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Background and Hypothesis Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway. Study Designs Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances. Study Results PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes’ expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains. Conclusions Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment.

Resting‐state functional magnetic resonance imaging (rsfMRI) is a promising task‐free functional imaging approach, which may complement or replace task‐based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real‐time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)‐echo planar imaging (EPI) with repetition time: 400 ms. Moving‐averaged sliding‐window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting‐state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement‐related false‐positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting‐state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task‐activation in motor cortex, Broca's, and Wernicke's areas was 5–10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real‐time high‐speed rsfMRI for presurgical mapping of eloquent cortex with real‐time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://neurodata.io/mri/.

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions—particularly prefrontal–parietal and basal ganglia—whereas the network pattern differs between genders.

Histological studies have reported microstructural hemispheric asymmetries in several cortical areas of the human brain, but reliable in vivo assessment methods have been lacking so far. Here, we used neurite orientation dispersion and density imaging (NODDI) to examine microstructural asymmetries in in vivo and determine if findings are in accordance with what has been reported in histological studies. We examined intra-neurite volume fraction (INVF), neurite orientation dispersion (ODI), and isotropic volume fraction (ISO) asymmetries in two independent samples of healthy adults (n = 269 and n = 251). Over both samples, we found greater left-hemispheric INVF in early auditory, inferior parietal and temporal-parietal-occipital areas. In contrast, we found greater right-hemispheric INVF in the fusiform and inferior temporal gyrus, reflecting what has been reported in histological studies. ODI was asymmetric towards the left hemisphere in frontal areas and towards the right hemisphere in early auditory areas. ISO showed less pronounced asymmetries. There were hardly any effects of sex or handedness on microstructural asymmetry as determined by NODDI. Taken together, these findings suggest substantial microstructural asymmetries in gray matter, making NODDI a promising marker for future genetic and behavioral studies on laterality.

Primary brain tumours often occur near eloquent regions, affecting language, motor and memory capacity, with awake mapping and tailored resection designed to preserve higher cognitive functioning. The effects of such tumours on subcortical structures, including the thalamus and basal ganglia, have been largely unexplored, in spite of the known importance of such structures to higher cognitive functioning. We sought to explore the effects of volume changes of subcortical structures on cognition, in 62 consecutive patients diagnosed with primary brain tumour and cavernous malformations, referred to our neurosurgical practice. We found right caudate to be highly predictive of intelligence, left pallidum of total neuropsychological function and right hippocampus of mood. Our study is the largest of its kind in exploring subcortical substrates of higher cognition in consecutive patients with brain tumours. This research supports prior literature, showing subcortical structures to be related to higher cognitive functioning, particularly measures of memory and executive functioning implicated in fronto-subcortical circuits. Furthermore, involvement of right mesial temporal structures in mood, further strengthens the central role of Papez circuit in emotional quality of cognition. Attention to subcortical integrity is likely to be important in discussing postsurgical cognitive outcome with patients and their families., We explored subcortical volume changes on cognition in 62 consecutive patients diagnosed with brain tumour and cavernous malformations. Right caudate volume predicted intelligence; left pallidum volume predicted total neuropsychological function; right hippocampus volume predicted mood. This research supports prior literature implicating fronto-subcortical circuits structural integrity in higher cognitive functioning., Graphical Abstract Graphical Abstract

Objective: We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. Methods: It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. Results: Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. Conclusion: N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. Significance: The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network’s collaborative informatics and neuroimaging suite (COINS). Electronic supplementary material The online version of this article (doi:10.1007/s12021-017-9338-9) contains supplementary material, which is available to authorized users.

While the behavior of “being musically creative”— improvising, composing, songwriting, etc.—is undoubtedly a complex and highly variable one, recent neuroscientific investigation has offered significant insight into the neural underpinnings of many of the creative processes contributing to such behavior. A previous study from our research group (Bashwiner et al., 2016), which examined two aspects of brain structure as a function of creative musical experience, found significantly increased cortical surface area or subcortical volume in regions of the default-mode network, a motor planning network, and a “limbic” network. The present study sought to determine how these regions coordinate with one another and with other regions of the brain in a large number of participants (n ​= ​218) during a task-neutral period, i.e., during the “resting state.” Deriving from the previous study’s results a set of eleven regions of interest (ROIs), the present study analyzed the resting-state functional connectivity (RSFC) from each of these seed regions as a function of creative musical experience (assessed via our Musical Creativity Questionnaire). Of the eleven ROIs investigated, nine showed significant correlations with a total of 22 clusters throughout the brain, the most significant being located in bilateral cerebellum, right inferior frontal gyrus, midline thalamus (particularly the mediodorsal nucleus), and medial premotor regions. These results support prior reports (by ourselves and others) implicating regions of the default-mode, executive, and motor-planning networks in musical creativity, while additionally—and somewhat unanticipatedly—including a potentially much larger role for the salience network than has been previously reported in studies of musical creativity.

Creative cognition, as measured through divergent thinking (DT), offers insight into one’s ability to generate novel ideas. Relatively little work has been done exploring the relationship between creative idea generation tasks and white matter integrity via fractional anisotropy (FA). Our previous work has shown that higher scores on DT tasks were related to reduced fractional anisotropy (FA) within the left hemisphere anterior thalamic radiation (Jung et al., 2010). However, Takeuchi et al., 2010, found positive correlations with FA and DT tasks in the prefrontal cortex and genu of the corpus callosum. The present study assessed subjects studying or working in science, technology, engineering and mathematics (STEM; N ​= ​178) for correlations in white matter FA, as related to a measure of DT. Healthy normal subjects aged (16–32 years, mean age ​= ​22.0 ​± ​3.8; F ​= ​89/178). Three idea generation DT measures were scored by three raters (α ​= ​0.71) using the consensual assessment technique, from which a composite creativity index (CCI) was derived. We found that CCI was inversely related to FA (all p ​

While there are minimal sex differences in overall intelligence, males, on average, have larger total brain volume and corresponding regional brain volumes compared to females, measures that are consistently related to intelligence. Limited research has examined which other brain characteristics may differentially contribute to intelligence in females to facilitate equal performance on intelligence measures. Recent reports of sex differences in the neural characteristics of the brain further highlight the need to differentiate how the structural neural characteristics relate to intellectual ability in males and females. The current study utilized a graph network approach in conjunction with structural equation modeling to examine potential sex differences in the relationship between white matter efficiency, fronto-parietal gray matter volume, and general cognitive ability (GCA). Participants were healthy adults (n = 244) who completed a battery of cognitive testing and underwent structural neuroimaging. Results indicated that in males, a latent factor of fronto-parietal gray matter was significantly related to GCA when controlling for total gray matter volume. In females, white matter efficiency and total gray matter volume were significantly related to GCA, with no specificity of the fronto-parietal gray matter factor over and above total gray matter volume. This work highlights that different neural characteristics across males and females may contribute to performance on intelligence measures. Hum Brain Mapp 37:4006-4016, 2016. © 2016 Wiley Periodicals, Inc.

Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249)

Nearly everyone has the ability for creative thought. Yet, certain individuals create works that propel their fields, challenge paradigms, and advance the world. What are the neurobiological factors that might underlie such prominent creative achievement? In this study, we focus on morphometric differences in brain structure between high creative achievers from diverse fields of expertise and a 'smart' comparison group of age-, intelligence-, and education-matched average creative achievers. Participants underwent a high-resolution structural brain imaging scan and completed a series of intelligence, creative thinking, personality, and creative achievement measures. We examined whether high and average creative achievers could be distinguished based on the relationship between morphometric brain measures (cortical area and thickness) and behavioral measures. Although participants' performance on the behavioral measures did not differ between the two groups aside from creative achievement, the relationship between posterior parietal cortex morphometry and creativity, intelligence, and personality measures depended on group membership. These results suggest that extraordinary creativity may be associated with measurable structural brain differences, especially within parietal cortex.

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning., Previous studies suggest that individual differences in intelligence correlate with circuit complexity and dendritic arborization in the brain. Here the authors use NODDI, a diffusion MRI technique, to confirm that neurite density and arborization are inversely related to measures of intelligence.

Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain structural connectivity matrices were calculated on a 5 mm(3) voxel scale from diffusion imaging data based on the probabilistic tractography method. The effect of the choice of the number of components (30 and 100) and their stability were examined. This method generated a set of spatially independent components that are consistent with the canonical brain tracts provided by previous anatomic descriptions, with the high order model yielding finer segmentations. The corpus-callosum example shows how this method leads to a robust parcellation of a brain structure based on its connectivity properties. We applied cmICA to study structural connectivity differences between a group of schizophrenia subjects and healthy controls. The connectivity profiles at both model orders showed similar regions with reduced connectivity in schizophrenia patients. These regions included forceps major, right inferior fronto-occipital fasciculus, uncinate fasciculus, thalamic radiation, and corticospinal tract. This paper provides a novel unsupervised data-driven framework that summarizes the information in a large global connectivity matrix and tests for brain connectivity differences. It has the potential for capturing important brain changes related to disease in connectivity-based disorders.

This study examined the association between size of the caudate nuclei and intelligence. Based on the central role of the caudate in learning, as well as neuroimaging studies linking greater caudate volume to better attentional function, verbal ability, and dopamine receptor availability, we hypothesized the existence of a positive association between intelligence and caudate volume in three large independent samples of healthy adults (total N = 517). Regression of IQ onto bilateral caudate volume controlling for age, sex, and total brain volume indicated a significant positive correlation between caudate volume and intelligence, with a comparable magnitude of effect across each of the three samples. No other subcortical structures were independently associated with IQ, suggesting a specific biological link between caudate morphology and intelligence. Hum Brain Mapp 36:1407–1416, 2015. © 2014 Wiley Periodicals, Inc.

The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence.

Openness/Intellect (i.e., openness to experience) is the Big Five personality factor most consistently associated with individual differences in creativity. Recent psychometric evidence has demonstrated that this factor consists of two distinct aspects-Intellect and Openness. Whereas Intellect reflects perceived intelligence and intellectual engagement, Openness reflects engagement with fantasy, perception, and aesthetics. We investigated the extent to which Openness and Intellect are associated with variations in brain structure as measured by cortical thickness, area, and volume (N = 185). Our results demonstrated that Openness was correlated inversely with cortical thickness and volume in left middle frontal gyrus (BA 6), middle temporal gyrus (MTG, BA 21), and superior temporal gyrus (BA 41), and exclusively with cortical thickness in left inferior parietal lobule (BA 40), right inferior frontal gyrus (IFG, BA 45), and MTG (BA 37). When age and sex were statistically controlled for, the inverse correlations between Openness and cortical thickness remained statistically significant for all regions except left MTG, whereas the correlations involving cortical volume remained statistically significant only for left middle frontal gyrus. There was no statistically significant correlation between Openness and cortical area, and no statistically significant correlation between Intellect and cortical thickness, area, or volume. Our results demonstrate that individual differences in Openness are correlated with variation in brain structure-particularly as indexed by cortical thickness. Given the involvement of the above regions in processes related to memory and cognitive control, we discuss the implications of our findings for the possible contribution of personality to creative cognition.

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p < 0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (electrophysiology) and structure (white matter integrity) and demonstrates the sensitivity of this multivariate, multimodal integration technique to group differences in function and structure.

There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance., 22 pages, 7 figures

Creative behaviors are among the most complex that humans engage in, involving not only highly intricate, domain-specific knowledge and skill, but also domain-general processing styles and the affective drive to create. This study presents structural imaging data indicating that musically creative people (as indicated by self-report) have greater cortical surface area or volume in a) regions associated with domain-specific higher-cognitive motor activity and sound processing (dorsal premotor cortex, supplementary and pre-supplementary motor areas and planum temporale), b) domain-general creative-ideation regions associated with the default mode network (dorsomedial prefrontal cortex, middle temporal gyrus and temporal pole) and c) emotion-related regions (orbitofrontal cortex, temporal pole and amygdala). These findings suggest that domain-specific musical expertise, default-mode cognitive processing style and intensity of emotional experience might all coordinate to motivate and facilitate the drive to create music.

We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural network underlying the functional DMN. The GM regions consisted mainly of the precuneus, superior and medial frontal gyri, middle temporal gyrus, hippocampus, cuneus, and cerebellum. The WM regions included the cingulum, corpus callosum, corona radiata, association fibers, and middle cerebellar peduncle. Significant gender differences in the relationship between intelligence quotient (IQ) and the identified structural network were observed. Our findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network. The constrained SBM approach is additionally applicable to a wide variety of problems identifying structural networks from seed regions.

Despite intensive study, the role of the dorsal medial frontal cortex (dMFC) in error monitoring and conflict processing remains actively debated. The current experiment manipulated conflict type (stimulus conflict only or stimulus and response selection conflict) and utilized a novel modeling approach to isolate error and conflict variance during a multimodal numeric Stroop task. Specifically, hemodynamic response functions resulting from two statistical models that either included or isolated variance arising from relatively few error trials were directly contrasted. Twenty-four participants completed the task while undergoing event-related functional magnetic resonance imaging on a 1.5-Tesla scanner. Response times monotonically increased based on the presence of pure stimulus or stimulus and response selection conflict. Functional results indicated that dMFC activity was present during trials requiring response selection and inhibition of competing motor responses, but absent during trials involving pure stimulus conflict. A comparison of the different statistical models suggested that relatively few error trials contributed to a disproportionate amount of variance (i.e., activity) throughout the dMFC, but particularly within the rostral anterior cingulate gyrus (rACC). Finally, functional connectivity analyses indicated that an empirically derived seed in the dorsal ACC/pre-SMA exhibited strong connectivity (i.e., positive correlation) with prefrontal and inferior parietal cortex but was anti-correlated with the default-mode network. An empirically derived seed from the rACC exhibited the opposite pattern, suggesting that sub-regions of the dMFC exhibit different connectivity patterns with other large scale networks implicated in internal mentations such as daydreaming (default-mode) versus the execution of top-down attentional control (fronto-parietal).

Transcranial direct current stimulation (TDCS) is a non-invasive form of brain stimulation applied via a weak electrical current passed between electrodes on the scalp. In recent studies, TDCS has been shown to improve learning when applied to the prefrontal cortex (e.g., Kincses et al. in Neuropsychologia 42:113-117, 2003; Clark et al. Neuroimage in 2010). The present study examined the effects of TDCS delivered at the beginning of training (novice) or after an hour of training (experienced) on participants' ability to detect cues indicative of covert threats. Participants completed two 1-h training sessions. During the first 30 min of each training session, either 0.1 mA or 2.0 mA of anodal TDCS was delivered to the participant. The anode was positioned near F8, and the cathode was placed on the upper left arm. Testing trials immediately followed training. Accuracy in classification of images containing and not-containing threat stimuli during the testing sessions indicated: (1) that mastery of threat detection significantly increased with training, (2) that anodal TDCS at 2 mA significantly enhanced learning, and (3) TDCS was significantly more effective in enhancing test performance when applied in novice learners than in experienced learners. The enhanced performance following training with TDCS persisted into the second session when TDCS was delivered early in training.

A 1H magnetic resonance spectroscopic imaging study at 3T and short echo time was conducted to evaluate both the reproducibility, as measured by the interscan coefficient of variation (CV), and test-retest reliability, as measured by the intraclass correlation coefficient (ICC), of measurements of glutamate (Glu), combined glutamate and glutamine (Glx), myo-inositol (mI), N-acetylaspartate, creatine, and choline in 21 healthy subjects. The effect of partial volume correction on these measures and the relationship of reproducibility and reliability to data quality were also examined. A 1H magnetic resonance spectroscopic imaging slice was prescribed above the lateral ventricles and single repeat scans were performed within 30 min to minimize physiologic variability. Interscan CVs based on all the voxels varied from 0.05 to 0.07 for N-acetylaspartate, creatine, and choline to 0.10-0.13 for mI, Glu, and Glx. Findings on the reproducibility of gray and white matter estimates of N-acetylaspartate, creatine, and choline are consistent with previous studies using longer echo times, with CVs in the range of 0.02-0.04 and ICC in the range of 0.65-0.90. CVs for Glu, Glx, and mI are much lower than reported in previous studies at 1.5 T, while white matter mI (CV=0.04, ICC=0.93) and gray matter Glx (CV=0.04, ICC=0.68) demonstrated both high reproducibility and test-retest reliability.

Background: Emerging evidence implicates white matter (WM) abnormalities in the pathophysiology of schizophrenia. However, there is considerable heterogeneity in the presentation of WM abnormalities in the existing studies. The object of this study was to evaluate WM integrity in a large sample of patients with first-episode (FE) and chronic schizophrenia in comparison to matched control groups. Our goal was to assess whether WM findings occurred early in the illness or whether these abnormalities developed with the illness over time. Methods: Participants included 114 patients with schizophrenia (31 FE and 83 chronic patients) and 138 matched controls. High-resolution structural and diffusion tensor images were obtained on all participants. Measures of fractional anisotropy (FA) were calculated for the 4 cortical lobes and the cerebellum and brain stem. Results: FA was significant lower in patients vs controls in the whole brain and individually in the frontal, parietal, occipital, and temporal lobes. FA was not significantly different in the brain stem or cerebellum. FA differences were significant only in patients with chronic schizophrenia and not in the FE group. Conclusions: We found global differences in the WM microstructure in patients with chronic but not FE schizophrenia. These findings suggest progressive alterations in WM microstructure.

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI-fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI-fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI-fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.

A broadly accepted definition of creativity refers to the production of something both novel and useful within a given social context. Studies of patients with neurological and psychiatric disorders and neuroimaging studies of healthy controls have each drawn attention to frontal and temporal lobe contributions to creativity. Based on previous magnetic resonance (MR) spectroscopy studies demonstrating relationships between cognitive ability and concentrations ofN-acetyl-aspartate (NAA), a common neurometabolite, we hypothesized that NAA assessed in gray and white matter (from a supraventricular slab) would relate to laboratory measures of creativity. MR imaging and divergent thinking measures were obtained in a cohort of 56 healthy controls. Independent judges ranked the creative products of each participant, from which a “Composite Creativity Index” (CCI) was created. Different patterns of correlations between NAA and CCI were found in higher verbal ability versus lower verbal ability participants, providing neurobiological support for a critical “threshold” regarding the relationship between intelligence and creativity. To our knowledge, this is the first report assessing the relationship between brain chemistry and creative cognition, as measured with divergent thinking, in a cohort comprised exclusively of normal, healthy participants.

Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidate effects that may not be seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.

The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self‐generated cognition, such as mind‐wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease‐related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting‐state fMRI data, we provide evidence that Openness to Experience—a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes—underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a robust predictor—even after controlling for intelligence, age, gender, and other personality variables—explaining 18% of the variance in DN functioning. These findings point to a biological basis of Openness to Experience, and suggest that normally distributed personality traits affect the intrinsic architecture of large‐scale brain systems. Hum Brain Mapp 37:773–779, 2016. © 2015 Wiley Periodicals, Inc.

The creativity research community is in search of a viable cognitive measure providing support for behavioral observations that higher ideational output is often associated with higher creativity (known as the equal-odds rule). One such measure has included divergent thinking: the production of many examples or uses for a common or single object or image. We sought to test the equal-odds rule using a measure of divergent thinking, and applied the consensual assessment technique to determine creative responses as opposed to merely original responses. We also sought to determine structural brain correlates of both ideational fluency and ideational creativity. Two-hundred forty-six subjects were subjected to a broad battery of behavioral measures, including a core measure of divergent thinking (Foresight), and measures of intelligence, creative achievement, and personality (i.e., Openness to Experience). Cortical thickness and subcortical volumes (e.g., thalamus) were measured using automated techniques (FreeSurfer). We found that higher number of responses on the divergent thinking task was significantly associated with higher creativity (r = 0.73) as independently assessed by three judges. Moreover, we found that creativity was predicted by cortical thickness in regions including the left frontal pole and left parahippocampal gyrus. These results support the equal-odds rule, and provide neuronal evidence implicating brain regions involved with "thinking about the future" and "extracting future prospects."

© The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both sourcebased morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain structural connectivity matrices were calculated on a 5 mm(3) voxel scale from diffusion imaging data based on the probabilistic tractography method. The effect of the choice of the number of components (30 and 100) and their stability were examined. This method generated a set of spatially independent components that are consistent with the canonical brain tracts provided by previous anatomic descriptions, with the high order model yielding finer segmentations. The corpus-callosum example shows how this method leads to a robust parcellation of a brain structure based on its connectivity properties. We applied cmICA to study structural connectivity differences between a group of schizophrenia subjects and healthy controls. The connectivity profiles at both model orders showed similar regions with reduced connectivity in schizophrenia patients. These regions included forceps major, right inferior fronto-occipital fasciculus, uncinate fasciculus, thalamic radiation, and corticospinal tract. This paper provides a novel unsupervised data-driven framework that summarizes the information in a large global connectivity matrix and tests for brain connectivity differences. It has the potential for capturing important brain changes related to disease in connectivity-based disorders.

The ability to reliably respond to stimuli could be an important biological determinant of differences in fluid intelligence (Gf). However, most electrophysiological studies of Gf employ event-related potential (ERP) measures that average brain activity over trials, and hence have limited power to quantify neural variability. Time-frequency analyses can capture cross-trial variation in the phase of neural activity, and thus can help address the importance of neural reliability to differences in Gf. This study recruited a community sample of healthy adults and measured inter-trial phase clustering (ITPC), total spectral power, and ERP amplitudes elicited by Repeated and Novel non-target stimuli during two visual oddball tasks. Condition effects, relations among the EEG measures, and relations with Gf were assessed. Early visual responses to Repeated stimuli elicited higher ITPC, yet only ITPC elicited by Novel stimuli was associated with Gf. Analyses of spectral power further highlighted the contribution of phase consistency to the findings. The link between Gf and reliable responding to changing inputs suggests an important role for flexible resource allocation in fluid intellectual skills.

Creative cognition emerges from a complex network of interacting brain regions. This study investigated the relationship between the structural organization of the human brain and aspects of creative cognition tapped by divergent thinking tasks. Diffusion weighted imaging (DWI) was used to obtain fiber tracts from 83 segmented cortical regions. This information was represented as a network and metrics of connectivity organization, including connectivity strength, clustering and communication efficiency were computed, and their relationship to individual levels of creativity was examined. Permutation testing identified significant sex differences in the relationship between global connectivity and creativity as measured by divergent thinking tests. Females demonstrated significant inverse relationships between global connectivity and creative cognition, whereas there were no significant relationships observed in males. Node specific analyses revealed inverse relationships across measures of connectivity, efficiency, clustering and creative cognition in widespread regions in females. Our findings suggest that females involve more regions of the brain in processing to produce novel ideas to solutions, perhaps at the expense of efficiency (greater path lengths). Males, in contrast, exhibited few, relatively weak positive relationships across these measures. Extending recent observations of sex differences in connectome structure, our findings of sexually dimorphic relationships suggest a unique topological organization of connectivity underlying the generation of novel ideas in males and females.

We examined the relationship between structural brain variation and general intelligence using voxel-based morphometric analysis of MRI data in men and women with equivalent IQ scores. Compared to men, women show more white matter and fewer gray matter areas related to intelligence. In men IQ/gray matter correlations are strongest in frontal and parietal lobes (BA 8, 9, 39, 40), whereas the strongest correlations in women are in the frontal lobe (BA10) along with Broca's area. Men and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to general intelligence and that different types of brain designs may manifest equivalent intellectual performance.

Total brain volume accounts for about 16% of the variance in general intelligence scores (IQ), but how volumes of specific regions-of-interest (ROIs) relate to IQ is not known. We used voxel-based morphometry (VBM) in two independent samples to identify substantial gray matter (GM) correlates of IQ. Based on statistical conjunction of both samples (N = 47; P < 0.05 corrected for multiple comparisons), more gray matter is associated with higher IQ in discrete Brodmann areas (BA) including frontal (BA 10, 46, 9), temporal (BA 21, 37, 22, 42), parietal (BA 43 and 3), and occipital (BA 19) lobes and near BA 39 for white matter (WM). These results underscore the distributed neural basis of intelligence and suggest a developmental course for volume--IQ relationships in adulthood.

One of the great joys of being a scien-tist is the hunt for an elusive signal withinthe noise of data, opinions, biases, andother human foibles associated with thepursuit of knowledge. It is inevitable thatthis imperfect quest will result in manyfalse starts along the way when looking“through a glass, darkly.” Our imperfectand incomplete knowledge of the worldmust look like an unpolished mirror,reflecting gibberish, at times. However, italsoreflectsanunderlyingsignalthatbearsfurther scrutiny, in spite of our instinctto discard a flawed image of reality. Thepursuit of the neural underpinnings ofcreative cognition is certainly that “darkglass” we peer into so intently, attempt-ing to grasp, through our meager instru-ments, some hidden truth. Many thinkersand researchers have found that creativityand madness seem somehow to be inter-twined, but the signal is weak, the imageblurry, and the propensity toward roman-tic stereotypes is high. And yet, as scien-tists, we can only follow the data, tryingtomakesenseofwhatittellsus.So,ratherthan entertain the premise outright let metake you on a bit of a journey (which willend back at madness, I promise).First: What if evolutionary processesselected for two types of reasoning?CosmidesandToobyhypothesizeda“ded-icated intelligence” that “refers to theability of a computational system tosolve predefined, target set of prob-lems.” These problems often involve wellestablished rules—like your mundanelife, and Raven’s Matrices problems, andacquiring a language (Pinker, 1991). Theother problems require “improvisationalintelligence” referring to “the ability of acomputational system to improvise solu-tions to novel problems” (Cosmides andTooby, 2002). These problems are moretransient and involve contingencies thatmay or may not persist over time—likefiguring out how to get into your car, hav-ing locked your keys inside. Philosopherscall the former type of problem solving“deductive reasoning”—the observationsnecessarily result in a conclusion beingmade based on the evidence. They are rulebased, deterministic, and the cause leadsnaturallytoeffect.Thelatterproblemsolv-ing is called “abductive reasoning”—thereare an infinite number of possible solu-tionstothemyriadchallengesfacedinthe world; therefore a theory best explainsthe observation, given the evidence. Thisreasoningisprobabilistic,involvesapprox-imation,and(importantly)guessing.Bothmethods are adaptive: one for problemsthat are familiar, the other for problemsthat have never been encountered before.Kanazawa (2004) views intelligence(incorrectly), the pinnacle of deductivereasoning, as THE domain-specific adap-tation to solving novel problems in theenvironment.However,itismycontentionthat intelligence and creativity occupy twoextremes of a dichotomy: intelligence sup-plies a “dedicated reasoning capacity” forproblems that possess rule-based, cause-effect relationships. Others have coveredwell, and provide empirical support for,the “general purpose problem solving”capacity of intelligence and “g” (Kaufmanet al., 2011): I am merely saying here thatthe mechanism is rather “dedicated” tocause-effectrelationships—acapacitywithbroad applicability to deductive reason-ing tasks. In contrast, creativity emergedas an adaptive cognitive mechanism forlow frequency, “improvisational reason-ing,” where solutions to problems areunsighted (Simonton, 2013), and proba-bilistic approximation could lead to novelsolutions. Creative reasoning solves theminority of problems that are unforeseenand yet of high adaptability: “The light-ning has struck the tree near the campand set it on fire. The fire is now spread-ing to the dry underbrush. What should Ido?” (Kanazawa, 2004). In this conceptu-alization, creativity is an evolved cognitivemechanism to abstract, to synthesize, tosolve non-recurrent problems in the envi-ronment. Finally, intelligence should beseen as a rather stable evolved mechanismover the last 1.6 million years (i.e., thesingular“innovation”beingtheAcheuleanhand ax), while creativity appears to haveappeared, in humans at least, in thelast ∼30,000 years (Gabora and Kaufman,2010). Intelligence may not be evolution-arily novel, but creativity certainly is.Perhaps the most parsimonious the-ory of creative cognition to incorporateevolutionary principles is that of BlindVariation and Selective Retention (BVSR)(Campbell, 1960). Indeed, his the-ory posits that creativity in humans“represent(s) cumulated inductiveachievements, stage by stage expansionsof knowledge beyond what could havebeen

Background Altered functional connectivity in critical networks has been associated with chronic alcohol abuse. In turn, changes in connectivity in executive control networks (ECNs) may undermine the ability to control alcohol consumption. It was hypothesized that network connectivity would be reduced in individuals with problematic alcohol use (ALC) compared with controls and that diminished network connectivity would be associated with greater failure to control drinking. Methods Resting-state functional magnetic resonance imaging was analyzed to identify 14 previously identified intrinsic connectivity networks (ICNs) using a priori regions of interest in cases ranging from binge drinkers to those with severe alcohol use disorder, as well as control subjects. Analyses tested for differences in network connectivity strength between 255 ALC cases and 87 age- and gender-matched controls. Further, structural equation analysis, using 383 ALC cases, tested whether functional connectivity strength mediated the relationship between years of regular drinking and alcohol problems. Results The age- and gender-matched analysis showed that ALC had significantly lower network connectivity strength than controls in the left executive control (LECN), basal ganglia, and primary visual networks. For all ALC, LECN connectivity strength is negatively correlated with failed control and alcohol disorder severity. Edges connecting parietal regions with dorsolateral prefrontal, middle frontal, and temporal regions within the LECN drove these relationships. A positive association between years of drinking and severity of alcohol problems was mediated by reduced ECN connectivity. Conclusions This study reports relationships between network strength and problematic alcohol use, suggesting that chronic drinking negatively impacts brain connectivity, specifically in the LECN. Altered functional connectivity, related to chronic alcohol abuse, may contribute to the etiology of alcohol dependence and relapse.

A lower level of intelligence, as measured by IQ, has historically been the central defining criterion of mental retardation (MR). The use of IQ scores in terms of standard deviation units from the mean is the basis for defining MR in the ICD-10 and DSM-IV-TR, and more recently for defining intellectual disability (ID) in the DSM-5. Similarly, ID is defined by the American Association on Intellectual and Developmental Disabilities as an IQ score approximately two standard deviations below the mean 1. However, in recent years, an increasing number of researchers and clinicians have expressed the view that measurements of IQ fail to capture individual differences in cognitive dysfunction. The heterogeneity of cognitive dysfunction and consequent adaptive behavior profile in persons with MR is one of the reasons leading the working group in charge of this issue within the revision of the ICD-10 to propose a new definition for intellectual developmental disorders (IDD) in the upcoming 11th edition of the diagnostic system 2. In 2011, the WPA Section on Psychiatry of Intellectual Disability started a mini-Delphi process with an international panel of experts to produce a consensus document on this issue. The present letter reports the results of the systematic mapping 3 of the international literature included in this process, focusing on current models of intelligence, multi-component and specific cognitive functions, and the relationship between intellectual and affective assessment, as relevant for defining IDD. A total of 7,948 articles matched the key words. After titles were checked, 3,179 were selected. After abstracts were read, 2,497 were excluded as they were not relevant to the mapping topic, and 114 were excluded because they were not in English. After reading the remaining articles in full, 177 papers were included as relevant to search questions. The mapping of current theoretical approaches identified limitations of IQ as an indicator of the adaptive complexity and dynamism of human intellectual functioning and pointed out the need for a shared model and comprehensive definition of intelligence. Of the available approaches, the most frequently used refers to a unitary capacity, articulated in complex functions. A second evolving group of theories identifies a key role of interdependent but specialized factors, such as specific cognitive functions. There is a neuro-bio-psychological evidence in support of both approaches, but multi-component models seem to prevail. Experimental data indicate that the same IQ score can correspond to very different cognitive profiles, and that functional limitations and problem behaviors associated with IDD correlate with impairment of specific cognitive functions more than with IQ 4,5. To address the limitations of the current conceptualization of MR, the ICD-11 working group proposed revised diagnostic criteria for IDD, based on a more articulated model of cognitive impairment. This approach juxtaposes a new concept of cognitive characterization to that of intelligence and complements the measurement of IQ with the assessment of specific cognitive functions and a contextualised description of consequent adaptive and learning difficulties 2. Within this new approach, cognitive skills should be assessed through tests, semi-structured observations, and direct clinical examination. The tests should combine the measurement of IQ with that of several aspects of executive functioning, including perceptual reasoning, processing speed, verbal comprehension, as well as the assessment of attention, perception and working memory. The evaluation should aim to identify the cognitive dysfunctions that have the greatest negative impact in terms of behavior, adjustment, autonomy, and above all quality of life, across the lifespan. The instruments to assess specific cognitive functions should have a low cost, in order to allow fast assimilation by professionals practicing in low-income countries 6. Production and distribution by international non-profit organizations could greatly facilitate this effort. In conclusion, within the proposed ICD-11 framework for characterization of IDD, there is a need for neuropsychological measures that can be readily adapted to different levels of severity, and that are easy to apply in clinical and research practice. The evolving understanding of how environmental and cultural factors influence development should promote a continuing search for assessment models and practices that capture developmental pathways of cognition in persons with IDD. Naturalistic, multidisciplinary and multicentric studies could provide useful data to this purpose 7.