The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both a group and individual level.

OverviewVirtually all working memory (WM) theorists agree that control processes are a critical component of WM function. Some set of internal mechanisms must be responsible for: 1) selecting information for active maintenance in WM; 2) ensuring that it can be stored for an appropriate length of time; 3) protecting it against sources of interference; 4) updating it at appropriate junctures; and 5) utilizing it to influence other cognitive systems (i.e., perception, attention, memory and action). Yet, equally clear to most theorists is the observation that the ability to exert control over WM varies substantially, both within individuals (across time and task situations) and across individuals. In some sense, this observation poses perhaps the core paradox regarding cognitive control: Why is cognitive control so important, yet simultaneously so fragile and vulnerable to disruption? Why does it appear that our ability to exert control is so strong in some cases, but so weak in others? If exerting cognitive control seems to be the optimal response in many situations, why does it seem as if behavior is sub-optimally controlled much of the time in many individuals, and at least some of the time in all individuals?In this chapter, we put forth a theory of cognitive control in WM that attempts to explain this variability. Our central hypothesis is that cognitive control operates via two distinct operating modes -proactive control and reactive control. We will present arguments suggesting that these two modes are dissociable on a number of dimensions, such as computational properties, neural substrates, temporal dynamics, and consequences for information processing. We will suggest that although most formulations of cognitive control in WM only consider proactive control, reactive control 3 mechanisms may be more dominant. We will further suggest that by distinguishing between these two modes we will be able to: 1) resolve some of the apparent inconsistencies in the existing WM literature; 2) understand how and why the impact of cognitive control processes in WM can vary so strongly within individuals across time and task situations; 3) gain insight into the nature of cognitive control impairments found in healthy aging (and possibly in other populations suffering from neuropsychiatric disorders); 4) understand some of the critical underlying mechanisms related to individual differences in WM function; and 5) account for potentially surprising data indicating that putatively "non-cognitive" variables such as mood states and personality traits (e.g., extraversion, neuroticism) may also influence WM function.The general theoretical framework that we advance here for understanding the sources of variation that affect WM and cognitive control is termed the D u a l Mechanisms of Control, or DMC account. It is worth noting that, although we have been developing this framework for several years now, this chapter marks the first comprehensive treatment of the theory and its empirical support. As such, we combine discussion of b...

Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being unconstrained by choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging datasets.

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22–35), but some imaging- related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.