Single-Subject Grey Matter Graphs in Alzheimer's Disease

Coordinated patterns of cortical morphology have been described as structural graphs and previous research has demonstrated that properties of such graphs are altered in Alzheimer's disease (AD). However, it remains unknown how these alterations are related to cognitive deficits in individuals, as such graphs are restricted to group-level analysis. In the present study we investigated this question in single-subject grey matter networks. This new method extracts large-scale structural graphs where nodes represent small cortical regions that are connected by edges when they show statistical similarity. Using this method, unweighted and undirected networks were extracted from T1 weighted structural magnetic resonance imaging scans of 38 AD patients (19 female, average age 72±4 years) and 38 controls (19 females, average age 72±4 years). Group comparisons of standard graph properties were performed after correcting for grey matter volumetric measurements and were correlated to scores of general cognitive functioning. AD networks were characterised by a more random topology as indicated by a decreased small world coefficient (p = ), decreased normalized clustering coefficient (p = 7.25×10−6) and decreased normalized path length (p = 1.91×10−7). Reduced normalized path length explained significantly (p = 0.004) more variance in measurements of general cognitive decline (32%) in comparison to volumetric measurements (9%). Altered path length of the parahippocampal gyrus, hippocampus, fusiform gyrus and precuneus showed the strongest relationship with cognitive decline. The present results suggest that single-subject grey matter graphs provide a concise quantification of cortical structure that has clinical value, which might be of particular importance for disease prognosis. These findings contribute to a better understanding of structural alterations and cognitive dysfunction in AD.

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title: Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features
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abstract: Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

title: Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity
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title: Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory
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title: Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease
abstract: Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-β deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.

title: Relationships between β-amyloid and functional connectivity in different components of the default mode network in aging.
abstract: Although beta-amyloid (Aβ) deposition is a characteristic feature of Alzheimer's disease (AD), this pathology is commonly found in elderly normal controls (NC). The pattern of Aβ deposition as detected with Pittsburgh compound-B positron emission tomography (PIB-PET) imaging shows substantial spatial overlap with the default mode network (DMN), a group of brain regions that typically deactivates during externally driven cognitive tasks. In this study, we show that DMN functional connectivity (FC) during rest is altered with increasing levels of PIB uptake in NC. Specifically, FC decreases were identified in regions implicated in episodic memory (EM) processing (posteromedial cortex, ventral medial prefrontal cortex, and angular gyrus), whereas connectivity increases were detected in dorsal and anterior medial prefrontal and lateral temporal cortices. This pattern of decreases is consistent with previous studies that suggest heightened vulnerability of EM-related brain regions in AD, whereas the observed increases in FC may reflect a compensatory response.

title: Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease
abstract: Brain connectivity studies have revealed that highly connected ‘hub’ regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. ‘Activity dependent degeneration’ (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.

title: The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease
abstract: The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all‐cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.

title: A global optimisation method for robust affine registration of brain images
abstract: Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework - one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.

title: Robust voxel similarity metrics for the registration of dissimilar single and multimodal images
abstract: In this paper, we develop data driven registration algorithms, relying on pixel similarity metrics, that enable an accurate (subpixel) rigid registration of dissimilar single or multimodal 2D/3D images. Gross dissimilarities are handled by considering similarity measures related to robust M-estimators. In particular, a novel (robust) similarity metric is proposed for comparing multimodal images. The proposed robust similarity metrics are compared to the most popular standard similarity metrics, on synthetic as well as on real-world image pairs showing gross dissimilarities. Three case studies are considered: the registration of single modal and multimodal 3D medical images, the matching of multispectral remotely sensed images, and the registration of intensity and range images. The proposed robust similarity measures compare favourably with the standard (non-robust) techniques.

title: Gray-Value Based Registration of CT and MR Images by Maximization of Local Correlation
abstract: For gray-value based multi-modality registration the similarity measure is essential. Excellent results have been obtained with mutual information for various modality combinations. In this contribution we consider local correlation as similarity measure for multi-modality registration. Using a software phantom it is analyzed why local correlation is suitable for this registration task whereas direct gray-value correlation itself is usually not. It is shown that registration with local correlation can be done using only a fraction of the image volume offering an opportunity to accelerate the algorithm. Within validation, registration of the phantom images, two simultaneously acquired dual contrast MR images, and a clinical CT-MR data set has been studied. For comparison, the data sets have also been registered with mutual information. The results show that not only mutual information, but also local correlation is suitable for gray-value based multi-modality registration.

title: A Comparison of Simularity Measures for use in 2D-3D Medical Image Registration
abstract: A comparison of six similarity measures for use in intensity based 2D-3D image registration is presented. The accuracy of the similarity measures are compared to a “gold-standard” registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a CT scan to a fluoroscopy image of a spine phantom. The registration is carried out within a region of interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modelled by a phantom image alone. More realistic “gold standard” data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2D-3D image registration. Two measures were able to register accurately and robustly even when soft tissue structures and interventional instruments were present as differences between the images. These measures are called pattern intensity and gradient difference.

title: The architecture of complex weighted networks.
abstract: Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.

title: Complex network measures of brain connectivity: Uses and interpretations
abstract: Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets.

title: Collective dynamics of ‘small-world’ networks
abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

title: Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
abstract: Many technological, biological, social, and information networks fall into the broad class of ‘small-world’ networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction (‘small/not-small’) rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model – the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified.  We defined a precise measure of ‘small-world-ness’ S based on the trade off between high local clustering and short path length. A network is now deemed a ‘small-world’ if S>1 - an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process.  We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.

title: Specificity and Stability in Topology of Protein Networks
abstract: Molecular networks guide the biochemistry of a living cell on multiple levels: Its metabolic and signaling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks, we quantified correlations between connectivities of interacting nodes and compared them to a null model of a network, in which all links were randomly rewired. We found that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, whereas those between a highly connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell and increases the overall robustness of a network by localizing effects of deleterious perturbations.

title: Emergence of scaling in random networks
abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

title: The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain
abstract: Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple "tool du jour." To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function.

title: Rank Transformations as a Bridge between Parametric and Nonparametric Statistics
abstract: Many of the more useful and powerful nonparametric procedures may be presented in a unified manner by treating them as rank transformation procedures. Rank transformation procedures are ones in which the usual parametric procedure is applied to the ranks of the data instead of to the data themselves. This technique should be viewed as a useful tool for developing nonparametric procedures to solve new problems.

title: Analysis of covariance using the rank transformation.
abstract: The rank transformation refers to the replacement of data by their ranks, with a subsequent analysis using the usual normal theory procedure, but calculated on the ranks rather than on the data. Rank transformation procedures have previously been shown by the authors to have properties of robustness and power in both regression and analysis of variance. It seems natural to consider the use of the rank transformation in analysis of covariance, which is a combination of regression and analysis of variance. In this paper the rank transformation approach to analysis of covariance is presented and examined. Comparisons are made with the rank transformation procedure given by Quade (1967, 1200), and some 'standard' data sets are used to compare the results of these two procedures. A Monte Carlo simulation study examines the behavior of these methods under the null hypothesis and under alternative hypotheses, with both normal and nonnormal distributions. All results are compared with the usual analysis of covariance procedure on the basis of robustness and power.

title: Whole-brain anatomical networks: Does the choice of nodes matter?
abstract: Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while white-matter fiber bundles serve as interconnecting network links. However, the lack of a gold standard for regional parcellation in brain MRI makes the definition of nodes arbitrary, meaning that network nodes are defined using templates employing either random or anatomical parcellation criteria. Consequently, the number of nodes included in networks studied by different authors has varied considerably, from less than 100 up to more than 10(4). Here, we systematically and quantitatively assess the behavior, structure and topological attributes of whole-brain anatomical networks over a wide range of nodal scales, a variety of grey-matter parcellations as well as different diffusion-MRI acquisition protocols. We show that simple binary decisions about network organization, such as whether small-worldness or scale-freeness is evident, are unaffected by spatial scale, and that the estimates of various organizational parameters (e.g. small-worldness, clustering, path length, and efficiency) are consistent across different parcellation scales at the same resolution (i.e. the same number of nodes). However, these parameters vary considerably as a function of spatial scale; for example small-worldness exhibited a difference of 95% between the widely-used automated anatomical labeling (AAL) template (approximately 100 nodes) and a 4000-node random parcellation (sigma(AAL)=1.9 vs. sigma(4000)=53.6+/-2.2). These findings indicate that any comparison of network parameters across studies must be made with reference to the spatial scale of the nodal parcellation.

title: Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
abstract: Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

title: Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain
abstract: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, ) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999,  Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.

title: Controlling the false discovery rate: a practical and powerful approach to multiple testing
abstract: The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.

title: THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY
abstract: Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased. 1.1. Simultaneous hypotheses testing. The control of the increased type I error when testing simultaneously a family of hypotheses is a central issue in the area of multiple comparisons. Rarely are we interested only in whether all hypotheses are jointly true or not, which is the test of the intersection null hypothesis. In most applications, we infer about the individual hypotheses, realizing that some of the tested hypotheses are usually true—we hope not all—and some are not. We wish to decide which ones are not true, indicating (statistical) discoveries. An important such problem is that of multiple endpoints in a clinical trial: a new treatment is compared with an existing one in terms of a large number of potential benefits (endpoints).

title: Correlated Size Variations in Human Visual Cortex, Lateral Geniculate Nucleus, and Optic Tract
abstract: We have examined several components of the human visual system to determine how the dimensions of the optic tract, lateral geniculate nucleus (LGN), and primary visual cortex (V1) vary within the same brain. Measurements were made of the cross-sectional area of the optic tract, the volumes of the magnocellular and parvocellular layers of the LGN, and the surface area and volume of V1 in one or both cerebral hemispheres of 15 neurologically normal human brains obtained at autopsy. Consistent with previous observations, there was a two- to threefold variation in the size of each of these visual components among the individuals studied. Importantly, this variation was coordinated within the visual system of any one individual. That is, a relatively large V1 was associated with a commensurately large LGN and optic tract, whereas a relatively small V1 was associated with a commensurately smaller LGN and optic tract. This relationship among the components of the human visual system indicates that the development of its different parts is interdependent. Such coordinated variation should generate substantial differences in visual ability among humans.

title: Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI
abstract: Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions implicated in this network, several, including the posterior cingulate cortex and inferior parietal lobes, have also shown decreased metabolism early in the course of Alzheimer's disease (AD). We reasoned that default-mode network activity might therefore be abnormal in AD. To test this hypothesis, we used independent component analysis to isolate the network in a group of 13 subjects with mild AD and in a group of 13 age-matched elderly controls as they performed a simple sensory-motor processing task. Three important findings are reported. Prominent coactivation of the hippocampus, detected in all groups, suggests that the default-mode network is closely involved with episodic memory processing. The AD group showed decreased resting-state activity in the posterior cingulate and hippocampus, suggesting that disrupted connectivity between these two regions accounts for the posterior cingulate hypometabolism commonly detected in positron emission tomography studies of early AD. Finally, a goodness-of-fit analysis applied at the individual subject level suggests that activity in the default-mode network may ultimately prove a sensitive and specific biomarker for incipient AD.

title: Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study
abstract: Activity and reactivity of the default mode network in the brain was studied using functional magnetic resonance imaging (fMRI) in 28 nondemented individuals with mild cognitive impairment (MCI), 18 patients with mild Alzheimer's disease (AD), and 41 healthy elderly controls (HC). The default mode network was interrogated by means of decreases in brain activity, termed deactivations, during a visual encoding task and during a nonspatial working memory task. Deactivation was found in the default mode network involving the anterior frontal, precuneus, and posterior cingulate cortex. MCI patients showed less deactivation than HC, but more than AD. The most pronounced differences between MCI, HC, and AD occurred in the very early phase of deactivation, reflecting the reactivity and adaptation of the network. The default mode network response in the anterior frontal cortex significantly distinguished MCI from both HC (in the medial frontal) and AD (in the anterior cingulate cortex). The response in the precuneus could only distinguish between patients and HC, not between MCI and AD. These findings may be consistent with the notion that MCI is a transitional state between healthy aging and dementia and with the proposed early changes in MCI in the posterior cingulate cortex and precuneus. These findings suggest that altered activity in the default mode network may act as an early marker for AD pathology.

title: Specificity of brain activation patterns in people at genetic risk for Alzheimer disease.
abstract: Previous studies with positron-emission tomography (PET) and functional magnetic resonance imaging (fMRI) have indicated differences in neural metabolism and activity between carriers of the APOE epsilon4 allele and those who are not at risk for Alzheimer disease (AD). Persons without dementia carrying the epsilon4 allele showed greater magnitude and extent of brain activation than noncarriers in regions required for memory, suggesting they performed additional cognitive work to accomplish the same task. To determine whether the fMRI differences were specific to a memory task or generalizable to any difficult cognitive task, the authors performed fMRI and compared images from 25 subjects with and without the APOE epsilon4 allele. In the most difficult conditions, all subjects showed increased MR signal in the prefrontal cortex, indicating increased cognitive effort. However, the two genetic groups showed no differences in activation patterns even at the most difficult task level, suggesting that additional cognitive effort in persons at genetic risk for AD is specific to episodic encoding and is not merely a reflection of task difficulty.

title: Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia
abstract: Fluorodeoxyglucose positron emission tomography (PET) studies have found that patients with Alzheimer's dementia (AD) have abnormally low rates of cerebral glucose metabolism in posterior cingulate, parietal, temporal, and prefrontal cortex. We previously found that cognitively normal, late-middle-aged carriers of the apolipoprotein E ε4 allele, a common susceptibility gene for late-onset Alzheimer's dementia, have abnormally low rates of glucose metabolism in the same brain regions as patients with probable AD. We now consider whether ε4 carriers have these regional brain abnormalities as relatively young adults. Apolipoprotein E genotypes were established in normal volunteers 20–39 years of age. Clinical ratings, neuropsychological tests, magnetic resonance imaging, and PET were performed in 12 ε4 heterozygotes, all with the ε3/ε4 genotype, and 15 noncarriers of the ε4 allele, 12 of whom were individually matched for sex, age, and educational level. An automated algorithm was used to generate an aggregate surface-projection map that compared regional PET measurements in the two groups. The young adult ε4 carriers and noncarriers did not differ significantly in their sex, age, educational level, clinical ratings, or neuropsychological test scores. Like previously studied patients with probable AD and late-middle-aged ε4 carriers, the young ε4 carriers had abnormally low rates of glucose metabolism bilaterally in the posterior cingulate, parietal, temporal, and prefrontal cortex. Carriers of a common Alzheimer's susceptibility gene have functional brain abnormalities in young adulthood, several decades before the possible onset of dementia.

title: A tension-based theory of morphogenesis and compact wiring in the central nervous system.
abstract: Many structural features of the mammalian central nervous system can be explained by a morphogenetic mechanism that involves mechanical tension along axons, dendrites and glial processes. In the cerebral cortex, for example, tension along axons in the white matter can explain how and why the cortex folds in a characteristic species-specific pattern. In the cerebellum, tension along parallel fibres can explain why the cortex is highly elongated but folded like an accordion. By keeping the aggregate length of axonal and dendritic wiring low, tension should contribute to the compactness of neural circuitry throughout the adult brain.

title: Developmental mechanics of the primate cerebral cortex
abstract: The idea that the brain is shaped through the interplay of predetermined ontogenetic factors and mechanisms of self-organization has a long tradition in biology, going back to the late-nineteenth century. Here we illustrate the substantial impact of mechanical forces on the development, morphology, and functioning of the primate cerebral cortex. Based on the analysis of quantitative structural data for prefrontal cortices of the adult rhesus monkey, we demonstrate that (1) the characteristic shape of cortical convolutions can be explained by the global minimization of axonal tension in corticocortical projections; (2) mechanical forces resulting from cortical folding have a significant impact on the relative and absolute thickness of cortical layers in gyri and sulci; (3) folding forces may affect the cellular migration during cortical development, resulting in a significantly larger number of neurons in gyral compared to non-gyral regions; and (4) mechanically induced variations of morphology at the cellular level may result in different modes of neuronal functioning in gyri and sulci. These results underscore the significant contribution of mechanical forces during the self-organization of the primate cerebral cortex. Taking such factors into account within a framework of developmental mechanics can lead to a better understanding of how genetic specification, the layout of connections, brain shape as well as brain function are linked in normal and pathologically transformed brains.

title: Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex
abstract: Cortical thickness correlation across individuals has been observed. So far, it remains unclear to what extent such a correlation in thickness is a reflection of underlying fiber connection. Here we explicitly compared the patterns of cortical thickness correlation and diffusion-based fiber connection across the entire cerebral cortex, in 95 normal adults. Interregional thickness correlations were extracted by using computational neuroanatomy algorithms based on structural MRI, and diffusion connections were detected by using diffusion probabilistic tractography. Approximately 35-40% of thickness correlations showed convergent diffusion connections across the cerebral cortex. Intriguingly, the observed convergences between thickness correlation and diffusion connection are mostly focused on the positive thickness correlations, while almost all of the negative correlations (>90%) did not have a matched diffusion connection, suggesting different mechanisms behind the positive and negative thickness correlations, the latter not being mediated by a direct fiber pathway. Furthermore, graph theoretic analysis reveals that the thickness correlation network has a more randomized overall topology, whereas the nodal characteristics of cortical regions in these two networks are statistically correlated. These findings indicate that thickness correlations partly reflect underlying fiber connections but they contains exclusive information, and therefore should not be simply taken as a proxy measure for fiber connections.

title: Transforming growth factor-alpha immunoreactivity in the developing and adult brain.
abstract: Transforming growth factor-alpha immunoreactivity is examined in the developing and adult brain of cats and rats, and in the adult human brain in cryostat sections immediately processed free-floating with a well-characterized monoclonal antibody which does not cross-react with epidermal growth factor. Transforming growth factor-alpha immunoreactivity is observed in neurons of the cerebral neocortex, subiculum, hippocampus, striatum, thalamus, amygdala, basal forebrain, mesencephalon, cerebellar cortex, dentate nucleus and brainstem during development and in adulthood. The intensity of the immunoreaction directly correlates with the size of the cytoplasm. Diffuse transforming growth factor-alpha immunoreactivity also occurs in the white matter of the cerebrum, cerebellum and brainstem in the kitten, but not in the adult cat. In addition to neurons, numbers of glial cells in the cerebellar white matter, brainstem and cerebral hemispheres during development, and a few glial cells in the cerebellar cortex, diencephalon, cerebral cortex and white matter in adults are strongly transforming growth factor-alpha immunoreactive. These results support the concept that transforming growth factor-alpha is widely distributed in the brain of mammals, localizes in both neurons and glial cells, and is development dependent. These findings also suggest that transforming growth factor-alpha may play a role in the developing and adult central nervous system.

title: A PROPOSED MECHANISM OF EMOTION
abstract: The work of Cannon,1Bard,2Penfield,3Ranson4and others has greatly advanced knowledge of the functions of the hypothalamus. In the light of these researches the connections of the hypothalamus to the medial wall of the cerebral cortex gain a new significance. The following discussion presents some anatomic, clinical and experimental data dealing with the hypothalamus, the gyrus cinguli, the hippocampus and their interconnections. Taken as a whole, this ensemble of structures is proposed as representing theoretically the anatomic basis of the emotions. It is generally recognized that in the brain of lower vertebrates the medial wall of the cerebral hemisphere is connected anatomically and integrated physiologically with the hypothalamus and that the lateral wall is similarly related to the dorsal thalamus (Herrick5). These fundamental relations are not only retained but greatly elaborated in the mammalian brain by the further development of the hippocampal formation

title: Abnormalities of the uncinate fasciculus and posterior cingulate fasciculus in mild cognitive impairment and early Alzheimer's disease: A diffusion tensor tractography study
abstract: Mild cognitive impairment (MCI) is considered the transitional stage between normal cognition and dementia. The aim of this study was to use tractography based analysis to elucidate alterations in subjects with MCI compared with subjects with early Alzheimer's disease (AD) and controls. Seventeen subjects with early AD, 16 with MCI and 16 controls underwent magnetic resonance diffusion tensor imaging (DTI) and neuropsychological assessment. Diffusion tensor tractographies were computed and fiber-tract maps were generated using "dTV II" DTI software. We measured mean fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values along the uncinate fasciculus (UNC), posterior cingulate fasciculus (PCF) and corticospinal tract (CST). There were statistically significant differences in the FA and ADC values of the UNC and PCF between subjects with early AD and controls. Subjects with MCI exhibited significantly lower FA values on both sides of the PCF relative to controls. However, there were no significant differences between subjects with early AD and MCI for any measurement. Our results suggest that alterations in the PCF precede the onset of dementia.

title: Mapping the Structural Core of Human Cerebral Cortex
abstract: Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

title: Age- and Gender-Related Differences in the Cortical Anatomical Network
abstract: Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited “small-world” character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

title: Functional connectivity in the resting brain: A network analysis of the default mode hypothesis
abstract: Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

title: Investigating the Functional Heterogeneity of the Default Mode Network Using Coordinate-Based Meta-Analytic Modeling
abstract: The default mode network (DMN) comprises a set of regions that exhibit ongoing, intrinsic activity in the resting state and task-related decreases in activity across a range of paradigms. However, DMN regions have also been reported as task-related increases, either independently or coactivated with other regions in the network. Cognitive subtractions and the use of low-level baseline conditions have generally masked the functional nature of these regions. Using a combination of activation likelihood estimation, which assesses statistically significant convergence of neuroimaging results, and tools distributed with the BrainMap database, we identified core regions in the DMN and examined their functional heterogeneity. Meta-analytic coactivation maps of task-related increases were independently generated for each region, which included both within-DMN and non-DMN connections. Their functional properties were assessed using behavioral domain metadata in BrainMap. These results were integrated to determine a DMN connectivity model that represents the patterns of interactions observed in task-related increases in activity across diverse tasks. Subnetwork components of this model were identified, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstrating that components of the DMN are differentially specialized. Affective and perceptual cliques of the DMN were identified, as well as the cliques associated with a reduced preference for motor processing. In summary, we used advanced coordinate-based meta-analysis techniques to explicate behavior and connectivity in the default mode network; future work will involve applying this analysis strategy to other modes of brain function, such as executive function or sensorimotor systems.

title: Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease
abstract: The relation between pathology and cognitive dysfunction in dementia is still poorly understood, although disturbed communication between different brain regions is almost certainly involved. In this study we combine magneto-encephalography (MEG) and network analysis to investigate the role of functional sub-networks (modules) in the brain with regard to cognitive failure in Alzheimer's disease. Whole-head resting-state (MEG) was performed in 18 Alzheimer patients (age 67 ± 9, 6 females, MMSE 23 ± 5) and 18 healthy controls (age 66 ± 9, 11 females, MMSE 29 ± 1). We constructed functional brain networks based on interregional synchronization measurements, and performed graph theoretical analysis with a focus on modular organization. The overall modular strength and the number of modules changed significantly in Alzheimer patients. The parietal cortex was the most highly connected network area, but showed the strongest intramodular losses. Nonetheless, weakening of intermodular connectivity was even more outspoken, and more strongly related to cognitive impairment. The results of this study demonstrate that particularly the loss of communication between different functional brain regions reflects cognitive decline in Alzheimer's disease. These findings imply the relevance of regarding dementia as a functional network disorder.

title: Conserved and variable architecture of human white matter connectivity
abstract: Whole-brain network analysis of diffusion imaging tractography data is an important new tool for quantification of differential connectivity patterns across individuals and between groups. Here we investigate both the conservation of network architectural properties across methodological variation and the reproducibility of individual architecture across multiple scanning sessions. Diffusion spectrum imaging (DSI) and diffusion tensor imaging (DTI) data were both acquired in triplicate from a cohort of healthy young adults. Deterministic tractography was performed on each dataset and inter-regional connectivity matrices were then derived by applying each of three widely used whole-brain parcellation schemes over a range of spatial resolutions. Across acquisitions and preprocessing streams, anatomical brain networks were found to be sparsely connected, hierarchical, and assortative. They also displayed signatures of topo-physical interdependence such as Rentian scaling. Basic connectivity properties and several graph metrics consistently displayed high reproducibility and low variability in both DSI and DTI networks. The relative increased sensitivity of DSI to complex fiber configurations was evident in increased tract counts and network density compared with DTI. In combination, this pattern of results shows that network analysis of human white matter connectivity provides sensitive and temporally stable topological and physical estimates of individual cortical structure across multiple spatial scales.

title: Network Scaling Effects in Graph Analytic Studies of Human Resting-State fMRI Data
abstract: Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable.

title: Globally Altered Structural Brain Network Topology in Grapheme-Color Synesthesia
abstract: Synesthesia is a perceptual phenomenon in which stimuli in one particular modality elicit a sensation within the same or another sensory modality (e.g., specific graphemes evoke the perception of particular colors). Grapheme-color synesthesia (GCS) has been proposed to arise from abnormal local cross-activation between grapheme and color areas because of their hyperconnectivity. Recently published studies did not confirm such a hyperconnectivity, although morphometric alterations were found in occipitotemporal, parietal, and frontal regions of synesthetes. We used magnetic resonance imaging surface-based morphometry and graph-theoretical network analyses to investigate the topology of structural brain networks in 24 synesthetes and 24 nonsynesthetes. Connectivity matrices were derived from region-wise cortical thickness correlations of 2366 different cortical parcellations across the whole cortex and from 154 more common brain divisions as well. Compared with nonsynesthetes, synesthetes revealed a globally altered structural network topology as reflected by reduced small-worldness, increased clustering, increased degree, and decreased betweenness centrality. Connectivity of the fusiform gyrus (FuG) and intraparietal sulcus (IPS) was changed as well. Hierarchical modularity analysis revealed increased intramodular and intermodular connectivity of the IPS in GCS. However, connectivity differences in the FuG and IPS showed a low specificity because of global changes. We provide first evidence that GCS is rooted in a reduced small-world network organization that is driven by increased clustering suggesting global hyperconnectivity within the synesthetes' brain. Connectivity alterations were widespread and not restricted to the FuG and IPS. Therefore, synesthetic experience might be only one phenotypic manifestation of the globally altered network architecture in GCS.

title: Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map.
abstract: Recent advances in functional neuroimaging of the human cerebral cortex revived interest in the study of the cortical morphology at both macro- and microscopic levels. By means of high-resolution magnetic resonance imaging (MRI), in vivo images of the human brain can be acquired and used to aid localization of the functional maps. The goal of the present study was to determine variability in the occurrence and location of the cingulate sulcus (CS) and the paracingulate sulcus (PCS). Brain MRIs of 247 healthy young volunteers were obtained and transformed into a standardized stereotaxic space (Talairach and Tournoux, 1988). The CS and PCS were marked in 494 hemispheres using software capable of real-time movement through a 3-D volume. The markers were used to generate a probabilistic map of the CS and PCS. The individual MRI images were also evaluated for the presence and location of the following morphological features: the continuity of the CS, the presence of vertically oriented branches of the CS, the presence of the PCS, and the presence of the intralimbic sulcus. The results revealed considerable variability in the location of some of the above morphological features and a striking hemispheric asymmetry in the prominence of the PCS. The results of four previous blood-flow activation studies of speech control were used to illustrate the relevance of our morphological findings for functional neuroimaging of the human anterior cingulate cortex.

title: Functional network disruption in the degenerative dementias
abstract: Despite advances towards understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and neuropathological changes to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is crucial for normal functioning. A better understanding of network disruption in the neurodegenerative dementias might help bridge the gap between molecular changes, pathological changes, and symptoms. Recent findings on functional network disruption as assessed with resting-state or intrinsic connectivity functional MRI and electroencephalography and magnetoencephalography have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are somewhat specific to the clinical syndromes and, in Alzheimer's disease and frontotemporal dementia, network disruption tracks the pattern of pathological changes. These findings might have practical implications for diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the presymptomatic stage, and tracking of disease progression.

title: Network-Based Classification Using Cortical Thickness of AD Patients
abstract: In this article we propose a framework for establishing individual structural networks. An individual network is established for each subject using the mean cortical thickness of cortical regions as defined by the AAL atlas. Specifically, for each subject, we compute a similarity matrix of mean cortical thickness between pairs of cortical regions, which we refer to hereafter as the individual's network. Such individual networks can be used for classification. We use a combination of two types of feature selection approaches to search for the most discriminative edges. These edges serve as the input to a support vector machine (SVM) for classification. We demonstrate the utility of the proposed method by a comparison with classifying the raw cortical thickness data, and individual networks, using a publically available dataset. In particular, 83 subjects from the OASIS database were chosen to validate this approach, 39 of which were diagnosed with either mild cognitive impairment (MCI) or moderate Alzheimer's disease (AD) and the remaining were age-matched controls. While using an SVM on the raw cortical thickness data or individual networks without hybrid feature selection resulted in less than or nearly 80% classification accuracy, our approach yielded 90.4% classification accuracy in leave-one-out analysis.

title: Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures
abstract: Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI) and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from (of conventional volumetric features) to (of hierarchical network features), evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset.

title: The Brain Matures with Stronger Functional Connectivity and Decreased Randomness of Its Network
abstract: We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998) graph parameters C (local clustering) and L (global path length) for alpha (∼10 Hz), beta (∼20 Hz), and theta (∼4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ∼50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ∼18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.

title: Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease
abstract: Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimer's brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.

title: Brain Anatomical Network and Intelligence
abstract: Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

title: Functional brain network efficiency predicts intelligence
abstract: The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small‐world network attributes (high clustering and short path length) and whether these small‐world network parameters are associated with intellectual performance. High‐density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph‐theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small‐world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto‐Frontal Integration Theory (P‐FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.