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https://www.readbyqxmd.com/read/28231626/default-mode-network-deactivation-to-smoking-cue-relative-to-food-cue-predicts-treatment-outcome-in-nicotine-use-disorder
#1
Claire E Wilcox, Eric D Claus, Vince D Calhoun, Srinivas Rachakonda, Rae A Littlewood, Jessica Mickey, Pamela B Arenella, Natalie Goodreau, Kent E Hutchison
Identifying predictors of treatment outcome for nicotine use disorders (NUDs) may help improve efficacy of established treatments, like varenicline. Brain reactivity to drug stimuli predicts relapse risk in nicotine and other substance use disorders in some studies. Activity in the default mode network (DMN) is affected by drug cues and other palatable cues, but its clinical significance is unclear. In this study, 143 individuals with NUD (male n = 91, ages 18-55 years) received a functional magnetic resonance imaging scan during a visual cue task during which they were presented with a series of smoking-related or food-related video clips prior to randomization to treatment with varenicline (n = 80) or placebo...
February 23, 2017: Addiction Biology
https://www.readbyqxmd.com/read/28231395/functional-connectivity-in-amygdalar-sensory-pre-motor-networks-at-rest-new-evidence-from-the-human-connectome-project
#2
Nicola Toschi, Andrea Duggento, Luca Passamonti
The word "e-motion" derives from the Latin word "ex-moveo" which literally means "moving away from something / somebody". Emotions are thus fundamental to prime action and goal-directed behavior with obvious implications for individual's survival. However, the brain mechanisms underlying the interactions between emotional and motor cortical systems remain poorly understood. A recent diffusion tensor imaging study in humans has reported the existence of direct anatomical connections between the amygdala and sensory/(pre)motor cortices, corroborating an initial observation in animal research...
February 23, 2017: European Journal of Neuroscience
https://www.readbyqxmd.com/read/28230844/network-neuroscience
#3
REVIEW
Danielle S Bassett, Olaf Sporns
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science...
February 23, 2017: Nature Neuroscience
https://www.readbyqxmd.com/read/28230841/magnetoencephalography-for-brain-electrophysiology-and-imaging
#4
REVIEW
Sylvain Baillet
We review the aspects that uniquely characterize magnetoencephalography (MEG) among the techniques available to explore and resolve brain function and dysfunction. While emphasizing its specific strengths in terms of millisecond source imaging, we also identify and discuss current practical challenges, in particular in signal extraction and interpretation. We also take issue with some perceived disadvantages of MEG, including the misconception that the technique is redundant with electroencephalography. Overall, MEG contributes uniquely to our deeper comprehension of both regional and large-scale brain dynamics: from the functions of neural oscillations and the nature of event-related brain activation, to the mechanisms of functional connectivity between regions and the emergence of modes of network communication in brain systems...
February 23, 2017: Nature Neuroscience
https://www.readbyqxmd.com/read/28229308/eeg-signatures-of-dynamic-functional-network-connectivity-states
#5
E A Allen, E Damaraju, T Eichele, L Wu, V D Calhoun
The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/patterns between different brain networks that reliably occur across time and subjects...
February 22, 2017: Brain Topography
https://www.readbyqxmd.com/read/28229131/discovering-cortical-folding-patterns-in-neonatal-cortical-surfaces-using-large-scale-dataset
#6
Yu Meng, Gang Li, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen
The cortical folding of the human brain is highly complex and variable across individuals. Mining the major patterns of cortical folding from modern large-scale neuroimaging datasets is of great importance in advancing techniques for neuroimaging analysis and understanding the inter-individual variations of cortical folding and its relationship with cognitive function and disorders. As the primary cortical folding is genetically influenced and has been established at term birth, neonates with the minimal exposure to the complicated postnatal environmental influence are the ideal candidates for understanding the major patterns of cortical folding...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28227771/tracking-intrinsic-connectivity-brain-network-features-during-successive-pseudo-resting-states-and-interoceptive-task-fmri
#7
Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini
Advanced multivariate analyses of functional magnetic resonance imaging (fMRI) data based on blood oxygen level-dependent (BOLD) contras have revealed that the human brain organizes its activities into multiple intrinsic connectivity networks (ICNs). Several fMRI studies have evaluated the modulations of these networks during different cognitive or emotional tasks using blind source separation techniques particularly the independent component analysis (ICA). In this exploratory study, we applied ICA methodology to examine ICN modulations under different interoceptive conditions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227769/identifying-the-effects-of-visceral-interoception-on-human-brain-connectome-a-multivariate-analysis-of-covariance-of-fmri-data
#8
Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini
Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome. Fifteen healthy right-handed subjects (all females, age range 21 - 48 years; mean age = 30.3, SD = 8.7 years) underwent a blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) that included continuous intravesical saline infusion and drainage...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227768/measures-of-the-brain-functional-network-that-correlate-with-alzheimer-s-neuropsychological-test-scores-an-fmri-and-graph-analysis-study
#9
Soroosh Golbabaei, Amin Dadashi, Hamid Soltanian-Zadeh, Soroosh Golbabaei, Amin Dadashi, Hamid Soltanian-Zadeh, Soroosh Golbabaei, Amin Dadashi, Hamid Soltanian-Zadeh
Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227759/mining-cross-frequency-coupling-microstates-cfc%C3%AE-states-from-eeg-recordings-during-resting-state-and-mental-arithmetic-tasks
#10
Stavros I Dimitriadis, Yu Sun, Nitish Thakor, Anastasios Bezerianos, Stavros I Dimitriadis, Yu Sun, Nitish Thakor, Anastasios Bezerianos, Stavros I Dimitriadis, Yu Sun, Nitish Thakor, Anastasios Bezerianos
The functional brain connectivity has been studied by analyzing synchronization between dynamic oscillations of identical frequency or between different frequencies of distinct brain areas. It has been hypothesized that cross-frequency coupling (CFC) between different frequency bands is the carrier mechanism for the coordination of global and local neural processes and hence supports the distributed information processing in the brain. In the present study, we attempt to study the dynamic evolution of CFC at resting-state and during a mental task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227758/mining-cross-frequency-coupling-microstates-from-resting-state-meg-an-application-to-mild-traumatic-brain-injury
#11
Marios Antonakakis, Stavros I Dimitriadis, Michalis Zervakis, Andrew C Papanicolaou, George Zouridakis, Marios Antonakakis, Stavros I Dimitriadis, Michalis Zervakis, Andrew C Papanicolaou, George Zouridakis, Michalis Zervakis, Marios Antonakakis, George Zouridakis, Andrew C Papanicolaou, Stavros I Dimitriadis
Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227749/reconstructing-multivariate-causal-structure-between-functional-brain-networks-through-a-laguerre-volterra-based-granger-causality-approach
#12
Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi, Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi, Andrea Duggento, Nicola Toschi, Maria Guerrisi, Luca Passamonti, Riccardo Barbieri, Gaetano Valenza
Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227516/identification-of-emotion-associated-brain-functional-network-with-phase-locking-value
#13
V Gonuguntla, R Mallipeddi, K C Veluvolu, V Gonuguntla, R Mallipeddi, K C Veluvolu, V Gonuguntla, K C Veluvolu, R Mallipeddi
Recognition of discriminative brain functional network pattern and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Emotion models mapping onto brain is possible with the help of emotion-specific network patterns and its corresponding brain regions. This paper presents a method to identify emotion related functional connectivity pattern and their distinctive associated regions using EEG phase synchrony (phase locking value (PLV)) connectivity analysis...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227511/toward-a-distributed-free-floating-wireless-implantable-neural-recording-system
#14
Pyungwoo Yeon, Xingyuan Tong, Byunghun Lee, Abdollah Mirbozorgi, Bruce Ash, Helmut Eckhardt, Maysam Ghovanloo, Pyungwoo Yeon, Xingyuan Tong, Byunghun Lee, Abdollah Mirbozorgi, Bruce Ash, Helmut Eckhardt, Maysam Ghovanloo, Maysam Ghovanloo, Byunghun Lee, Bruce Ash, Xingyuan Tong, Pyungwoo Yeon, Abdollah Mirbozorgi, Helmut Eckhardt
To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm(3) neural recording probes, floating with the brain and operating wirelessly across the cortical surface...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227408/structural-brain-network-analysis-in-schizophrenia-using-minimum-spanning-tree
#15
Ali Anjomshoa, Mahsa Dolatshahi, Fatemeh Amirkhani, Farzaneh Rahmani, Mehdi M Mirbagheri, Mohammad Hadi Aarabi, Ali Anjomshoa, Mahsa Dolatshahi, Fatemeh Amirkhani, Farzaneh Rahmani, Mehdi M Mirbagheri, Mohammad Hadi Aarabi, Mehdi M Mirbagheri, Mahsa Dolatshahi, Ali Anjomshoa, Farzaneh Rahmani, Fatemeh Amirkhani, Mohammad Hadi Aarabi
Schizophrenia is a mental disorder in which functional and structural brain networks are disrupted. Classical network analysis has been used by many researchers to quantify brain networks and to study the network changes in schizophrenia, but unfortunately metrics used in this classical method highly depend on the networks' density and weight; the comparisons made by this method are biased. The minimum spanning tree (MST) is an alternative method to solve this problem, but its usefulness in studying the schizophrenic brain network has not been examined yet...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227407/recursive-feature-elimination-for-biomarker-discovery-in-resting-state-functional-connectivity
#16
Hariharan Ravishankar, Radhika Madhavan, Rakesh Mullick, Teena Shetty, Luca Marinelli, Suresh E Joel, Hariharan Ravishankar, Radhika Madhavan, Rakesh Mullick, Teena Shetty, Luca Marinelli, Suresh E Joel, Suresh E Joel, Teena Shetty, Luca Marinelli, Rakesh Mullick, Hariharan Ravishankar, Radhika Madhavan
Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227399/functional-connectivity-analysis-using-whole-brain-and-regional-network-metrics-in-ms-patients
#17
V C Chirumamilla, V Fleischer, A Droby, T Anjum, M Muthuraman, F Zipp, S Groppa, V C Chirumamilla, V Fleischer, A Droby, T Anjum, M Muthuraman, F Zipp, S Groppa, A Droby, S Groppa, F Zipp, V C Chirumamilla, T Anjum, V Fleischer, M Muthuraman
In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227396/supervised-multimodal-fusion-and-its-application-in-searching-joint-neuromarkers-of-working-memory-deficits-in-schizophrenia
#18
Shile Qi, Vince D Calhoun, Theo G M van Erp, Eswar Damaraju, Juan Bustillo, Yuhui Du, Jessica A Turner, Daniel H Mathalon, Judith M Ford, James Voyvodic, Bryon A Mueller, Aysenil Belger, Sarah Mc Ewen, Steven G Potkin, Adrian Preda, F Birn, Tianzi Jiang, Jing Sui, Shile Qi, Vince D Calhoun, Theo G M van Erp, Eswar Damaraju, Juan Bustillo, Yuhui Du, Jessica A Turner, Daniel H Mathalon, Judith M Ford, James Voyvodic, Bryon A Mueller, Aysenil Belger, Sarah McEwen, Steven G Potkin, Adrian Preda, F Birn, Tianzi Jiang, Jing Sui
Multimodal fusion is an effective approach to better understand brain disease. To date, most current fusion approaches are unsupervised; there is need for a multivariate method that can adopt prior information to guide multimodal fusion. Here we proposed a novel supervised fusion model, called "MCCAR+jICA", which enables both identification of multimodal co-alterations and linking the covarying brain regions with a specific reference signal, e.g., cognitive scores. The proposed method has been validated on both simulated and real human brain data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227395/impaired-episodic-memory-network-in-subjects-at-high-risk-for-alzheimer-s-disease
#19
Yafeng Zhan, Jianhua Ma, Kaibin Xu, Yanhui Ding, Yue Cui, Zhengyi Yang, Yong Liu, Yafeng Zhan, Jianhua Ma, Kaibin Xu, Yanhui Ding, Yue Cui, Zhengyi Yang, Yong Liu, Yanhui Ding, Kaibin Xu, Yue Cui, Yafeng Zhan, Jianhua Ma, Zhengyi Yang, Yong Liu
Episodic memory dysfunction is one of the hallmark symptoms of Alzheimer's disease (AD) and mild cognitive impairment (MCI). This cognitive impairment may be related to abnormal brain structure and activity. Functional connectivity mapping (FCM) analysis provides a powerful tool for exploring the topology of human brain function using magnetic resonance imaging (MRI). Thus, it would be advantageous to investigate the changes in functional connectivity within the episodic memory network in a longitudinal MCI dataset, as it may be helpful in identifying a potential marker of disease progress...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227295/classification-of-obsessive-compulsive-disorder-from-resting-state-fmri
#20
Bhaskar Sen, Gail A Bernstein, Tingting Xu, Bryon A Mueller, Mindy W Schreiner, Kathryn R Cullen, Keshab K Parhi, Bhaskar Sen, Gail A Bernstein, Tingting Xu, Bryon A Mueller, Mindy W Schreiner, Kathryn R Cullen, Keshab K Parhi, Keshab K Parhi, Gail A Bernstein, Tingting Xu, Kathryn R Cullen, Bryon A Mueller, Mindy W Schreiner, Bhaskar Sen
Obssesive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of the patients' daily lives. Accurate diagnosis of this disorder is a primary step towards effective treatment. Diagnosing OCD is a lengthy procedure that involves interviews, symptom rating scales and behavioral observation as well as the experience of a clinician. Discovering signal processing and network based biomarkers from functional magnetic resonance scans of patients may greatly assist the clinicians in their diagnostic assessments...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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