Negative content enhances stimulus‐specific cerebral activity during free viewing of pictures,faces, and words

Negative visual stimuli have been found to elicit stronger brain activation than do neutral stimuli. Such emotion effects have been shown for pictures, faces, and words alike, but the literature suggests stimulus‐specific differences regarding locus and lateralization of the activity. In the current functional magnetic resonance imaging study, we directly compared brain responses to passively viewed negative and neutral pictures of complex scenes, faces, and words (nouns) in 43 healthy participants (21 males) varying in age and demographic background. Both negative pictures and faces activated the extrastriate visual cortices of both hemispheres more strongly than neutral ones, but effects were larger and extended more dorsally for pictures, whereas negative faces additionally activated the superior temporal sulci. Negative words differentially activated typical higher‐level language processing areas such as the left inferior frontal and angular gyrus. There were small emotion effects in the amygdala for faces and words, which were both lateralized to the left hemisphere. Although pictures elicited overall the strongest amygdala activity, amygdala response to negative pictures was not significantly stronger than to neutral ones. Across stimulus types, emotion effects converged in the left anterior insula. No gender effects were apparent, but age had a small, stimulus‐specific impact on emotion processing. Our study specifies similarities and differences in effects of negative emotional content on the processing of different types of stimuli, indicating that brain response to negative stimuli is specifically enhanced in areas involved in processing of the respective stimulus type in general and converges across stimuli in the left anterior insula.     

Differentially categorized structural brain hubs are involved in different microstructural,functional, and cognitive characteristics and contribute to individual identification

Very little is known regarding whether structural hubs of human brain networks that enable efficient information communication may be classified into different categories. Using three multimodal neuroimaging data sets, we construct individual structural brain networks and further identify hub regions based on eight widely used graph‐nodal metrics, followed by comprehensive characteristics and reproducibility analyses. We show the three categories of structural hubs in the brain network, namely, aggregated, distributed, and connector hubs. Spatially, these distinct categories of hubs are primarily located in the default‐mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. These categorized hubs exhibit various distinct characteristics to support their differentiated roles, involving microstructural organization, wiring costs, topological vulnerability, functional modular integration, and cognitive flexibility; moreover, these characteristics are better in the hubs than nonhubs. Finally, all three categories of hubs display high across‐session spatial similarities and act as structural fingerprints with high predictive rates (100%, 100%, and 84.2%) for individual identification. Collectively, we highlight three categories of brain hubs with differential microstructural, functional and, cognitive associations, which shed light on topological mechanisms of the human connectome.     

Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting‐state fMRI study

Children exposed to natural disasters are vulnerable to the development of posttraumatic stress disorder (PTSD). Recent studies of other neuropsychiatric disorders have used graph‐based theoretical analysis to investigate the topological properties of the functional brain connectome. However, little is known about this connectome in pediatric PTSD. Twenty‐eight pediatric PTSD patients and 26 trauma‐exposed non‐PTSD patients were recruited from 4,200 screened subjects after the 2008 Sichuan earthquake to undergo a resting‐state functional magnetic resonance imaging scan. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole‐brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was then used to examine the group‐specific topological properties of the two functional connectomes. Both the PTSD and non‐PTSD control groups exhibited “small‐world” brain network topology. However, the functional connectome of the PTSD group showed a significant increase in the clustering coefficient and a normalized characteristic path length and local efficiency, suggesting a shift toward regular networks. Furthermore, the PTSD connectomes showed both enhanced nodal centralities, mainly in the default mode‐ and salience‐related regions, and reduced nodal centralities, mainly in the central‐executive network regions. The clustering coefficient and nodal efficiency of the left superior frontal gyrus were positively correlated with the Clinician‐Administered PTSD Scale. These disrupted topological properties of the functional connectome help to clarify the pathogenesis of pediatric PTSD and could be potential biomarkers of brain abnormalities. Hum Brain Mapp 36:3677–3686, 2015. © 2015 Wiley Periodicals, Inc .     

Reduced striatal activation in females with major depression during the processing of affective stimuli

The extent to which affective reactivity and associated neural underpinnings are altered by depression remains equivocal. This study assessed striatal activation in fifty-one unmedicated female participants meeting DSM-IV criteria for Major Depressive Disorder (MDD) and 61 age-matched healthy females (HC) aged 17–63 years. Participants completed an affective reactivity functional magnetic resonance imaging task. Data were preprocessed using SPM8, and region-of-interest analyses were completed using MarsBaR to extract caudate, putamen, and nucleus accumbens (NAcc) activation. General linear repeated measure ANOVAs were used to assess group differences and correlational analyses were used to measure the association between activation, depression severity, and anhedonia. Main effects of hemisphere, valence, and group status were observed, with MDD participants demonstrating decreased striatal activation compared with HC. Across groups and valence types, the left hemisphere demonstrated greater activation than the right hemisphere in the putamen and nucleus accumbens, whereas the right hemisphere demonstrated greater activation than the left in the caudate. Additionally, unpleasant stimuli elicited greater activation than pleasant and neutral stimuli in the caudate and putamen, and unpleasant stimuli elicited greater activation than neutral stimuli in the NAcc. There were no significant associations between activation, depression severity, and anhedonia. Overall, depression was characterized by reduced affective reactivity in the striatum, regardless of stimuli valence, supporting the emotion context insensitivity model of depression.     

Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes

Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task‐functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task‐fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data‐driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion‐MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree‐based classifiers produced significant above‐chance predictions both during cross‐validation (NN: AUC–ROC[CI] = 0.68[0.64–0.73], accuracy[CI] = 68.34%[63–73.2%]; RF: AUC–ROC[CI] = 0.7[0.66–0.73], accuracy[CI] = 64.81%[60.9–68.5]) and testing (NN: AUC–ROC[CI] = 0.69[0.53–0.84], accuracy[CI] = 68.09[53.2–80.9]; RF: AUC–ROC[CI] = 0.68[0.53–0.84], accuracy[CI] = 68.09[55.3–80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above‐chance predictions of functional language lateralization in the ATL are possible based on diffusion‐MRI connectomes alone. Increased degree within the right pMTG as a right‐sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward‐lateralized semantic processing.     

Neural signatures of shared subjective affective engagement and disengagement during movie viewing

When watching a negative emotional movie, we differ from person to person in the ease with which we engage and the difficulty with which we disengage throughout a temporally evolving narrative. We investigated neural responses of emotional processing, by considering inter-individual synchronization in subjective emotional engagement and disengagement. The neural underpinnings of these shared responses are ideally studied in naturalistic scenarios like movie viewing, wherein individuals emotionally engage and disengage at their own time and pace throughout the course of a narrative. Despite the rich data that naturalistic designs can bring to the study, there is a challenge in determining time-resolved behavioral markers of subjective engagement and disengagement and their underlying neural responses. We used a within-subject cross-over design instructing 22 subjects to watch clips of either neutral or sad content while undergoing functional magnetic resonance imaging (fMRI). Participants watched the same movies a second time while continuously annotating the perceived emotional intensity, thus enabling the mapping of brain activity and emotional experience. Our analyses revealed that between-participant similarity in waxing (engagement) and waning (disengagement) of emotional intensity was directly related to the between-participant similarity in spatiotemporal patterns of brain activation during the movie(s). Similar patterns of engagement reflected common activation in the bilateral ventromedial prefrontal cortex, regions often involved in self-referenced evaluation and generation of negative emotions. Similar patterns of disengagement reflected common activation in central executive and default mode network regions often involved in top-down emotion regulation. Together this work helps to better understand cognitive and neural mechanisms underpinning engagement and disengagement from emotionally evocative narratives.     

The neural substrates of affective processing toward positive and negative affective pictures in patients with major depressive disorder

Previous studies examining neural responses to emotional stimuli in individuals with major depressive disorder (MDD) have indicated increased responses within the left amygdala to sad faces, and increased activity within the visual cortex and striatum to expressions of happiness. Using functional magnetic resonance imaging (fMRI), the current study measured neural responses to neutral, positive and negative pictures of the International Affective Picture System in 15 healthy individuals and 15 patients with MDD. Depressed individuals demonstrated lower activity in the right hippocampus and the right insula to negative affective pictures, whereas they showed lower activity in the right anterior cingulate cortex and the left insula to positive pictures. However, within the MDD group, the severity of depression correlated with the activity of the left amygdala, bilateral inferior orbitofrontal areas, and the left insula to negative pictures, whereas there were no clear indications of association between specific cerebral regions and positive pictures. Our findings indicate that preferential decreases in the left amygdala in response to negative pictures might be involved in the processing of emotional stimuli in depressed individuals. Also, these findings suggest that the bilateral inferior orbitofrontal cortices and left amygdala may be preferentially recruited in MDD patients, but not in healthy individuals.     

The balance between feeling and knowing: affective and cognitive empathy are reflected in the brain's intrinsic functional dynamics

Affective empathy (AE) is distinguished clinically and neurally from cognitive empathy (CE). While AE is selectively disrupted in psychopathy, autism is associated with deficits in CE. Despite such dissociations, AE and CE together contribute to normal human empathic experience. A dimensional measure of individual differences in AE 'relative to' CE captures this interaction and may reveal brain-behavior relationships beyond those detectable with AE and CE separately. Using resting-state fMRI and measures of empathy in healthy adults, we show that relative empathic ability (REA) is reflected in the brain's intrinsic functional dynamics. Dominance of AE was associated with stronger functional connectivity among social-emotional regions (ventral anterior insula, orbitofrontal cortex, amygdala, perigenual anterior cingulate). Dominance of CE was related to stronger connectivity among areas implicated in interoception, autonomic monitoring and social-cognitive processing (brainstem, superior temporal sulcus, ventral anterior insula). These patterns were distinct from those observed with AE and CE separately. Finally, REA and the strength of several functional connections were associated with symptoms of psychopathology. These findings suggest that REA provides a dimensional index of empathic function and pathological tendencies in healthy adults, which are reflected in the intrinsic functional dynamics of neural systems associated with social and emotional cognition.     

ALE meta‐analysis reveals dissociable networks for affective and discriminative aspects of touch

Emotionally‐laden tactile stimulation—such as a caress on the skin or the feel of velvet—may represent a functionally distinct domain of touch, underpinned by specific cortical pathways. In order to determine whether, and to what extent, cortical functional neuroanatomy supports a distinction between affective and discriminative touch, an activation likelihood estimate (ALE) meta‐analysis was performed. This meta‐analysis statistically mapped reported functional magnetic resonance imaging (fMRI) activations from 17 published affective touch studies in which tactile stimulation was associated with positive subjective evaluation (n = 291, 34 experimental contrasts). A separate ALE meta‐analysis mapped regions most likely to be activated by tactile stimulation during detection and discrimination tasks (n = 1,075, 91 experimental contrasts). These meta‐analyses revealed dissociable regions for affective and discriminative touch, with posterior insula (PI) more likely to be activated for affective touch, and primary somatosensory cortices (SI) more likely to be activated for discriminative touch. Secondary somatosensory cortex had a high likelihood of engagement by both affective and discriminative touch. Further, meta‐analytic connectivity (MCAM) analyses investigated network‐level co‐activation likelihoods independent of task or stimulus, across a range of domains and paradigms. Affective‐related PI and discriminative‐related SI regions co‐activated with different networks, implicated in dissociable functions, but sharing somatosensory co‐activations. Taken together, these meta‐analytic findings suggest that affective and discriminative touch are dissociable both on the regional and network levels. However, their degree of shared activation likelihood in somatosensory cortices indicates that this dissociation reflects functional biases within tactile processing networks, rather than functionally and anatomically distinct pathways. Hum Brain Mapp 37:1308‐1320, 2016. © 2016 Wiley Periodicals, Inc.     

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting‐state functional connectivity

The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large‐scale region‐to‐region resting‐state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M₁) and inhibitory (GABA_A, M₂) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting‐state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting‐state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting‐state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103–3113, 2016. © 2016 Wiley Periodicals, Inc.     

Disrupted structural and functional rich club organization of the brain connectome in patients with generalized tonic‐clonic seizure

Network studies have demonstrated that a small set of highly connected regions may play a central role in global information integration, together forming a rich club organization. Given that generalized tonic‐clonic seizure (GTCS) has been conceptualized as a network disorder, we hypothesized that the rich club disturbances may be related to the network abnormalities of GTCS. Here, we used graph theoretical analysis to investigate the rich club organization of both structural and functional connectome in patients with GTCS (n = 50) and healthy controls (n = 60). We further measured the level of global efficiency and clustering in rich club and non‐rich club organization. We, respectively, identified a small number of highly connected hubs as rich club organization from structural and functional networks. Patients were found to exhibit significantly reduced rich club connectivity among the central hubs. Meanwhile, both structural and functional network showed changed levels of global efficiency and clustering of rich club organization in GTCS. Furthermore, in patients, lower levels of rich club connectivity were found to be correlated with longer duration of illness and seizure frequency. Together, these findings suggest that GTCS is characterized by a selective disruption of rich club organization due to the long‐term injurious effects of epileptic actions on the central hub regions, which potentially contribute to a reduced level of brain integration capacity among different functional domains and an added effect of illness on a preexisting vulnerability. Our findings emphasize a central role for abnormal rich club organization in the pathophysiological mechanism underlying GTCS. Hum Brain Mapp 37:4487–4499, 2016. © 2016 Wiley Periodicals, Inc.     

Temporal stability of functional brain modules associated with human intelligence

Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time‐averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting‐state data (N = 281) and estimated subject‐specific time‐varying functional connectivity networks. Modularity optimization was applied to determine individual time‐variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post‐hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity — which are characterized by greater disconnection of task‐positive from task‐negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.     

Breakdown of the affective‐cognitive network in functional dystonia

Previous studies suggested that brain regions subtending affective‐cognitive processes can be implicated in the pathophysiology of functional dystonia (FD). In this study, the role of the affective‐cognitive network was explored in two phenotypes of FD: fixed (FixFD) and mobile dystonia (MobFD). We hypothesized that each of these phenotypes would show peculiar functional connectivity (FC) alterations in line with their divergent disease clinical expressions. Resting state fMRI (RS‐fMRI) was obtained in 40 FD patients (12 FixFD; 28 MobFD) and 43 controls (14 young FixFD‐age‐matched [yHC]; 29 old MobFD‐age‐matched [oHC]). FC of brain regions of interest, known to be involved in affective‐cognitive processes, and independent component analysis of RS‐fMRI data to explore brain networks were employed. Compared to HC, all FD patients showed reduced FC between the majority of affective‐cognitive seeds of interest and the fronto‐subcortical and limbic circuits; enhanced FC between the right affective‐cognitive part of the cerebellum and the bilateral associative parietal cortex; enhanced FC of the bilateral amygdala with the subcortical and posterior cortical brain regions; and altered FC between the left medial dorsal nucleus and the sensorimotor and associative brain regions (enhanced in MobFD and reduced in FixFD). Compared with yHC and MobFD patients, FixFD patients had an extensive pattern of reduced FC within the cerebellar network, and between the majority of affective‐cognitive seeds of interest and the sensorimotor and high‐order function (“cognitive”) areas with a unique involvement of dorsal anterior cingulate cortex connectivity. Brain FC within the affective‐cognitive network is altered in FD and presented specific features associated with each FD phenotype, suggesting an interaction between brain connectivity and clinical expression of the disease.     

Reorganization of brain functional small-world networks during finger movements

A functional measure of brain organization is the efficiency of functional connectivity. The degree of functional connectivity can differ during a task compared to the rest, and to study this issue, we investigated the functional connectivity networks in healthy subjects during a simple, right-handed, sequential finger-tapping task using graph theoretic measures. EEGs were recorded from 58 channels in 15 healthy subjects at rest and during a motor task. We estimated mutual information values of wavelet coefficients to create an association matrix between EEG electrodes and produced a series of adjacency matrices or graphs, A, by thresholding with network cost. These graphs are called small-world networks, and we assessed their efficiency measures. We found economical small-world properties in brain functional connectivity networks in the alpha and beta band networks. The efficiency of the brain networks was enhanced during the task in the beta band networks, but not in the alpha band networks. A regional efficiency analysis during the task showed that the bilateral primary motor and left sensory areas showed increased nodal efficiency, Enodal, whereas decreased Enodal was found over the posterior parietal areas. The present study provides evidence for the reorganization of brain functional connectivity networks in a motor task with the greatest increase in Enodal in motor executive areas.     

The effects of antidepressant treatment on resting‐state functional brain networks in patients with major depressive disorder

Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first‐episode drug‐naive major depressive disorder (MDD) patients and performed resting‐state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor—escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8‐week interval. Whole‐brain connectivity was analyzed using a graph‐theory approach—functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment‐related changes. We observed significant group‐by‐time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal‐limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging‐based biomarkers for treatment evaluation in MDD. Hum Brain Mapp 36:768–778, 2015. © 2014 Wiley Periodicals, Inc.     

Brain activation during direct and indirect processing of positive and negative words

The effects of task conditions on brain activation to emotional stimuli are poorly understood. In this event-related fMRI study, brain activation to negative and positive words (matched for arousal) and neutral words was investigated under two task conditions. Subjects either had to attend to the emotional meaning (direct task) or to non-emotional features of the words (indirect task). Regardless of task, positive vs. negative words led to increased activation in the ventral medial prefrontal cortex, while negative vs. positive words induced increased activation of the insula. Compared to neutral words, all emotional words were associated with increased activation of the amygdala. Finally, the direct condition, as compared to the indirect condition, led to enhanced activation to emotional vs. neutral words in the dorsomedial prefrontal cortex and the anterior cingulate cortex. These results suggest valence and arousal dependent brain activation patterns that are partially modulated by participants’ processing mode of the emotional stimuli.     

Reduced functional coupling in the default‐mode network during self‐referential processing

Activity within the default‐mode network (DMN) is thought to be related to self‐referential processing, such as thinking about one's preferences or personality traits. Although the DMN is generally considered to function as a network, evidence is starting to accumulate that suggests that areas of the DMN are each specialized for different subfunctions of self‐referential processing. Here, we address the issue of functional specialization by investigating changes in coupling between areas of the DMN during self‐referential processing. To this aim, brain activity was assessed during a task in which subjects had to indicate whether a trait adjective described their own personality (self‐referential, Self condition), that of another person (other‐referential, Other condition), or whether the trait was socially desirable (nonreferential, Control condition). To exclude confounding effects of cardiorespiratory processes on activity and functional coupling, we corrected the fMRI signal for these effects. Activity within areas of the DMN was found to be modulated by self‐referential processing. More specifically, during the Self condition compared to the Other and Control condition, activity within the dorsal medial prefrontal cortex, ventral medial prefrontal cortex, and posterior cingulate cortex was increased. Moreover, coupling between areas of the DMN was reduced during the Self condition compared to the Other and Control condition, while coupling between regions of the DMN and regions outside the network was increased. As such, these results provide an indication for functional specialization within the DMN and support the notion that each area of the DMN is involved in different subfunctions of self‐referential processing. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Preprocessing strategy influences graph‐based exploration of altered functional networks in major depression

Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422‐1442, 2016. © 2016 Wiley Periodicals, Inc.     

Small-world properties of nonlinear brain activity in schizophrenia

A disturbance in the interactions between distributed cortical regions may underlie the cognitive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia "disconnection hypothesis." This is achieved by analyzing the spatiotemporal structure of resting state scalp EEG data previously acquired from 40 young subjects with a recent first episode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the topography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of "cliquishness"), and hub structure of these graphs were used as metrics of the underlying brain network activity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths--features consistent with a "small-world" topology--as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still "small-world," these effects are consistent with a subtle randomization in the underlying network architecture--likely associated with a greater number of links connecting disparate clusters. This randomization may underlie the cognitive disturbances characteristic of schizophrenia.