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In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, "Dimensional connectomics of anxious misery" (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, "Human connectome Project for disordered emotional states" (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety. The third study, "Perturbation of the treatment resistant depression connectome by fast-acting therapies" (HCP-MDD), quantifies alterations of the structural and functional connectome as a result of three fast-acting interventions: electroconvulsive therapy, serial ketamine therapy, and total sleep deprivation. Finally, the fourth study, "Connectomes related to anxiety and depression in adolescents" (HCP-ADA), investigates developmental trajectories of subtypes of anxiety and depression in adolescence. The four projects use comparable and standardized Human Connectome Project magnetic resonance imaging (MRI) protocols, including structural MRI, diffusion-weighted MRI, and both task and resting state functional MRI. All four projects also conducted comprehensive and convergent clinical and neuropsychological assessments, including (but not limited to) demographic information, clinical diagnoses, symptoms of mood and anxiety disorders, negative and positive affect, cognitive function, and exposure to early life stress. The first round of analyses conducted in the four projects offered novel methods to investigate relations between functional connectomes and self-reports in large datasets, identified new functional correlates of symptoms of mood and anxiety disorders, characterized the trajectory of connectome-symptom profiles over time, and quantified the impact of novel treatments on aberrant connectivity. Taken together, the data obtained and reported by the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders describe a rich constellation of convergent biological, clinical, and behavioral phenotypes that span the peak ages for the onset of emotional disorders. These data are being prepared for open sharing with the scientific community following screens for quality by the Connectome Coordinating Facility (CCF). The CCF also plans to release data from all projects that have been pre-processed using identical state-of-the-art pipelines. The resultant dataset will give researchers the opportunity to pool complementary data across the four projects to study circuit dysfunctions that may underlie mood and anxiety disorders, to map cohesive relations among circuits and symptoms, and to probe how these relations change as a function of age and acute interventions. This large and combined dataset may also be ideal for using data-driven analytic approaches to inform neurobiological targets for future clinical trials and interventions focused on clinical or behavioral outcomes., Competing Interests: Declarations of Competing Interest The Authors have no conflicts of interest to declare., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The development of scanners with ultra-high gradient strength, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts., Competing Interests: Declarations of Competing Interest None., (Copyright © 2021. Published by Elsevier Inc.)

Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Jazz improvisation offers a model for creative cognition, as it involves the real-time creation of a novel, information-rich product. Previous research has shown that when musicians improvise, they recruit regions in the Default Mode Network (DMN) and Executive Control Network (ECN). Here, we ask whether these findings from task-fMRI studies might extend to intrinsic differences in resting state functional connectivity. We compared Improvising musicians, Classical musicians, and Minimally Musically Trained (MMT) controls in seed-based functional connectivity and network analyses in resting state functional MRI. We also examined the functional correlates of behavioral performance in musical improvisation and divergent thinking. Seed-based analysis consistently showed higher connectivity in ventral DMN (vDMN) and bilateral ECN in both groups of musically trained individuals as compared to MMT controls, with additional group differences in primary visual network. In particular, primary visual network connectivity to DMN and ECN was highest in Improvisational musicians, as was connectivity between ECN and DMN; in contrast, connectivity between vDMN and frontal pole was highest in Classical musicians. Furthermore, graph-theoretical analysis indicated heightened network measures in both musician groups, with betweenness centrality, clustering, and local efficiency showing highest levels in Classical musicians, and degrees and strengths showing highest levels in Improvisational musicians. Taken together, results suggest that heightened functional connectivity among musicians can be explained by higher within-network connectivity (more tight-knit cortical networks) in Classical musicians, as opposed to more disperse, globally-connected cortical networks in Improvisational musicians., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Foreign language learning in adulthood often takes place in classrooms where learning outcomes vary widely among students, for both initial learning and long-term retention. Despite the fundamental role of speech perception in first language acquisition, its role in foreign language learning outcomes remains unknown. Using a speech discrimination functional magnetic resonance imaging (fMRI) task and resting-state fMRI before and after an intensive, classroom-based, Mandarin Chinese course, we examined how variations in pre-training organization and pre-to-post reorganization of brain functions predicted successful language learning in male and female native English-speakers. Greater pre-training activation in right inferior frontal gyrus (IFG) to Mandarin speech was associated with better Mandarin attainment at the end of the course. After four weeks of class, learners showed overall increased activation in left IFG and left superior parietal lobule (SPL) to Mandarin speech, but in neither region was variation related to learning outcomes. Immediate attainment was associated with greater pre-to-post reduction of right IFG activation to Mandarin speech but also greater enhancement of resting-state connectivity between this region and both left IFG and left SPL. Long-term retention of Mandarin skills measured three months later was more accurately predicted by models using features of neural preparedness (pre-training activation) and neural plasticity (pre-to-post activation change) than models using behavior preparedness and plasticity features (pre-training speech discrimination accuracy and Mandarin attainment, respectively). These findings suggest that successful holistic foreign language acquisition in human adulthood requires right IFG engagement during initial learning but right IFG disengagement for long-term retention of language skills., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development., (Copyright © 2018. Published by Elsevier Inc.)

Personality traits are stable predictors of many life outcomes that are associated with important decisions that involve tradeoffs over time. Therefore, a fundamental question is how tradeoffs over time vary from person to person in relation to stable personality traits. We investigated the influence of personality, as measured by the Five-Factor Model, on time preferences and on neural activity engaged by intertemporal choice. During functional magnetic resonance imaging (fMRI), participants made choices between smaller-sooner and larger-later monetary rewards. For each participant, we estimated a constant-sensitivity discount function that dissociates impatience (devaluation of future consequences) from time sensitivity (consistency with rational, exponential discounting). Overall, higher neuroticism was associated with a relatively greater preference for immediate rewards and higher conscientiousness with a relatively greater preference for delayed rewards. Specifically, higher conscientiousness correlated positively with lower short-term impatience and more exponential time preferences, whereas higher neuroticism (lower emotional stability) correlated positively with higher short-term impatience and less exponential time preferences. Cognitive-control and reward brain regions were more activated when higher conscientiousness participants selected a smaller-sooner reward and, conversely, when higher neuroticism participants selected a larger-later reward. The greater activations that occurred when choosing rewards that contradicted personality predispositions may reflect the greater recruitment of mental resources needed to override those predispositions. These findings reveal that stable personality traits fundamentally influence how rewards are chosen over time., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Task-induced deactivation of the default-mode network (DMN) has been associated in adults with successful episodic memory formation, possibly as a mechanism to focus allocation of mental resources for successful encoding of external stimuli. We investigated developmental changes of deactivation of the DMN (posterior cingulate, medial prefrontal, and bilateral lateral parietal cortices) during episodic memory formation in children, adolescents, and young adults (ages 8-24), who studied scenes during functional magnetic resonance imaging (fMRI). Recognition memory improved with age. We defined DMN regions of interest from a different sample of participants with the same age range, using resting-state fMRI. In adults, there was greater deactivation of the DMN for scenes that were later remembered than scenes that were later forgotten. In children, deactivation of the default-network did not differ reliably between scenes that were later remembered or forgotten. Adolescents exhibited a pattern of activation intermediate to that of children and adults. The hippocampal region, often considered part of the DMN, showed a functional dissociation with the rest of the DMN by exhibiting increased activation for later remembered than later forgotten scene that was similar across age groups. These findings suggest that development of memory ability from childhood through adulthood may involve increased deactivation of the neocortical DMN during learning., (© 2013.)

Alterations in neuronal circuitry are recognized as an important substrate of many neurological disorders, including epilepsy. Patients with the developmental brain malformation of periventricular nodular heterotopia (PNH) often have both seizures and dyslexia, and there is evidence to suggest that aberrant neuronal connectivity underlies both of these clinical features. We used task-based functional MRI (fMRI) to determine whether heterotopic nodules of gray matter in this condition are integrated into functional cortical circuits. Blood oxygenation level-dependent (BOLD) fMRI was acquired in eight participants with PNH during the performance of reading-related tasks. Evidence of neural activation within heterotopic gray matter was identified, and regions of cortical coactivation were then mapped systematically. Findings were correlated with resting-state functional connectivity results and with performance on the fMRI reading-related tasks. Six participants (75%) demonstrated activation within at least one region of gray matter heterotopia. Cortical areas directly overlying the heterotopia were usually coactivated (60%), as were areas known to have functional connectivity to the heterotopia in the task-free resting state (73%). Six of seven (86%) primary task contrasts resulted in heterotopia activation in at least one participant. Activation was most commonly seen during rapid naming of visual stimuli, a characteristic impairment in this patient population. Our findings represent a systematic demonstration that heterotopic gray matter can be metabolically coactivated in a neuronal migration disorder associated with epilepsy and dyslexia. Gray matter nodules were most commonly coactivated with the anatomically overlying cortex and other regions with resting-state connectivity to heterotopia. These results have broader implications for understanding the network pathogenesis of both seizures and reading disabilities., (© 2013.)

Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The rate of learning or memory formation varies over time for any individual, partly due to moment-to-moment fluctuation of brain state. Functional neuroimaging has revealed the neural correlates of learning and memory, but here we asked if neuroimaging can causally enhance human learning by detection of brain states that reveal when a person is prepared or not prepared to learn. The parahippocampal cortex (PHC) is essential for memory formation for scenes. Here, activation in PHC was monitored in real-time, and scene presentations were triggered when participants entered "good" or "bad" brain states for learning of novel scenes. Subsequent recognition memory was more accurate for scenes presented in "good" than "bad" brain states. These findings show that neuroimaging can identify in real-time brain states that enhance or depress learning and memory formation, and knowledge about such brain states may be useful for accelerating education and training. Further, the use of functional neuroimaging as a causal, rather than correlative, tool to study the human brain may open new insights into the neural basis of human cognition., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Neuroimaging has revealed consistent activations in medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) extending to precuneus both during explicit self-reference tasks and during rest, a period during which some form of self-reference is assumed to occur in the default mode of brain function. The similarity between these two patterns of midline cortical activation may reflect a common neural system for explicit and default-mode self-reference, but there is little direct evidence about the similarities and differences between the neural systems that mediate explicit self-reference versus default-mode self-reference during rest. In two experiments, we compared directly the brain regions activated by explicit self-reference during judgments about trait adjectives and by rest conditions relative to a semantic task without self-reference. Explicit self-reference preferentially engaged dorsal MPFC, rest preferentially engaged precuneus, and both self-reference and rest commonly engaged ventral MPFC and PCC. These findings indicate that there are both associations (shared components) and dissociations between the neural systems underlying explicit self-reference and the default mode of brain function., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The human amygdala responds to first impressions of people as judged from their faces, such as normative judgments about the trustworthiness of strangers. It is unknown, however, whether amygdala responses to first impressions can be validated by objective criteria. Here, we examined amygdala responses to faces of Chief Executive Officers (CEOs) where real-world outcomes could be measured objectively by the amounts of profits made by each CEO's company. During fMRI scanning, participants made incidental judgments about the symmetry of each CEO's face. After scanning, participants rated each CEO's face on leadership ability. Parametric analyses showed that greater left amygdala response to the CEOs' faces was associated with higher post-scan ratings of the CEOs' leadership ability. In addition, greater left amygdala response was also associated with greater profits made by the CEOs' companies and this relationship was statistically mediated by external raters' perceptions of arousal. Thus, amygdala response reflected both subjective judgments and objective measures of leadership ability based on first impressions., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Older adults often show bilateral brain activation, compared to unilateral activation in younger adults, when performing tasks in domains of age-associated cognitive impairment, such as episodic and working memory. Less is known about activation associated with performance in cognitive domains that are typically unaffected by healthy aging. We used event-related functional magnetic resonance imaging to examine age-related patterns in brain activation associated with a form of implicit memory, repetition priming, which is typically preserved in healthy aging. Sixteen younger adults and 15 nondemented older adults performed semantic judgments (abstract/concrete) on single words in a study phase. In a test phase, identical judgments were made for repeated and new words. Younger and older adults showed similar response-time benefits (repetition priming) from repeated semantic classification. Repetition priming was associated with repetition-related reductions of prefrontal activation in both groups, but the patterns of activation differed between groups. Both groups showed similar activation reductions in dorsal left inferior prefrontal cortex (LIPFC), but older adults showed less reduction than younger adults in ventral and anterior LIPFC. Activation reductions were exclusively left-lateralized for younger adults, whereas older adults showed additional reductions in multiple regions of right frontal cortices. Right prefrontal activation reductions in older adults correlated with better repetition priming and better performance on independent tests of semantic processing. Thus, reduced asymmetry of prefrontal activation reductions in healthy aging was related to conceptual repetition priming, a form of learning that is spared in aging, and with the sparing of semantic memory.

We investigated the relation between cognitive processing speed and structural properties of white matter pathways via convergent imaging studies in healthy and brain-injured groups. Voxel-based morphometry (VBM) was applied to diffusion tensor imaging data from thirty-nine young healthy subjects in order to investigate the relation between processing speed, as assessed with the Digit-Symbol subtest from WAIS-III, and fractional anisotropy, an index of microstructural organization of white matter. Digit-Symbol performance was positively correlated with fractional anisotropy of white matter in the parietal and temporal lobes bilaterally and in the left middle frontal gyrus. Fiber tractography indicated that these regions are consistent with the trajectories of the superior and inferior longitudinal fasciculi. In a second investigation, we assessed the effect of white matter damage on processing speed using voxel-based lesion-symptom mapping (VLSM) analysis of data from seventy-two patients with left-hemisphere strokes. Lesions in left parietal white matter, together with cortical lesions in supramarginal and angular gyri were associated with impaired performance. These findings suggest that cognitive processing speed, as assessed by the Digit-Symbol test, is closely related to the structural integrity of white matter tracts associated with parietal and temporal cortices and left middle frontal gyrus. Further, fiber tractography applied to VBM results and the patient findings suggest that the superior longitudinal fasciculus, a major tract subserving fronto-parietal integration, makes a prominent contribution to processing speed.