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Brain Connectivity

Anna Custo, Dimitri van der Ville, William M Wells, Ioana M Tomescu, Christoph Michel
Using EEG to elucidate the spontaneous activation of brain resting state networks is non trivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting state topographies, so-called microstates). We estimated seven resting state topographies explaining the EEG dataset with k-means clustering (N=164, 256 electrodes)...
September 22, 2017: Brain Connectivity
Kaundinya Gopinath, Venkatagiri Krishnamurthy, K Sathian
In a recent study Eklund et al. (Eklund et al., 2016) employing resting state functional magnetic resonance imaging (rsfMRI) data as a surrogate for null fMRI datasets posited that cluster-wise family-wise error (FWE) rate corrected inferences made using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds (CDTs) less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise (Eklund et al...
September 19, 2017: Brain Connectivity
Satoru Hayasaka
fMRI-based functional connectivity networks are often constructed by thresholding a correlation matrix of nodal time courses. In a typical thresholding approach known as hard thresholding, a single threshold is applied to the entire correlation matrix to identify edges representing super-threshold correlations. However, hard thresholding is known to produce a network with uneven allocation of edges, resulting in a fragmented network with a large number of disconnected nodes. It is suggested that an alternative network thresholding approach, node-wise thresholding, is able to overcome these problems...
September 13, 2017: Brain Connectivity
Stavros I Dimitriadis, Marios Antonakakis, Panagiotis Simos, Jack M Fletcher, Andrew C Papanicolaou
In the present study a novel data-driven topological filtering technique is introduced to derive the backbone of functional brain networks relying on orthogonal minimal spanning trees (OMST). The method aims to identify the essential functional connections to ensure optimal information flow via the objective criterion of global efficiency minus the cost of surviving connections. The OMST technique was applied to multichannel, resting-state neuromagnetic recordings from four groups of participants: healthy adults (n=50), adults who have suffered mild traumatic brain injury (n=30), typically developing children (n=27), and reading-disabled children (n=25)...
September 11, 2017: Brain Connectivity
Tanzil Arefin, Anna E Mechling, Carole Aura Meirsman, Thomas Bienert, Neele Saskia Huebner, Hsu-Lei Lee, Sami Ben Hamida, Aliza Ehrlich, Daniel Roquet, Juergen Hennig, Dominik von Elverfeldt, Brigitte Lina Kieffer, Laura-Adela Harsan
Recent studies have demonstrated that orchestrated gene activity and expression supports synchronous activity of brain networks. However, there is a paucity of information on the consequences of single gene function on overall brain functional organization and connectivity, and how this translates at behavioral level. Here we combined mouse mutagenesis with functional and structural magnetic resonance imaging (MRI) to determine whether targeted inactivation of a single gene would modify whole brain connectivity in live animals...
September 7, 2017: Brain Connectivity
Silvana Silva Pereira, Rikkert Hindriks, Stefanie Mühlberg, Eric Maris, Freek Van Ede, Alessandra Griffa, Patrick Hagmann, Gustavo Deco
A popular way to analyze resting-state EEG and MEG data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time-series with the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time-series are mixtures of source activity. It is therefore of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks...
September 6, 2017: Brain Connectivity
Shella Dawn Keilholz, Cesar Caballero-Gaudes, Peter Bandettini, Gustavo Deco, Vince D Calhoun
Time-resolved analysis of resting state fMRI data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This manuscript briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to the interpretation of the network dynamics observed with resting state fMRI and the role that resting state fMRI can play in elucidating the large-scale organization of brain activity...
September 6, 2017: Brain Connectivity
Maxime Chamberland, Gabriel Girard, Michael Bernier, David Fortin, Maxime Descoteaux, Kevin Whittingstall
Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. Yet, the source of FC variability inferred from resting-state functional MRI (rs-fMRI) remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC to SC is challenging given the many potential biases associated with quantifying their respective strengths...
August 21, 2017: Brain Connectivity
Reinder Vos de Wael, Fahmeed Hyder, Garth John Thompson
Neuroimaging studies typically consider white matter as unchanging in different neural and metabolic states. However, a recent study (Brain Connect. 6(6):435-447) demonstrated that white matter signal regression (WMSR) produced a similar loss of neurometabolic information to "global" (whole-brain) signal regression (GSR) in resting state fMRI (R-fMRI) data. This was unexpected, as the loss of information would normally be attributed to neural activity within gray matter correlating with the global R-fMRI signal...
August 21, 2017: Brain Connectivity
Afrooz Jahedi, Chanond A Nasamran, Brian Faires, Juanjuan Fan, Ralph-Axel Müller
Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We employed conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, non-verbal IQ, and head motion...
August 21, 2017: Brain Connectivity
Keiichi Onoda, Nobuhiro Yada, Kentaro Ozasa, Shinji Hara, Yasushi Yamamoto, Hajime Kitagaki, Shuhei Yamaguchi
Resting-state functional connectivity is one promising biomarker for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution, and compared performance for AD and MCI identification with multi-voxel pattern analysis...
June 30, 2017: Brain Connectivity
Lauren Schweizer, Christine H Meyer-Frieβem, Peter K Zahn, Martin Tegenthoff, Tobias Schmidt-Wilcke
Transcutaneous spinal direct current stimulation (tsDCS) is a noninvasive method that can modulate spinal reflexes, sensory afferent conduction, and even pain perception. Although neurophysiological evidence suggests that tsDCS alters somatosensory and nociceptive afferent conduction to the cortex, its supraspinal effects have not yet been investigated by using functional imaging to investigate tsDCS-induced alterations in intrinsic functional connectivity (FC). Therefore, we hypothesize that tsDCS-induced changes in neurophysiological measures might also be reflected in spontaneous brain activity...
June 28, 2017: Brain Connectivity
(no author information available yet)
No abstract text is available yet for this article.
September 2017: Brain Connectivity
Varina L Boerwinkle, Deepankar Mohanty, Stephen T Foldes, Danielle Guffey, Charles G Minard, Aditya Vedantam, Jeffrey S Raskin, Sandi Lam, Margaret Bond, Lucia Mirea, P David Adelson, Angus A Wilfong, Daniel J Curry
The purpose of this study was to prospectively investigate the agreement between the epileptogenic zone(s) (EZ) localization by resting-state functional magnetic resonance imaging (rs-fMRI) and the seizure onset zone(s) (SOZ) identified by intracranial electroencephalogram (ic-EEG) using novel differentiating and ranking criteria of rs-fMRI abnormal independent components (ICs) in a large consecutive heterogeneous pediatric intractable epilepsy population without an a priori alternate modality informing EZ localization or prior declaration of total SOZ number...
September 2017: Brain Connectivity
Katja Brodmann, Oliver Gruber, Roberto Goya-Maldonado
A growing body of evidence indicates that the neuropeptide oxytocin (OT) alters the neural correlates of socioemotional and salience processing. Yet the effects of OT over important large-scale networks involved in these processes, such as the default mode (DM), ventral attention (VA), and cingulo-opercular (CO) networks, remain unknown. Therefore, we conducted a placebo-controlled crossover study with intranasal 24 IU OT in 38 healthy male subjects using a resting-state functional magnetic resonance imaging paradigm to investigate its impact over these three networks candidates...
September 2017: Brain Connectivity
Julie Coloigner, Ronald Phlypo, Thomas D Coates, Natasha Lepore, John C Wood
Sickle cell disease (SCD) is a vascular disorder that is often associated with recurrent ischemia-reperfusion injury, anemia, vasculopathy, and strokes. These cerebral injuries are associated with neurological dysfunction, limiting the full developing potential of the patient. However, recent large studies of SCD have demonstrated that cognitive impairment occurs even in the absence of brain abnormalities on conventional magnetic resonance imaging (MRI). These observations support an emerging consensus that brain injury in SCD is diffuse and that conventional neuroimaging often underestimates the extent of injury...
September 2017: Brain Connectivity
Ganesh B Chand, Junjie Wu, Ihab Hajjar, Deqiang Qiu
Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals...
September 2017: Brain Connectivity
Mayank Kaushal, Akinwunmi Oni-Orisan, Gang Chen, Wenjun Li, Jack Leschke, Doug Ward, Benjamin Kalinosky, Matthew Budde, Brian Schmit, Shi-Jiang Li, Vaishnavi Muqeet, Shekar Kurpad
Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned...
September 2017: Brain Connectivity
Moo K Chung, Jamie L Hanson, Nagesh Adluru, Andrew L Alexander, Richard J Davidson, Seth D Pollak
In diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length, and FA values into the connectivity model. Using various node-degree-based graph theory features, the three connectivity models are compared...
August 2017: Brain Connectivity
Shruti Agarwal, Hanzhang Lu, Jay J Pillai
The aim of this study was to explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) (rsfMRI) may also affect the resting-state fMRI (rsfMRI) frequency domain metrics the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). Twelve de novo brain tumor patients, who underwent clinical fMRI examinations, including task-based fMRI (tbfMRI) and rsfMRI, were included in this Institutional Review Board-approved study...
August 2017: Brain Connectivity
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