Functional Network Integrity Research Articles

Altered Functional Brain Network Integration, Segregation, and Modularity in Infants Born Very Preterm at Term-Equivalent Age

To determine the functional network organization of the brain in infants born very pretermat term-equivalent age and to relate network alterations to known clinical risk factors for poor neurologic outcomes in prematurity. Resting-state functional magnetic resonance imaging data from 66 infants born very preterm (gestational age <32weeks and birth weight <1500g) and 66 healthy neonates born at full term, acquired as part of a prospective, cross-sectional study, were compared at term age using graph theory. Features of resting-state networks, including integration, segregation, and modularity, were derived from correlated hemodynamic activity arising from 93 cortical and subcortical regions of interest and compared between groups. Despite preserved small-world topology and modular organization, resting-state networks of infants born very preterm at term-equivalent age were less segregated and less integrated than those of infants born full term. Chronic respiratory illness (ie, bronchopulmonary dysplasia and the length of oxygen support) was associated with decreased global efficiency and increased path lengths (P<.05). In both cohorts, 4 functional modules with similar composition were observed (parietal/temporal, frontal, subcortical/limbic, and occipital). The density of connections in 3 of the 4 modules was decreased in the very preterm network (P<.01); however, in the occipital/visual cortex module, connectivity was increased in infants born very preterm relative to control infants (P<.0001). Early exposure to the ex utero environment is associated with altered resting-state network functional organization in infants born very preterm at term-equivalent age, likely reflecting disrupted brain maturational processes.

Functional network connectivity impairments and core cognitive deficits in schizophrenia.

Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.

O32. Functional Network Connectivity Impairments and Core Cognitive Deficits in Schizophrenia

Cognitive impairment contributes to disability in patients with schizophrenia and may be related to altered functional networks that subserve cognitive functions. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM).

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

BackgroundCurrent diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function.MethodFive-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level.ResultsNo significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences.ConclusionsUsing clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.

Electrophysiological correlates of cognitive symptoms in multiple sclerosis through neuromagnetic resting-state functional connectivity

Electrophysiological correlates of cognitive symptoms in multiple sclerosis through neuromagnetic resting-state functional connectivity

Disrupted Brain Functional Networks in Drug-Naïve Children with ADHD Assessed Using Graph Theory Analysis

Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in childhood characterized by multiple behavioral problems and executive dysfunctions. Neuroimaging studies revealed a variety of abnormalities, but the results were inconsistent, partly owing to confounding medication effects and psychiatric comorbidities. Thus, studies of drug- naive ADHD with limited comorbidities is needed to better define disorder-related abnormalities in brain networks. Methods: Resting state functional magnetic resonance imaging was performed on 51 drug-naive ADHD patients (age 6 to 16) and 55 age-matched healthy controls. Brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and graph theory was used to analyze the network topological properties. The Stroop test was used to assess the cognitive inhibitory abilities in the subjects. The non-parametric permutation tests were used to compare the topological architectures in the two groups. Results: Compared with the healthy subjects, the brain networks in the ADHD patients demonstrated altered topological characteristics including lower global and local efficiency ( p=.002,.026), and longer path length (p=.002). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (p<.05, corrected for the FDR). Altered global and nodal topological efficiency were associated with severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p<.05). Conclusions: Alterations in network typologies in drug-naive ADHD indicate a weaker small-worldization with decreased segregation and integration of functional brain networks, notably involving a dysfunction of the cingulo-fronto-parietal attention network that was associated with inhibitory control deficits. Funding Statement: This study was supported by the National Natural Science Foundation (Grant Nos. 81621003，81761128023, 81220108013, 81227002, 81030027, 81671669, 81371536 and 81801358), the Humboldt Foundation, and the Program for Changjiang Scholars and Innovative Research Teams in Universities of China (PCSIRT, Grant No. IRT16R52). Dr. Gong would also like to acknowledge the support from his Changjiang Scholar Professorship Award of China (Award No. T2014190) and American CMB Distinguished Professorship Award (Award No. F510000/ G16916411) administered by the Institute of International Education, USA Declaration of Interests: None of the authors has conflicts of interests to report with regard to this manuscript. Ethics Approval Statement: This study was approved by the research ethics committee of West China Hospital of Sichuan University, written informed consent was obtained from the parents/guardians of all participants, and the assents were obtained from study participants prior to participation.

Corrigendum to "On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification."

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Age Related Changes in Topological Properties of Brain Functional Network and Structural Connectivity

Introduction: There are still uncertainties about the true nature of age related changes in topological properties of the brain functional network and its structural connectivity during various developmental stages. In this cross- sectional study, we investigated the effects of age and its relationship with regional nodal properties of the functional brain network and white matter integrity.Method: DTI and fMRI data were acquired from 458 healthy Chinese participants ranging from age 8 to 81 years. Tractography was conducted on the DTI data using FSL. Graph Theory analyses were conducted on the functional data yielding topological properties of the functional network using SPM and GRETNA toolbox. Two multiple regressions were performed to investigate the effects of age on nodal topological properties of the functional brain network and white matter integrity.Result: For the functional studies, we observed that regional nodal characteristics such as node betweenness were decreased while node degree and node efficiency was increased in relation to increasing age. Perversely, we observed that the relationship between nodal topological properties and fasciculus structures were primarily positive for nodal betweenness but negative for nodal degree and nodal efficiency. Decrease in functional nodal betweenness was primarily located in superior frontal lobe, right occipital lobe and the global hubs. These brain regions also had both direct and indirect anatomical relationships with the 14 fiber bundles. A linear age related decreases in the Fractional anisotropy (FA) value was found in the callosum forceps minor.Conclusion: These results suggests that age related differences were more pronounced in the functional than in structural measure indicating these measures do not have direct one-to-one mapping. Our study also indicates that the fiber bundles with longer fibers exhibited a more pronounced effect on the properties of functional network.

Functional network integration and attention skills in young children

Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood.

The Integration of Functional Brain Activity from Adolescence to Adulthood.

Age-related changes in human functional neuroanatomy are poorly understood. This is partly due to the limits of interpretation of standard fMRI. These limits relate to age-related variation in noise levels in data from different subjects, and the common use of standard adult brain parcellations for developmental studies. Here we used an emerging MRI approach called multiecho (ME)-fMRI to characterize functional brain changes with age. ME-fMRI acquires blood oxygenation level-dependent (BOLD) signals while also quantifying susceptibility-weighted transverse relaxation time (T2*) signal decay. This approach newly enables reliable detection of BOLD signal components at the subject level as opposed to solely at the group-average level. In turn, it supports more robust characterization of the variability in functional brain organization across individuals. We hypothesized that BOLD components in the resting state are not stable with age, and would decrease in number from adolescence to adulthood. This runs counter to the current assumptions in neurodevelopmental analyses of brain connectivity that the number of BOLD signal components is a random effect. From resting-state ME-fMRI of 51 healthy subjects of both sexes, between 8.3 and 46.2 years of age, we found a highly significant (r = -0.55, p ≪ 0.001) exponential decrease in the number of BOLD components with age. The number of BOLD components were halved from adolescence to the fifth decade of life, stabilizing in middle adulthood. The regions driving this change were dorsolateral prefrontal cortices, parietal cortex, and cerebellum. The functional network of these regions centered on the cerebellum. We conclude that an age-related decrease in BOLD component number concurs with the hypothesis of neurodevelopmental integration of functional brain activity. We show evidence that the cerebellum may play a key role in this process.SIGNIFICANCE STATEMENT Human brain development is ongoing from childhood to at least 30 years of age. Functional MRI (fMRI) is key for characterizing changes in brain function that accompany development. However, developmental fMRI studies have relied on reference maps of adult brain organization in the analysis of data from younger subjects. This approach may limit the characterization of functional activity patterns that are particular to children and adolescents. Here we used an emerging fMRI approach called multi-echo fMRI that is not susceptible to such biases when analyzing the variation in functional brain organization over development. We hypothesized an integration of the components of brain activity over development, and found that the number of components decreases exponentially, halving from 8 to 35 years of age. The brain regions most affected underlie executive function and coordination. In summary, we show major changes in the organization and integration of functional networks over development into adulthood, with both methodological and neurobiological implications for future lifespan and disease studies on brain connectivity.

Developmental trends of theta-beta interelectrode power correlation during resting state in normal children.

The possibility that power-to-power (theta-beta) frequency coupling increases during development was analyzed. Three minutes of spontaneous EEG in an open eyes condition were recorded in a sample of 160 subjects ranging from 6 to 26years old. Theta (4-7Hz) and beta band (15-20Hz) power was calculated in a trial-by-trial basis. Inter-electrode power correlations (IPC) were computed in each subject as the correlation between the power of two frequency bands recorded in two electrodes. An increase in theta-beta IPC with age was obtained. IPCs were higher when theta was seeded in posterior regions than in anterior or central regions. Moreover, the significant correlations between each individual IPC and age were calculated, making it possible to draw IPC versus age correlation maps in order to capture the IPC development topography. An increase was found in significant correlations in the left hemisphere compared to the right hemisphere. There were no differences in the inter-hemispheric versus intra-hemispheric IPC maturation spatial patterns. An increase in power-to-power-frequency coupling in theta-beta occurs during development, suggesting an increase in functional connectivity with age. Frequency coupling between theta and beta rhythms would be one of the mechanisms facilitating integration of long distance functional networks during development.

Disrupted functional network integrity and flexibility after stroke: Relation to motor impairments

Previous studies investigating brain activation present during upper limb movement after stroke have greatly detailed activity alterations in the ipsi- and contralesional primary motor cortices (M1). Despite considerable interest in M1, investigations into the integration and coordination of large-scale functional networks subserving motor, sensory, and cognitive control after stroke remain scarce. The purpose of this study was to assess non-static functional connectivity within whole-brain networks involved in the production of isometric, visually-paced hand grips. Seventeen stroke patients and 24 healthy controls underwent functional MRI while performing a series of 50 isometric hand grips with their affected hand (stroke patients) or dominant hand (control subjects). We used task-based multivariate functional connectivity to derive spatial and temporal information of whole-brain networks specifically underlying hand movement. This technique has the advantage of extracting within-network commonalities across groups and identifying connectivity differences between these groups. We further used a nonparametric statistical approach to identify group differences in regional activity within these task-specific networks and assess whether such alterations were related to the degree of motor impairment in stroke patients. Our whole-brain multivariate analysis revealed group differences in two networks: (1) a motor network, including pre- and postcentral gyri, dorsal and ventral premotor cortices, as well as supplementary motor area, in which stroke patients showed reduced task-related activation compared to controls, and (2) a default-mode network (DMN), including the posterior cingulate cortex, precuneus, and medial prefrontal cortex, in which patients showed less deactivation than controls. Within-network group differences revealed decreased activity in ipsilesional primary sensorimotor cortex in stroke patients, which also positively correlated with lower levels of motor impairment. Moreover, the temporal information extracted from the functional networks revealed that stroke patients did not show a reciprocal DMN deactivation peak following activation of their motor network. This finding suggests that allocation of functional resources to motor areas during hand movement may impair their ability to efficiently switch from one network to another. Taken together, our study expands our understanding of functional reorganization during motor recovery after a stroke, and suggests that modulation of ipsilesional sensorimotor activity may increase the integrity of a whole-brain motor network, contribute to better motor performance, and optimize network flexibility.

Resting-state fMRI reveals network disintegration during delirium.

Delirium is characterized by inattention and other cognitive deficits, symptoms that have been associated with disturbed interactions between remote brain regions. Recent EEG studies confirm that disturbed global network topology may underlie the syndrome, but lack an anatomical basis. The aim of this study was to increase our understanding of the global organization of functional connectivity during delirium and to localize possible alterations. Resting-state fMRI data from 44 subjects were analyzed, and motion-free data were available in nine delirious patients, seven post delirium patients and thirteen non-delirious clinical controls. We focused on the functional network backbones using the minimum spanning tree, which allows unbiased network comparisons. During delirium a longer diameter (mean (M) = 0.30, standard deviation (SD) = 0.05, P = .024) and a lower leaf fraction (M = 0.32, SD = 0.03, P = .027) was found compared to the control group (M = 0.28, SD = 0.04 respectively M = 0.35, SD = 0.03), suggesting reduced functional network integration and efficiency. Delirium duration was strongly related to loss of network hierarchy (rho = −0.92, P = .001). Connectivity strength was decreased in the post delirium group (M = 0.16, SD = 0.01) compared to the delirium group (M = 0.17, SD = 0.03, P = .024) and the control group (M = 0.19, SD = 0.02, P = .001). Permutation tests revealed a decreased degree of the right posterior cingulate cortex during delirium and complex regional alterations after delirium. These findings indicate that delirium reflects disintegration of functional interactions between remote brain areas and suggest long-term impact after the syndrome resolves.

BOLD temporal variability differentiates wakefulness from anesthesia-induced unconsciousness.

Even though a number of findings, based on information content or information integration, are shown to define neural underpinnings characteristic of a conscious experience, the neurophysiological mechanism of consciousness is still poorly understood. Here, we investigated the brain activity and functional connectivity changes that occur in the isoflurane-anesthetized unconscious state in contrast to the awake state in rats (awake and/or anesthetized, n = 68 rats). We examined nine information measures previously shown to distinguish between conscious states: blood oxygen level-dependent (BOLD) variability, functional connectivity strength, modularity, weighted modularity, efficiency, clustering coefficient, small-worldness, and spatial and temporal Lempel-Ziv complexity measure. We also identified modular membership, seed-based network connectivity, and absolute and normalized power spectrums to assess the integrity of the BOLD functional networks between awake and anesthesia. fMRI BOLD variability and related absolute power were the only information measures significantly higher during the awake state compared with isoflurane anesthesia across animals, and with varying levels of anesthesia, after correcting for motion and respiration confounds. Thus, we conclude that, at least under the specific conditions examined here, global measures of information integration/sharing do not properly distinguish the anesthetized state from wakefulness, and heightened overall, global and local, BOLD variability is the most reliable determinant of conscious brain activity relative to isoflurane anesthesia. NEW & NOTEWORTHY Multiple metrics previously suggested to be able to distinguish between states of consciousness were compared, within and across rats in awake and isoflurane anesthesia-induced unconsciousness. All measures tested showed sensitivity to confounds, correcting for motion and for respiration changes due to anesthesia. Resting state local BOLD variability and the related absolute power were the only information measures that robustly differentiated wakefulness states. These results caution against the general applicability of global information measures in identifying levels of consciousness, thus challenging the popular concept that these measures reflect states of consciousness, and also pointing to local signal variability as a more reliable indicator of states of wakefulness.

Disrupted intrinsic and remote functional connectivity in heterotopia-related epilepsy.

Several neuroimaging studies have examined neural interactions in patients with periventricular nodular heterotopia (PNH). However, features of the underlying functional network remain poorly understood. In this study, we examined alterations in the local (regional) and remote (interregional) cerebral networks in this disorder. Twenty-eight subjects all having suffered from PNH with epilepsy, as well as 28 age- and sex- matched healthy controls, were enrolled in this study. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) were calculated to detect regional neural function and functional network integration, respectively. Compared with healthy controls, patients with PNH-related epilepsy showed decreased ALFF in the ventromedial prefrontal cortex (vmPFC) and precuneus areas. ALFF values in both areas were negative correlated with epilepsy duration (P<.05, Bonferroni-corrected). Furthermore, patients with PNH-related epilepsy had increased remote interregional FC mainly in bilateral prefrontal and parietal cortices, supramarginal gyrus, dorsal cingulate gyrus, and right insula; lower FC was found in posterior brain regions including bilateral parahippocampal gyrus and inferior temporal gyrus. Focal spontaneous hypofunction, as assessed by ALFF, correlates with epilepsy duration in patients with PNH-related epilepsy. Abnormalities existed both within the default-mode network and then across the whole brain, demonstrating that intrinsic brain dysfunction may be related to specific network interactions. Our findings provide novel understanding of the connectivity-based pathophysiological mechanisms of PNH.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

MultiXplore: Visual exploration platform for multimodal neuroimaging data

BackgroundConstruction of brain functional and structural networks by neuroimaging methods facilitates inter-modal studies. These type of studies often demand exploration tools to carry out functional–structural discoveries and answer questions regarding the anatomical basis of brain networks. New methodThis paper describes the design and development of a software module for interactive visualization and exploration of dual-modal brain networks. Our objective was to equip the user with a research tool to investigate brain connectivity matrices while visualizing relevant anatomical landmarks within a 3D volumetric view. In order to create this view, MultiXplore was designed to load data from both structural and diffusion MRI and connectivity matrices. ResultsOnce user starts to select desired cells through an interactive matrix unit, associated axonal fiber pathways and grey matter regions are generated and displayed. Integration and visualization of functional and structural networks in this 3D interactive framework was successfully implemented and tested. Comparison with existing method(s)MultiXplore contributes to the transition of connectivity visualization techniques from node-link format to an anatomically more realistic graphical form and assists scientists in relating connectivity matrices to their anatomical correlates. This module also benefits from additional novel functionalities to annotate and differentiate fibers in a large bundle. Unlike traditional graph displays, interactive functionality helps in the inspection and visualization of relevant structures without cluttering the scene with excessive items. ConclusionThis module was designed and developed as a plugin to 3D Slicer imaging platform and is accessible for neuroimaging researchers through NITRC (http://www.nitrc.org/projects/multixplore/).

Spotlight on the July 4 issue

#### Notable in Neurology This issue features an article detailing a survey focused on the long-term unmet needs in survivors of stroke or TIA; another article presents the association of chronic polyneuropathy with activities of daily living, falls, and gait. A featured review examines the 4th consensus report of the DLB (dementia with Lewy bodies) Consortium. ### Functional network integrity presages cognitive decline in preclinical Alzheimer disease Using functional connectivity MRI to assess integrity of cortical networks, the authors identified a specific set of …

CancerGD: A Resource for Identifying and Interpreting Genetic Dependencies in Cancer

SummaryGenes whose function is selectively essential in the presence of cancer-associated genetic aberrations represent promising targets for the development of precision therapeutics. Here, we present CancerGD, a resource that integrates genotypic profiling with large-scale loss-of-function genetic screens in tumor cell lines to identify such genetic dependencies. CancerGD provides tools for searching, visualizing, and interpreting these genetic dependencies through the integration of functional interaction networks. CancerGD includes different screen types (siRNA, shRNA, CRISPR), and we describe a simple format for submitting new datasets.