The Future of the Human Connectome

1.1. Vignettes from the ‘pre-connectome’ era

As a prelude to discussing the future of the human connectome, it is appropriate for this special issue to reflect briefly on the state of primate connectivity studies two decades ago, when fMRI was born. One of the authors (DVE) had just published a map of cortical areas and an analysis of cortical connectivity in macaque visual cortex (Fig. 1) in which evidence was reported for several hundred cortico-cortical connections linking 32 visual areas (Felleman & Van Essen, 1991). This circuitry was much more complex than many neuroscientists had anticipated, yet important organizational principles could be elucidated by considering the pattern of connections within the overall 32 × 32 connectivity matrix (a ‘parcellated connectome’ in current parlance). Around this time, the other author (KU) was laying the foundations of fMRI as a tool for studying human brain function (Ogawa et al., 1992; Tank et al., 1992; Ugurbil, this issue). The first fMRI studies were considered a success when any cortical activation was detected using full-field or hemifield visual stimulation. Nevertheless, it was apparent from the outset that fMRI offered great potential for elucidating human brain function; developing it to the point of mapping the cortical areas identified by DVE (Fig. 1) was an objective frequently mentioned by KU. There were even clues early on that spontaneous fluctuations in the fMRI BOLD signal might enable exploration of functional connectivity. Specifically, Ogawa et al. (1993) commented “If [these] oscillations represent a neural origin, it may be possible to measure spatial correlations of the responses across the cortex. This would provide an MRI method of mapping patterns of functional connections between cortical areas.” However, the notion that a comprehensive high-resolution mapping of brain connectivity would be feasible using fMRI in particular or, for that matter, any other MR-based method would have been considered far-fetched at the time. Indeed, around that time Francis Crick and Ted Jones bemoaned the lack of promising methods for exploring long-distance connections in the human brain (Crick and Jones, 1993).

Remarkable advances on many fronts now make it feasible to decipher fundamental aspects of the human connectome and its variability in health and disease. Two methods in particular can be used to make strong, albeit indirect, inferences about brain connectivity. The first, diffusion imaging (dMRI), estimates the orientation of fiber bundles in white matter based on anisotropies in water diffusion. It provides the inputs for tractography analyses that can be used to infer ‘structural connectivity’ between gray matter regions (Wansapura et al., 1999; Jbabdi and Johansen-Berg, 2011). Sporns et al. (2005) first proposed that tractography could be used for a systematic investigation of the human connectome and its variability. The second method, resting-state fMRI (R-fMRI), uses temporal correlations in the slow fluctuations of the BOLD fMRI signal to infer ‘functional connectivity’. R-fMRI serves as an indirect but nonetheless invaluable indicator of gray-matter regions that interact strongly and in many cases are connected anatomically (Biswal et al., 1995; Fox and Raichle, 2007; Vincent et al., 2007; Beckmann et al., 2005; Smith, 2011). A growing number of studies have revealed important insights through systematic analyses of whole-brain connectivity using dMRI and/or R-fMRI. This includes analyses of brain networks, modularity, and hubs, as well as demonstration of connectivity-based parcellation (Hagmann et al. 2008; Van den Heuvel and Hulshoff, 2010; Cohen et al., 2008; Nelson et al., 2010; Yeo et al., 2011; Mars et al., 2011; Power et al., 2011).

Despite their promise and potential, current methods for assessing structural and functional connectivity face serious technical limitations at multiple levels. Analyses of structural connectivity must cope with a high incidence of false positives and false negatives, combined with an inherent difficulty in making quantitative estimates of connection strength (Iturria-Medina et al., 2008; Jones and Cercignani, 2010; Jbabdi and Johansen-Berg, 2011). Analyses of functional connectivity are limited by the indirect nature of neurovascular coupling to neural activity and the presence of confounding long-range correlations of vascular origin (Mitra et al., 1995). In addition, functional correlations reflect more than direct anatomical connectivity (Vincent et al., 2007), as they can be influenced by common inputs and/or interactions via serially connected areas. Both approaches also face limitations imposed by the difficulty of accurately parcellating the brain into functionally distinct subdivisions (parcels) and in aligning data across subjects (intersubject registration).

1.2. Early days of the HCP

Given the uniqueness of the Human Connectome Project, a few comments on its origins and early days are warranted. In the summer of 2009, a Request For Applications (RFA) was announced by the NIH Neuroscience Blueprint Institutes and Centers, with the intent of awarding a single large grant to characterize the human connectome in ‘up to hundreds’ of healthy adults. The rationale for launching such a bold plan at that time was based on the impressive technological advances in neuroimaging just described, coupled with the prospect of accelerating progress by a judicious investment of resources. Importantly in this regard, the RFA encouraged the use of multiple imaging methodologies and allowed for an extended period for optimizing and refining the data acquisition and analysis methods, rather than relying on state-of-the-art at the time of the grant award. It also mandated that the resultant datasets be made freely available via a robust and user-friendly informatics platform. Many neuroscientists and NIH leaders contributed to the framing of the HCP RFA, but Mike Huerta (then at NIMH) deserves special recognition for his central role in articulating and advocating the vision.

NIH ultimately awarded two complementary HCP grants (http://humanconnectome.org/consortia/). The authors are PI's for the ‘WU-Minn’ HCP consortium, led by Washington University and University of Minnesota and involving several dozen investigators from 11 institutions. This is a 5-year, $30M effort to chart the human connectome and its variability in 1,200 healthy adults (twins and their non-twin siblings) using dMRI, R-fMRI, Task-fMRI, and MEG/EEG. A smaller 3-year grant was awarded to a consortium involving Massachusetts General Hospital (MGH) and UCLA, with a focus on developing a ‘next-generation’ 3T MR scanner for diffusion imaging.

From a personal perspective, we have found it extremely rewarding to bring together an extraordinarily talented multidisciplinary team of enthusiastic and dedicated investigators. This team was able in a few months to formulate a high-level vision, translate it into a detailed concept for the many sub-projects needed for eventual success of the HCP, and even generate promising preliminary data using the complementary expertise among consortium members. Since the grant was awarded, the excitement of moving forward has been coupled with a sobering appreciation of the daunting scope of the endeavor. Currently, 84 consortium members work on the WU-Minn project (36 investigators, plus students, postdocs, and technical staff). Each consortium member participates in at least one of seven operational teams (from pulse-sequence development to informatics - http://www.humanconnectome.org/about/teams.html) plus various ad hoc working groups established to deal with concrete issues. Coordination of these efforts is facilitated by a full-time project manager, intensive 2-day ‘All-Hands’ meetings (two held to date), regular meetings of a Steering Committee and an Operations Committee, monthly ‘science’ teleconference meetings to keep the consortium apprised of ongoing progress, and countless emails, conference calls, and ad hoc small-group meetings to address specific issues. This has enabled rapid progress on many fronts during the early stages of the project (e.g., Feinberg et al., 2010; Marcus et al., 2011; Van Essen et al., 2011c; Power et al., 2011). Critical to this progress has been an excellent esprit de corps and high level of commitment among all members of the consortium.

In the remainder of this article we concentrate on two complementary topics. The first addresses a number of neurobiological issues that are relevant to elucidating human brain connectivity. They provide a sobering reminder of many facets of the overall problem, but also suggest profitable directions to pursue in meeting these challenges. The second topic highlights some of the advances to be anticipated via methodological improvements in MR data acquisition and analysis. Other aspects of the HCP endeavor that are important for its success are outside the scope of this article; many of these are covered elsewhere (Marcus et al., 2011; Van Essen et al., 2011c; Behrens and Sporns, 2011; Akil et al., 2011). Our overarching objective is to provide a better appreciation of the types of advances in human connectomics that can be anticipated in the coming decade as well as the limitations that will be difficult to overcome in this time frame.

2.1. Scales of analysis

In considering spatial scales (and reflecting our penchant for neuroanatomical quantification!), it is useful to summarize key facts about major components of the human brain (Table 1). For the cerebral cortex, each of the two topologically separate hemispheres is a highly convoluted sheet with average thickness of ~2.4 mm and surface area of ~1,000 cm² (Van Essen et al., 2011b), about the size of a medium pizza. Cerebellar cortex is a single, topologically continuous sheet spanning both hemispheres. It is only ~1 mm thick and has an estimated surface area of ~1,100 cm², comparable to that of an individual cerebral hemisphere. Subcortical structures (including basal ganglia, thalamus, brainstem) occupy ~80 cm³, one-sixth the volume of cerebral cortical gray matter. Subcortical white matter, i.e., the long-distance cables between gray-matter regions, occupies ~400 cm³, more than half the volume of the combined gray matter structures.

To characterize brain circuitry as accurately as possible, it is desirable to analyze structural and functional connectivity across the whole brain at the finest spatial resolution supported by the data acquisition and analysis methods. This leads to the concept of a ‘dense connectome’ as a fine-grained connectivity matrix between each gray matter voxel or surface vertex and every other gray matter voxel or vertex (Marcus et al., 2011). At the spatial resolution supported by conventional 3T R-fMRI data acquisition (3 mm cubic voxels), a dense connectome can be represented by a matrix of ~45,000 × 45,000 elements. Analysis and visualization of dense connectome datasets are facilitated by the new ‘CIFTI’ data format (http://www.nitrc.org/projects/cifti/) that can handle surface vertices and gray matter voxels in a single data file (Marcus et al., 2011). Methodological improvements discussed below should substantially improve resolution, resulting in much larger dense connectome datasets, for example, just improving the resolution to 2 mm voxel dimensions results in a dense connectome exceeding over 90,000 × 90,000 elements.

At a cellular level, cerebral neocortex contains on average ~90,000 neurons per mm² surface area and white matter contains ~300,000 axons/mm³ (Table 1). The density of synapses is far greater (~3 × 10⁸/mm³). Hence, even with the most optimistic scenarios for improved spatial resolution, a vast gulf will remain between the macro-connectome domain of in vivo neuroimaging and the micro-connectome domain dealing with the 3D arrangement of neurons, axons, dendrites, synapses, and glia.

2.2. Brain parcellations

The analysis of dense connectomes will be one major thrust of human connectome studies. However, many analyses will capitalize on more compact representations provided by ‘parcellated connectomes’ that describe average connectivity between different brain subregions (parcels). It is obviously important to have parcellation schemes that are as accurate as possible, so that connectivity patterns can be related to functionally meaningful parcels (Wig et al., 2011; Behrens and Sporns, 2011). The challenges in meeting this objective differ for each brain region.

Cerebral cortex contains a mosaic of many different areas that are distinct in their architecture, topography, connectivity, and/or function (Felleman & Van Essen, 1991). Despite a century of progress, determining the precise layout of cortical areas is far from complete, even for the intensively studied macaque monkey. By one recent estimate, there are ~140 distinct neocortical and transitional areas in each hemisphere of the macaque (Van Essen et al., 2011a). Human cortex may contain 150 - 200 areas/hemisphere, based on accurate maps of 52 areas that encompass ~1/3 of neocortical surface area (Fig. 2; Van Essen et al., 2011b). The spatial extent of accurately mapped human areas averages ~7 cm² per parcel; but in both humans and macaques cortical areas span a more than 100-fold range in average surface area.

Subcortical gray matter includes a handful of major structures (basal ganglia, thalamus, hypothalamus, midbrain, and brainstem) that can be segmented using structural MR. Each of these structures contain numerous finer-grained parcels (nuclei and subnuclei) that are more difficult to delineate. The total number that are large enough to be identifiable using in vivo neuroimaging is approximately 75 on each side of the brain (cf. Mai et al., 2007).

Cerebellar cortex has proven difficult to parcellate because it is remarkably uniform in its cellular architecture, completely lacks cortico-cortical connections, and is connected to other brain regions via compact, poorly understood nuclei (Strick et al., 2009). Nonetheless, it is functionally heterogeneous and contains multiple topographically organized subdivisions. Buckner et al., (2011) used a functional connectivity analysis to infer that cerebellar cortex contains three ‘homotopic’ maps of the ‘full cerebrum’ (most of the neocortical sheet). Within this coarse mapping, they identified cerebellar subregions corresponding to each of 17 networks and many more spatially discrete subregions previously derived from a functional connectivity analysis of cerebral cortex (Yeo et al., 2011). Altogether, it seems likely that there are at least 20 and perhaps 50 or more functionally distinct cerebellar parcels (Table 1).

In aggregate, these observations suggest that the human brain contains about 500 (+/- 100) neurobiologically distinct parcels accessible to in vivo neuroimaging. A connectivity matrix based on such a parcellation would have roughly 25,000 (+/- ~10,000) possible entries. Information from nonhuman primates provides important constraints on the connectivity patterns to be expected within this matrix.

2.3. Long-distance anatomical connectivity

Even though white-matter tracts have been studied in humans for more than a century, there is very little hard evidence (‘ground truth’) on the detailed pattern of long-distance connections between gray-matter regions. However, a wealth of relevant data comes from anatomical tracer studies in nonhuman primates. For cerebral cortex, quantitative analyses of cortico-cortical connections from retrograde tracer injections in the macaque are especially informative. Four general observations warrant mention. (i) Numerous cortical inputs and outputs. Areas V1, V2, and V4 each receive inputs from 25 other cortical areas within the same hemisphere (Markov et al., 2011), which is many more than were reported in previous studies. The number of inputs to any given area in temporal, parietal, and frontal cortex is even greater (H. Kennedy, N. Markov, personal communication; Lewis and Van Essen, 2000; Gerbella et al., 2010, 2011). There are also numerous interhemispheric connections though these are not as well studied (e.g., Lewis & Van Essen, 2000). (ii) Wide range of connection strengths. Remarkably, the inputs to each area span more than five orders of magnitude in strength, as assessed by the fraction of labeled neurons, yet the connection strength from any given area is reproducible, within 1 – 2 orders of magnitude, when tested by repeat injections (Markov et al., 2011). (iii) Similar connectivity profiles for adjacent areas. The connectivity profiles for foveal portions of areas V1, V2, and V4 are strikingly similar in terms of the other areas to which they are connected; the differences are mainly quantitative (strength of inputs) rather than qualitative (Markov et al., 2011). (iv) Within-area heterogeneity. The connectivity of an individual cortical area can show considerable internal heterogeneity. For example, large regional differences in the connectivity of topographically organized areas have been demonstrated for central vs. peripheral vision in extrastriate cortex (Baizer et al., 1991) and for somatotopic sensory and motor areas (He et al., 1993, 1995). In addition, some visual areas, including V1, V2, and V4, have a finer-grained, repetitive modular substructure with distinct connectivity patterns (Livingstone and Hubel, 1984; DeYoe et al., 1994; Sincich et al., 2010). Such heterogeneity has major implications for using connectivity to distinguish among cortical areas.

Analogous issues arise in considering the many complex pathways that involve subcortical structures and the cerebellum. In the interests of brevity, these are not considered further here, except to emphasize the need to interpret human connectivity studies in relation to the full set of anatomical realities inferred from nonhuman primate connectivity studies.

Extrapolating from these observations on the parcellated macaque connectome to human cortex suggests that a large fraction (possibly half or more) of the possible cortico-cortical connections within each hemisphere might actually exist. However, most of these pathways are likely to be weak and may be difficult to detect reliably. The complexity will be even greater when considering connections between the two hemispheres and among subcortical structures. In short, the parcellated human connectome is likely to be a highly distributed network in which each parcel has many inputs and outputs (perhaps 100 or more of each), most of which are relatively weak. Hence, it is of paramount importance to have in vivo imaging methods that can chart complex connectivity patterns quantitatively and with high spatial fidelity. This will require improvements in sensitivity, spatial resolution, acquisition time, and analysis methods (see Section 3).

2.4 Axonal trajectories

Tractography analyses based on diffusion imaging estimate the trajectories of fiber bundles that have coherent orientations (i.e., are approximately collinear) across adjacent white matter voxels. When using tractography to infer connectivity between distant gray matter regions, a critical assumption is that the axons mediating the anatomical pathway are tightly fasciculated along their entire length - even in regions where crossing fibers occur. This assumption is supported to a limited degree by classical dissections of human white matter (Déjérine, 1895; Gluhbegovic, 1980). Analysis of fiber trajectories after anterograde tracer injections in the macaque (Schmahmann and Pandya, 2006; Lehman et al., 2011) indicate that fibers are indeed fasciculated over extended lengths in many cases but in other cases axonal trajectories may be distributed more diffusely. Another important issue that affects the accuracy of non-invasively reconstructed cortico-cortico connections is the angle at which fibers enter and leave cerebral cortex. Do fibers typically traverse the gray-white boundary approximately at right angles, as has been assumed (e.g., Tuch et al., 2003)? Or do they typically ‘peel off’ and traverse this boundary obliquely, as suggested by examination of myelinated axon trajectories (K. Dikranian, M. Glasser, D. Van Essen, unpublished observations)? Detailed analysis of such issues may provide improved, anatomically grounded constraints that can be used to better constrain the non-invasive reconstruction of long-distance pathways.

2.5 Functional connectivity and neurovascular coupling

Analyses of brain circuits have benefited greatly by the emergence of methods for inferring ‘functional connectivity’ using correlations in the fMRI BOLD signal (e.g., Smith et al, 2011). The fundamental underpinnings of this approach is that correlations in the low-frequency BOLD signal provide evidence for which brain regions are functionally coupled through direct or indirect anatomical connections (Biswal et al., 1995; Vincent et al., 2007). For such analyses, spatial resolution is limited not only by the voxel size used for data acquisition, but also by the spatial relationships associated with neurovascular coupling. In particular, in gradient echo (GRE) BOLD fMRI images (the most commonly used approach for functional brain imaging and to date the only approach successfully used for resting state fMRI) the functional signals are largest in draining vessels (Menon et al., 1993; Uludag et al., 2009) that, for cerebral cortex, lie on the pial surface. This is particularly true at magnetic fields of 1.5 – 4 Tesla (Duong et al., 2003). This is a serious problem for analyzing buried cortex, because larger vessels drain from both banks of a given sulcus and thus reflect neural activity of regions that reside in close proximity in space but may be widely separated in function. The confound can be reduced by restricting analysis to gray-matter voxels (e.g., Kolster et al., 2010), but it is not eliminated because the BOLD effect from blood vessels extends outside blood vessel boundaries by about the blood vessel diameter (up to ~1 mm; Ogawa et al., 1993)¹.

Of course, having strong signals of truly cortical origin will help. For ultrahigh magnetic fields of 7 Tesla (7T) and above, there is a stronger microvascular contribution from capillaries, small post-capillary venules, and small intracortical veins (Yacoub et al., 2005; Uludag et al., 2009). Mapping signals associated with this intracortical vasculature may be distinguishable in analysis methods such as independent component analysis (ICA) and segregated from contributions originating from the pial vessels based on their different spatio-temporal signatures given sufficient spatial and temporal resolution (De Martino et al., 2011). Laminar-specific analyses can also further improve spatial fidelity of the GRE BOLD functional signals at 7T, by excluding not only the pial surface but also superficial cortical layers as well to eliminate the extravascular BOLD effect from the surface veins extending over the cortex (Polimeni et al., 2010); however, this approach sacrifices information about the superficial layers, whose connections differ from those of deeper layers.

2.6. Individual variability

The nature and magnitude of individual variability is markedly different for each major brain structure. Variability is greatest for the cerebral cortex, where it involves large differences in the pattern of cortical convolutions, in the location of cortical areas relative to these convolutions, in the size of each area, and presumably also in the patterns of long-distance connectivity. Current methods used to compensate for individual variability involve registration to a standard atlas using volume-based or surface-based algorithms that are constrained by shape features. In general, surface-based registration outperforms volume-based registration (nonlinear or linear) in achieving good intersubject alignment (Fischl et al., 2008; Van Essen et al., 2011b). However, the quality of intersubject registration is variable and is worst in regions of high folding variability (Fig. 4). Approaches that capitalize on measures more closely related to cortical areas than folding patterns, e.g. connectivity data or cortical myelination (Glasser and Van Essen 2011), should in principle be able to achieve much better alignment.

3.1 Data acquisition at 3T

A number of refinements are expected to significantly improve the quality of fMRI and dMRI data relative to conventional 3T scanners and data acquisition methods. These include improved pulse sequences and hardware.

3.2 Data acquisition at ultrahigh fields

While major improvements in fMRI and dMRI data acquisition can be achieved using 3T scanners, the rapidly evolving arena of ultrahigh field MR scanning opens additional exciting opportunities. The WU-Minn consortium plans to scan 200 subjects (a subset of the 1,200 scanned at 3T at WashU) using a 7T scanner at UMinn. The 7T system, while less mature and more challenging to work with than the 3T scanner, offers major advantages for the resting and task-based fMRI studies, and potentially also for diffusion-based techniques if sufficiently short echo times can be achieved for diffusion weighting. In addition, a new 10.5T scanner is currently under development at UMinn via a separately funded grant. Some HCP subjects will be scanned at 10.5T if it becomes operational in time. In any event it is important to anticipate how this higher field strength will serve connectomic studies in the future.

For all of the MRI approaches to be used in the HCP, a key parameter is spatial resolution. This is ultimately determined by the available SNR, since the minimum voxel volume achievable is directly related to SNR. For fMRI studies, another critical issue is the spatial fidelity of the BOLD signal relative to neuronal activity, which improves with increasing magnetic field. At low fields, BOLD signals originate mainly from large draining vessels. When acquired at high resolution, vessels on the pial surface are clearly discernible, as in venograms (Segebarth et al., 1994; Hoogenraad et al., 1999). At ultrahigh fields, this large vessel contribution persists in GRE fMRI images, but the contribution of the microvasculature increases supra-linearly with magnetic field and is also robustly detectable (Ugurbil et al, 2003).

Another potential gain at 7T may come from using spin-echo (SE) BOLD pulse sequences rather than GRE BOLD in order to enhance microvascular and suppress macrovascular contributions (Duong et al., 2003; Yacoub et al., 2003; Uludag et al., 2009). This approach can reveal robust signals for task based functional mapping at 7T (Yacoub et al., 2007; 2008), approximately comparable to what is obtained at 3T with conventional GRE BOLD. SE BOLD, however, does not have the spatial accuracy at lower fields such as 3T owing to persisting intravascular blood T2 changes coupled to alterations in neuronal activity (Norris et al., 2002; Duong et al., 2003). Nor does it have the sensitivity to yield robust resting state fMRI maps even at 7T (Yacoub et al., unpublished observations) where the temporal inefficiencies (due to RF power deposition limits) of SE imaging limit whole brain acquisitions.

The increased spatial fidelity achieved at ultrahigh fields and sequences like (SE) EPI and 3D GRASE, combined with the field-dependent gains in SNR has allowed functional mapping at the level of cortical columns, including orientation domains (Yacoub 2007, 2008) and axis of motion features in MT with laminar differentiation (Zimmermann et al., 2011). These capabilities have yet to be extensively used in cognitive neuroscience, in part because of the challenge in achieving high resolution over the whole brain. However, engineering and methodological advances over the last decade now enable the acquisition of excellent, high resolution (mm or submillimeter isotropic) whole brain images at 7T, at least with the GRE approach. For example, a whole-brain analysis of resting state networks at 1 mm isotropic resolution (De Martino et al., 2011) revealed signals primarily within cortical gray matter, reducing the problem of cross-talk between opposite banks of sulci (Section 2.5). Increasing the number of channels for the RF coils (e.g., to 48 to 64 channels) combined with better 7T electronics should lead to further improvements in SNR.

Additional gains are expected at 10.5T in image SNR (Vaughan et al., 2001) and accuracy of mapping signals due to further improvements in the microvascular contribution (Uludag et al., 2009). Like 7T, 10.5T has the potential to shine for high resolution resting and task based fMRI. However, the technical challenges to be faced at 10.5T should not be underestimated, given the experience with existing 9.4T scanners. At UMinn, the 9.4T system has been abandoned as an fMRI scanner because gradients compatible with the “head-only” 65 cm bore space have failed to tolerate EPI type sequences. However, 9.4T has been a great learning experience from which the 10.5T has and will continue to benefit. Provided the magnet meets technical specifications, as is likely, given the record of the manufacturer (Agilent), we are optimistic that the new 10.5T scanner will avoid the difficulties encountered at 9.4T and will provide resting-state and task fMRI data that will permit analysis at the level of cortical layers and columns. Besides the many technical challenges with data acquisition, there will be additional challenges in dealing with the incredibly large amount of data to be generated at high resolution with highly parallel and highly accelerated acquisitions.

3.3. Other data modalities and analyses

In this article, we have concentrated on R-fMRI and dMRI data acquisition because these domains are fundamentally the most rate-limiting for estimating human brain connectivity in vivo and for addressing the neurobiological challenges outlined in Section 2. However, R-fMRI and dMRI data acquisition are entry points to a much broader effort that is essential to a better understanding of the human connectome. The wider effort includes many levels of data analysis and visualization as well as cross-modal integration with other important modalities based on imaging, behavior, and genetics. For the WU-Minn HCP consortium, this includes task-fMRI to be carried out on all 1,200 subjects and MEG/EEG on 100 subjects. Extensive behavioral assessments will be carried out in all subjects. Moreover, blood sample collection will allow genotyping in year 5 of the grant, likely including whole genome sequencing. Together, this will provide a dataset of unprecedented richness for exploring the relationships between brain connectivity and behavioral phenotypes as well as genetic underpinnings. The subjects will be 300 twin pairs and 600 non-twin siblings, which will allow exploration of the heritability of brain connections and will enhance the statistical power of the genetic analyses. This is described in greater detail elsewhere (Van Essen et al., 2011c).

The data analysis efforts for the HCP will span a broad range of efforts that we can only briefly touch upon here. Of particular import are efforts to address (i) the brain parcellation problem, (ii) intersubject variability and correlating connectivity with other phenotypes, and (iii) providing an informatics and visualization platform that will enable a new type of discovery science through powerful data mining capabilities (Marcus et al., 2011). These methods and approaches are under active development and will be considered further in future publications.

3.4. The human connectome in a decade

Funding for the current WU-Minn HCP will conclude in 2015, but the ConnectomeDB database will be maintained at Washington University for an additional 5 years. How will our understanding of the human connectome expand and evolve over the coming decade? Will the advances be incremental, or can we anticipate transformative neurobiological insights to emerge? Before speculating briefly on these issues, it is instructive to consider the Human Genome Project, launched in 1990 and concluded in 2003 at a cost of ~3 billion dollars, two orders of magnitude greater than the current HCP investment. The genome sequencing effort has been extraordinarily successful, including many ways that were not anticipated in its early days. It has led to deep and surprising insights into fundamental aspects of genome organization and function, enabled numerous medical advances, and powered new biotechnology industries.

Among the many differences between human brain macro-connectomics and genome sequencing is the fidelity with which data are acquired and analyzed. Genome sequencing is extremely accurate (99.99% or better), making it feasible to distinguish differences between individuals at the single nucleotide level. However, this is coupled with a very low level of nucleotide diversity across individuals -- only about 1 part in 1,000. The accuracy with which human brain connectivity can be quantitatively assessed is vastly inferior to genome sequencing. On the other hand, the degree of individual variability in connectivity patterns is likely to be far greater. Each cortical area varies in surface area by two-fold or more across individuals (Van Essen et al., 2011b) and the strength of pathways between a pair of cortical areas can vary by one or two orders of magnitude (Markov et al., 2011). Until the HCP data are acquired and analyzed, we simply will not know what sensitivity and reliability to expect in comparing connectomes across individuals and relating connectivity to behavioral phenotypes and genetic differences. Despite these caveats, we are optimistic that major insights will emerge from mining of HCP data. In broad strokes, this will include (i) a far more accurate charting of brain parcellations (particularly neocortical and cerebellar parcels), brain networks, and their dynamics; (ii) a quantitative characterization of network variability across individuals; and (iii) correlations between behavioral phenotypes and brain networks that provide a deeper understanding of the neural basis of individual variability. Such insights might be related to working memory, perceptual categorization, emotion, personality, or any of the many other phenotypes that will be available for data mining.

Our focus in this article has been on the healthy adult human brain, based on the mandate from NIH to the HCP. However, the dramatic changes in brain circuits that occur during development and aging will be amenable to analysis using the approaches described here. Even more important in the long run will be the advances in understanding the neurobiological bases of brain diseases and disorders that afflict hundreds of millions of individuals worldwide and extract an extraordinary toll in terms of human suffering and economic impact. The prospect of providing improvements in diagnosis and treatment of brain disorders by capitalizing on a broad spectrum of methodological advances provides ample motivation for continued investment in human connectomics.