scholarly journals Whole brain mapping of somatosensory responses in awake marmosets investigated with ultra-high-field fMRI

2020 ◽  
Vol 124 (6) ◽  
pp. 1900-1913
Author(s):  
Justine C. Cléry ◽  
Yuki Hori ◽  
David J. Schaeffer ◽  
Joseph S. Gati ◽  
J. Andrew Pruszynski ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Mapping ◽  
Resting State ◽  
High Field ◽  
Body Representation ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Ultra High Field ◽  
Somatosensory Stimulation ◽  
Somatosensory Responses

We used somatosensory stimulation combined with functional MRI (fMRI) in awake marmosets to reveal the topographic body representation in areas S1, S2, thalamus, and putamen. We showed the existence of a body representation organization within the thalamus and the cingulate cortex by computing functional connectivity maps from seeds defined in S1/S2 using resting-state fMRI data. This noninvasive approach will be essential for chronic studies by guiding invasive recording and manipulation techniques.

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

2018 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data ◽  
Extensive Literature ◽  
Topographic Organization ◽  
Lateral Organization

AbstractIn accordance with the concept of topographic organization of neuroanatomical structures, there is an increased interest in estimating and delineating continuous changes in the functional connectivity patterns across neighboring voxels within a region of interest using resting-state fMRI data. Fundamental to this functional connectivity gradient analysis is the assumption that the functional organization is stable and uniform across the region of interest. To evaluate this assumption, we developed a model testing procedure to arbitrate between overlapping, shifted, or different topographic connectivity gradients across subdivisions of a structure. We tested the procedure using the striatum, a subcortical structure consisting of the caudate nucleus and putamen, in which an extensive literature, primarily from rodents and non-human primates, suggest to have a shared topographic organization of a single diagonal gradient. We found, across multiple resting state fMRI data samples of different spatial resolutions in humans, and one macaque resting state fMRI data sample, that the models with different functional connectivity gradients across the caudate and putamen was the preferred model. The model selection procedure was validated in control conditions of checkerboard subdivisions, demonstrating the expected overlapping gradient. More specifically, while we replicated the diagonal organization of the functional connectivity gradients in both the caudate and putamen, our analysis also revealed a medial-lateral organization within the caudate. Not surprisingly, performing the same analysis assuming a unitary gradient obfuscates the medial-lateral organization of the caudate, producing only a diagonal gradient. These findings demonstrate the importance of testing basic assumptions and evaluating interpretations across species. The significance of differential topographic gradients across the putamen and caudate and the medial-lateral gradient of the caudate in humans should be tested in future studies.

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

NeuroImage ◽  
2019 ◽  
Vol 185 ◽  
pp. 102-110 ◽  
Author(s):  
Jonathan F. O'Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data

Human Brain Functional Connectivity in Resting-State fMRI Data across the Range of Weeks

2017 ◽  
Vol 15 (8) ◽  
pp. 148-153
Author(s):  
Nasrin Borumandnia ◽  
Hamid Alavi Majd ◽  
Farid Zayeri
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Brain Functional Connectivity

scholarly journals An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD

2013 ◽  
Vol 35 (4) ◽  
pp. 1261-1272 ◽  
Author(s):  
Elseline Hoekzema ◽  
Susana Carmona ◽  
J. Antoni Ramos-Quiroga ◽  
Vanesa Richarte Fernández ◽  
Rosa Bosch ◽  
...  
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Resting State ◽  
Adult Adhd ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Independent Components ◽  
Functional Connectivity Analysis ◽  
Data Points

Presurgical brain mapping of the language network in pediatric patients with epilepsy using resting-state fMRI

2021 ◽  
pp. 1-10
Author(s):  
Daiana R. Pur ◽  
Roy Eagleson ◽  
Marcus Lo ◽  
Michael T. Jurkiewicz ◽  
Andrea Andrade ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Resting State ◽  
Template Matching ◽  
Pediatric Patients ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Epileptogenic Zone ◽  
Anatomical Landmarks ◽  
Language Lateralization ◽  
Language Network

OBJECTIVEEpilepsy affects neural processing and often causes intra- or interhemispheric language reorganization, rendering localization solely based on anatomical landmarks (e.g., Broca’s area) unreliable. Preoperative brain mapping is necessary to weigh the risk of resection with the risk of postoperative deficit. However, the use of conventional mapping methods (e.g., somatosensory stimulation, task-based functional MRI [fMRI]) in pediatric patients is technically difficult due to low compliance and their unique neurophysiology. Resting-state fMRI (rs-fMRI), a “task-free” technique based on the neural activity of the brain at rest, has the potential to overcome these limitations. The authors hypothesized that language networks can be identified from rs-fMRI by applying functional connectivity analyses.METHODSCases in which both task-based fMRI and rs-fMRI were acquired as part of the preoperative clinical protocol for epilepsy surgery were reviewed. Task-based fMRI consisted of 2 language tasks and 1 motor task. Resting-state fMRI data were acquired while the patients watched an animated movie and were analyzed using independent component analysis (i.e., data-driven method). The authors extracted language networks from rs-fMRI data by performing a similarity analysis with functionally defined language network templates via a template-matching procedure. The Dice coefficient was used to quantify the overlap.RESULTSThirteen children underwent conventional task-based fMRI (e.g., verb generation, object naming), rs-fMRI, and structural imaging at 1.5T. The language components with the highest overlap with the language templates were identified for each patient. Language lateralization results from task-based fMRI and rs-fMRI mapping were comparable, with good concordance in most cases. Resting-state fMRI–derived language maps indicated that language was on the left in 4 patients (31%), on the right in 5 patients (38%), and bilateral in 4 patients (31%). In some cases, rs-fMRI indicated a more extensive language representation.CONCLUSIONSResting-state fMRI–derived language network data were identified at the patient level using a template-matching method. More than half of the patients in this study presented with atypical language lateralization, emphasizing the need for mapping. Overall, these data suggest that this technique may be used to preoperatively identify language networks in pediatric patients. It may also optimize presurgical planning of electrode placement and thereby guide the surgeon’s approach to the epileptogenic zone.

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