While fMRI studies typically collapse data from many subjects, brain functional organization varies between individuals. Here, we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a “fingerprint” that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual’s connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but notably, the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence; the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects based on functional connectivity fMRI.

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person’s overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10-100 minutes

This paper reports on three studies on the development and validation of the Parental Reflective Functioning Questionnaire (PRFQ), a brief, multidimensional self-report measure that assesses parental reflective functioning or mentalizing, that is, the capacity to treat the infant as a psychological agent. Study 1 investigated the factor structure, reliability, and relationships of the PRFQ with demographic features, symptomatic distress, attachment dimensions, and emotional availability in a socially diverse sample of 299 mothers of a child aged 0–3. In Study 2, the factorial invariance of the PRFQ in mothers and fathers was investigated in a sample of 153 first-time parents, and relationships with demographic features, symptomatic distress, attachment dimensions, and parenting stress were investigated. Study 3 investigated the relationship between the PRFQ and infant attachment classification as assessed with the Strange Situation Procedure (SSP) in a sample of 136 community mothers and their infants. Exploratory and confirmatory factor analyses suggested three theoretically consistent factors assessing pre-mentalizing modes, certainty about the mental states of the infant, and interest and curiosity in the mental states of the infant. These factors were generally related in theoretically expected ways to parental attachment dimensions, emotional availability, parenting stress, and infant attachment status in the SSP. Yet, at the same time, more research on the PRFQ is needed to further establish its reliability and validity.

Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.

Animal studies suggest that structural changes occur in the maternal brain during the early postpartum period in regions such as the hypothalamus, amygdala, parietal lobe, and prefrontal cortex and such changes are related to the expression of maternal behaviors. In an attempt to explore this in humans, we conducted a prospective longitudinal study to examine gray matter changes using voxel-based morphometry on high resolution magnetic resonance images of mothers’ brains at two time points: 2–4 weeks postpartum and 3–4 months postpartum. Comparing gray matter volumes across these two time points, we found increases in gray matter volume of the prefrontal cortex, parietal lobes, and midbrain areas. Increased gray matter volume in the midbrain including the hypothalamus, substantia nigra, and amygdala was associated with maternal positive perception of her baby. These results suggest that the first months of motherhood in humans are accompanied by structural changes in brain regions implicated in maternal motivation and behaviors.

Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization—or “functional connectivity.” Standards have been proposed based on test–retest reliability, but open questions remain. These include how amount of data per subject influences whole-brain reliability, the influence of increasing runs versus sessions, the spatial distribution of reliability, the reliability of multivariate methods, and, crucially, how reliability maps onto prediction of behavior. We collected a dataset of 12 extensively sampled individuals (144 min data each across 2 identically configured scanners) to assess test–retest reliability of whole-brain connectivity within the generalizability theory framework. We used Human Connectome Project data to replicate these analyses and relate reliability to behavioral prediction. Overall, the historical 5-min scan produced poor reliability averaged across connections. Increasing the number of sessions was more beneficial than increasing runs. Reliability was lowest for subcortical connections and highest for within-network cortical connections. Multivariate reliability was greater than univariate. Finally, reliability could not be used to improve prediction; these findings are among the first to underscore this distinction for functional connectivity. A comprehensive understanding of test–retest reliability, including its limitations, supports the development of best practices in the field.

In this paper we focus on the first wave of outcomes in a pilot phase randomized control trial of a home-based intervention for infants and their families, Minding the Baby® (MTB), an interdisciplinary, mentalization-based intervention in which home visiting services are provided by a team that includes a nurse practitioner and a clinical social worker. Families are recruited during mother's pregnancy and continue through the child's second birthday. Analyses revealed that intervention families were more likely to be on track with immunization schedules at 12 months, had lower rates of rapid subsequent childbearing, and were less likely to be referred to child protective services. In addition, mother-infant interactions were less likely to be disrupted at 4 months when mothers were teenagers, and all intervention infants were more likely to be securely attached, and less likely to be disorganized in relation to attachment at one year. Finally, mothers’ capacity to reflect on their own and their child's experience improved over the course of the intervention in the most high-risk mothers.