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We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find for the first-time strong evidence relating inter-individual brain structural variations to a wide range of demographic and behavioral variates across a large cohort of young healthy human volunteers. Our analyses reveal that a robust ‘positive-negative’ spectrum of behavioral and demographic variates, recently associated to covariation in brain function, can already be identified using only structural features, highlighting the importance of careful integration of structural features in any analysis of inter-individual differences in functional connectivity and downstream associations with behavioral/demographic variates. For years, scientists have tried to explain human behavior by measuring brain characteristics. During the first half of the 19th century, craniometry, the science of taking measurements of the skull, was a popular field of research and cognitive abilities as well as many behaviors were associated with different skull sizes and shapes. Although craniometry has been broadly discredited as a science, the study of brain structure and function, and their correlation to human behavior, continues to this day. Currently, one of the most powerful tools used in the study of the brain is magnetic resonance imaging (MRI), which relies on strong magnetic fields and radio waves to produce detailed imaging. These images can provide functional information, by measuring changes in blood flow to different parts of the brain, as well as structural information such as the amount of gray or white matter or the size of different brain regions. Many studies have shown correlations between functional MRI (fMRI) data and behavioral and demographic traits, such as years of education, lifestyle habits or stress. Another advance in the study of the relationship between behaviors and the brain has been the emergence of better statistical analysis tools thanks to increasing computing power. These tools have made it possible to integrate data from different sources and analyze many variables at the same time, allowing patterns to emerge that would have been previously missed. Llera et al. have analyzed a large dataset from young healthy volunteers to show that changes in behavioral traits can be predicted by brain structure, and not just by brain function as previously shown. Different types of brain structural data, including what the surface of the brain looks like and relative volumes of gray and white matter, were integrated and analyzed, and correlations between changes in these variables and changes in the demographic and behavioral traits of the subjects were found. Previously, a robust relationship had been established between specific patterns of connections and activity in the brain and a group of characteristics such as life satisfaction, working memory, weight and strength, loneliness, family history of drugs and alcohol use, etc. Llera et al. show that this relationship also holds between the traits and structural brain data. As an example, there is a positive correlation between changes in the number of years of education and the income of the subjects and changes in a pattern of integrated structural data that include the amount of gray matter, white matter integrity and size of specific brain structures. Given these findings it becomes important to reconsider whether differences between individuals previously attributed to brain function could simply explained by the shape or size of the brain and its parts. These findings show that physical brain characteristics, including its size or the shape of its surface, could predict information such as individuals’ lifestyle decisions or their income; also implying that these characteristics are not simply a product of brain function. The results also demonstrate the power of combining different types of brain data to predict patterns in behavior.

Head motion during functional MRI (fMRI) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting (‘scrubbing’) or regressing out (‘spike regression’) motion volumes from fMRI time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the fMRI time-series and reducing temporal degrees of freedom. ICA-based fMRI denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e.g. ICA-FIX; Salimi-Khorshidi et al. (2014)). Here, we propose an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) that uses a small (n=4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe ICA-AROMA, its implementation, and initial validation. ICA-AROMA identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated ICA-AROMA in resting-state (100 participants) and task-based fMRI data (118 participants). Our approach removed (motion-related) spurious noise from both rfMRI and task-based fMRI data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, ICA-AROMA increased sensitivity to group-level activation. Our results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the fMRI data. •ICA-AROMA: ICA-based strategy for motion artifact removal from fMRI data.•ICA-AROMA preserves degrees of freedom, prevents heteroscedasticity.•ICA-AROMA is generalizable across datasets, does not require re-training.•ICA-AROMA is applicable to resting-state fMRI and task-based fMRI data.

A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health. Wang et al. present a new open resource from the UK Biobank using quantitative susceptibility mapping, a neuroimaging marker sensitive to iron and myelin. They demonstrate a broad range of phenotypic and genetic associations in 35,885 participants.

The striatum receives dense dopaminergic projections, making it a key region of the dopaminergic system. Its dysfunction has been implicated in various conditions including Parkinson’s disease (PD) and substance use disorder. However, the investigation of dopamine-specific functioning in humans is problematic as current MRI approaches are unable to differentiate between dopaminergic and other projections. Here, we demonstrate that ‘connectopic mapping’ – a novel approach for characterizing fine-grained, overlapping modes of functional connectivity – can be used to map dopaminergic projections in striatum. We applied connectopic mapping to resting-state functional MRI data of the Human Connectome Project (population cohort; N = 839) and selected the second-order striatal connectivity mode for further analyses. We first validated its specificity to dopaminergic projections by demonstrating a high spatial correlation ( r = 0.884) with dopamine transporter availability – a marker of dopaminergic projections – derived from DaT SPECT scans of 209 healthy controls. Next, we obtained the subject-specific second-order modes from 20 controls and 39 PD patients scanned under placebo and under dopamine replacement therapy (L-DOPA), and show that our proposed dopaminergic marker tracks PD diagnosis, symptom severity, and sensitivity to L-DOPA. Finally, across 30 daily alcohol users and 38 daily smokers, we establish strong associations with self-reported alcohol and nicotine use. Our findings provide evidence that the second-order mode of functional connectivity in striatum maps onto dopaminergic projections, tracks inter-individual differences in PD symptom severity and L-DOPA sensitivity, and exhibits strong associations with levels of nicotine and alcohol use, thereby offering a new biomarker for dopamine-related (dys)function in the human brain.

Wealth inequality is one of the most profound challenges confronting society today. However, an important issue in addressing inequality lies in formalizing the diversity of individual perspectives regarding what constitutes a fair distribution of resources. We tackle this topic by simulating wealth inequality through the allocation of bonus endowments in both Dictator Game (DG) and Ultimatum Game (UG) settings and capturing distributive decisions. By integrating a computational model, we quantify individual differences in the interplay between financial self-interest and competing pro-social motivations that emerge in the context of pre-existing wealth inequity. Our behavioral results show that, on average, pre-existing wealth influences distributive preferences across both allocations and proposals. Yet, inequality elicits non-uniform fairness concerns. Using a hierarchical clustering approach, we objectively categorise participants’ behavior elucidating four distinct decision strategies: ‘Pro-Self’, ‘Table Egalitarianism’, ‘Total Egalitarianism’, and ‘Moral Opportunism’. A balanced distribution of strategies is observed during allocations (DG), whereas Table Egalitarianism prevails in strategic proposals (UG), highlighting the influence of strategic considerations on decision strategy. Furthermore, we demonstrate an association between strategies across decision contexts. Our findings thus contribute a principled framework to formalize distributive preferences, revealing that, with respect to both altruistic allocations and strategic proposals, competing ideals of fairness underlie distributive preferences under wealth inequality.

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.

Personality factors as defined by the “five-factor model” are some of the most investigated characteristics that underlie various types of complex behavior. These are, however, often investigated as isolated traits that are conceptually independent, yet empirically are typically strongly related to each other. We apply Independent Component Analysis to these personality factors as measured by the NEO-FFI in 471 healthy subjects from the Human Connectome Project to investigate independent personality profiles that incorporate all five original factors. Subsequently we examine how these profiles are related to patterns of resting-state brain activity in specific networks-of-interest related to cognition and emotion. We find that a personality profile of contrasting openness and agreeableness is associated with engagement of a subcortical-medial prefrontal network and the dorsolateral prefrontal cortex. Likewise, a profile of contrasting extraversion and conscientiousness is associated with activity in the precuneus. This study shows a novel approach to investigating personality and how it is related to patterns of activity in the resting brain.

Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.

Pre-reading language skills develop rapidly in early childhood and are related to brain structure and functional architecture in young children prior to formal education. However, the early neurobiological development that supports these skills is not well understood. Here we acquired anatomical, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) from 35 children at 3.5 years of age. Children were assessed for pre-reading abilities using the NEPSY-II subtests one year later (4.5 years). We applied a data-driven linked independent component analysis to explore the shared co-variation of gray and white matter measures. Two sources of structural variation at 3.5 years of age demonstrated relationships with Speeded Naming scores at 4.5 years of age. The first imaging component involved volumetric variability in reading-related cortical regions alongside microstructural features of the superior longitudinal fasciculus. The second component was dominated by cortical volumetric variations within the cerebellum and visual association area. In a subset of children with rs-fMRI data, we evaluated the inter-network functional connectivity of the left-lateralized fronto-parietal language network and its relationship with pre-reading measures. Higher functional connectivity between the fronto-parietal language network and the default mode and visual networks at 3.5 years significantly predicted better Phonological Processing scores at 4.5 years. Together, these results suggest that the integration of functional networks, as well as the co-development of white and gray matter brain structures in early childhood, support the emergence of pre-reading measures in preschool children.

In line with the Research Domain Criteria (RDoC) , we set out to investigate the brain basis of psychopathology within a transdiagnostic, dimensional framework. We performed an integrative structural-functional linked independent component analysis to study the relationship between brain measures and a broad set of biobehavioral measures in a sample (n = 295) with both mentally healthy participants and patients with diverse non-psychotic psychiatric disorders (i.e. mood, anxiety, addiction, and neurodevelopmental disorders). To get a more complete understanding of the underlying brain mechanisms, we used gray and white matter measures for brain structure and both resting-state and stress scans for brain function. The results emphasize the importance of the executive control network (ECN) during the functional scans for the understanding of transdiagnostic symptom dimensions. The connectivity between the ECN and the frontoparietal network in the aftermath of stress was correlated with symptom dimensions across both the cognitive and negative valence domains, and also with various other health-related biological and behavioral measures. Finally, we identified a multimodal component that was specifically associated with the diagnosis of autism spectrum disorder (ASD). The involvement of the default mode network, precentral gyrus, and thalamus across the different modalities of this component may reflect the broad functional domains that may be affected in ASD, like theory of mind, motor problems, and sensitivity to sensory stimuli, respectively. Taken together, the findings from our extensive, exploratory analyses emphasize the importance of a dimensional and more integrative approach for getting a better understanding of the brain basis of psychopathology.