The WU-Minn Human Connectome Project: An Overview

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

A multi-modal parcellation of human cerebral cortex

Function in the human connectome: Task-fMRI and individual differences in behavior

Functional connectomics from resting-state fMRI

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

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The Human Connectome Project's neuroimaging approach

https://pure.mpg.de/pubman/item/item_1747052_7/component/file_1835422/Schoffelen_2013_neuroimage.pdf

Adding dynamics to the Human Connectome Project with MEG.

Diagnosis of mild traumatic brain injuries (TBIs) has been difficult because of the absence of obvious focal brain lesions, using conventional computed tomography (CT) or magnetic resonance imaging (MRI) scans, in a large percentage of TBIs. One useful measure that can characterize potential tissue and neural network damage objectively is Lempel–Ziv complexity (LZC) applied to magnetoencephalography (MEG) signals. LZC is a model-independent estimator of system complexity that estimates the number of different patterns in a sequence. We hypothesized that because of the potential network damage, TBIs would show a reduced level of complexity in regions that are impaired. We included 18 healthy controls and 18 military veterans with TBI in the study. Resting state MEG data were acquired, and the LZCs were analyzed across the whole brain. Our results indicated reduced complexity in multiple brain areas in TBI patients relative to the healthy controls. In addition, we detected several neuropsychologica...

Complexity analysis of resting state magnetoencephalography activity in traumatic brain injury patients.

Neuronal oscillations in the theta and gamma bands have been shown to be important for cognition. Here we examined the temporal and spatial relationship between the two frequency bands in emotional processing using magnetoencephalography and an advanced dynamic beamformer source imaging method called synthetic aperture magnetometry. We found that areas including the amygdala, visual and frontal cortex showed significant event-related synchronization in both bands, suggesting a functional association of neuronal oscillations in the same areas in the two bands. However, while the temporal profile in both bands was similar in the amygdala, the peak in gamma band power was much earlier within both visual and frontal areas. Our results do not support a traditional view that the localizations of lower and higher frequencies are spatially distinct. Instead, they suggest that in emotional processing, neuronal oscillations in the gamma and theta bands may reflect, at least in visual and frontal cortex either different but related functional processes or, perhaps more probably, different computational components of the same functional process.

/pdf/theta-band-activity-in-response-to-emotional-expressions-and-1nxuvqhvrj.pdf

Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging.

Qian Luo

Papers

Adding dynamics to the Human Connectome Project with MEG.

Complexity analysis of resting state magnetoencephalography activity in traumatic brain injury patients.

Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging.