gradient echo, echoplanar imaging (EPI) pulse sequence before and after 1 week of training in performing the task.Results. Bilaterally, patients showed significantly less blood oxygenation level-dependent (BOLD) signal response in the premotor area (PMA) before beginning motor training than controls. BOLD signal response increased in the left PMA of schizophrenic patients after 1 week of motor training; in contrast, the signal decreased in the left PMA of control subjects. Training effects concerning the number of finger movement sequences achieved did not differ between groups. Daily neuroleptic dose did not significantly affect changes with training in BOLD signal response in the PMA.Conclusions. These preliminary results suggest that schizophrenic patients have dysfunction of neural networks in areas including the PMA that are involved in executing a complex motor task. In terms of brain activity, motor learning may be less efficient or slower in the patients than in healthy subjects.

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Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks

NeuroImage ◽

10.1016/j.neuroimage.2010.01.093 ◽

2010 ◽

Vol 52 (4) ◽

pp. 1149-1161 ◽

Cited By ~ 178

Author(s):

F. Musso ◽

J. Brinkmeyer ◽

A. Mobascher ◽

T. Warbrick ◽

G. Winterer

Keyword(s):

Resting State ◽

Brain Activity ◽

Analysis Approach ◽

Resting State Networks ◽

Spontaneous Brain Activity ◽

Fmri Analysis

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Human non-REM sleep and the mean global BOLD signal

Journal of Cerebral Blood Flow & Metabolism ◽

10.1177/0271678x18791070 ◽

2018 ◽

Vol 39 (11) ◽

pp. 2210-2222 ◽

Cited By ~ 5

Author(s):

Mark P McAvoy ◽

Enzo Tagliazucchi ◽

Helmut Laufs ◽

Marcus E Raichle

Keyword(s):

Blood Flow ◽

Rem Sleep ◽

Brain Activity ◽

Oxygen Metabolism ◽

Blood Oxygen Level Dependent ◽

Blood Oxygen ◽

Bold Signal ◽

Blood Oxygen Level ◽

The Mean ◽

Uniform Manner

A hallmark of non-rapid eye movement (REM) sleep is the decreased brain activity as measured by global reductions in cerebral blood flow, oxygen metabolism, and glucose metabolism. It is unknown whether the blood oxygen level dependent (BOLD) signal undergoes similar changes. Here we show that, in contrast to the decreases in blood flow and metabolism, the mean global BOLD signal increases with sleep depth in a regionally non-uniform manner throughout gray matter. We relate our findings to the circulatory and metabolic processes influencing the BOLD signal and conclude that because oxygen consumption decreases proportionately more than blood flow in sleep, the resulting decrease in paramagnetic deoxyhemoglobin accounts for the increase in mean global BOLD signal.

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Specific Patterns of Bold Variability Associated with the Processing of Pain Stimuli

10.1101/157222 ◽

2017 ◽

Author(s):

Tommaso Costa ◽

Andrea Nani ◽

Jordi Manuello ◽

Ugo Vercelli ◽

Mona-Karina Tatu ◽

...

Keyword(s):

Complex Dynamics ◽

Brain Activity ◽

Periaqueductal Gray ◽

Anterior Cingulate ◽

Entropy Rate ◽

Bold Signal ◽

Dorsal Anterior Cingulate Cortex ◽

Brain Areas ◽

Posterior Insula ◽

The Brain

ABSTRACTIt is well known that the blood oxygen level dependent (BOLD) signal varies according to task performance and region specificity. This ongoing and fluctuating activity reflects the organization of functional brain networks. Peculiar dynamics of BOLD signal are therefore supposed to characterize brain activity in different conditions. Within this framework, we investigated through a multivoxel pattern analysis whether patterns of BOLD variability convey information that may allow an efficient discrimination between task (i.e., painful stimulation) and rest conditions. We therefore identified the most discriminative brain areas between the two conditions, which turned out to be the anterior insula, dorsal anterior cingulate cortex, posterior insula, the thalamus, and the periaqueductal gray. Then, on the basis of information theory, we calculated the entropy of their different time series. Entropy was found to distribute differently between these brain areas. The posterior insula was found to be is the smaller contributor to the entropy rate, whereas the system formed by the thalamus and periaqueductal gray was found to be the major contributor. Overall, the brain system reaches a higher level of entropy during the rest condition, which suggests that cerebral activity is characterized by a larger informational space when the brain is at rest than when it is engaged in a specific task. Thus, this study provides evidence that: i) the pattern of BOLD variance allow a good discrimination between the conditions of rest and pain stimulation; ii) the discriminative pattern resembles closely that of the functional network that has been called pain matrix; iii) brain areas with high and low variability are characterized by a different sample entropy; iv) the entropy rate of cerebral regions can be an insightful parameter to better understand the complex dynamics of the brain.

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