Find research datasets worth reusing

Resting-state Functional Connectivity Throughout a Course of iTBS in Major Depression

ClinicalTrials.gov study NCT03944213. IPD Sharing: NO. Countries: 1. Publications: 11.

Can we Use Resting-state fMRI and CSD Fiber Tractography for Presurgical Mapping?

ClinicalTrials.gov study NCT06040580. IPD Sharing: NO. Countries: 1. Publications: 2.

A Comparison of Two Treatments for CRPS and Changes in Resting-State Connectivity of Cerebral Networks.

ClinicalTrials.gov study NCT02753335. IPD Sharing: NO. Countries: 1. Publications: 1.

Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP

ClinicalTrials.gov study NCT05949528. IPD Sharing: NO. Countries: 1. Publications: 0.

Efficacy of Deep-coil Based Repetitive Transcranial Magnetic Stimulation in the Treatment of Parkinson's Disease With Freezing of Gait and Its Neural Regulation Mechanism Study of Resting-state Cerebr

ClinicalTrials.gov study NCT06888778. IPD Sharing: NO. Countries: 1. Publications: 1.

publicJul 2020View details →

dryad32/100

Data from: Brain states govern the spatio-temporal dynamics of resting-state functional connectivity

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2020View details →

A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol

This dataset contains preprocessed resting state fMRI data (.nii.gz) with accompanying confound files (.tsv) from the Single Individual volunteer for Multiple Observations across Networks (SIMON; http://fcon_1000.projects.nitrc.org/indi/retro/SIMON.html) dataset that has been minimally preprocessed using the NeuroImaging Analysis Kit (NIAK; http://niak.simexp-lab.org/build/html/PREPROCESSING.html). Preprocessing steps included:  (1) Slice timing correction; (2) Estimation of rigid-body motion in fMRI runs, both within- and between sessions; (3) Linear or non-linear coregistration of the structural scan in stereotaxic space; (4) Individual coregistration between structural and functional scans; (5) Resampling of functional scans in stereotaxic space. Confound files (.tsv) have been included for purposes of scrubbing and regression of confounds using NIAK or other analysis software, allowing for further processing without conflicts.

opencc-by-4.0Jun 2020View details →

A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system

A brief session of rightward prismatic adaptation (R-PA) has been shown to alleviate neglect symptoms in patients with right hemispheric damage, very likely by switching hemispheric dominance of the ventral attentional network (VAN) from the right to the left and by changing task-related activity within the dorsal attentional network (DAN). We have investigated this very rapid change in functional organisation with a network approach by comparing resting-state connectivity before and after a brief exposure i) to R-PA (14 normal subjects; experimental condition) or ii) to plain glasses (12 normal subjects; control condition). A whole brain analysis (comprising all 129 regions of interest) highlighted R-PA-induced changes within a bilateral, fronto-temporal network, which consisted of 13 nodes and 11 edges; all edges involved one of 4 frontal nodes, which were part of VAN. The analysis of network characteristics within VAN and DAN revealed a R-PA-induced decrease in connectivity strength between nodes and a decrease in local efficiency within VAN but not within DAN. These results indicate that the resting-state connectivity configuration of VAN is modulated by R-PA, possibly by decreasing its modularity.

opencc-zeroJul 2020View details →

Data from: Cognitive correlates of cerebellar resting-state functional connectivity in Parkinson disease.

Objective: The purpose of this cross-sectional study was to investigate the contributions of altered cerebellar resting-state functional connectivity (FC) to cognitive impairment in Parkinson disease (PD). Methods: We conducted morphometric and resting-state functional connectivity MRI (FC-MRI) analyses contrasting 81 PD and 43 age-matched healthy controls using rigorous quality assurance measures. To investigate the relationship of cerebellar FC to cognitive status, we compared PD participants without cognitive impairment (Clinical Dementia Rating scale, CDR=0; n=47) to PD participants with impaired cognition (CDR≥0.5; n=34). Comprehensive measures of cognition across the five cognitive domains were assessed for behavioral correlations. Results: The PD participants had significantly weaker FC between the vermis and peristriate visual association cortex compared to controls, and the strength of this FC correlated with visuospatial function and global cognition. In contrast, weaker FC between vermis and dorsolateral prefrontal cortex was found in the cognitively impaired PD group compared to PD participants without cognitive impairment. This effect correlated with deficits in attention, executive functions and global cognition. No group differences in cerebellar lobular volumes or regional cortical thickness of the significant cortical clusters were observed. Conclusion: These results demonstrate a correlation between cerebellar vermal FC and cognitive impairment in PD. The absence of significant atrophy in cerebellum or relevant cortical areas suggests this could be related to local pathophysiology such as neurotransmitter dysfunction.

opencc-zeroDec 2017View details →

Data from: Resting-state gamma-band power alterations in schizophrenia reveal E/I-balance abnormalities across illness-stages

We examined alterations in E/I-balance in schizophrenia (ScZ) through measurements of resting-state gamma-band activity in participants meeting clinical high-risk (CHR) criteria (n = 88), 21 first episode (FEP) patients and 34 chronic ScZ-patients. Furthermore, MRS-data were obtained in CHR-participants and matched controls. Magnetoencephalographic (MEG) resting-state activity was examined at source level and MEG-data were correlated with neuropsychological scores and clinical symptoms. CHR-participants were characterized by increased 64–90 Hz power. In contrast, FEP- and ScZ-patients showed aberrant spectral power at both low- and high gamma-band frequencies. MRS-data showed a shift in E/I-balance toward increased excitation in CHR-participants, which correlated with increased occipital gamma-band power. Finally, neuropsychological deficits and clinical symptoms in FEP and ScZ-patients were correlated with reduced gamma band-activity, while elevated psychotic symptoms in the CHR group showed the opposite relationship. The current study suggests that resting-state gamma-band power and altered Glx/GABA ratio indicate changes in E/I-balance parameters across illness stages in ScZ.

opencc-zeroDec 2015View details →

Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

opencc-zeroDec 2013View details →

Data from: Maximizing negative correlations in resting-state functional connectivity MRI by time-lag

This paper aims to better understand the physiological meaning of negative correlations in resting state functional connectivity MRI (r-fcMRI). The correlations between anatomy-based brain regions of 18 healthy humans were calculated and analyzed with and without a correction for global signal and with and without spatial smoothing. In addition, correlations between anatomy-based brain regions of 18 naïve anesthetized rats were calculated and compared to the human data. T-statistics were used to differentiate between positive and negative connections. The application of spatial smoothing and global signal correction increased the number of significant positive connections but their effect on negative connections was complex. Positive connections were mainly observed between cortical structures while most negative connections were observed between cortical and non-cortical structures with almost no negative connections between non-cortical structures. In both human and rats, negative connections were never observed between bilateral homologous regions. The main difference between positive and negative connections in both the human and rat data was that positive connections became less significant with time-lags, while negative connections became more significant with time-lag. This effect was evident in all four types of analyses (with and without global signal correction and spatial smoothing) but was most significant in the analysis with no correction for the global signal. We hypothesize that the valence of r-fcMRI connectivity reflects the relative contributions of cerebral blood volume (CBV) and flow (CBF) to the BOLD signal and that these relative contributions are location-specific. If cerebral circulation is primarily regulated by CBF in one region and by CBV in another, a functional connection between these regions can manifest as an r-fcMRI negative and time-delayed correlation. Similarly, negative correlations could result from spatially inhomogeneous responses of rCBV or rCBF alone. Consequently, neuronal regulation of brain circulation may be deduced from the valence of r-fcMRI connectivity.

opencc-by-4.0Mar 2017View details →

Figure 3 from: Leonard J, Flournoy J, Lewis-de los Angeles CP, Whitaker K (2017) How much motion is too much motion? Determining motion thresholds by sample size for reproducibility in developmental resting-state MRI. Research Ideas and Outcomes 3: e12569. https://doi.org/10.3897/rio.3.e12569

Figure 3 - Out of sample prediction accuracy of autism diagnosis using resting state data as a function of sample size and motion-based exclusion criteria (percentage of fMRI, whole-brain volumes exceeding threshold). Red line is a naive classifier that assumes that all participants share the modal diagnosis (in this case, non-ASD). The black line spans the 5th to 95th percentile accuracy across iterations using a linear SVM, with the black points at the median value. Code and output can be found on GitHub (Flournoy and Leonard 2017).

opencc-by-4.0Mar 2017View details →

Figure 2 from: Leonard J, Flournoy J, Lewis-de los Angeles CP, Whitaker K (2017) How much motion is too much motion? Determining motion thresholds by sample size for reproducibility in developmental resting-state MRI. Research Ideas and Outcomes 3: e12569. https://doi.org/10.3897/rio.3.e12569

Figure 2 - Split-half reliability results showing how sample size (N) has a large effect on R squared (median R squared from 100 permutations) while motion threshold does not. Error bars represent average 95% confidence intervals across 100 permutations. Code and output can be found on GitHub (Flournoy and Leonard 2017).