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"effective connectivity"

Valerio Santangelo
Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets...
2018: Frontiers in Integrative Neuroscience
Edmund T Rolls, Wei Cheng, Matthieu Gilson, Jiang Qiu, Zicheng Hu, Hongtao Ruan, Yu Li, Chu-Chung Huang, Albert C Yang, Shih-Jen Tsai, Xiaodong Zhang, Kaixiang Zhuang, Ching-Po Lin, Gustavo Deco, Peng Xie, Jianfeng Feng
BACKGROUND: Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects. METHODS: We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints...
February 2018: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Christopher S Parker, Jonathan D Clayden, M Jorge Cardoso, Roman Rodionov, John S Duncan, Catherine Scott, Beate Diehl, Sebastien Ourselin
Patients with medically-refractory focal epilepsy may be candidates for neurosurgery and some may require placement of intracranial EEG electrodes to localise seizure onset. Assessing cerebral responses to single pulse electrical stimulation (SPES) may give diagnostically useful data. SPES produces cortico-cortical evoked potentials (CCEPs), which infer effective brain connectivity. Diffusion-weighted images and tractography may be used to estimate structural brain connectivity. This combination provides the opportunity to observe seizure onset and its propagation throughout the brain, spreading contiguously along the cortex explored with electrodes, or non-contiguously...
2018: NeuroImage: Clinical
Xiangfei Geng, Junhai Xu, Baolin Liu, Yonggang Shi
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN)...
2018: Frontiers in Neuroscience
Joohyun Kim, Hye Jin Kam, Yu Rang Park, Soyoung Yoo, Ji Seon Oh, Young-Hak Kim, Jae-Ho Lee
Objectives: Developments in advanced technology have unlocked an era of smart health, transforming healthcare practices inside and outside hospitals for both medical staff and patients. It is now possible for patients to collect detailed health data using smartphones and wearable devices, regardless of their physical location or time zone. The use of these patient-generated data holds great promise for future healthcare advancements in many ways; however, current strategies for smart-health technologies tend to focus on the smartness of the technology itself and on managing a particular disease or condition...
January 2018: Healthcare Informatics Research
Nicolas Honnorat, Christos Davatzikos
The introduction of graph theory in neuroimaging has provided invaluable tools for the study of brain connectivity. These methods require the definition of a graph, which is typically derived by estimating the effective connectivity between brain regions through the optimization of an ill-posed inverse problem. Considerable efforts have been devoted to the development of methods extracting sparse connectivity graphs. The present paper aims at highlighting the benefits of an alternative approach. We investigate low-rank L2 regularized matrices recently introduced under the denomination of Riccati regularized precision matrices...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
Joan Guàrdia-Olmos, Maribel Peró-Cebollero, Esteve Gudayol-Ferré
Structural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity. Results: A 100 papers were found, of which 25 were rejected due to a lack of sufficient data on basic aspects of the construction of SEM...
2018: Frontiers in Behavioral Neuroscience
Dana Mastrovito, Catherine Hanson, Stephen Jose Hanson
Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects...
2018: NeuroImage: Clinical
Fangyun Tian, Tiecheng Liu, Gang Xu, Duan Li, Talha Ghazi, Trevor Shick, Azeem Sajjad, Michael M Wang, Peter Farrehi, Jimo Borjigin
Sudden cardiac arrest is a leading cause of death in the United States. The neurophysiological mechanism underlying sudden death is not well understood. Previously we have shown that the brain is highly stimulated in dying animals and that asphyxia-induced death could be delayed by blocking the intact brain-heart neuronal connection. These studies suggest that the autonomic nervous system plays an important role in mediating sudden cardiac arrest. In this study, we tested the effectiveness of phentolamine and atenolol, individually or combined, in prolonging functionality of the vital organs in CO2 -mediated asphyxic cardiac arrest model...
2018: Frontiers in Physiology
Christina Andreou, Saskia Steinmann, Katharina Kolbeck, Jonas Rauh, Gregor Leicht, Steffen Moritz, Christoph Mulert
Reports linking a 'jumping-to-conclusions' bias to delusions have led to growing interest in the neurobiological correlates of probabilistic reasoning. Several brain areas have been implicated in probabilistic reasoning; however, findings are difficult to integrate into a coherent account. The present study aimed to provide additional evidence by investigating, for the first time, effective connectivity among brain areas involved in different stages of evidence gathering. We investigated evidence gathering in 25 healthy individuals using fMRI and a new paradigm (Box Task) designed such as to minimize the effects of cognitive effort and reward processing...
February 19, 2018: NeuroImage
Jean-Didier Lemaréchal, Nathalie George, Olivier David
Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system...
February 17, 2018: NeuroImage
Valentina Bruno, Carlotta Fossataro, Francesca Garbarini
Motor imagery (MI) is the mental simulation of an action without any overt movement. Functional evidences show that brain activity during MI and motor execution (ME) largely overlaps. However, the role of the primary motor cortex (M1) during MI is controversial. Effective connectivity techniques show a facilitation on M1 during ME and an inhibition during MI, depending on whether an action should be performed or suppressed. Conversely, Transcranial Magnetic Stimulation (TMS) studies report facilitatory effects during both ME and MI...
February 17, 2018: Neuropsychologia
Nasibeh Talebi, Ali Motie Nasrabadi, Iman Mohammad-Rezazadeh
Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output...
February 2018: Cognitive Neurodynamics
Julia Neitzel, Rachel Nuttall, Christian Sorg
Previous animal research suggests that the spread of pathological agents in Alzheimer's disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans...
2018: Frontiers in Neurology
Liwen Zhang, Esther M Opmeer, Lisette van der Meer, André Aleman, Branislava Ćurčić-Blake, Henricus G Ruhé
OBJECTIVES: Sufficient prefrontal top-down control of limbic affective areas, especially the amygdala, is essential for successful effortful emotion regulation (ER). Difficulties in effortful ER have been seen in patients with bipolar disorder (BD), which could be suggestive of a disturbed prefrontal-amygdala regulation circuit. The aim of this study was to investigate whether BD patients show abnormal effective connectivity from the prefrontal areas to the amygdala during effortful ER (reappraisal)...
February 11, 2018: Bipolar Disorders
Georgios Naros, Florian Grimm, Daniel Weiss, Alireza Gharabaghi
BACKGROUND: Both the cerebello-thalamo-cortical circuit and the basal ganglia/cortical motor loop have been postulated to be generators of tremor in PD. The recent suggestion that the basal ganglia trigger tremor episodes and the cerebello-thalamo-cortical circuitry modulates tremor amplitude combines both competing hypotheses. However, the role of the STN in tremor generation and the impact of proprioceptive feedback on tremor suppression during voluntary movements have not been considered in this model yet...
February 2018: Movement Disorders: Official Journal of the Movement Disorder Society
Volker Pernice, Rava Azeredo da Silveira
Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations...
February 6, 2018: PLoS Computational Biology
Jehan Alswaihli, Roland Potthast, Ingo Bojak, Douglas Saddy, Axel Hutt
Understanding the neural field activity for realistic living systems is a challenging task in contemporary neuroscience. Neural fields have been studied and developed theoretically and numerically with considerable success over the past four decades. However, to make effective use of such models, we need to identify their constituents in practical systems. This includes the determination of model parameters and in particular the reconstruction of the underlying effective connectivity in biological tissues.In this work, we provide an integral equation approach to the reconstruction of the neural connectivity in the case where the neural activity is governed by a delay neural field equation...
February 5, 2018: Journal of Mathematical Neuroscience
Lena Palaniyappan, Gopikrishna Deshpande, Pradyumna Lanka, D Rangaprakash, Sarina Iwabuchi, Susan Francis, Peter F Liddle
OBJECTIVE: Schizophrenia spectrum disorders (SSD) and psychotic bipolar disorder share a number of genetic and neurobiological features, despite a divergence in clinical course and outcome trajectories. We studied the diagnostic classification potential that can be achieved on the basis of the structure and connectivity within a triple network system (the default mode, salience and central executive network) in patients with SSD and psychotic bipolar disorder. METHODS: Directed static connectivity and its dynamic variance was estimated among 8 nodes of the three large-scale networks...
February 2, 2018: Schizophrenia Research
Kit Melissa Larsen, Morten Mørup, Michelle Rosgaard Birknow, Elvira Fischer, Oliver Hulme, Anders Vangkilde, Henriette Schmock, William Frans Christiaan Baaré, Michael Didriksen, Line Olsen, Thomas Werge, Hartwig R Siebner, Marta I Garrido
22q11.2 deletion syndrome (22q11.2DS) is one of the most common copy number variants and confers a markedly increased risk for schizophrenia. As such, 22q11.2DS is a homogeneous genetic liability model which enables studies to delineate functional abnormalities that may precede disease onset. Mismatch negativity (MMN), a brain marker of change detection, is reduced in people with schizophrenia compared to healthy controls. Using dynamic causal modelling (DCM), previous studies showed that top-down effective connectivity linking the frontal and temporal cortex is reduced in schizophrenia relative to healthy controls in MMN tasks...
January 30, 2018: Schizophrenia Research
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