We combined quantitative relaxation rate (
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= 1/
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) mapping—to measure local myelination—with fMRI-based retinotopy. Gray–white and pial surfaces were reconstructed and used to sample
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at ...different cortical depths. Like myelination,
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decreased from deeper to superficial layers.
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decreased passing from V1 and MT, to immediately surrounding areas, then to the angular gyrus. High
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was correlated across the cortex with convex local curvature so the data was first “de-curved”. By overlaying
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and retinotopic maps, we found that many visual area borders were associated with significant
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increases including V1, V3A, MT, V6, V6A, V8/VO1, FST, and VIP. Surprisingly, retinotopic MT occupied only the posterior portion of an oval-shaped lateral occipital
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maximum.
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maps were reproducible within individuals and comparable between subjects without intensity normalization, enabling multi-center studies of development, aging, and disease progression, and structure/function mapping in other modalities.
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their ...geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•The data-driven pipeline is used to identify white matter clusters.•The altered bundle properties in AD are involved multiple fiber clusters.•The altered clusters correlated with clinical scale.
...Diffusion magnetic resonance imaging tractography allows investigating brain structural connections in a noninvasive way and has been widely used for understanding neurological disease. Quantification of brain connectivity along with its length by dividing a fiber bundle into multiple segments (node) is a powerful approach to assess biological properties, which is termed as tractometry. However, current tractometry methods face challenges in node identification along with the length of complex bundles whose morphology is difficult to summarize. In addition, the anatomic measure reflecting the macroscopic fiber cross-section has not been followed in previous tractometry. In this paper, we propose an automated fiber bundle quantification, which we refer to as ClusterMetric. The ClusterMetric uses a data-driven approach to identify fiber clusters corresponding to subdivisions of the white matter anatomy and identify consistent space nodes along the length of clusters across individuals. The proposed method is demonstrated by applicating to our collected dataset including 23 Alzheimer's disease (AD) patients and 22 healthy controls (HCs) and a public dataset of ADNI including 53 AD patients and 85 HCs. The altered white matter tracts in AD group are observed using both datasets, which involve several major fiber tracts including the corpus callosum, corona-radiata-frontal, arcuate fasciculus, inferior occipito-frontal fasciculus, uncinate fasciculus, thalamo-frontal, superior longitudinal fasciculus, inferior cerebellar peduncle, cingulum bundle, and extreme capsule. These fiber clusters represent the white matter connections that could be most affected in AD, suggesting the ability of our method in identifying potential abnormalities specific to local regions within a fiber cluster.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Abstract
A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers ...the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional ...segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting‐state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta‐analysis also identified functional distinction between subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior subregions mainly participated in social cognition and semantic processing; the ventral middle subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro‐posterior subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
About a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale spatial organization of visual cortex can be measured with functional magnetic resonance imaging ...(fMRI) while subjects view spatially modulated visual stimuli, also known as "retinotopic mapping." One of the datasets collected by the Human Connectome Project involved ultrahigh-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other Human Connectome Project measures acquired in these same participants.
The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing ...multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.
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