Effect of cocaine dependence on brain connections: clinical implications

Cocaine dependence (CD) is associated with several cognitive deficits. Accumulating evidence, based on human and animal studies, has led to models for interpreting the neural basis of cognitive functions as interactions between functionally related brain regions. In this review, we focus on magnetic resonance imaging (MRI) studies using brain connectivity techniques as related to CD. The majority of these brain connectivity studies indicated that cocaine use is associated with altered brain connectivity between different structures, including cortical-striatal regions and default mode network. In cocaine users some of the altered brain connectivity measures are associated with behavioral performance, history of drug use, and treatment outcome. The implications of these brain connectivity findings to the treatment of CD and the pros and cons of the major brain connectivity techniques are discussed. Finally potential future directions in cocaine use disorder research using brain connectivity techniques are briefly described.

fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity

Down-regulation of the amygdala with real-time fMRI neurofeedback (rtfMRI NF) potentially allows targeting brain circuits of emotion processing and may involve prefrontal-limbic networks underlying effective emotion regulation. Little research has been dedicated to the effect of rtfMRI NF on the functional connectivity of the amygdala and connectivity patterns in amygdala down-regulation with neurofeedback have not been addressed yet. Using psychophysiological interaction analysis of fMRI data, we present evidence that voluntary amygdala down-regulation by rtfMRI NF while viewing aversive pictures was associated with increased connectivity of the right amygdala with the ventromedial prefrontal cortex (vmPFC) in healthy subjects (N=16). In contrast, a control group (N=16) receiving sham feedback did not alter amygdala connectivity (Group×Condition t-contrast: p<.05 at cluster-level). Task-dependent increases in amygdala-vmPFC connectivity were predicted by picture arousal (β=.59, p<.05). A dynamic causal modeling analysis with Bayesian model selection aimed at further characterizing the underlying causal structure and favored a bottom-up model assuming predominant information flow from the amygdala to the vmPFC (xp=.90). The results were complemented by the observation of task-dependent alterations in functional connectivity of the vmPFC with the visual cortex and the ventrolateral PFC in the experimental group (Condition t-contrast: p<.05 at cluster-level). Taken together, the results underscore the potential of amygdala fMRI neurofeedback to influence functional connectivity in key networks of emotion processing and regulation. This may be beneficial for patients suffering from severe emotion dysregulation by improving neural self-regulation.

Foundational perspectives on causality in large-scale brain networks

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Estimating Directed Connectivity from Cortical Recordings and Reconstructed Sources

In cognitive neuroscience, electrical brain activity is most commonly recorded at the scalp. In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. Several approaches to this reconstruction have been developed, thereby solving the so-called implicit inverse problem Michel et al. (Clin Neurophysiol 115:2195–2222, 2004). However, a unifying premise against which to validate these source reconstructions is seldom available. The dataset provided in this work, in which brain activity is simultaneously recorded on the scalp (non-invasively) by electroencephalography (EEG) and on the cortex (invasively) by electrocorticography (ECoG), can be of a great help in this direction. These multimodal recordings were obtained from a macaque monkey under wakefulness and sedation. Our primary goal was to establish the connectivity architecture between two sources of interest (frontal and parietal), and to assess how their coupling changes over the conditions. We chose these sources because previous studies have shown that the connections between them are modified by anaesthesia Boly et al. (J Neurosci 32:7082–7090, 2012). Our secondary goal was to evaluate the consistency of the connectivity results when analyzing sources recorded from invasive data (128 implanted ECoG sources) and source activity reconstructed from scalp recordings (19 EEG sensors) at the same locations as the ECoG sources. We conclude that the directed connectivity in the frequency domain between cortical sources reconstructed from scalp EEG is qualitatively similar to the connectivity inferred directly from cortical recordings, using both data-driven (directed transfer function) and biologically grounded (dynamic causal modelling) methods. Furthermore, the connectivity changes identified were consistent with previous findings Boly et al. (J Neurosci 32:7082–7090, 2012). Our findings suggest that inferences about directed connectivity based upon non-invasive electrophysiological data have construct validity in relation to invasive recordings.

Functional connectivity indicates differential roles for the intraparietal sulcus and the superior parietal lobule in multiple object tracking

Attentive tracking requires sustained object-based attention, rather than passive vigilance or rapid attentional shifts to brief events. Several theories of tracking suggest a mechanism of indexing objects that allows for attentional resources to be directed toward the moving targets. Imaging studies have shown that cortical areas belonging to the dorsal frontoparietal attention network increase BOLD-signal during multiple object tracking (MOT). Among these areas, some studies have assigned IPS a particular role in object indexing, but the neuroimaging evidence has been sparse. In the present study, we tested participants on a continuous version of the MOT task in order to investigate how cortical areas engage in functional networks during attentional tracking. Specifically, we analyzed the data using eigenvector centrality mapping (ECM) analysis, which provides estimates of individual voxels' connectedness with hub-like parts of the functional network. The results obtained using permutation based voxel-wise statistics support the proposed role for the IPS in object indexing as this region displayed increased centrality during tracking as well as increased functional connectivity with both prefrontal and visual perceptual cortices. In contrast, the opposite pattern was observed for the SPL, with decreasing centrality, as well as reduced functional connectivity with the visual and frontal cortices, in agreement with a hypothesized role for SPL in attentional shifts. These findings provide novel evidence that IPS and SPL serve different functional roles during MOT, while at the same time being highly engaged during tracking as measured by BOLD-signal changes.

Altered functional neuronal activity in neuropsychiatric lupus: A systematic review of the fMRI investigations

OBJECTIVE

Recent years have seen a rapid increase in the investigation of neuropsychiatric lupus (NPSLE) through the use of functional magnetic resonance imaging (fMRI). Measuring specific neuronal activity in regional brain structures during a cognitive task may identify possible biomarker for NPSLE.

METHODS

A systematic review of fMRI studies of systemic lupus erythematosus (SLE) is carried out to address common findings that characterize NPSLE.

RESULTS

A disturbance to the working memory and executive function brain regions is among the most well-replicated finding. Differences in brain activation may relate to an early primary dysfunction of these regions. Increased functional connectivity strength in the fronto-parietal cortex in the resting state is correlated with SLE disease activity in one study. Decrease functional connectivity is observed in lupus patients with long-term disease. However, there is strong evidence that points toward a lack of effective integration of distributed functional brain regions and disruptions in the subtle modulation of brain function in relation to task demands in SLE. Limitations of the literature to date include the use of small sample size and the lack of addressing the effect of confounding variables, including immunosuppressive treatment.

CONCLUSION

Careful definitions of the fMRI technique used both in the design, analyses, and interpretation of high dimensional data is needed, when dealing with a limited number of SLE subjects with heterogeneous manifestations and unknown pathophysiology.

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain's wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using compute unified device architecture, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets' features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.

The Effects of Taekwondo Training on Brain Connectivity and Body Intelligence

OBJECTIVE

Many studies have reported that Taekwondo training could improve body perception, control and brain activity, as assessed with an electroencephalogram. This study aimed to assess body intelligence and brain connectivity in children with Taekwondo training as compared to children without Taekwondo training.

METHODS

Fifteen children with Taekwondo training (TKD) and 13 age- and sex-matched children who had no previous experience of Taekwondo training (controls) were recruited. Body intelligence, clinical characteristics and brain connectivity in all children were assessed with the Body Intelligence Scale (BIS), self-report, and resting state functional magnetic resonance imaging.

RESULTS

The mean BIS score in the TKD group was higher than that in the control group. The TKD group showed increased low-frequency fluctuations in the right frontal precentral gyrus and the right parietal precuneus, compared to the control group. The TKD group showed positive cerebellum vermis (lobe VII) seed to the right frontal, left frontal, and left parietal lobe. The control group showed positive cerebellum seed to the left frontal, parietal, and occipital cortex. Relative to the control group, the TKD group showed increased functional connectivity from cerebellum seed to the right inferior frontal gyrus.

CONCLUSION

To the best of our knowledge, this is the first study to assess the effect of Taekwondo training on brain connectivity in children. Taekwondo training improved body intelligence and brain connectivity from the cerebellum to the parietal and frontal cortex.

Altered structure of dynamic electroencephalogram oscillatory pattern in major depression

Research on electroencephalogram (EEG) characteristics associated with major depressive disorder (MDD) has accumulated diverse neurophysiologic findings related to the content, topography, neurochemistry, and functions of EEG oscillations. Significant progress has been made since the first landmark EEG study on affective disorders by Davidson 35 years ago. A systematic account of these data is important and necessary for building a consistent neuropsychophysiologic model of MDD and other affective disorders. Given the extensive data on frequency-dependent functional significance of EEG oscillations, a frequency domain approach may reveal the types of brain functions involved and disturbed in MDD. In this review, we systematize and integrate diverse and often unconnected observations on the content, topography, neurochemistry, and functions of EEG oscillations involved in MDD within the general concept of an EEG oscillatory pattern.

Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease

There is significant interest, both from basic and applied research perspectives, in understanding how structural/functional connectivity changes can explain behavioral symptoms and predict decline in neurodegenerative diseases such as Alzheimer's disease (AD). The first step in most such analyses is to encode the connectivity information as a graph; then, one may perform statistical inference on various 'global' graph theoretic summary measures (e.g., modularity, graph diameter) and/or at the level of individual edges (or connections). For AD in particular, clear differences in connectivity at the dementia stage of the disease (relative to healthy controls) have been identified. Despite such findings, AD-related connectivity changes in preclinical disease remain poorly characterized. Such preclinical datasets are typically smaller and group differences are weaker. In this paper, we propose a new multi-resolution method for performing statistical analysis of connectivity networks/graphs derived from neuroimaging data. At the high level, the method occupies the middle ground between the two contrasts - that is, to analyze global graph summary measures (global) or connectivity strengths or correlations for individual edges similar to voxel based analysis (local). Instead, our strategy derives a Wavelet representation at each primitive (connection edge) which captures the graph context at multiple resolutions. We provide extensive empirical evidence of how this framework offers improved statistical power by analyzing two distinct AD datasets. Here, connectivity is derived from diffusion tensor magnetic resonance images by running a tractography routine. We first present results showing significant connectivity differences between AD patients and controls that were not evident using standard approaches. Later, we show results on populations that are not diagnosed with AD but have a positive family history risk of AD where our algorithm helps in identifying potentially subtle differences between patient groups. We also give an easy to deploy open source implementation of the algorithm for use within studies of connectivity in AD and other neurodegenerative disorders.

Local dimension-reduced dynamical spatio-temporal models for resting state network estimation

To overcome the limitations of independent component analysis (ICA), today’s most popular analysis tool for investigating whole-brain spatial activation in resting state functional magnetic resonance imaging (fMRI), we present a new class of local dimension-reduced dynamical spatio-temporal model which dispenses the independence assumptions that severely limit deeper connectivity descriptions between spatial components. The new method combines novel concepts of group sparsity with contiguity-constrained clusterization to produce physiologically consistent regions of interest in illustrative fMRI data whose causal interactions may then be easily estimated, something impossible under the usual ICA assumptions.

Differences in functional connectivity between alcohol dependence and internet gaming disorder

INTRODUCTION

Internet gaming disorder (IGD) and alcohol dependence (AD) have been reported to share clinical characteristics including craving and over-engagement despite negative consequences. However, there are also clinical factors that differ between individuals with IGD and those with AD in terms of chemical intoxication, prevalence age, and visual and auditory stimulation.

METHODS

We assessed brain functional connectivity within the prefrontal, striatum, and temporal lobe in 15 patients with IGD and in 16 patients with AD. Symptoms of depression, anxiety, and the attention deficit hyperactivity disorder were assessed in patients with IGD and in patients with AD.

RESULTS

Both AD and IGD subjects have positive functional connectivity between the dorsolateral prefrontal cortex (DLPFC), cingulate, and cerebellum. In addition, both groups have negative functional connectivity between the DLPFC and the orbitofrontal cortex. However, the AD subjects have positive functional connectivity between the DLPFC, temporal lobe and striatal areas while IGD subjects have negative functional connectivity between the DLPFC, temporal lobe and striatal areas.

CONCLUSIONS

AD and IGD subjects may share deficits in executive function, including problems with self-control and adaptive responding. However, the negative connectivity between the DLPFC and the striatal areas in IGD subjects, different from the connectivity observed in AD subjects, may be due to the earlier prevalence age, different comorbid diseases as well as visual and auditory stimulation.

Seemingly unrelated regression empowers detection of network failure in dementia

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.

Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence

Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i.e., the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents.

The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults

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.

EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study

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.

Structural connectivity patterns associated with the putative visual word form area and children's reading ability

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.

High-resolution prediction of mouse brain connectivity using gene expression patterns

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.

Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams

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.

Disruption of structure-function coupling in the schizophrenia connectome

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.

Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition

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.

Reprint of: Mapping connectivity in the developing brain

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.

Functional neuroanatomy of disorders of consciousness

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.

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

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.