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https://www.readbyqxmd.com/read/28635541/association-between-abnormal-brain-functional-connectivity-in-children-and-psychopathology-a-study-based-on-graph-theory-and-machine-learning
#1
João Ricardo Sato, Claudinei Eduardo Biazoli, Giovanni Abrahão Salum, Ary Gadelha, Nicolas Crossley, Gilson Vieira, André Zugman, Felipe Almeida Picon, Pedro Mario Pan, Marcelo Queiroz Hoexter, Edson Amaro, Mauricio Anés, Luciana Monteiro Moura, Marco Antonio Gomes Del'Aquilla, Philip Mcguire, Luis Augusto Rohde, Euripedes Constantino Miguel, Andrea Parolin Jackowski, Rodrigo Affonseca Bressan
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). METHODS: We applied this approach to resting-state fMRI data from 622 children and adolescents...
February 8, 2017: World Journal of Biological Psychiatry
https://www.readbyqxmd.com/read/28633299/structural-covariance-networks-in-children-with-autism-or-adhd
#2
R A I Bethlehem, R Romero-Garcia, E Mak, E T Bullmore, S Baron-Cohen
Background: While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Method: Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69)...
June 13, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28622528/band-specific-atypical-functional-connectivity-pattern-in-childhood-autism-spectrum-disorder
#3
Tetsuya Takahashi, Teruya Yamanishi, Sou Nobukawa, Shinya Kasakawa, Yuko Yoshimura, Hirotoshi Hiraishi, Chiaki Hasegawa, Takashi Ikeda, Tetsu Hirosawa, Toshio Munesue, Haruhiro Higashida, Yoshio Minabe, Mitsuru Kikuchi
OBJECTIVE: Altered brain connectivity has been theorized as a key neural underpinning of autism spectrum disorder (ASD), but recent investigations have revealed conflicting patterns of connectivity, particularly hyper-connectivity and hypo-connectivity across age groups. The application of graph theory to neuroimaging data has become an effective approach for characterizing topographical patterns of large-scale functional networks. We used a graph approach to investigate alteration of functional networks in childhood ASD...
May 23, 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
https://www.readbyqxmd.com/read/28620239/shared-and-specific-intrinsic-functional-connectivity-patterns-in-unmedicated-bipolar-disorder-and-major-depressive-disorder
#4
Ying Wang, Junjing Wang, Yanbin Jia, Shuming Zhong, Meiqi Niu, Yao Sun, Zhangzhang Qi, Ling Zhao, Li Huang, Ruiwang Huang
Identifying brain differences and similarities between bipolar disorder (BD) and major depressive disorder (MDD) is necessary for increasing our understanding of the pathophysiology and for developing more effective treatments. However, the features of whole-brain intrinsic functional connectivity underlying BD and MDD have not been directly compared. We collected resting-state fMRI data from 48 BD patients, 48 MDD patients, and 51 healthy subjects. We constructed voxel-wise whole-brain functional networks and computed regional functional connectivity strength (FCS) using graph-theory and further divided the regional FCS into long-range FCS (lFCS) and short-range FCS (sFCS)...
June 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28618557/scale-free-networks-emerging-from-multifractal-time-series
#5
Marcello A Budroni, Andrea Baronchelli, Romualdo Pastor-Satorras
Methods connecting dynamical systems and graph theory have attracted increasing interest in the past few years, with applications ranging from a detailed comparison of different kinds of dynamics to the characterization of empirical data. Here we investigate the effects of the (multi)fractal properties of a signal, common in time series arising from chaotic dynamics or strange attractors, on the topology of a suitably projected network. Relying on the box-counting formalism, we map boxes into the nodes of a network and establish analytic expressions connecting the natural measure of a box with its degree in the graph representation...
May 2017: Physical Review. E
https://www.readbyqxmd.com/read/28618512/cavity-master-equation-for-the-continuous-time-dynamics-of-discrete-spin-models
#6
E Aurell, G Del Ferraro, E Domínguez, R Mulet
We present an alternate method to close the master equation representing the continuous time dynamics of interacting Ising spins. The method makes use of the theory of random point processes to derive a master equation for local conditional probabilities. We analytically test our solution studying two known cases, the dynamics of the mean-field ferromagnet and the dynamics of the one-dimensional Ising system. We present numerical results comparing our predictions with Monte Carlo simulations in three different models on random graphs with finite connectivity: the Ising ferromagnet, the random field Ising model, and the Viana-Bray spin-glass model...
May 2017: Physical Review. E
https://www.readbyqxmd.com/read/28617222/an-extensive-assessment-of-network-alignment-algorithms-for-comparison-of-brain-connectomes
#7
Marianna Milano, Pietro Hiram Guzzi, Olga Tymofieva, Duan Xu, Christofer Hess, Pierangelo Veltri, Mario Cannataro
BACKGROUND: Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation...
June 6, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28612393/network-theory-and-metapopulation-persistence-incorporating-node-self-connections
#8
Jessica Zamborain-Mason, Garry R Russ, Rene A Abesamis, Abner A Bucol, Sean R Connolly
Network analysis is gaining increasing importance in conservation planning. However, which network metrics are the best predictors of metapopulation persistence is still unresolved. Here, we identify a critical limitation of graph theory-derived network metrics that have been proposed for this purpose: their omission of node self-connections. We resolve this by presenting modifications of existing network metrics, and developing entirely new metrics, that account for node self-connections. Then, we illustrate the performance of these new and modified metrics with an age-structured metapopulation model for a real-world marine reserve network case study, and we evaluate the robustness of our findings by systematically varying particular features of that network...
July 2017: Ecology Letters
https://www.readbyqxmd.com/read/28607055/brain-circuit-gene-expression-relationships-and-neuroplasticity-of-multisensory-cortices-in-blind-children
#9
Laura Ortiz-Terán, Ibai Diez, Tomás Ortiz, David L Perez, Jose Ignacio Aragón, Victor Costumero, Alvaro Pascual-Leone, Georges El Fakhri, Jorge Sepulcre
Sensory deprivation reorganizes neurocircuits in the human brain. The biological basis of such neuroplastic adaptations remains elusive. In this study, we applied two complementary graph theory-based functional connectivity analyses, one to evaluate whole-brain functional connectivity relationships and the second to specifically delineate distributed network connectivity profiles downstream of primary sensory cortices, to investigate neural reorganization in blind children compared with sighted controls. We also examined the relationship between connectivity changes and neuroplasticity-related gene expression profiles in the cerebral cortex...
June 12, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28602817/on-memories-neural-ensembles-and-mental-flexibility
#10
Dimitris A Pinotsis, Scott L Brincat, Earl K Miller
Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed...
June 9, 2017: NeuroImage
https://www.readbyqxmd.com/read/28575716/structural-network-connectivity-impairment-and-depressive-symptoms-in-cerebral-small-vessel-disease
#11
Xiaofeng Xie, Yulu Shi, Junjian Zhang
BACKGROUND: Cerebral small vessel disease (SVD) can disrupt mood regulation circuits and cause depressive symptoms which may occur prior to onset of other symptoms. However, the topological network alterations in SVD with depressive symptoms remained unclear. We aim to investigate how these changes in structural network were related to depressive symptoms in SVD. METHODS: We recruited 20 SVD with depressive symptoms (SVD+D), 20 SVD without depressive symptoms (SVD-D) and 16 healthy control (HC) individuals...
May 26, 2017: Journal of Affective Disorders
https://www.readbyqxmd.com/read/28573962/anatomical-integration-and-rich-club-connectivity-in-euthymic-bipolar-disorder
#12
S O'Donoghue, L Kilmartin, D O'Hora, L Emsell, C Langan, S McInerney, N J Forde, A Leemans, B Jeurissen, G J Barker, P McCarthy, D M Cannon, C McDonald
BACKGROUND: Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the 'rich-club', represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses...
July 2017: Psychological Medicine
https://www.readbyqxmd.com/read/28569530/network-analysis-for-the-visualization-and-analysis-of-qualitative-data
#13
Jennifer J Pokorny, Alex Norman, Anthony P Zanesco, Susan Bauer-Wu, Baljinder K Sahdra, Clifford D Saron
We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures...
June 1, 2017: Psychological Methods
https://www.readbyqxmd.com/read/28562217/deep-restricted-kernel-machines-using-conjugate-feature-duality
#14
Johan A K Suykens
The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal components analysis (PCA), matrix singular value decomposition, and Parzen-type models...
May 31, 2017: Neural Computation
https://www.readbyqxmd.com/read/28553837/inflexible-functional-connectivity-of-the-dorsal-anterior-cingulate-cortex-in-adolescent-major-depressive-disorder
#15
Tiffany C Ho, Matthew D Sacchet, Colm G Connolly, Daniel S Margulies, Olga Tymofiyeva, Martin P Paulus, Alan N Simmons, Ian H Gotlib, Tony T Yang
Recent evidence suggests that anterior cingulate cortex (ACC) maturation during adolescence contributes to or underlies the development of major depressive disorder (MDD) during this sensitive period. The ACC is a structure that sits at the intersection of several task-positive networks (eg, central executive network, CEN) which are still developing during adolescence. While recent work using seed-based approaches indicate that depressed adolescents show limited task-evoked versus resting-state connectivity (termed 'inflexibility') between the ACC and task-negative networks, no study has used network-based approaches to investigate inflexibility of the ACC in task-positive networks to understand adolescent MDD...
May 29, 2017: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
https://www.readbyqxmd.com/read/28531764/disrupted-resting-brain-graph-measures-in-individuals-at-high-risk-for-alcoholism
#16
Bharath Holla, Rajanikant Panda, Ganesan Venkatasubramanian, Bharat Biswal, Rose Dawn Bharath, Vivek Benegal
Familial susceptibility to alcoholism is likely to be linked to the externalizing diathesis seen in high-risk offspring from high-density alcohol use disorder (AUD) families. The present study aimed at comparing resting brain functional connectivity and their association with externalizing symptoms and alcoholism familial density in 40 substance-naive high-risk (HR) male offspring from high-density AUD families and 30 matched healthy low-risk (LR) males without a family history of substance dependence using graph theory-based network analysis...
May 14, 2017: Psychiatry Research
https://www.readbyqxmd.com/read/28520668/structural-connectivity-related-to-persistent-symptoms-after-mild-tbi-in-adolescents-and-response-to-aerobic-training-preliminary-investigation
#17
Weihong Yuan, Shari L Wade, Catherine Quatman-Yates, Jason A Hugentobler, Paul J Gubanich, Brad G Kurowski
OBJECTIVE: To quantify structural connectivity abnormalities in adolescents with mild traumatic brain injury (mTBI) and to investigate connectivity changes following aerobic training using graph theory and diffusion tensor imaging tractography. SETTING: Outpatient research setting. PARTICIPANTS: Twenty-two children (age: 15.83 ± 1.77 years, 10 females) with 4 to 16 weeks of persistent symptoms after mTBI and a matched healthy comparison group...
May 17, 2017: Journal of Head Trauma Rehabilitation
https://www.readbyqxmd.com/read/28515708/disrupted-structural-and-functional-networks-and-their-correlation-with-alertness-in-right-temporal-lobe-epilepsy-a-graph-theory-study
#18
Wenyu Jiang, Jianping Li, Xuemei Chen, Wei Ye, Jinou Zheng
Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls...
2017: Frontiers in Neurology
https://www.readbyqxmd.com/read/28499176/brain-networks-structural-realism-and-local-approaches-to-the-scientific-realism-debate
#19
Karen Yan, Jonathon Hricko
We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge)...
May 9, 2017: Studies in History and Philosophy of Biological and Biomedical Sciences
https://www.readbyqxmd.com/read/28491032/topological-filtering-of-dynamic-functional-brain-networks-unfolds-informative-chronnectomics-a-novel-data-driven-thresholding-scheme-based-on-orthogonal-minimal-spanning-trees-omsts
#20
Stavros I Dimitriadis, Christos Salis, Ioannis Tarnanas, David E Linden
The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or "nodes," and quantifying the strength of the connections between nodes, or "edges," as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory...
2017: Frontiers in Neuroinformatics
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