Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.

This study aims to reveal the association between sleep quality and crystallized intelligence (Gc), fluid intelligence (Gf), and the underlying brain structural basis. Using the data from the Human Connectome Project (N = 1087), we performed mediation analysis to explore whether regional brain structure related to sleep quality mediate the association between sleep quality and intellectual abilities, and further examined whether socioeconomic status (i.e., income and education level) moderate the mediation effect. Results showed that poorer sleep quality was associated with lower Gc rather than Gf, and worse sleep quality was associated with smaller volume and surface area in temporal lobe, including inferior temporal gyrus and middle temporal gyrus. Notably, temporal lobe structures mediated the association between sleep quality and Gc rather than Gf. Furthermore, socioeconomic status (i.e., income and education level) moderated the mediating effect, showing low socioeconomic status has a more significant mediating effect with stronger association between sleep quality and Gc as well as stronger association between temporal lobe structure and Gc in low socioeconomic status group. These findings suggest that individuals with higher socioeconomic status are less susceptible to the effect of sleep quality on Gc.

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Recent studies have established the moment-to-moment turnover of the blood-oxygen-level-dependent signal (TBOLD) at resting state as a key measure of local cortical brain function. Here, we sought to extend that line of research by evaluating TBOLD in 70 cortical areas with respect to corresponding brain volume, age, and sex across the lifespan in 1,344 healthy participants including 633 from the Human Connectome Project (HCP)-Development cohort (294 males and 339 females, age range 8-21 yr) and 711 healthy participants from HCP-Aging cohort (316 males and 395 females, 36-90 yr old). In both groups, we found that 1) TBOLD increased with age, 2) volume decreased with age, and 3) TBOLD and volume were highly significantly negatively correlated, independent of age. The inverse association between TBOLD and volume was documented in nearly all 70 brain areas and for both sexes, with slightly stronger associations documented for males. The strong correspondence between TBOLD and volume across age and sex suggests a common influence such as chronic neuroinflammation contributing to reduced cortical volume and increased TBOLD across the lifespan.NEW & NOTEWORTHY We report a significant negative association between resting functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signal turnover (TBOLD) and cortical gray matter volume across the lifespan, such that TBOLD increased whereas volume decreased. We attribute this association to a hypothesized chronic, low-grade neuroinflammation, probably induced by various neurotropic pathogens, including human herpes viruses known to be dormant in the brain in a latent state and reactivated by stress, fever, and various environmental exposures, such as ultraviolet light.

BACKGROUND: The zona incerta (ZI) is a subcortical structure primarily investigated in rodents that is implicated in various behaviors, ranging from motor control to survival-associated activities, partly due to its integration in multiple neural circuits. In the current study, we used diffusion magnetic resonance imaging tractography to segment the ZI and gain insight into its connectivity in various circuits in humans.
METHODS: We performed probabilistic tractography in 7T diffusion MRI on 178 participants from the Human Connectome Project to validate the ZI\'s anatomical subdivisions and their respective tracts. K-means clustering segmented the ZI based on each voxel\'s connectivity profile. We further characterized the connections of each ZI subregion using probabilistic tractography with each subregion as a seed.
RESULTS: We identified 2 dominant clusters that delineated the whole ZI into rostral and caudal subregions. The caudal ZI primarily connected with motor regions, while the rostral ZI received a topographic distribution of projections from prefrontal areas, notably the anterior cingulate and medial prefrontal cortices. We generated a probabilistic ZI atlas that was registered to a patient-participant\'s magnetic resonance imaging scan for placement of stereoencephalographic leads for electrophysiology-guided deep brain stimulation to treat their obsessive-compulsive disorder. Rostral ZI stimulation improved the patient\'s core symptoms (mean improvement 21%).
CONCLUSIONS: We present a tractography-based atlas of the rostral and caudal ZI subregions constructed using high-resolution diffusion magnetic resonance imaging from 178 healthy participants. Our work provides an anatomical foundation to explore the rostral ZI as a novel target for deep brain stimulation to treat refractory obsessive-compulsive disorder and other disorders associated with dysfunctional reward circuitry.

In a segregated society, marked by a historical background of inequalities, there is a consistent under-representation of ethnic and racial minorities in biomedical research, causing disparities in understanding genetic and acquired diseases as well as in the effectiveness of clinical treatments affecting different groups. The repeated inclusion of small and non-representative samples of the population in neuroimaging research has led to generalization bias in the morphological characterization of the human brain. A few brain morphometric studies between Whites and African Americans have reported differences in orbitofrontal volumetry and insula cortical thickness. Nevertheless, these studies are mostly conducted in small samples and populations with cognitive impairment. For this reason, this study aimed to identify brain morphological variability due to racial identity in representative samples. We hypothesized that, in neurotypical young adults, there are differences in brain morphometry between participants with distinct racial identities. We analyzed the Human Connectome Project (HCP) database to test this hypothesis. Brain volumetry, cortical thickness, and cortical surface area measures of participants identified as Whites (n = 338) or African Americans (n = 56) were analyzed. Non-parametrical permutation analysis of covariance between these racial identity groups adjusting for age, sex, education, and economic income was implemented. Results indicated volumetric differences in choroid plexus, supratentorial, white matter, and subcortical brain structures. Moreover, differences in cortical thickness and surface area in frontal, parietal, temporal, and occipital brain regions were identified between groups. In this regard, the inclusion of sub-representative minorities in neuroimaging research, such as African American persons, is fundamental for the comprehension of human brain morphometric diversity and to design personalized clinical brain treatments for this population.

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the \"fingerprint of FC\" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.