scholarly journals Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

2018 ◽  
Author(s):  
Javier Rasero ◽  
Hannelore Aerts ◽  
Jesus M. Cortes ◽  
Sebastiano Stramaglia ◽  
Daniele Marinazzo
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
The Other ◽  
Intrinsic Connectivity ◽  
Supervised Classifiers ◽  
Oxygenation Level ◽  
Intrinsic Connectivity Networks ◽  
Hidden Layer ◽  
Simple Neural Network

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" Blood Oxygenation Level Dependent time series, are increasingly being associated to cognitive, clinical, and behavioral aspects, and compared with the pattern of activity elicited by specific tasks. We study the reconfiguration of the brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. We use a large cohort of publicly available data in both resting and task-based fMRI paradigms; by trying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90\% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

scholarly journals High spatial correspondence at a columnar level between activation and resting state fMRI signals and local field potentials

2017 ◽  
Vol 114 (20) ◽  
pp. 5253-5258 ◽  
Author(s):  
Zhaoyue Shi ◽  
Ruiqi Wu ◽  
Pai-Feng Yang ◽  
Feng Wang ◽  
Tung-Lin Wu ◽  
...  
Keyword(s):  
Local Field ◽  
Resting State ◽  
Local Field Potentials ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Spatial Relationships ◽  
Field Potentials ◽  
Lateral Direction ◽  
Oxygenation Level ◽  
Local Correlations

Although blood oxygenation level-dependent (BOLD) fMRI has been widely used to map brain responses to external stimuli and to delineate functional circuits at rest, the extent to which BOLD signals correlate spatially with underlying neuronal activity, the spatial relationships between stimulus-evoked BOLD activations and local correlations of BOLD signals in a resting state, and whether these spatial relationships vary across functionally distinct cortical areas are not known. To address these critical questions, we directly compared the spatial extents of stimulated activations and the local profiles of intervoxel resting state correlations for both high-resolution BOLD at 9.4 T and local field potentials (LFPs), using 98-channel microelectrode arrays, in functionally distinct primary somatosensory areas 3b and 1 in nonhuman primates. Anatomic images of LFP and BOLD were coregistered within 0.10 mm accuracy. We found that the point spread functions (PSFs) of BOLD and LFP responses were comparable in the stimulus condition, and both estimates of activations were slightly more spatially constrained than local correlations at rest. The magnitudes of stimulus responses in area 3b were stronger than those in area 1 and extended in a medial to lateral direction. In addition, the reproducibility and stability of stimulus-evoked activation locations within and across both modalities were robust. Our work suggests that the intrinsic resolution of BOLD is not a limiting feature in practice and approaches the intrinsic precision achievable by multielectrode electrophysiology.

Functional Connectivity Pattern of the Internal Hippocampal Network in Awake Pigeons: A Resting-State fMRI Study

2017 ◽  
Vol 90 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Mehdi Behroozi ◽  
Felix Ströckens ◽  
Martin Stacho ◽  
Onur Güntürkün
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Hippocampal Formation ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Lateral Septum ◽  
Connectivity Pattern ◽  
Oxygenation Level ◽  
Hippocampal Network

In the last two decades, the avian hippocampus has been repeatedly studied with respect to its architecture, neurochemistry, and connectivity pattern. We review these insights and conclude that we unfortunately still lack proper knowledge on the interaction between the different hippocampal subregions. To fill this gap, we need information on the functional connectivity pattern of the hippocampal network. These data could complement our structural connectivity knowledge. To this end, we conducted a resting-state fMRI experiment in awake pigeons in a 7-T MR scanner. A voxel-wise regression analysis of blood oxygenation level-dependent (BOLD) fluctuations was performed in 6 distinct areas, dorsomedial (DM), dorsolateral (DL), triangular shaped (Tr), dorsolateral corticoid (CDL), temporo-parieto-occipital (TPO), and lateral septum regions (SL), to establish a functional connectivity map of the avian hippocampal network. Our study reveals that the system of connectivities between CDL, DL, DM, and Tr is the functional backbone of the pigeon hippocampal system. Within this network, DM is the central hub and is strongly associated with DL and CDL BOLD signal fluctuations. DM is also the only hippocampal region to which large Tr areas are functionally connected. In contrast to published tracing data, TPO and SL are only weakly integrated in this network. In summary, our findings uncovered a structurally otherwise invisible architecture of the avian hippocampal formation by revealing the dynamic blueprints of this network.

scholarly journals The Role of Cerebrovascular-Reactivity Mapping in Functional MRI: Calibrated fMRI and Resting-State fMRI

2021 ◽  
Vol 12 ◽  
Author(s):  
J. Jean Chen ◽  
Claudine J. Gauthier
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Cerebrovascular Reactivity ◽  
Resting State Functional Connectivity ◽  
Non Invasive ◽  
Oxygenation Level ◽  
Calibrated Fmri

Task and resting-state functional MRI (fMRI) is primarily based on the same blood-oxygenation level-dependent (BOLD) phenomenon that MRI-based cerebrovascular reactivity (CVR) mapping has most commonly relied upon. This technique is finding an ever-increasing role in neuroscience and clinical research as well as treatment planning. The estimation of CVR has unique applications in and associations with fMRI. In particular, CVR estimation is part of a family of techniques called calibrated BOLD fMRI, the purpose of which is to allow the mapping of cerebral oxidative metabolism (CMRO2) using a combination of BOLD and cerebral-blood flow (CBF) measurements. Moreover, CVR has recently been shown to be a major source of vascular bias in computing resting-state functional connectivity, in much the same way that it is used to neutralize the vascular contribution in calibrated fMRI. Furthermore, due to the obvious challenges in estimating CVR using gas challenges, a rapidly growing field of study is the estimation of CVR without any form of challenge, including the use of resting-state fMRI for that purpose. This review addresses all of these aspects in which CVR interacts with fMRI and the role of CVR in calibrated fMRI, provides an overview of the physiological biases and assumptions underlying hypercapnia-based CVR and calibrated fMRI, and provides a view into the future of non-invasive CVR measurement.

scholarly journals Identifying components that vary in space and time from resting-state functional MRI

2021 ◽  
Author(s):  
Gregory Scott ◽  
Robert Leech
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Building Blocks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Time Varying ◽  
Spatiotemporal Evolution ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

A widespread assumption of fMRI-derived large-scale intrinsic connectivity networks (ICNs) is that they are spatially static over time. However, the assumption of spatial stationarity of ICNs has been challenged by a range of techniques that allow for time-varying connectivity between brain regions and demonstration that canonical networks like the default model network (DMN) can be fractionated according to time-varying connectivity relationships of their subcomponents. Previously, we developed a simple spatiotemporal ICA (stICA) technique to allow the discovery of patterns of spatiotemporal evolution in task fMRI data in a way that avoided the traditional constraint of spatial stationarity on brain networks, and we validated the approach in fMRI of task-to-rest transitions. Here, we apply our stICA technique to resting-state fMRI datasets to explore whether spatiotemporally evolving components of brain activity can be identified in the absence of an overt behavioural task. We found that stICA components could generally be described in terms of graded onsets and offsets of ICNs that had been calculated based on techniques that assumed spatial stationarity. Our results suggest that, to a reasonable approximation, stable ICNs can be taken to be building blocks of the spatiotemporal patterns measured with resting-state fMRI.

scholarly journals Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI

2020 ◽  
Author(s):  
Simon Schwab
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Strongly Correlated ◽  
Thalamic Nuclei ◽  
Frequency Bands ◽  
Beta Frequency Band ◽  
Oxygenation Level ◽  
Beta Frequency

Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks.

scholarly journals Behavioral Interpretations of Intrinsic Connectivity Networks

2011 ◽  
Vol 23 (12) ◽  
pp. 4022-4037 ◽  
Author(s):  
Angela R. Laird ◽  
P. Mickle Fox ◽  
Simon B. Eickhoff ◽  
Jessica A. Turner ◽  
Kimberly L. Ray ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Resting State ◽  
Brain Networks ◽  
Data Driven ◽  
Resting State Networks ◽  
Independent State ◽  
Intrinsic Connectivity ◽  
Mental Operations ◽  
Intrinsic Connectivity Networks ◽  
Insight Into

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

scholarly journals Measuring vascular reactivity with resting-state blood oxygenation level-dependent (BOLD) signal fluctuations: A potential alternative to the breath-holding challenge?

2016 ◽  
Vol 37 (7) ◽  
pp. 2526-2538 ◽  
Author(s):  
Hesamoddin Jahanian ◽  
Thomas Christen ◽  
Michael E Moseley ◽  
Nicholas M Pajewski ◽  
Clinton B Wright ◽  
...  
Keyword(s):  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Vascular Reactivity ◽  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Breath Holding ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Blood Oxygenation Level ◽  
Oxygenation Level

Measurement of the ability of blood vessels to dilate and constrict, known as vascular reactivity, is often performed with breath-holding tasks that transiently raise arterial blood carbon dioxide (PaCO2) levels. However, following the proper commands for a breath-holding experiment may be difficult or impossible for many patients. In this study, we evaluated two approaches for obtaining vascular reactivity information using blood oxygenation level-dependent signal fluctuations obtained from resting-state functional magnetic resonance imaging data: physiological fluctuation regression and coefficient of variation of the resting-state functional magnetic resonance imaging signal. We studied a cohort of 28 older adults (69 ± 7 years) and found that six of them (21%) could not perform the breath-holding protocol, based on an objective comparison with an idealized respiratory waveform. In the subjects that could comply, we found a strong linear correlation between data extracted from spontaneous resting-state functional magnetic resonance imaging signal fluctuations and the blood oxygenation level-dependent percentage signal change during breath-holding challenge ( R2 = 0.57 and 0.61 for resting-state physiological fluctuation regression and resting-state coefficient of variation methods, respectively). This technique may eliminate the need for subject cooperation, thus allowing the evaluation of vascular reactivity in a wider range of clinical and research conditions in which it may otherwise be impractical.

scholarly journals Resting state functional connectivity in the human spinal cord

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Robert L Barry ◽  
Seth A Smith ◽  
Adrienne N Dula ◽  
John C Gore
Keyword(s):  
Spinal Cord ◽  
Functional Connectivity ◽  
Resting State ◽  
Low Frequency ◽  
Blood Oxygenation ◽  
Resting State Functional Connectivity ◽  
Human Spinal Cord ◽  
Oxygenation Level ◽  
Left And Right ◽  
The Brain

Functional magnetic resonance imaging using blood oxygenation level dependent (BOLD) contrast is well established as one of the most powerful methods for mapping human brain function. Numerous studies have measured how low-frequency BOLD signal fluctuations from the brain are correlated between voxels in a resting state, and have exploited these signals to infer functional connectivity within specific neural circuits. However, to date there have been no previous substantiated reports of resting state correlations in the spinal cord. In a cohort of healthy volunteers, we observed robust functional connectivity between left and right ventral (motor) horns, and between left and right dorsal (sensory) horns. Our results demonstrate that low-frequency BOLD fluctuations are inherent in the spinal cord as well as the brain, and by analogy to cortical circuits, we hypothesize that these correlations may offer insight into the execution and maintenance of sensory and motor functions both locally and within the cerebrum.

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