ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Stimulating learning: A functional MRI and behavioral investigation of the effects of transcranial direct current stimulation on stochastic learning in schizophrenia

Transcranial direct current stimulation (tDCS) of the medial prefrontal cortex (mPFC) is under clinical investigation as a treatment for cognitive deficits. We investigate the effects of tDCS over the mPFC on performance SSLT in individuals with schizophrenia, and the underlying neurophysiological effect in regions associated with learning values and stimulus-outcome relationships. In this parallel-design double-blind pilot study, 49 individuals with schizophrenia, of whom 28 completed a fMRI, were randomized into active or sham tDCS stimulation groups. Subjects participated in 4 days of SSLT training (days 1, 2, 14, 56) with tDCS applied at day-1, and during a concurrent MRI scan at day-14. The SSLT demonstrated a significant mean difference in performance in the tDCS treatment group: at day-2 and at day-56. Active tDCS was associated with increased insular activity, and reduced amygdala activation. tDCS may offer an important novel approach to modulating brain networks to ameliorate cognitive deficits in schizophrenia, with this study being the first to show a longer-term effect on SSLT

Different shades of default mode disturbance in schizophrenia : Subnodal covariance estimation in structure and function

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients

The Relation of Ongoing Brain Activity, Evoked Neural Responses, and Cognition

Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a “segregationist” view on ongoing activity, both in time and space, which would selectively associate certain frequency bands or levels of spatial organization with specific functional roles. Instead, we emphasize the functional importance of the full range, from differentiation to integration, of intrinsic activity within a hierarchical spatiotemporal structure. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function – provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behavior

Alterations in cerebellar grey matter structure and covariance networks in young people with Tourette syndrome

© 2020 The Authors Tourette syndrome (TS) is a childhood-onset neurological disorder characterised by the occurrence of motor and vocal tics and the presence of premonitory sensory/urge phenomena. Functional neuroimaging studies in humans, and experimental investigations in animals, have shown that the genesis of tics in TS involve a complex interaction between cortical-striatal-thalamic-cortical brain circuits and additionally appears to involve the cerebellum. Furthermore, structural brain imaging studies have demonstrated alterations in grey matter (GM) volume in TS across a wide range of brain areas, including alterations in GM volume within the cerebellum. Until now, no study to our knowledge has yet investigated how GM structural covariance networks linked to the cerebellum may be altered in individuals with TS. In this study we employed voxel-based morphometry, and a ‘seed-to-voxel’ structural covariance network (SCN) mapping approach, to investigate alterations in GM cerebellar volume in people with TS, and alterations in cerebellar SCNs associated with TS. Data from 64 young participants was entered in the final analysis, of which 28 had TS while 36 were age-and sex-matched healthy volunteers. Using the spatially unbiased atlas template of the cerebellum and brainstem (SUIT) atlas, we found reduced GM volume in cerebellar lobule involved in higher-order cognitive functions and sensorimotor processing, in patients. In addition, we found that several areas located in frontal and cingulate cortices and sensorimotor network in addition to subcortical areas show altered structural covariance with our cerebellar seed compared to age-matched controls. These results add to the increasing evidence that cortico-basal ganglia–cerebellar interactions play an important role in tic symptomology

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis

A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI

Non-parametric data-driven analysis techniques can be used to study datasets with few assumptions about the data and underlying experiment. Variations of independent component analysis (ICA) have been the methods mostly used on fMRI data, e.g., in finding resting-state networks thought to reflect the connectivity of the brain. Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. It is a generic method with few underlying assumptions about the data. The results are built from the statistical relations between all input voxels, resulting in a whole-brain analysis on a voxel level. It has good scalability properties and the parallel implementation is capable of handling large datasets and databases. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to put the voxels in a lower-dimensional space reflecting the dependency relations based on the distance matrix. By performing clustering in this space we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks. The decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components

Brain imaging of chronic pain : from the association with immune mechanisms to brain networks

Musculoskeletal chronic pain encompasses the two mechanistically distinct categories of nociplastic pain and nociceptive pain. The former is classically typified by fibromyalgia whereas the latter is characteristic of rheumatoid arthritis. Both conditions are heterogeneous in their clinical manifestation with a multitude of factors potentially contributing to pain. This thesis aims to provide a well-rounded investigation of the pain-related mechanisms in fibromyalgia and rheumatoid arthritis from a clinical and methodological perspective. The primary focus of the works in this thesis was to study the function and biochemistry of the human brain in the presence of pain through the use of brain imaging techniques such as functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (MRS). In fibromyalgia, pain-related neural activity (assessed via fMRI) and metabolite content of pain-related brain areas (assessed via proton MRS) were examined in relation to a combination of immune-related mechanisms and behavioral/clinical pain measures. Conversely, in rheumatoid arthritis, fMRI was used to explore the temporal changes in brain network organization across different spatial scales (brain communities and areas) during painful stimulation at an inflamed body site (joint) and a neutral body site (thumbnail). In Study I, the Ala147Thr polymorphism of the gene encoding the translocator protein (TSPO, a biomarker of glial activation) was found to be involved in fundamental aspects of human pain regulation and metabolic content in thalamus and rostral anterior cingulate cortex, but not in the cerebral processing of evoked pain. Fibromyalgia patients and healthy subjects that were genetically inferred as high-affinity TSPO binders presented with a less efficient descending pain regulation including reduced conditioned pain modulation and expectancy-induced reduction of pain. The same subjects were also found to be featured by elevated thalamic glutamate concentrations and positive associations between glutamate and GABA in rostral anterior cingulate cortex. Altogether, these findings indicate that a less efficient endogenous pain modulation and brain-region specific changes in metabolite content might be ascribable to subjects determined as TSPO high-affinity binders regardless of baseline pain levels. In Study II, fibromyalgia patients had higher levels of anti-satellite glia cells immunoglobulin G antibodies (anti-SGC IgG, proposed to be pathogenic autoantibodies in fibromyalgia) than healthy subjects. Patients with elevated anti-SGC IgG levels presented with high ongoing pain intensity and disease severity, and anti-SGC IgG levels correlated positively with these clinical measures. An inverse relation was found between the levels of these antibodies and baseline thalamic concentrations of metabolites such as scylloinositol, total choline and macromolecule 12. These findings and the fact that anti-SGC IgG levels were not found to relate to pressure pain sensitivity and cerebral pain processing (both assessing evoked pain) support the potential clinical relevance of these antibodies in ongoing pain and propose their functional relation with the central nervous system in fibromyalgia patients. In Study III, the degree of interaction among brain communities was generally higher during and after painful pressure on the joint in rheumatoid arthritis patients as opposed to healthy subjects. The cerebral high demand that is often associated with a constant elevated interaction of brain communities might possibly be contributing to the maintenance of pain and the fatigue seen in these patients. However, in rheumatoid arthritis patients, the network organization was not found to reconfigure differently depending on the stimulation site. This might indicate that the cerebral processing of pain in these patients is unspecific to the clinical relevance of the area being stimulated. At the level of single pain-related brain areas and not communities, six tested brain areas were not found to bring significant contribution to the temporal changes of the network architecture. The explorative nature of Study III profited from the visualization of brain communities and the selected pain-related brain areas by means of NetPlotBrain, a tool which we developed in Python for visualizing brain networks and viewing brain anatomy. All the works in this thesis concern efforts to increase the understanding regarding potential parameters contributing to multiple aspects of pain in two mechanistically distinct chronic pain conditions