The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
"We based our analysis on volumetric data from the preselected bundle of 100 unrelated human subjects from the S500 release of the Human Connectome Project , in which each subject went through four rs-fMRI sessions lasting 14min 33s resulting in 1200 volumes per session and n = 400 sessions in total. "
[Show abstract][Hide abstract] ABSTRACT: Investigating temporal variability of functional connectivity is an emerging
field in connectomics. Entering dynamic functional connectivity by applying
sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged
from this topic. We introduce frequency-resolved dynamic functional
connectivity (frdFC) by means of multivariate empirical mode decomposition
(MEMD) followed up by filter-bank investigations. We develop our method on the
most canonical form by applying a sliding window approach to the intrinsic mode
functions (IMFs) resulting from MEMD. We explore two modifications:
uniform-amplitude frequency scales by normalizing the IMFs by their
instantaneous amplitude and cumulative scales. By exploiting the well
established concept of scale-invariance in resting-state parameters, we compare
our frdFC approaches. In general, we find that MEMD is capable of generating
time courses to perform frdFC and we discover that the structure of
connectivity-states is robust over frequency scales and even becomes more
evident with decreasing frequency. This scale-stability varies with the number
of extracted clusters when applying k-means. We find a scale-stability drop-off
from k = 4 to k = 5 extracted connectivity-states, which is corroborated by
null-models, simulations, theoretical considerations, filter-banks, and
scale-adjusted windows. Our filter-bank studies show that filter design is more
delicate in the rs-fMRI than in the simulated case. Besides offering a baseline
for further frdFC research, we suggest and demonstrate the use of
scale-stability as a quality criterion for connectivity-state and model
selection. We present first evidence showing that scale-invariance plays an
important role in connectivity-state considerations. A data repository of our
frequency-resolved time-series is provided.
"Future studies with larger sample sizes should be carried out to validate the present results. In particular, the Human Connectome Project plans to release a large dataset of multimodal data 1 [Van Essen et al., 2012], which will provide a unique opportunity to test our results on the similarity between SC and resting-state fMRI-FC and MEG-FC, as well as to analyze whether these similarities change with different cognitive tasks . It would be interesting as well to test whether SC influences the test-retest reliability of fMRI-FC and MEG-FC estimates. "
"the years that followed the publication of this paper, the idea to establish a " comprehensive structural description of the network of elements and connections forming the human brain " (Sporns et al., 2005) has gained rapidly growing interest in the field, yielding large-scale data-collection initiatives (Biswal et al., 2010; Nooner et al., 2012; Toga et al., 2012; Van Essen et al., 2013; 2012) as well as analyses (Glasser et al., 2013; Setsompop et al., 2013; Smith et al., 2013; Zuo et al., 2011), which illustrates the interest in structural connectivity databases of the human brain. Thus, in the past, several dMRIbased white matter atlases have been introduced (Mori et al., 2008) which were usually based on single subject data (Bürgel et al., 2006; Catani et al., 2002; Hagmann et al., 2003; Makris et al., 1997; Pajevic and Pierpaoli, 2000; Stieltjes et al., 2001; Wakana et al., 2004). "
[Show abstract][Hide abstract] ABSTRACT: The analysis of the structural architecture of the human brain in terms of connectivity between its sub-regions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute - Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. As a second experiment, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to prior literature findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) to perform fiber tracking directly within MNI space. Hence, it may be used for various purposes such as the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step towards a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.