Explore the vast range of titles available.

Every brain is unique, having its structural and functional organization shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under the common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. We investigate this assumption: can the structural properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects, considering parcellations of different granularity, to understand the connectivity misalignment between regions. First, we compare the obtained permutations with four different algorithm initializations: Spatial Adjacency, Identity, Barycenter, and Random. Our results suggest that applying an alignment strategy improves the similarity across subjects when the number of parcels is above 100 and when using Spatial Adjacency and Identity initialization (the most plausible priors). Second, we characterize the obtained permutations, revealing that the majority of permutations happens between neighbors parcels. Lastly, we study the spatial distribution of the permutations. By visualizing the results on the cortex, we observe no clear spatial patterns on the permutations and all the regions across the context are mostly permuted with first and second order neighbors. Can the structural properties of a specific region of an atlas be assumed to be the same across subjects? Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects and with parcellations of different granularity to understand which is the connectivity misalignment between regions.

The human insula has been parcellated on the basis of resting state functional connectivity and diffusion tensor imaging. Little is known about the organization of the insula when involved in active tasks. We explored this issue using a novel meta-analytic clustering approach. We queried the BrainMap database asking for papers involving normal subjects that recorded activations in the insular cortex, retrieving 1305 papers, involving 22,872 subjects and a total of 2957 foci. Data were analyzed with several different methodologies, some of which expressly designed for this work. We used meta-analytic connectivity modeling and meta-analytic clustering of data obtained from the BrainMap database. We performed cluster analysis to subdivide the insula in areas with homogeneous connectivity, and density analysis of the activated foci using Voronoi tessellation. Our results confirm and extend previous findings obtained investigating the resting state connectivity of the anterior-posterior and left-right insulae. They indicate, for the first time, that some blocks of the anterior insula play the role of hubs between the anterior and the posterior insulae, as confirmed by their activation in several different paradigms. This finding supports the view that the network to which the anterior insula belongs is related to saliency detection. The insulae of both sides can be parcellated in two clusters, the anterior and the posterior: the anterior is characterized by an attentional pattern of connectivity with frontal, cingulate, parietal, cerebellar and anterior insular highly connected areas, whereas the posterior is characterized by a more local connectivity pattern with connections to sensorimotor, temporal and posterior cingulate areas. This antero-posterior subdivision, better characterized on the right side, results sharper with the connectivity based clusterization than with the behavioral based clusterization. The circuits belonging to the anterior insula are very homogeneous and their blocks in multidimensional scaling of MACM-based profiles are in central position, whereas those belonging to the posterior insula, especially on the left, are located at the periphery and sparse, thus suggesting that the posterior circuits bear a more heterogeneous connectivity. The anterior cluster is mostly activated by cognition, whereas the posterior is mostly activated by interoception, perception and emotion.

One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct. We propose a new unbiased evaluation criterion (distance‐controlled boundary coefficient [DCBC]) for brain parcellations to overcome the drawback of existing evaluation criteria that are biased by spatial smoothness. Using DCBC, task‐evoked, and resting‐state functional magnetic resonance imaging data, we found resting‐state group parcellations predict task‐based functional boundaries very well, anatomical atlases predict functional boundaries no better than chance, and multi‐modal parcellations do not improve on resting‐state parcellations.

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the “WU-Minn-Ox” HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The “HCP-style” neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

Removal of nuisance signals (such as motion) from the BOLD time series is an important aspect of preprocessing to obtain meaningful resting‐state functional connectivity (rs‐FC). The nuisance signals are commonly removed using denoising procedures at the finest resolution, that is the voxel time series. Typically, the voxel‐wise time series are then aggregated into predefined regions or parcels to obtain an rs‐FC matrix as the correlation between pairs of regional time series. Computational efficiency can be improved by denoising the aggregated regional time series instead of the voxel time series. However, a comprehensive comparison of the effects of denoising on these two resolutions is missing. In this study, we systematically investigate the effects of denoising at different time series resolutions (voxel‐level and region‐level) in 370 unrelated subjects from the HCP‐YA dataset. Alongside the time series resolution, we considered additional factors such as aggregation method (Mean and first eigenvariate [EV]) and parcellation granularity (100, 400, and 1000 regions). To assess the effect of those choices on the utility of the resulting whole‐brain rs‐FC, we evaluated the individual specificity (fingerprinting) and the capacity to predict age and three cognitive scores. Our findings show generally equal or better performance for region‐level denoising with notable differences depending on the aggregation method. Using Mean aggregation yielded equal individual specificity and prediction performance for voxel‐level and region‐level denoising. When EV was employed for aggregation, the individual specificity of voxel‐level denoising was reduced compared to region‐level denoising. Increasing parcellation granularity generally improved individual specificity. For the prediction of age and cognitive test scores, only fluid intelligence indicated worse performance for voxel‐level denoising in the case of aggregating with the EV. Based on these results, we recommend the adoption of region‐level denoising for brain‐behavior investigations when using Mean aggregation. This approach offers equal individual specificity and prediction capacity with reduced computational resources for the analysis of rs‐FC patterns. Region‐level denoising is computationally efficient and effective: Denoising aggregated regional time series (as opposed to voxel‐level) generally yields equal or better results in individual specificity and predictive capacity.

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community. Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Surface‐based parcellation approaches fare substantially better than volumetric approaches but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures.

Humans have the largest cerebral cortex among primates. The question of whether association cortex, particularly prefrontal cortex (PFC), is disproportionately larger in humans compared with nonhuman primates is controversial: Some studies report that human PFC is relatively larger, whereas others report a more uniform PFC scaling. We address this controversy using MRI-derived cortical surfaces of many individual humans, chimpanzees, and macaques. We present two parcellation-based PFC delineations based on cytoarchitecture and function and show that a previously used morphological surrogate (cortex anterior to the genu of the corpus callosum) substantially underestimates PFC extent, especially in humans. We find that the proportion of cortical gray matter occupied by PFC in humans is up to 1.9-fold greater than in macaques and 1.2-fold greater than in chimpanzees. The disparity is even more prominent for the proportion of subcortical white matter underlying the PFC, which is 2.4-fold greater in humans than in macaques and 1.7-fold greater than in chimpanzees.

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously. •A systematic comparison of state-of-the-art parcellation methods is provided.•10 subject- and 24 group-level methods are evaluated using publicly available data.•Experiments consist of quantitative assessments of parcellations at varying scales.•Several criteria are simultaneously considered to evaluate parcellations.•Results suggest that there is no optimal method able to address all the challenges.

“In vivo Brodmann mapping” or non-invasive cortical parcellation using MRI, especially by measuring cortical myelination, has recently become a popular research topic, though myeloarchitectonic cortical parcellation in humans previously languished in favor of cytoarchitecture. We review recent in vivo myelin mapping studies and discuss some of the different methods for estimating myelin content. We discuss some ways in which myelin maps may improve surface registration and be useful for cross-modal and cross-species comparisons, including some preliminary cross-species results. Next, we consider neurobiological aspects of why some parts of cortex are more myelinated than others. Myelin content is inversely correlated with intracortical circuit complexity — in general, more myelin content means simpler and perhaps less dynamic intracortical circuits. Using existing PET data and functional network parcellations, we examine metabolic differences in the differently myelinated cortical functional networks. Lightly myelinated cognitive association networks tend to have higher aerobic glycolysis than heavily myelinated early sensory-motor ones, perhaps reflecting greater ongoing dynamic anabolic cortical processes. This finding is consistent with the hypothesis that intracortical myelination may stabilize intracortical circuits and inhibit synaptic plasticity. Finally, we discuss the future of the in vivo myeloarchitectural field and cortical parcellation – “in vivo Brodmann mapping” – in general. •Interest in in vivo myelin mapping using MRI is rapidly growing.•Many architectural properties of cerebral cortex are correlated with myelin content.•Cortical aerobic glycolysis is correlated with myelin content.•Cortical myelin may serve as an inhibitor of synaptic plasticity.•Multi-modal cortical parcellation will provide many new insights in the future.

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state. •Functional parcel boundaries reconfigure with cognitive state.•Parcel reconfigurations are robust and reliable across sessions and subjects.•Parcel sizes can significantly predict task condition and task performance.•State-dependent functional atlases have implications for functional connectivity.