Development of the selection and manipulation of self-generated thoughts in adolescence

The ability to select and manipulate self-generated (stimulus-independent, SI), as opposed to stimulus-oriented (SO), information, in a controlled and flexible way has previously only been studied in adults. This ability is thought to rely in part on the rostrolateral prefrontal cortex (RLPFC), which continues to mature anatomically during adolescence. We investigated (1) the development of this ability behaviorally, (2) the associated functional brain development, and (3) the link between functional and structural maturation. Participants classified according to their shape letters either presented visually (SO phases) or that they generated in their head by continuing the alphabet sequence (SI phases). SI phases were performed in the presence or absence of distracting letters. A total of 179 participants (7–27 years old) took part in a behavioral study. Resistance to visual distractors exhibited small improvements with age. SI thoughts manipulation and switching between SI and SO thoughts showed steeper performance improvements extending into late adolescence. Thirty-seven participants (11–30 years old) took part in a functional MRI (fMRI) study. SI thought manipulation and switching between SO and SI thought were each associated with brain regions consistently recruited across age. A single frontal brain region in each contrast exhibited decreased activity with age: left inferior frontal gyrus/anterior insula for SI thought manipulation, and right superior RLPFC for switching between SO and SI thoughts. By integrating structural and functional data, we demonstrated that the observed functional changes with age were not purely consequences of structural maturation and thus may reflect the maturation of neurocognitive strategies

Meta-analytic methods for neuroimaging data explained

The number of neuroimaging studies has grown exponentially in recent years and their results are not always consistent. Meta-analyses are helpful to summarize this vast literature and also offer insights that are not apparent from the individual studies. In this review, we describe the main methods used for meta-analyzing neuroimaging data, with special emphasis on their relative advantages and disadvantages. We describe and discuss meta-analytical methods for global brain volumes, methods based on regions of interest, label-based reviews, voxel-based meta-analytic methods and online databases. Regions of interest-based methods allow for optimal statistical analyses but are affected by a limited and potentially biased inclusion of brain regions, whilst voxel-based methods benefit from a more exhaustive and unbiased inclusion of studies but are statistically more limited. There are also relevant differences between the different available voxel-based meta-analytic methods, and the field is rapidly evolving to develop more accurate and robust methods. We suggest that in any meta-analysis of neuroimaging data, authors should aim to: only include studies exploring the whole brain; ensure that the same threshold throughout the whole brain is used within each included study; and explore the robustness of the findings via complementary analyses to minimize the risk of false positives

Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. View Full-Text Keywords: neuropsychiatric systemic lupus erythematosus; rs-fMRI; graph theory; functional connectivity; surrogate data; machine learning; visuomotor ability; mental flexibilit

Morphometricity as a measure of the neuroanatomical signature of a trait

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute for Biomedical Imaging and Bioengineering (U.S.) (P41EB015896)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R21EB018907)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB019956)National Institute on Aging (5R01AG008122)National Institute on Aging (R01AG016495)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS0525851)National Institute of Neurological Diseases and Stroke (U.S.) (R21NS072652)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institute of Neurological Diseases and Stroke (U.S.) (5U01NS086625)United States. National Institutes of Health (5U01-MH093765)United States. National Institutes of Health (R01NS083534)United States. National Institutes of Health (R01NS070963)United States. National Institutes of Health (R41AG052246)United States. National Institutes of Health (1K25EB013649-01

Multilocus Genetic Analysis of Brain Images

The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders

DTI measurements for Alzheimer's classification

Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimer's disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value < 0.01), leading to overoptimistic results (10% up to 30% relative increase in AUC). We observed that this effect is manifest regardless of the choice of diffusion index, specifically fractional anisotropy and mean diffusivity. Secondly, we performed a test on an independent mixed cohort consisting of 119 ADNI scans; thus, we evaluated the informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test