226
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The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults. Cortex 2014; 64:293-309. [PMID: 25569764 DOI: 10.1016/j.cortex.2014.11.005] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 09/14/2014] [Accepted: 11/11/2014] [Indexed: 12/28/2022]
Abstract
Neuroimaging evidences posit human intelligence as tightly coupled with several structural and functional brain properties, also suggesting its potential protective role against aging and neurodegenerative conditions. However, whether higher order cognition might in fact lead to a more resilient brain has not been quantitatively demonstrated yet. Here we document a relationship between individual intelligence quotient (IQ) and brain resilience to targeted and random attacks, as measured through resting-state fMRI graph-theoretical analysis in 102 healthy individuals. In this modeling context, enhanced brain robustness to targeted attacks (TA) in individuals with higher IQ is supported by an increased distributed processing capacity despite the systematic loss of the most important node(s) of the system. Moreover, brain resilience in individuals with higher IQ is supported by a set of neocortical regions mainly belonging to language and memory processing network(s), whereas regions related to emotional processing are mostly responsible for lower IQ individuals. Results suggest intelligence level among the predictors of post-lesional or neurodegenerative recovery, also promoting the evolutionary role of higher order cognition, and simultaneously suggesting a new framework for brain stimulation interventions aimed at counteract brain deterioration over time.
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227
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Ventouras EC, Margariti A, Chondraki P, Kalatzis I, Economou NT, Tsekou H, Paparrigopoulos T, Ktonas P. EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study. Cogn Neurodyn 2014; 9:231-48. [PMID: 25852781 DOI: 10.1007/s11571-014-9319-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Revised: 10/22/2014] [Accepted: 11/05/2014] [Indexed: 11/27/2022] Open
Abstract
Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.
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228
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Fan Q, Anderson AW, Davis N, Cutting LE. Structural connectivity patterns associated with the putative visual word form area and children's reading ability. Brain Res 2014; 1586:118-29. [PMID: 25152466 PMCID: PMC4190016 DOI: 10.1016/j.brainres.2014.08.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Revised: 07/19/2014] [Accepted: 08/17/2014] [Indexed: 01/22/2023]
Abstract
With the advent of neuroimaging techniques, especially functional MRI (fMRI), studies have mapped brain regions that are associated with good and poor reading, most centrally a region within the left occipito-temporal/fusiform region (L-OT/F) often referred to as the visual word form area (VWFA). Despite an abundance of fMRI studies of the putative VWFA, research about its structural connectivity has just started. Provided that the putative VWFA may be connected to distributed regions in the brain, it remains unclear how this network is engaged in constituting a well-tuned reading circuitry in the brain. Here we used diffusion MRI to study the structural connectivity patterns of the putative VWFA and surrounding areas within the L-OT/F in children with typically developing (TD) reading ability and with word recognition deficits (WRD; sometimes referred to as dyslexia). We found that L-OT/F connectivity varied along a posterior-anterior gradient, with specific structural connectivity patterns related to reading ability in the ROIs centered upon the putative VWFA. Findings suggest that the architecture of the putative VWFA connectivity is fundamentally different between TD and WRD, with TD showing greater connectivity to linguistic regions than WRD, and WRD showing greater connectivity to visual and parahippocampal regions than TD. Findings thus reveal clear structural abnormalities underlying the functional abnormalities in the putative VWFA in WRD.
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229
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High-resolution prediction of mouse brain connectivity using gene expression patterns. Methods 2014; 73:71-8. [PMID: 25109429 DOI: 10.1016/j.ymeth.2014.07.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Revised: 07/12/2014] [Accepted: 07/26/2014] [Indexed: 01/30/2023] Open
Abstract
The brain is a multi-level system in which the high-level functions are generated by low-level genetic mechanisms. Thus, elucidating the relationship among multiple brain levels via correlative and predictive analytics is an important area in brain research. Currently, studies in multiple species have indicated that the spatiotemporal gene expression patterns are predictive of brain wiring. Specifically, results on the worm Caenorhabditis elegans have shown that the prediction of neuronal connectivity using gene expression signatures yielded statistically significant results. Recent studies on the mammalian brain produced similar results at the coarse regional level. In this study, we provide the first high-resolution, large-scale integrative analysis of the transcriptome and connectome in a single mammalian brain at a fine voxel level. By using the Allen Brain Atlas data, we predict voxel-level brain connectivity based on the gene expressions in the adult mouse brain. We employ regularized models to show that gene expression is predictive of connectivity at the voxel-level with an accuracy of 93%. We also identify a set of genes playing the most important role in connectivity prediction. We use only this small number of genes to predict the brain wiring with an accuracy over 80%. We discover that these important genes are enriched in neurons as compared to glia, and they perform connectivity-related functions. We perform several interesting correlative studies to further elucidate the transcriptome-connectome relationship.
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230
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Bezgin G, Rybacki K, van Opstal AJ, Bakker R, Shen K, Vakorin VA, McIntosh AR, Kötter R. Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams. BRAIN AND LANGUAGE 2014; 135:73-84. [PMID: 24980416 DOI: 10.1016/j.bandl.2014.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2013] [Revised: 04/26/2014] [Accepted: 05/26/2014] [Indexed: 06/03/2023]
Abstract
Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.
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231
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Disruption of structure-function coupling in the schizophrenia connectome. NEUROIMAGE-CLINICAL 2014; 4:779-87. [PMID: 24936428 PMCID: PMC4055899 DOI: 10.1016/j.nicl.2014.05.004] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2014] [Revised: 04/04/2014] [Accepted: 05/04/2014] [Indexed: 11/20/2022]
Abstract
Neuroimaging studies have demonstrated that the phenomenology of schizophrenia maps onto diffuse alterations in large-scale functional and structural brain networks. However, the relationship between structural and functional deficits remains unclear. To answer this question, patients with established schizophrenia and matched healthy controls underwent resting-state functional and diffusion weighted imaging. The network-based statistic was used to characterize between-group differences in whole-brain functional connectivity. Indices of white matter integrity were then estimated to assess the structural correlates of the functional alterations observed in patients. Finally, group differences in the relationship between indices of functional and structural brain connectivity were determined. Compared to controls, patients with schizophrenia showed decreased functional connectivity and impaired white matter integrity in a distributed network encompassing frontal, temporal, thalamic, and striatal regions. In controls, strong interregional coupling in neural activity was associated with well-myelinated white matter pathways in this network. This correspondence between structure and function appeared to be absent in patients with schizophrenia. In two additional disrupted functional networks, encompassing parietal, occipital, and temporal cortices, the relationship between function and structure was not affected. Overall, results from this study highlight the importance of considering not only the separable impact of functional and structural connectivity deficits on the pathoaetiology of schizophrenia, but also the implications of the complex nature of their interaction. More specifically, our findings support the core nature of fronto-striatal, fronto-thalamic, and fronto-temporal abnormalities in the schizophrenia connectome.
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232
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Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition. J Neurosci Methods 2014; 228:86-99. [PMID: 24675050 DOI: 10.1016/j.jneumeth.2014.03.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 03/12/2014] [Accepted: 03/13/2014] [Indexed: 11/23/2022]
Abstract
BACKGROUND Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation. NEW METHOD We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies. RESULTS The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity. CONCLUSIONS We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern.
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233
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Abstract
Recently, there has been a wealth of research into structural and functional brain connectivity, and how they change over development. While we are far from a complete understanding, these studies have yielded important insights into human brain development. There is an ever growing variety of methods for assessing connectivity, each with its own advantages. Here we review research on the development of structural and/or functional brain connectivity in both typically developing subjects and subjects with neurodevelopmental disorders. Space limitations preclude an exhaustive review of brain connectivity across all developmental disorders, so we review a representative selection of recent findings on brain connectivity in autism, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Turner syndrome, and ADHD. Major strides have been made in understanding the developmental trajectory of the human connectome, offering insight into characteristic features of brain development and biological processes involved in developmental brain disorders. We also discuss some common themes, including hemispheric specialization - or asymmetry - and sex differences. We conclude by discussing some promising future directions in connectomics, including the merger of imaging and genetics, and a deeper investigation of the relationships between structural and functional connectivity.
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234
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Di Perri C, Stender J, Laureys S, Gosseries O. Functional neuroanatomy of disorders of consciousness. Epilepsy Behav 2014; 30:28-32. [PMID: 24100252 DOI: 10.1016/j.yebeh.2013.09.014] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Accepted: 09/06/2013] [Indexed: 10/26/2022]
Abstract
Our understanding of the mechanisms of loss and recovery of consciousness, following severe brain injury or during anesthesia, is changing rapidly. Recent neuroimaging studies have shown that patients with chronic disorders of consciousness and subjects undergoing general anesthesia present a complex dysfunctionality in the architecture of brain connectivity. At present, the global hallmark of impaired consciousness appears to be a multifaceted dysfunctional connectivity pattern with both within-network loss of connectivity in a widespread frontoparietal network and between-network hyperconnectivity involving other regions such as the insula and ventral tegmental area. Despite ongoing efforts, the mechanisms underlying the emergence of consciousness after severe brain injury are not thoroughly understood. Important questions remain unanswered: What triggers the connectivity impairment leading to disorders of consciousness? Why do some patients recover from coma, while others with apparently similar brain injuries do not? Understanding these mechanisms could lead to a better comprehension of brain function and, hopefully, lead to new therapeutic strategies in this challenging patient population.
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235
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Abdelnour F, Voss HU, Raj A. Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage 2013; 90:335-47. [PMID: 24384152 DOI: 10.1016/j.neuroimage.2013.12.039] [Citation(s) in RCA: 147] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 11/04/2013] [Accepted: 12/16/2013] [Indexed: 01/09/2023] Open
Abstract
The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain's long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways.
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236
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Connectivity analysis of multichannel EEG signals using recurrence based phase synchronization technique. Comput Biol Med 2013; 46:11-21. [PMID: 24529201 DOI: 10.1016/j.compbiomed.2013.10.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Revised: 10/25/2013] [Accepted: 10/26/2013] [Indexed: 11/20/2022]
Abstract
Real world biological systems such as the human brain are inherently nonlinear and difficult to model. However, most of the previous studies have either employed linear models or parametric nonlinear models for investigating brain function. In this paper, a novel application of a nonlinear measure of phase synchronization based on recurrences, correlation between probabilities of recurrence (CPR), to study connectivity in the brain has been proposed. Being non-parametric, this method makes very few assumptions, making it suitable for investigating brain function in a data-driven way. CPR's utility with application to multichannel electroencephalographic (EEG) signals has been demonstrated. Brain connectivity obtained using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity between (a) epileptic seizure and pre-seizure and (b) eyes open and eyes closed states. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the superior ability of CPR for discriminating seizure from pre-seizure. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value.
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237
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Dennis EL, Thompson PM. Mapping connectivity in the developing brain. Int J Dev Neurosci 2013; 31:525-42. [PMID: 23722009 PMCID: PMC3800504 DOI: 10.1016/j.ijdevneu.2013.05.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Accepted: 05/14/2013] [Indexed: 02/07/2023] Open
Abstract
Recently, there has been a wealth of research into structural and functional brain connectivity, and how they change over development. While we are far from a complete understanding, these studies have yielded important insights into human brain development. There is an ever growing variety of methods for assessing connectivity, each with its own advantages. Here we review research on the development of structural and/or functional brain connectivity in both typically developing subjects and subjects with neurodevelopmental disorders. Space limitations preclude an exhaustive review of brain connectivity across all developmental disorders, so we review a representative selection of recent findings on brain connectivity in autism, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Turner syndrome, and ADHD. Major strides have been made in understanding the developmental trajectory of the human connectome, offering insight into characteristic features of brain development and biological processes involved in developmental brain disorders. We also discuss some common themes, including hemispheric specialization - or asymmetry - and sex differences. We conclude by discussing some promising future directions in connectomics, including the merger of imaging and genetics, and a deeper investigation of the relationships between structural and functional connectivity.
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238
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Sui J, Huster R, Yu Q, Segall JM, Calhoun VD. Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies. Neuroimage 2013; 102 Pt 1:11-23. [PMID: 24084066 DOI: 10.1016/j.neuroimage.2013.09.044] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 09/18/2013] [Accepted: 09/20/2013] [Indexed: 12/13/2022] Open
Abstract
Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.
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239
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Abstract
Studies using fMRI at rest and during task performance have revealed a set of brain areas and their connections that can be linked to the ability of children to regulate their thoughts, actions and emotions. Higher self-regulation has also been related favorable outcomes in adulthood. These findings have set the occasion for methods of improving self-regulation via training. A tool kit of such methods is now available. It remains to be seen if educators will use these new findings and tools to forge practical methods for improving the lives of the world's children.
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240
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Koush Y, Rosa MJ, Robineau F, Heinen K, W Rieger S, Weiskopf N, Vuilleumier P, Van De Ville D, Scharnowski F. Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI. Neuroimage 2013; 81:422-430. [PMID: 23668967 PMCID: PMC3734349 DOI: 10.1016/j.neuroimage.2013.05.010] [Citation(s) in RCA: 123] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Revised: 04/25/2013] [Accepted: 05/01/2013] [Indexed: 11/30/2022] Open
Abstract
Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. We adapt DCM for use in neurofeedback experiments. Participants can control a DCM-based neurofeedback signal. Real-time DCM allows for voluntary control over brain connectivity.
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241
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Meskaldji DE, Fischi-Gomez E, Griffa A, Hagmann P, Morgenthaler S, Thiran JP. Comparing connectomes across subjects and populations at different scales. Neuroimage 2013; 80:416-25. [PMID: 23631992 DOI: 10.1016/j.neuroimage.2013.04.084] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Revised: 04/12/2013] [Accepted: 04/16/2013] [Indexed: 01/24/2023] Open
Abstract
Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.
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242
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Bakhtiari R, Zürcher NR, Rogier O, Russo B, Hippolyte L, Granziera C, Araabi BN, Nili Ahmadabadi M, Hadjikhani N. Differences in white matter reflect atypical developmental trajectory in autism: A Tract-based Spatial Statistics study. NEUROIMAGE-CLINICAL 2012; 1:48-56. [PMID: 24179736 PMCID: PMC3757732 DOI: 10.1016/j.nicl.2012.09.001] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Revised: 08/20/2012] [Accepted: 09/03/2012] [Indexed: 12/03/2022]
Abstract
Autism is a neurodevelopmental disorder in which white matter (WM) maturation is affected. We assessed WM integrity in 16 adolescents and 14 adults with high-functioning autism spectrum disorder (ASD) and in matched neurotypical controls (NT) using diffusion weighted imaging and Tract-based Spatial Statistics. Decreased fractional anisotropy (FA) was observed in adolescents with ASD in tracts involved in emotional face processing, language, and executive functioning, including the inferior fronto-occipital fasciculus and the inferior and superior longitudinal fasciculi. Remarkably, no differences in FA were observed between ASD and NT adults. We evaluated the effect of age on WM development across the entire age range. Positive correlations between FA values and age were observed in the right inferior fronto-occipital fasciculus, the left superior longitudinal fasciculus, the corpus callosum, and the cortical spinal tract of ASD participants, but not in NT participants. Our data underscore the dynamic nature of brain development in ASD, showing the presence of an atypical process of WM maturation, that appears to normalize over time and could be at the basis of behavioral improvements often observed in high-functioning autism.
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Key Words
- ADI-R, Autism Diagnostic Interview-Revised
- ADOS, Autism Diagnostic Observation Schedule
- AQ, Autism Quotient
- ASD, Autism Spectrum Disorders
- ATR, anterior thalamic radiations
- Autism spectrum disorder
- Brain connectivity
- Brain development
- Brain maturation
- CC, corpus callosum
- CT, corticospinal tract
- DTI, Diffusion Tensor Imaging
- DTT, Diffusion Tensor Tractography
- Diffusion Tensor Imaging
- EF, executive functions
- FA, fractional anisotropy
- Fractional anisotropy
- IFOF, inferior froto-occipital fasciculus
- ILF, inferior longitudinal fasciculus
- NT, neurotypical
- PIQ, Performance Intelligence Quotient
- SLF, superior longitudinal fasciculus
- TBSS, Tract-based Spatial Statistics
- TE, echo time
- TFCE, Threshold-free Cluster Enhancement
- TR, repetition time
- UNC, uncinate fasciculus
- VBM, Voxel-Based Morphometry
- VBS, Voxel based Statistics of FA Images (VBM-like)
- WM, white matter
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243
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Lindquist MA. Functional Causal Mediation Analysis With an Application to Brain Connectivity. J Am Stat Assoc 2012; 107:1297-1309. [PMID: 25076802 DOI: 10.1080/01621459.2012.695640] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Mediation analysis is often used in the behavioral sciences to investigate the role of intermediate variables that lie on the causal path between a randomized treatment and an outcome variable. Typically, mediation is assessed using structural equation models (SEMs), with model coefficients interpreted as causal effects. In this article, we present an extension of SEMs to the functional data analysis (FDA) setting that allows the mediating variable to be a continuous function rather than a single scalar measure, thus providing the opportunity to study the functional effects of the mediator on the outcome. We provide sufficient conditions for identifying the average causal effects of the functional mediators using the extended SEM, as well as weaker conditions under which an instrumental variable estimand may be interpreted as an effect. The method is applied to data from a functional magnetic resonance imaging (fMRI) study of thermal pain that sought to determine whether activation in certain brain regions mediated the effect of applied temperature on self-reported pain. Our approach provides valuable information about the timing of the mediating effect that is not readily available when using the standard nonfunctional approach. To the best of our knowledge, this work provides the first application of causal inference to the FDA framework.
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244
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Micheloyannis S. Graph-based network analysis in schizophrenia. World J Psychiatry 2012; 2:1-12. [PMID: 24175163 PMCID: PMC3782171 DOI: 10.5498/wjp.v2.i1.1] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2011] [Revised: 12/10/2011] [Accepted: 01/21/2012] [Indexed: 02/05/2023] Open
Abstract
Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods’ therapeutic effects.
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