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The cortical patterning principle has been a long-standing question in neuroscience, yet how this translates to macroscale functional specialization in the human brain remains largely unknown. Here we examine age-dependent differences in resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks during early life, using a primarily cross-sectional but also longitudinal approach. We show that thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically influenced axonal projection. This pattern changes in childhood, when connectivity is established with the salience network, while decoupling externally and internally oriented functional systems. A developmental simulation using generative network models corroborated these findings, demonstrating that thalamic connectivity contributes to developing key features of the mature brain, such as functional segregation and the sensory-association axis, especially across 12–18 years of age. Our study suggests that the thalamus plays an important role in functional specialization during development, with potential implications for studying conditions with compromised internal and external processing.

Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX.

Objective: The Social Responsiveness Scale--Adult Version (SRS-A) measures autistic traits that are continuously distributed in the general population. Based on increased recognition of the dimensional nature of autistic traits, the authors examined the neural correlates of these traits in neurotypical individuals using the SRS-A and established a novel approach to assessing the neural basis of autistic characteristics, attempting to directly relate SRS-A scores to patterns of functional connectivity observed in the pregenual anterior cingulate cortex, a region commonly implicated in social cognition. Methods: Resting state functional magnetic resonance imaging scans were collected for 25 neurotypical adults. All participants provided SRS-A ratings completed by an informant who had observed them in natural social settings. Whole brain-corrected connectivity analyses were then conducted using SRS-A scores as a covariate of interest. Results: Across participants, a significant negative relationship between SRS-A scores and the functional connectivity of the pregenual anterior cingulate cortex with the anterior portion of the mid-insula was found. Specifically, low levels of autistic traits were observed when a substantial portion of the anterior mid-insula showed positive connectivity with the pregenual anterior cingulate cortex. In contrast, elevated levels of autistic traits were associated with negative connectivity between these two regions. Conclusions: Resting state functional connectivity of the pregenual anterior cingulate cortex-insula social network was related to autistic traits in neurotypical adults. Application of this approach in samples with autism spectrum disorders is needed to confirm whether this circuit is dimensionally related to the severity of autistic traits in clinical populations.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants’ choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants’ choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Nonhuman primate (NHP) models are commonly used to advance our understanding of brain function and organization. However, to date, they have offered few insights into individual differences among NHPs. In large part, this is due to the logistical challenges of NHP research, which limit most studies to 5 subjects or fewer.

Complementing long-standing traditions centered on histology, fMRI approaches are rapidly maturing in delineating brain areal organization at the macroscale. The non-human primate (NHP) provides the opportunity to overcome critical barriers in translational research. Here, we establish the data requirements for achieving reproducible and internally valid parcellations in individuals. We demonstrate that functional boundaries serve as a functional fingerprint of the individual animals and can be achieved under anesthesia or awake conditions (rest, naturalistic viewing), though differences between awake and anesthetized states precluded the detection of individual differences across states. Comparison of awake and anesthetized states suggested a more nuanced picture of changes in connectivity for higher-order association areas, as well as visual and motor cortex. These results establish feasibility and data requirements for the generation of reproducible individual-specific parcellations in NHPs, provide insights into the impact of scan state, and motivate efforts toward harmonizing protocols.

Schizophrenia, a neurodevelopmental disorder, involves abnormalities in functional connectivity (FC) across distributed neural networks, which are thought to antedate the emergence of psychosis. In a cohort of adolescents and young adults at clinical high risk (CHR) for psychosis, we applied data-driven approaches to resting-state functional magnetic resonance imaging data in order to systematically characterize FC abnormalities during this period and to determine whether these abnormalities are associated with psychosis risk and severity of psychotic symptoms.

Neuroimaging studies have shown structural alterations in several brain regions in children and adults with attention deficit hyperactivity disorder (ADHD). Through the formation of the international ENIGMA ADHD Working Group, we aimed to address weaknesses of previous imaging studies and meta-analyses, namely inadequate sample size and methodological heterogeneity. We aimed to investigate whether there are structural differences in children and adults with ADHD compared with those without this diagnosis.

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

Objectives: To highlight recent advances in the conceptualization of attention-deficit hyperactivity disorder (ADHD) emerging from neuroimaging and endophenotypic approaches.Methods: We selectively reviewed recent published literature on the phenomena of resting-state functional connectivity, intrasubject variability, and diffusion tensor imaging pertaining to ADHD.Results: Recent advances based on the novel approach of resting-state functional connectivity appear to be highly promising and likely to link to studies of intrasubject variability.Conclusions: Endophenotypic fractionation may offer a means of addressing the complex heterogeneity of ADHD on the path to testable models of pathophysiology. Such models focusing on intrasubject variability, intrinsic brain activity, and reward-related processing are progressing rapidly.

Split-brain patients present a unique opportunity to address controversies regarding subcortical contributions to interhemispheric coordination. We characterized residual functional connectivity in a complete commissurotomy patient by examining patterns of low-frequency BOLD functional MRI signal. Using independent components analysis and region-of-interest-based functional connectivity analyses, we demonstrate bilateral resting state networks in a patient lacking all major cerebral commissures. Compared with a control group, the patient's interhemispheric correlation scores fell within the normal range for two out of three regions examined. Thus, we provide evidence for bilateral resting state networks in a patient with complete commissurotomy. Such continued interhemispheric interaction suggests that, at least in part, cortical networks in the brain can be coordinated by subcortical mechanisms.

Abstract: In this research, we investigated the degree to which brain systems involved in ignoring emotionally salient information differ from those involved in ignoring nonemotional information. The design allowed examination of regional brain activity, using fMRI during color-word and emotional Stroop tasks. Twelve participants indicated the color of words while ignoring word meaning in conditions in which neutral words were contrasted to emotionally negative, emotionally positive, and incongruent color words. Dorsolateral frontal lobe activity was increased by both negative and incongruent color words, indicating a common system for maintaining an attentional set in the presence of salient distractors. In posterior regions of the brain, activity depended on the nature of the information to be ignored. Ignoring color-incongruent words increased left parietal activity and decreased parahippocampal gyrus activity, whereas ignoring negative emotional words increased bilateral occipito-temporal activity and decreased amygdala activity. The results indicate that emotion and attention are intimately related via a network of regions that monitor for salient information, maintain attention on the task, suppress irrelevant information, and select appropriate responses.

Performance deteriorates when subjects must shift between two different tasks relative to performing either task separately. This switching cost is thought to result from executive processes that are not inherent to the component operations of either task when performed alone. Medial and dorsolateral frontal cortices are theorized to subserve these executive processes. Here we show that larger areas of activation were seen in dorsolateral and medial frontal cortex in both younger and older adults during switching than repeating conditions, confirming the role of these frontal brain regions in executive processes. Younger subjects activated these medial and dorsolateral frontal cortices only when switching between tasks; in contrast, older subjects recruited similar frontal regions while performing the tasks in isolation as well as alternating between them. Older adults recruit medial and dorsolateral frontal areas, and the processes computed by these areas, even when no such demands are intrinsic to the current task conditions. This neural recruitment may be useful in offsetting the declines in cognitive function associated with ageing.

Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is challenging to establish and compare the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate the respective roles of brain structural and functional indices, along with their contributions to the prediction of demographic variables (age/sex) and clinical changes in schizophrenia patients. The present study enrolled 492 healthy people from the Southwest University Adult Lifespan Dataset (SALD) for demographic variable analysis and 39 patients with schizophrenia from the West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed variance partitioning on the voxels that could be predicted sufficiently. Permutation tests were applied to compare the difference in contribution between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices with respect to particular variables. In sum, for the variables reflecting long-term changes (age) and constant biological features (sex), the voxel-based structural metrics would contribute more than voxel-based functional metrics, but for the variable reflecting short-term changes (schizophrenia treatment), the functional metrics could contribute more.