Sporns; Networks of the Brain
Book	Page		Topic
Sporns; Networks of the Brain	3		When a person is cognitively at rest,    quietly awake and alert,    the brain engages in a characteristic pattern of dynamic neural activity.    The spatiotemporal profile of this pattern is molded by an intricate structural network of nerve fibers and pathways.
Sporns; Networks of the Brain	3		Changes in sensory input or cognitive task    result in highly specific patterns of brain activation.    These patterns are the effects of dynamic perturbations of a complex and continually active network.		0
Sporns; Networks of the Brain	4		Behavior and cognition    change over development    and the entire lifespan.    The growth and maturation of anatomical connections in the brain modify the range of your responses and cognitive capacities.		1
Sporns; Networks of the Brain	4		Brain and body    are dynamically coupled    through continual cycles of action and perception.    By causing bodily movement,    brain networks can structure their own inputs    and modulate their internal dynamics.		0
Sporns; Networks of the Brain	5		Network Measures and Architectures		1
Sporns; Networks of the Brain	7		Graphs and Networks -- Definitions		2
Sporns; Networks of the Brain	7		A graph is a mathematical representation of a real world network or, more generally, of some system composed of interconnected elements.		0
Sporns; Networks of the Brain	7		A simple graph comprises a set of nodes and a set of edges.		0
Sporns; Networks of the Brain	8		Edges can be undirected or directed.		1
Sporns; Networks of the Brain	8		One of the most elementary representations of a graph    is the adjacency matrix,     also called a connection matrix.		0
Sporns; Networks of the Brain	9		Nodes can be linked directly by a single edges    or indirectly by sequences of intermediate nodes and edges.		1
Sporns; Networks of the Brain	9		Ordered sequences    of unique edges and intermediate nodes    are call paths.		0
Sporns; Networks of the Brain	9		Sequences of nonunique edges or call walks.		0
Sporns; Networks of the Brain	9		Paths can connect a node to itself,    in which case the path is called a cycle.		0
Sporns; Networks of the Brain	11		Local Segregation -- Clustering and Modularity		2
Sporns; Networks of the Brain	11		A large number of processing characteristics and functional contributions of a node are determined by its interactions within a local neighborhood.		0
Sporns; Networks of the Brain	11		The neighborhood is defined in terms of topological distance    and does not necessarily imply close physical proximity.		0
Sporns; Networks of the Brain	11		Several measures of local connectivity evaluate extent to which the network is organized into the densely coupled neighborhoods,    also known as clusters,    communities,    or modules.		0
Sporns; Networks of the Brain	11		Densely interconnected neighborhoods form a cluster around the node, while sparsely interconnected neighbors do not.		0
Sporns; Networks of the Brain	11		The average of the clustering coefficients for each individual node is the clustering coefficient of the graph.		0
Sporns; Networks of the Brain	12		Clustering, motifs, and modularity    capture aspects of the local connectivity structure of a graph.		1
Sporns; Networks of the Brain	12		Clustering is significant in a neurobiological context    because neuronal units or brain regions that form a densely connected cluster or module    communicate a lot of shared information     and are therefore likely to constitute a functionally coherent brain system.		0
Sporns; Networks of the Brain	13		Clustering and modularity highlight a particular aspect of the functional organization of the brain,    its tendency to form segregated subsystems with specialized functional properties.		1
Sporns; Networks of the Brain	13		Measures of the capacity of the network to engage in global interactions that transcend modules and enable network-wide integration are based on paths and distances between nodes.		0
Sporns; Networks of the Brain	13		One of the most commonly used measures of integration in brain networks is the characteristic path length, usually computed as the global average of the graph's distance matrix.		0
Sporns; Networks of the Brain	15		Hub nodes are densely connected to the rest of the network,    facilitate global integrative processes,    or play a critical compensatory role    when the network is damaged.		2
Sporns; Networks of the Brain	15		In networks that are composed of local communities or modules,    within-module and between-modules connectivity    can provide information about the specific contributions of individual nodes.		0
Sporns; Networks of the Brain	15		Several measures of centrality are based on the notion of shortest paths.		0
Sporns; Networks of the Brain	15		A node is central if it has great control over the flow of information    within the network.		0
Sporns; Networks of the Brain	20		Power-law degree distributions    are shared across many networks and indicate a "scale-free" organization.		5
Sporns; Networks of the Brain	20		The term "scale-free"    refers to the fact that a power-law distribution    has no characteristic scale --    zooming in on any segment of the distribution    does not change its shape.		0
Sporns; Networks of the Brain	31		Brain Networks -- Structure and Dynamics		11
Sporns; Networks of the Brain	35		Nervous systems are organized on multiple scales,    from synaptic connections between single cells,    to the organization of cell populations within individual anatomical regions,    and finally to large-scale architecture brain regions and their interconnecting pathways.		4
Sporns; Networks of the Brain	35		The multiscale aspect of the nervous system is an essential feature of its organization and network architecture.		0
Sporns; Networks of the Brain	35		Brain connectivity at the large-scale (among regions and systems) describes neural processes that are the outcome of dynamic coordination among smaller elements.		0
Sporns; Networks of the Brain	36		Structural connectivity refers to a set of physical or structural (anatomical) connections    linking neural elements.		1
Sporns; Networks of the Brain	37		Functional connectivity captures patterns of deviations from statistical independence between distributed and often spatially remote neuronal units.		1
Sporns; Networks of the Brain	37		Effective connectivity describes a network of causal effects between neural elements,     which can be inferred through time series analysis,    statistical modeling,    or experimental perturbations.		0
Sporns; Networks of the Brain	37		The close relationship between structure and function in the brain    can create some ambiguity as to whether a neural parameter is best classified as structural or functional.		0
Sporns; Networks of the Brain	37		Neural communication    is significantly affected by    axonal conduction delays.		0
Sporns; Networks of the Brain	38		One of the most fundamental problems of graph analysis in the brain    is the definition of nodes and edges.		1
Sporns; Networks of the Brain	40		Techniques that resolve single neurons    currently permit the observation of only a small number of cells    embedded within a vast and mostly unobserved network.		2
Sporns; Networks of the Brain	40		All noninvasive techniques,    while covering a large part of the brain,    record signals that originate from neuronal populations.		0
Sporns; Networks of the Brain	40		All studies of structural and functional brain networks    require a parcellation of the recorded brain volume    into distinct regions and connections.		0
Sporns; Networks of the Brain	40		Node definition    generally involves an anatomical parcellation    into coherent regions    on the basis of histological or imaging data.		0
Sporns; Networks of the Brain	40		Defining nodes as individual voxels in fMRI data or electrodes or sensors in electrophysiological or MEG experiments.		0
Sporns; Networks of the Brain	40		Edge definition    involves the estimation of pairwise associations between nodes.		0
Sporns; Networks of the Brain	41		The use of perturbations offers one approach for discerning causal patterns.		1
Sporns; Networks of the Brain	47		The extraordinary variety and complexity of neural network activity patterns    requires computational modeling of empirical data to achieve an understanding of the system that is both explanatory and predictive.		6
Sporns; Networks of the Brain	47		No experimental manipulation is devised without recourse to some sort of model or representation of the essential components and interactions and their expected behavior.		0
Sporns; Networks of the Brain	47		Dynamic connectivity-based models    are indispensable for understanding how the local activity of neural units    is coordinated and integrated    to achieve global patterns.		0
Sporns; Networks of the Brain	47		The basis of all computational models is a set of state equations that govern the temporal evolution of the dynamic variables.		0
Sporns; Networks of the Brain	48		Given a set of initial conditions,    the trajectory of the system will flow toward a bounded set of points that constitute an attractor.		1
Sporns; Networks of the Brain	48		An attractor    may be as simple as a single fixed point    or have a more elaborate geometric shape    such as limit cycles (in the case at periodic dynamics)    or strange attractors (in the case that chaotic dynamics).		0
Sporns; Networks of the Brain	48		The set of points from which the system slows to a given attractor    is its basin of attraction.		0
Sporns; Networks of the Brain	51		In the history of neuroscience the concept of functional localization pitches those who view brain function as resulting from action of specialized centers against those who conceptualize brain function as fundamentally nonlocal and distributed.		3
Sporns; Networks of the Brain	52		The functional specialization of each local element is determined in part by the intrinsic properties of the element    and in part by its extrinsic network interactions.		1
Sporns; Networks of the Brain	52		Mapping the anatomy of the brain networks offers important clues as to the functional specialization of each of the network elements.		0
Sporns; Networks of the Brain	53		Connectivity carries information about the functionality of elements    in different kinds of biological networks.		1
Sporns; Networks of the Brain	53		Phrenology,    the identification of psychological and personality traits    on the basis of protrusions or bumps on a person's skull,    has been thoroughly debunked as a pseudoscience.		0
Sporns; Networks of the Brain	54		Brodmann's cortical maps and his regional classification remains an important reference system for cortical localization.		1
Sporns; Networks of the Brain	56		Karl Lashley's studies    of the behavioral effects of ablations and white matter cuts in rat brain    led him to reject localization of function    and to emphasize the distributed nature of brain function.		2
Sporns; Networks of the Brain	60		Connectivity profiles and the resulting cross correlation matrix    identified clusters of voxels with shared connectivity patterns.		4
Sporns; Networks of the Brain	61		Parcellation of Broca's area in the left inferior frontal cortex.    Broca's area appears segregated into three distinct subregions,    derived on the basis of the similarities and dissimilarities of their long-range structural connections    estimated from diffusion imaging followed by computational tractography. (diagram)		1
Sporns; Networks of the Brain	62		Connectivity-based parcellation,    in conjunction with probabilistic maps of cellular microanatomy,    has great promise for relating brain structure to function.		1
Sporns; Networks of the Brain	66		Variability in Brain Connectivity		4
Sporns; Networks of the Brain	66		Variability is an essential feature of many biological systems,    and is one of the major driving forces of evolution.		0
Sporns; Networks of the Brain	67		A significant proportion of variable neuronal morphology and network structure    is likely the result of experience- and activity-dependent processes.		1
Sporns; Networks of the Brain	67		Brain networks combine a strong tendency toward functional homeostasis with the capacity to express variations in behavior.		0
Sporns; Networks of the Brain	68		Structurally variable but functionally equivalent networks, are an example of degeneracy,    defined as the capacity of systems to perform similar functions despite differences in the way they are configured and connected.		1
Sporns; Networks of the Brain	69		Degeneracy is widespread among biological systems    and can be found in molecular,    cellular,    and large-scale networks.		1
Sporns; Networks of the Brain	69		Human brain networks display degeneracy, since different sets of brain regions can support a given cognitive function.		0
Sporns; Networks of the Brain	69		Different individuals utilize different (degenerate) networks.		0
Sporns; Networks of the Brain	69		Individual brain regions may not be necessary or, alternatively, the recovery processes following brain injury can configure structurally different but functionally equivalent networks.		0
Sporns; Networks of the Brain	69		Degeneracy in cognitive networks    is suggestive of the idea that mechanisms promoting functional homeostasis    may operate at the scale of the whole brain    to ensure that structural variations or disturbances    do not lead to uncontrolled divergence of functional outcomes.		0
Sporns; Networks of the Brain	69		Nervous systems exhibit striking diversity of neuronal cell types,    distinguished by their characteristic cellular morphology.		0
Sporns; Networks of the Brain	69		Diversity and variability in cortical interneurons has been shown to affect network dynamics,    with greater variability leading to less pronounced network synchrony.		0
Sporns; Networks of the Brain	69		Diverse and variable cell morphology    may help to regulate the excitability of nervous tissue, a potentially important factor in preventing pathological states such as epilepsy.		0
Sporns; Networks of the Brain	69		Heterogeneity of interneurons has been invoked as a source of greater "computational power" for cortical networks.		0
Sporns; Networks of the Brain	70		Anatomical tracing and staining techniques    that allow the visualization of the fine structure    of morphologically and physiologically identified neurons and local circuits    has provided abundant evidence that cells of different types    form and maintain    specific connection patterns.		1
Sporns; Networks of the Brain	70		Computational studies suggest that specific neuronal morphologies --  e.g. dendritic branching patterns and synaptic distributions -- support specific elementary computations.		0
Sporns; Networks of the Brain	71		As the cellular architecture of the brain is probed with ever more refined methods, the structure of every neuron will reveal unique patterns of neuronal processes and intracellular junctions.		1
Sporns; Networks of the Brain	71		Homeostatic and coordinative processes within the nervous system ensure that variability at molecular or cellular scales generally does not perturb processes unfolding on larger scales.		0
Sporns; Networks of the Brain	71		The modularity of brain's architecture effectively insulates functionally bound subsystems from spreading perturbations due to small fluctuations in structure or dynamics.		0
Sporns; Networks of the Brain	71		Individual neurons, even those belonging to the same class, must remain different from one another to continually create dynamic variability as a substrate for adaptive change.		0
Sporns; Networks of the Brain