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Objective: Tissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes or treatment‐related side effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilized quantitative magnetic resonance imaging (MRI), which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug‐resistant focal epilepsy and, secondarily, whether changes were related to disease severity. Methods: To assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug‐resistant focal epilepsy (age range = 4–18 years) and 46 typically developing children (age range = 2–18 years). We assessed depth‐dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof‐of‐concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically defined abnormalities (MRI positive) and healthy controls, and tested whether this could classify patients without reported radiological abnormalities (MRI negative). Results: We uncovered depth‐dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI‐positive patients and controls, sensitivity was 71.4% at 89.4% specificity on held‐out MRI‐negative patients. Significance: These findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically identified abnormalities. [ABSTRACT FROM AUTHOR]

The ability to monitor performance during a goal‐directed behavior differs among children and adults in ways that can be measured with several tasks and techniques. As well, recent work has shown that individual differences in error monitoring moderate temperamental risk for anxiety and that this moderation changes with age. We investigated age differences in neural responses linked to performance monitoring using a multimodal approach. The approach combined functional MRI and source localization of event‐related potentials (ERPs) in 12‐year‐old, 15‐year‐old, and adult participants. Neural generators of two components related to performance and error monitoring, the N2 and ERN, lay within specific areas of fMRI clusters. Whereas correlates of the N2 component appeared similar across age groups, age‐related differences manifested in the location of the generators of the ERN component. The dorsal anterior cingulate cortex (dACC) was the predominant source location for the 12‐year‐old group; this area manifested posteriorly for the 15‐year‐old and adult groups. A fMRI‐based ROI analysis confirmed this pattern of activity. These results suggest that changes in the underlying neural mechanisms are related to developmental changes in performance monitoring. Our research shows that developmental differences occur in neural responses during performance monitoring, with more posterior brain areas showing activation in adults and anterior regions in 12‐ and 15‐year‐old subjects. The fMRI cluster of results showed large activation in the ACC across ages. Source localization of ERP responses identified the ACC as the generator of the N2 ERP in all age groups, whereas generators of the ERN component revealed that the ACC is active during error monitoring in adolescence, whereas more posterior areas (PCC) are active in adults. [ABSTRACT FROM AUTHOR]

This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke. [ABSTRACT FROM AUTHOR]

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA‐Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA‐Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA‐Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders. [ABSTRACT FROM AUTHOR]

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach "Accelerated Permutation Inference for the ACE Model (APACE)" where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset. [ABSTRACT FROM AUTHOR]

Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice. [ABSTRACT FROM AUTHOR]

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia ( n = 226) was associated with wide-spread surface area reductions, bipolar disorder ( n = 227) and healthy comparison subjects ( n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose Blood oxygen level dependent (BOLD) brain activity, measured using functional MRI (fMRI), is dependent on the echo time (TE) and the reversible spin-spin relaxation time constant ( [ABSTRACT FROM AUTHOR]

Psychopharmacological research, if properly designed, may offer insight into both timing and area of effect, increasing our understanding of the brain's neurotransmitter systems. For that purpose, the acute influence of the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was repeatedly measured in 12 healthy young volunteers with resting state functional magnetic resonance imaging (RS-fMRI). Eighteen RS-fMRI scans were acquired per subject during this randomized, double blind, placebo-controlled, crossover study. Within-group comparisons of voxelwise functional connectivity with 10 functional networks were examined ( P < 0.05, FWE-corrected) using a non-parametric multivariate approach with cerebrospinal fluid, white matter, heart rate, and baseline measurements as covariates. Although both compounds did not change cognitive performance on several tests, significant effects were found on connectivity with multiple resting state networks. Serotonergic stimulation primarily reduced connectivity with the sensorimotor network and structures that are related to self-referential mechanisms, whereas galantamine affected networks and regions that are more involved in learning, memory, and visual perception and processing. These results are consistent with the serotonergic and cholinergic trajectories and their functional relevance. In addition, this study demonstrates the power of using repeated measures after drug administration, which offers the chance to explore both combined and time specific effects. Hum Brain Mapp 38:308-325, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background Diffusion tensor imaging studies show reductions in fractional anisotropy (FA) in individuals with bipolar disorder and their unaffected siblings. However, the use of various analysis methods is an important source of between-study heterogeneity. Using tract-based spatial statistics, we previously demonstrated widespread FA reductions in patients and unaffected relatives. To better interpret the neuroanatomical pattern of this previous finding and to assess the influence of methodological heterogeneity, we here applied tractography to the same sample. Methods Diffusion-weighted images were acquired for 96 patients, 69 unaffected siblings and 56 controls. We applied TRACULA, an extension of a global probabilistic tractography algorithm, to automatically segment 18 major fiber tracts. Average FA within each tract and at each cross-section along each tract was compared between groups. Results Patients had reduced FA compared to healthy controls and their unaffected siblings in general, and in particular in the parietal part of the superior longitudinal fasciculus. In unaffected siblings, FA was nominally reduced compared to controls in the corpus callosum. Point-wise analyses indicated that similar effects were present along extended sections, but with variable effect sizes. Current symptom severity negatively correlated with FA in several fronto-limbic association tracts. Conclusions The differential sensitivity of analysis techniques likely explains between-study heterogeneity in anatomical localization of FA reductions. The present tractography analysis confirms the presence of overall FA reductions in patients with bipolar disorder, which are most pronounced in the superior longitudinal fasciculus. Unaffected siblings may display similar, albeit more subtle and anatomically restricted FA reductions. Hum Brain Mapp 37:3474-3485, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction. Hum Brain Mapp 37:1486-1511, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Reduced speed of cerebral information processing is a cognitive deficit associated with schizophrenia. Normal information processing speed (PS) requires intact white matter (WM) physiology to support information transfer. In a cohort of 107 subjects (47/60 patients/controls), we demonstrate that PS deficits in schizophrenia patients are explained by reduced WM integrity, which is measured using diffusion tensor imaging, mediated by the mismatch in WM/gray matter blood perfusion, and measured using arterial spin labeling. Our findings are specific to PS, and testing this hypothesis for patient-control differences in working memory produces no explanation. We demonstrate that PS deficits in schizophrenia can be explained by neurophysiological alterations in cerebral WM. Whether the disproportionately low WM integrity in schizophrenia is due to illness or secondary due to this disorder deserves further examination. Hum Brain Mapp 36:3793-3804, 2015. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Individuals with a family history of substance use disorders (FH+) are at a greater risk of developing substance use disorders than their peers with no such family histories (FH−) and this vulnerability is proportional to the number of affected relatives (FH density). The risk for developing substance use disorders peaks during adolescence to early adulthood in the general population, and that is thought to be related to delayed maturation of frontocortical and frontostriatal functional circuits. We hypothesized that FH+ youth and young adults have impaired myelination of frontocortical and frontostriatal white matter tracts. We examined fractional anisotropy (FA) data in 80 FH+ and 34 FH− youths (12.9 ± 1.0 years) and in 25 FH+ and 30 FH− young adults (24.3 ± 3.4 years). FH+ youths had lower FA values in both frontocortical and frontostriatal tracts as well as parietocortical tracts including the anterior, superior and posterior corona radiata and the superior frontal-occipital fasciculus. Moreover, FA values in these tracts were negatively correlated with FH density. FH+ adults had lower FA values in two frontocortical tracts: the genu of the corpus callosum and anterior corona radiata and also significant negative correlations between FA and FH density in these same tracts. In both groups, lower FA values corresponded to higher radial diffusivity suggesting reduced axonal myelination. We interpreted our findings as evidence for impaired myelination of frontal white matter that was proportional to FH density. Our data suggest that deficits may partially resolve with age, paralleling an age-related decline in risk for developing substance use disorders. Hum Brain Mapp 35:5401-5413, 2014. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]