scholarly journals Lag structure in resting-state fMRI

2014 ◽  
Vol 111 (11) ◽  
pp. 2374-2391 ◽  
Author(s):  
A. Mitra ◽  
A. Z. Snyder ◽  
C. D. Hacker ◽  
M. E. Raichle
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Brain Activity ◽  
Temporal Structure ◽  
Motor Task ◽  
Time Of Day ◽  
Intrinsic Activity ◽  
Button Press ◽  
Bold Signal ◽  
Spontaneous Fluctuations

The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Beyond the low frequency fluctuations morning and evening differences in human brain

2019 ◽  
Author(s):  
Magdalena Fafrowicz ◽  
Bartosz Bohaterewicz ◽  
Anna Ceglarek ◽  
Monika Cichocka ◽  
Koryna Lewandowska ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Human Performance ◽  
Brain Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Basic Mechanism ◽  
Time Of Day ◽  
Resting State Functional Connectivity ◽  
Circadian Typology

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-hour-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-hour clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. 29 extreme morning- and 34 evening-type participants underwent two fMRI sessions: about one hour after wake-up time (morning) and about ten hours after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning and evening sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology.

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.

Brain activity at rest: a multiscale hierarchical functional organization

2011 ◽  
Vol 105 (6) ◽  
pp. 2753-2763 ◽  
Author(s):  
Gaëlle Doucet ◽  
Mikaël Naveau ◽  
Laurent Petit ◽  
Nicolas Delcroix ◽  
Laure Zago ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Brain Activity ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Hierarchical Level ◽  
Spontaneous Brain Activity ◽  
Dorsal Attentional Network

Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.

scholarly journals Brain connectivity at rest predicts individual differences in normative activity during movie watching

2021 ◽  
Author(s):  
David C Gruskin ◽  
Gaurav H Patel
Keyword(s):  
Spatial Distribution ◽  
Individual Differences ◽  
Resting State ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Sensory Information ◽  
Resting State Functional Connectivity ◽  
Similar Region ◽  
Human Connectome Project

When multiple individuals are exposed to the same sensory event, some are bound to have less typical experiences than others. These atypical experiences are underpinned by atypical stimulus-evoked brain activity, the extent of which is often indexed by intersubject correlation (ISC). Previous research has attributed individual differences in ISC to variation in trait-like behavioral phenotypes. Here, we extend this line of work by showing that an individual's degree and spatial distribution of ISC are closely related to their brain's intrinsic functional architecture. Using resting state and movie watching fMRI data from 176 Human Connectome Project participants, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC with considerable accuracy. Similar region-level analyses demonstrate that the amount of ISC a brain region exhibits during movie watching is associated with its connectivity to others at rest, and that the nature of these connectivity-activity relationships varies as a function of the region's role in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings suggest that the brain's ability to process complex sensory information is tightly linked to its baseline functional organization and motivate a more comprehensive understanding of individual responses to naturalistic stimuli.

scholarly journals Altered resting-state network dynamics in Schizophrenia

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.

scholarly journals Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

2018 ◽  
Author(s):  
Antonio Ulloa ◽  
Barry Horwitz
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Short Term Memory ◽  
Brain Activity ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Task Execution ◽  
Short Term ◽  
Visual Short Term Memory ◽  
And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

scholarly journals Opposing Changes in the Functional Architecture of Large-Scale Networks in Bipolar Mania and Depression

2020 ◽  
Vol 46 (4) ◽  
pp. 971-980
Author(s):  
Daniel Russo ◽  
Matteo Martino ◽  
Paola Magioncalda ◽  
Matilde Inglese ◽  
Mario Amore ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Depressive Symptomatology ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Functional Architecture ◽  
Sensorimotor Network ◽  
Large Scale Networks ◽  
Intrinsic Brain Activity ◽  
Affective Dimensions

Abstract Objective Manic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. Methods Using resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated—in the standard frequency band (SFB: 0.01–0.10 Hz), as well as in Slow5 (0.01–0.027 Hz) and Slow4 (0.027–0.073 Hz)—and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. Results Mania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. Conclusions Our data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).

scholarly journals Resting-state Spontaneous Fluctuations in Brain Activity

2009 ◽  
Vol 16 (5) ◽  
pp. 578-583 ◽  
Author(s):  
Joshua S. Shimony ◽  
Dongyang Zhang ◽  
James M. Johnston ◽  
Michael D. Fox ◽  
Abhik Roy ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Spontaneous Fluctuations

scholarly journals Intrinsic Functional Relations Between Human Cerebral Cortex and Thalamus

2008 ◽  
Vol 100 (4) ◽  
pp. 1740-1748 ◽  
Author(s):  
Dongyang Zhang ◽  
Abraham Z. Snyder ◽  
Michael D. Fox ◽  
Mark W. Sansbury ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Resting State ◽  
Brain Activity ◽  
Critical Role ◽  
Resting State Fmri ◽  
Intrinsic Activity ◽  
Bold Imaging ◽  
Functional Relations ◽  
State Activity ◽  
Human Thalamus

The brain is active even in the absence of explicit stimuli or overt responses. This activity is highly correlated within specific networks of the cerebral cortex when assessed with resting-state functional magnetic resonance imaging (fMRI) blood oxygen level–dependent (BOLD) imaging. The role of the thalamus in this intrinsic activity is unknown despite its critical role in the function of the cerebral cortex. Here we mapped correlations in resting-state activity between the human thalamus and the cerebral cortex in adult humans using fMRI BOLD imaging. Based on this functional measure of intrinsic brain activity we partitioned the thalamus into nuclear groups that correspond well with postmortem human histology and connectional anatomy inferred from nonhuman primates. This structure/function correspondence in resting-state activity was strongest between each cerebral hemisphere and its ipsilateral thalamus. However, each hemisphere was also strongly correlated with the contralateral thalamus, a pattern that is not attributable to known thalamocortical monosynaptic connections. These results extend our understanding of the intrinsic network organization of the human brain to the thalamus and highlight the potential of resting-state fMRI BOLD imaging to elucidate thalamocortical relationships.

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