New resting-state fMRI related studies at PubMed

Subscribe to New resting-state fMRI related studies at PubMed feed New resting-state fMRI related studies at PubMed
NCBI: db=pubmed; Term=resting state fMRI
Updated: 3 hours 14 min ago

Structural Brain Connectivity Constrains within-a-Day Variability of Direct Functional Connectivity.

Wed, 08/30/2017 - 15:00
Related Articles

Structural Brain Connectivity Constrains within-a-Day Variability of Direct Functional Connectivity.

Front Hum Neurosci. 2017;11:408

Authors: Park B, Eo J, Park HJ

Abstract
The idea that structural white matter connectivity constrains functional connectivity (interactions among brain regions) has widely been explored in studies of brain networks; studies have mostly focused on the "average" strength of functional connectivity. The question of how structural connectivity constrains the "variability" of functional connectivity remains unresolved. In this study, we investigated the variability of resting state functional connectivity that was acquired every 3 h within a single day from 12 participants (eight time sessions within a 24-h period, 165 scans per session). Three different types of functional connectivity (functional connectivity based on Pearson correlation, direct functional connectivity based on partial correlation, and the pseudo functional connectivity produced by their difference) were estimated from resting state functional magnetic resonance imaging data along with structural connectivity defined using fiber tractography of diffusion tensor imaging. Those types of functional connectivity were evaluated with regard to properties of structural connectivity (fiber streamline counts and lengths) and types of structural connectivity such as intra-/inter-hemispheric edges and topological edge types in the rich club organization. We observed that the structural connectivity constrained the variability of direct functional connectivity more than pseudo-functional connectivity and that the constraints depended strongly on structural connectivity types. The structural constraints were greater for intra-hemispheric and heterologous inter-hemispheric edges than homologous inter-hemispheric edges, and feeder and local edges than rich club edges in the rich club architecture. While each edge was highly variable, the multivariate patterns of edge involvement, especially the direct functional connectivity patterns among the rich club brain regions, showed low variability over time. This study suggests that structural connectivity not only constrains the strength of functional connectivity, but also the within-a-day variability of functional connectivity and connectivity patterns, particularly the direct functional connectivity among brain regions.

PMID: 28848416 [PubMed]

Altered Brain Functional Activity in Infants with Congenital Bilateral Severe Sensorineural Hearing Loss: A Resting-State Functional MRI Study under Sedation.

Wed, 08/30/2017 - 15:00
Related Articles

Altered Brain Functional Activity in Infants with Congenital Bilateral Severe Sensorineural Hearing Loss: A Resting-State Functional MRI Study under Sedation.

Neural Plast. 2017;2017:8986362

Authors: Xia S, Song T, Che J, Li Q, Chai C, Zheng M, Shen W

Abstract
Early hearing deprivation could affect the development of auditory, language, and vision ability. Insufficient or no stimulation of the auditory cortex during the sensitive periods of plasticity could affect the function of hearing, language, and vision development. Twenty-three infants with congenital severe sensorineural hearing loss (CSSHL) and 17 age and sex matched normal hearing subjects were recruited. The amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo) of the auditory, language, and vision related brain areas were compared between deaf infants and normal subjects. Compared with normal hearing subjects, decreased ALFF and ReHo were observed in auditory and language-related cortex. Increased ALFF and ReHo were observed in vision related cortex, which suggest that hearing and language function were impaired and vision function was enhanced due to the loss of hearing. ALFF of left Brodmann area 45 (BA45) was negatively correlated with deaf duration in infants with CSSHL. ALFF of right BA39 was positively correlated with deaf duration in infants with CSSHL. In conclusion, ALFF and ReHo can reflect the abnormal brain function in language, auditory, and visual information processing in infants with CSSHL. This demonstrates that the development of auditory, language, and vision processing function has been affected by congenital severe sensorineural hearing loss before 4 years of age.

PMID: 28255465 [PubMed - indexed for MEDLINE]

Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design.

Wed, 08/30/2017 - 15:00
Related Articles

Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design.

J Psychiatr Res. 2016 Jul;78:11-23

Authors: Trivedi MH, McGrath PJ, Fava M, Parsey RV, Kurian BT, Phillips ML, Oquendo MA, Bruder G, Pizzagalli D, Toups M, Cooper C, Adams P, Weyandt S, Morris DW, Grannemann BD, Ogden RT, Buckner R, McInnis M, Kraemer HC, Petkova E, Carmody TJ, Weissman MM

Abstract
UNLABELLED: Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 300 participants with early onset (≤30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository.
CLINICAL TRIAL REGISTRATION: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). Identifier: NCT01407094. URL: http://clinicaltrials.gov/show/NCT01407094.

PMID: 27038550 [PubMed - indexed for MEDLINE]

Multiple functional networks modeling for autism spectrum disorder diagnosis.

Tue, 08/29/2017 - 13:40

Multiple functional networks modeling for autism spectrum disorder diagnosis.

Hum Brain Mapp. 2017 Aug 28;:

Authors: Kam TE, Suk HI, Lee SW

Abstract
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

PMID: 28845892 [PubMed - as supplied by publisher]

Local and Extensive Neuroplasticity in Carpal Tunnel Syndrome: A Resting-State fMRI Study.

Tue, 08/29/2017 - 13:40

Local and Extensive Neuroplasticity in Carpal Tunnel Syndrome: A Resting-State fMRI Study.

Neurorehabil Neural Repair. 2017 Aug 01;:1545968317723749

Authors: Lu YC, Zhang H, Zheng MX, Hua XY, Qiu YQ, Shen YD, Jiang S, Xu JG, Gu YD, Xu WD

Abstract
Carpal tunnel syndrome (CTS) is a most common peripheral nerve entrapment neuropathy characterized by sensorimotor deficits in median nerve innervated digits. Block-design task-related functional magnetic resonance imaging (fMRI) studies have been used to investigate CTS-related neuroplasticity in the primary somatosensory cortices. However, considering the persistence of digital paresthesia syndrome caused by median nerve entrapment, spontaneous neuronal activity might provide a better understanding of CTS-related neuroplasticity, which remains unexplored. The present study aimed to investigate both local and extensive spontaneous neuronal activities with resting-state fMRI. A total of 28 bilateral CTS patients and 24 normal controls were recruited, and metrics, including amplitude of low-frequency fluctuation (ALFF) and voxel-wise functional connectivity (FC), were used to explore synaptic activity at different spatial scales. Correlations with clinical measures were further investigated by linear regression. Decreased amplitudes of low-frequency fluctuation were observed in the bilateral primary sensory cortex (SI) and secondary sensory cortex (SII) in CTS patients (AlphaSim corrected P < .05). This was found to be negatively related to the sensory thresholds of corresponding median nerve innervated fingers. In the voxel-wise FC analysis, with predefined seed regions of interest in the bilateral SI and primary motor cortex, we observed decreased interhemispheric and increased intrahemispheric FC. Additionally, both interhemispheric and intrahemispheric FC were found to be significantly correlated with the mean ALFF.

PMID: 28845734 [PubMed - as supplied by publisher]

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network.

Tue, 08/29/2017 - 13:40
Related Articles

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network.

Front Phys. 2017 Jul;5:

Authors: Tommasin S, Mascali D, Gili T, Assan IE, Moraschi M, Fratini M, Wise RG, Macaluso E, Mangia S, Giove F

Abstract
Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task.

PMID: 28845420 [PubMed]

Balance Deficit and Brain Connectivity in Children with Attention-Deficit/Hyperactivity Disorder.

Tue, 08/29/2017 - 13:40
Related Articles

Balance Deficit and Brain Connectivity in Children with Attention-Deficit/Hyperactivity Disorder.

Psychiatry Investig. 2017 Jul;14(4):452-457

Authors: Kim SM, Hyun GJ, Jung TW, Son YD, Cho IH, Kee BS, Han DH

Abstract
OBJECTIVE: We aimed to assess disturbances in postural and gait balance and functional connectivity within the brain regions controlling balance in children with attention-deficit/hyperactivity disorder (ADHD).
METHODS: Thirteen children with ADHD and 13 age- and sex-matched controls were recruited. Gait balance was assessed by the difference in the center of pressure (COP) between the left and right foot, as well as the difference in plantar pressure between the left and right foot during gait. Neuroimaging data were acquired using a 3.0 Tesla MRI scanner. Functional connectivity between the vermis of the cerebellum and all other brain regionswas assessed.
RESULTS: The difference in plantar pressure between the left foot and right foot in the ADHD group was greater than that observed in the control group. The average COP jerk score of the right foot in the ADHD group was higher than that observed in the control group. A higher functional connectivity between the cerebellum and the right middle frontal gyrus (premotor cortex) and medial frontal gyrus (cingulate gyrus) was observed in the control group relative to the ADHD group. In the ADHD group, the difference in plantar pressure between the left and right foot was also negatively correlated with the beta-value within the middle frontal gyrus.
CONCLUSION: Children with ADHD had disturbance of balance as assessed by plantar pressure. Decreased brain connectivity from the cerebellum to the premotor cortex and anterior cingulate was associated with disturbances of posture and balance in children with ADHD.

PMID: 28845172 [PubMed]

Using resting-state fMRI to assess the effect of aerobic exercise on functional connectivity of the DLPFC in older overweight adults.

Tue, 08/29/2017 - 13:40
Related Articles

Using resting-state fMRI to assess the effect of aerobic exercise on functional connectivity of the DLPFC in older overweight adults.

Brain Cogn. 2017 Aug 23;:

Authors: Prehn K, Lesemann A, Krey G, Witte AV, Köbe T, Grittner U, Flöel A

Abstract
Cardiovascular fitness is thought to exert beneficial effects on brain function and might delay the onset of cognitive decline. Empirical evidence of exercise-induced cognitive enhancement, however, has not been conclusive, possibly due to short intervention times in clinical trials. Resting-state functional connectivity (RSFC) has been proposed asan early indicator for intervention-induced changes. Here, we conducted a study in which healthy older overweight subjects took either part in a moderate aerobic exercise program over 6months (AE group; n=11) or control condition of non-aerobic stretching and toning (NAE group; n=18). While cognitive and gray matter volume changes were rather small (i.e., appeared only in certain sub-scores without Bonferroni correction for multiple comparisons or using small volume correction), we found significantly increased RSFC after training between dorsolateral prefrontal cortex and superior parietal gyrus/precuneus in the AE compared to the NAE group. This intervention study demonstrates an exercise-induced modulation of RSFC between key structures of the executive control and default mode networks, which might mediate an interaction between task-positive and task-negative brain activation required for task switching. Results further emphasize the value of RSFC asa sensitive biomarker for detecting early intervention-related cognitive improvements in clinical trials.

PMID: 28844505 [PubMed - as supplied by publisher]

"God has sent me to you": Right temporal epilepsy, left prefrontal psychosis.

Tue, 08/29/2017 - 13:40
Related Articles

"God has sent me to you": Right temporal epilepsy, left prefrontal psychosis.

Epilepsy Behav. 2016 Jul;60:7-10

Authors: Arzy S, Schurr R

Abstract
Religious experiences have long been documented in patients with epilepsy, though their exact underlying neural mechanisms are still unclear. Here, we had the rare opportunity to record a delusional religious conversion in real time in a patient with right temporal lobe epilepsy undergoing continuous video-EEG. In this patient, a messianic revelation experience occurred several hours after a complex partial seizure of temporal origin, compatible with postictal psychosis (PIP). We analyzed the recorded resting-state EEG epochs separately for each of the conventional frequency bands. Topographical analysis of the bandpass filtered EEG epochs revealed increased activity in the low-gamma range (30-40Hz) during religious conversion compared with activity during the patient's habitual state. The brain generator underlying this activity was localized to the left prefrontal cortex. This suggests that religious conversion in PIP is related to control mechanisms in the prefrontal lobe-related processes rather than medial temporal lobe-related processes.

PMID: 27176877 [PubMed - indexed for MEDLINE]

Delay discounting is predicted by scale-free dynamics of default mode network and salience network.

Mon, 08/28/2017 - 12:40

Delay discounting is predicted by scale-free dynamics of default mode network and salience network.

Neuroscience. 2017 Aug 23;:

Authors: Chen Z, Guo Y, Feng T

Abstract
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is frequently used as a powerful technology to detect individual differences in many cognitive functions. Recently, some studies have explored the association between scale-free dynamic properties of resting-state brain activation and individual personality traits. However, little is known about whether the scale-free dynamics of resting-state function networks is associated with delay discounting. To address this question, we calculated the Hurst exponent which quantifies long-term memory of the time series in resting-state networks (RSNs) identified via independent component analysis (ICA) and examined what relationship between delay discounting and the Hurst exponent of RSNs is. ICA results showed that entire nine RSNs were successfully recognized and extracted from independent components. After controlling some covariates, including gender, age, education, personality and trait anxiety, partial correlation analysis revealed that the Hurst exponent in default mode network (DMN) and salience network (SN) was positively correlated with the delay discounting rates. No significant correlation between delay discounting and mean Hurst exponent of the whole brain was found. Thus, our results suggest the individual delay discounting is associated with the dynamics of inner-network interactions in the DMN and SN, and highlight the crucial role of scale-free dynamic properties of function networks on intertemporal choice.

PMID: 28844008 [PubMed - as supplied by publisher]

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

Mon, 08/28/2017 - 12:40

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

Neuroscience. 2017 Aug 23;:

Authors: Wang XH, Jiao Y, Li L

Abstract
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is two-fold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken as an intra-ICN feature, and phase synchronization (PS) was used as an inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r = 0.79 (p < 10(-8)), and the performance of the predictive model for impulsivity is r = 0.48 (p < 10(-8)). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications.

PMID: 28843999 [PubMed - as supplied by publisher]

Neuroanatomical foundations of delayed reward discounting decision making.

Mon, 08/28/2017 - 12:40

Neuroanatomical foundations of delayed reward discounting decision making.

Neuroimage. 2017 Aug 23;:

Authors: Owens MM, Gray JC, Amlung MT, Oshri A, Sweet LH, MacKillop J

Abstract
Resolving tradeoffs between smaller immediate rewards and larger delayed rewards is ubiquitous in daily life and steep discounting of future rewards is associated with several psychiatric conditions. This form of decision-making is referred to as delayed reward discounting (DRD) and the features of brain structure associated with DRD are not well understood. The current study characterized the relationship between gray matter volume (GMV) and DRD in a sample of 1038 healthy adults (54.7% female) using cortical parcellation, subcortical segmentation, and voxelwise cortical surface-based group analyses. The results indicate that steeper DRD was significantly associated with lower total cortical GMV, but not subcortical GMV. In parcellation analyses, less GMV in 20 discrete cortical regions was associated with steeper DRD. Of these regions, only GMV in the middle temporal gyrus (MTG) and entorhinal cortex (EC) were uniquely associated with DRD. Voxelwise surface-based analyses corroborated these findings, again revealing significant associations between steeper DRD and less GMV in the MTG and EC. To inform the roles of MTG and EC in DRD, connectivity analysis of resting state data (N = 1003) using seed regions from the structural findings was conducted. This revealed that spontaneous activity in the MTG and EC was correlated with activation in the ventromedial prefrontal cortex, posterior cingulate cortex, and inferior parietal lobule, regions associated with the default mode network, which involves prospection, self-reflective thinking and mental simulation. Furthermore, meta-analytic co-activation analysis using Neurosynth revealed a similar pattern across 11,406 task-fMRI studies. Collectively, these findings provide robust evidence that morphometric characteristics of the temporal lobe are associated with DRD preferences and suggest it may be because of their role in mental activities in common with default mode activity.

PMID: 28843539 [PubMed - as supplied by publisher]

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

Sun, 08/27/2017 - 11:40

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

Med Image Anal. 2017 Aug 18;42:200-211

Authors: Zhao Y, Dong Q, Chen H, Iraji A, Li Y, Makkie M, Kou Z, Liu T

Abstract
State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data.

PMID: 28843214 [PubMed - as supplied by publisher]

Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study.

Sun, 08/27/2017 - 11:40
Related Articles

Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study.

Sci Rep. 2017 Aug 25;7(1):9422

Authors: Nilsonne G, Tamm S, Schwarz J, Almeida R, Fischer H, Kecklund G, Lekander M, Fransson P, Åkerstedt T

Abstract
Sleep deprivation has been reported to affect intrinsic brain connectivity, notably reducing connectivity in the default mode network. Studies to date have however shown inconsistent effects, in many cases lacked monitoring of wakefulness, and largely included young participants. We investigated effects of sleep deprivation on intrinsic brain connectivity in young and older participants. Participants aged 20-30 (final n = 30) and 65-75 (final n = 23) years underwent partial sleep deprivation (3 h sleep) in a cross-over design, with two 8-minutes eyes-open resting state functional magnetic resonance imaging (fMRI) runs in each session, monitored by eye-tracking. We assessed intrinsic brain connectivity using independent components analysis (ICA) as well as seed-region analyses of functional connectivity, and also analysed global signal variability, regional homogeneity, and the amplitude of low-frequency fluctuations. Sleep deprivation caused increased global signal variability. Changes in investigated resting state networks and in regional homogeneity were not statistically significant. Younger participants had higher connectivity in most examined networks, as well as higher regional homogeneity in areas including anterior and posterior cingulate cortex. In conclusion, we found that sleep deprivation caused increased global signal variability, and we speculate that this may be caused by wake-state instability.

PMID: 28842597 [PubMed - in process]

Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity.

Sun, 08/27/2017 - 11:40
Related Articles

Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity.

Neuroimage. 2017 Aug 22;:

Authors: Wirsich J, Ridley B, Besson P, Jirsa V, Bénar C, Ranjeva JP, Guye M

Abstract
While averaged dynamics of brain function are known to estimate the underlying structure, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. These complex functional dynamics, measured by EEG and fMRI, are thought to arise from a shared underlying structural architecture, which can be measured by diffusion MRI (dMRI). While simulation and data transformation (e.g. graph theory measures) have been proposed to refine the understanding of the underlying function-structure relationship, the potential complementary and/or independent contribution of EEG and fMRI to this relationship is still poorly understood. As such, we explored this relationship by analyzing the function-structure correlation in fourteen healthy subjects with simultaneous resting-state EEG-fMRI and dMRI acquisitions. We show that the combination of EEG and fMRI connectivity better explains dMRI connectivity and that this represents a genuine model improvement over fMRI-only models for both group-averaged connectivity matrices and at the individual level. Furthermore, this model improves the prediction within each resting-state network. The best model fit to underlying structure is mediated by fMRI and EEG-δ connectivity in combination with Euclidean distance and interhemispheric connectivity with more local contributions of EEG-γ at the scale of resting state networks. This highlights that the factors mediating the relationship between functional and structural metrics of connectivity are context and scale dependent, influenced by topological, geometric and architectural features. It also suggests that fMRI studies employing simultaneous EEG measures may characterize additional and essential parts of the underlying neuronal activity of the resting-state, which might be of special interest for both clinical studies and the investigation of resting-state dynamics.

PMID: 28842386 [PubMed - as supplied by publisher]

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.

Sun, 08/27/2017 - 11:40
Related Articles

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.

J Neurosci Methods. 2017 Aug 22;:

Authors: Javed E, Faye I, Malik AS, Abdullah JM

Abstract
BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

PMID: 28842191 [PubMed - as supplied by publisher]

Connectome-scale Functional Intrinsic Connectivity Networks in Macaques.

Sun, 08/27/2017 - 11:40
Related Articles

Connectome-scale Functional Intrinsic Connectivity Networks in Macaques.

Neuroscience. 2017 Aug 22;:

Authors: Zhang W, Jiang X, Zhang S, Howell BR, Zhao Y, Zhang T, Guo L, Sanchez MM, Hu X, Liu T

Abstract
There have been extensive studies of intrinsic connectivity networks (ICNs) in the human brains using resting state fMRI in the literature. However, the functional organization of ICNs in macaque brains has been less explored so far, despite growing interests in the field. In this work, we propose a computational framework to identify connectome-scale group-wise consistent ICNs in macaques via sparse representation of whole-brain resting state fMRI data. Experimental results demonstrate that 70 group-wise consistent ICNs are successfully identified in macaque brains via the proposed framework. These 70 ICNs are interpreted based on two publicly available parcellation maps of macaque brains and our work significantly expand currently known macaque ICNs already reported in the literature. In general, this set of connectome-scale group-wise consistent ICNs can potentially benefit a variety of studies in the neuroscience and brain mapping fields, and they provide a foundation to better understand brain evolution in the future.

PMID: 28842187 [PubMed - as supplied by publisher]

Pages