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

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.

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WE-FG-206-02: Brief Variations of BOLD Signal in Resting State FMRI Leading to Functional Connectivity Pattern Identification

Medical Physics ◽

10.1118/1.4957932 ◽

2016 ◽

Vol 43 (6Part41) ◽

pp. 3831-3831

Author(s):

N Ball ◽

N Chen

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Pattern Identification ◽

Connectivity Pattern ◽

Bold Signal

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

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1620520114 ◽

2017 ◽

Vol 114 (20) ◽

pp. 5253-5258 ◽

Cited By ~ 8

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.

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Resting state functional connectivity in the human spinal cord

eLife ◽

10.7554/elife.02812 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 53

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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Caffeine alters resting‐state functional connectivity measured by blood oxygenation level‐dependent MRI

NMR in Biomedicine ◽

10.1002/nbm.3080 ◽

2014 ◽

Vol 27 (4) ◽

pp. 444-452 ◽

Cited By ~ 13

Author(s):

Wen‐Chau Wu ◽

Shu‐Hua Lien ◽

Jia‐Horng Chang ◽

Shun‐Chung Yang

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Blood Oxygenation Level Dependent ◽

Blood Oxygenation ◽

Resting State Functional Connectivity ◽

Blood Oxygenation Level ◽

Oxygenation Level

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Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

10.20944/preprints202112.0080.v2 ◽

2022 ◽

Author(s):

Matthew J. Hoptman ◽

Umit Tural ◽

Kelvin O. Lim ◽

Daniel C. Javitt ◽

Lauren E. Oberlin

Keyword(s):

Diffusion Tensor Imaging ◽

Functional Connectivity ◽

Resting State ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Resting State Fmri ◽

Resting State Functional Connectivity ◽

Syndrome Scale ◽

Healthy Control ◽

Negative Syndrome

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

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Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

10.1101/2020.03.06.980193 ◽

2020 ◽

Author(s):

Jakub Kopal ◽

Anna Pidnebesna ◽

David Tomeček ◽

Jaroslav Tintěra ◽

Jaroslav Hlinka

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Motion Artifacts ◽

Connectivity Pattern ◽

Head Motion ◽

Connectivity Matrix ◽

Normal Brain ◽

Functional Connections ◽

Human Connectome Project

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

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Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

10.20944/preprints202112.0080.v1 ◽

2021 ◽

Author(s):

Matthew J. Hoptman ◽

Umit Tural ◽

Kelvin O. Lim ◽

Daniel C. Javitt ◽

Lauren E. Oberlin

Keyword(s):

Diffusion Tensor Imaging ◽

Functional Connectivity ◽

Resting State ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Resting State Fmri ◽

Resting State Functional Connectivity ◽

Syndrome Scale ◽

Healthy Control ◽

Negative Syndrome

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

Frontiers in Physiology ◽

10.3389/fphys.2021.657362 ◽

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.

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

10.1101/259077 ◽

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.

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