scholarly journals Cortical chemoarchitecture shapes macroscale effective functional connectivity patterns in macaque cerebral cortex

2016 ◽  
Vol 37 (5) ◽  
pp. 1856-1865 ◽  
Author(s):  
Elise Turk ◽  
Lianne H. Scholtens ◽  
Martijn P. van den Heuvel
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Connectivity Patterns

scholarly journals Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI

2017 ◽  
Author(s):  
Alexander Schaefer ◽  
Ru Kong ◽  
Evan M. Gordon ◽  
Timothy O. Laumann ◽  
Xi-Nian Zuo ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Spatial Proximity ◽  
Brain Organization ◽  
Cortical Areas ◽  
Connectivity Patterns ◽  
Almost All ◽  
Local Gradient ◽  
Global Similarity

AbstractA 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 one of two 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 four previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured sub-areal (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. Multi-resolution parcellations generated from 1489 participants are available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal)

scholarly journals The organization of the human cerebral cortex estimated by intrinsic functional connectivity

2011 ◽  
Vol 106 (3) ◽  
pp. 1125-1165 ◽  
Author(s):  
B. T. Thomas Yeo ◽  
Fenna M. Krienen ◽  
Jorge Sepulcre ◽  
Mert R. Sabuncu ◽  
Danial Lashkari ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Large Scale ◽  
Distributed Networks ◽  
Association Cortex ◽  
Resting State Functional Connectivity ◽  
Local Networks ◽  
Temporal Area ◽  
Hierarchical Relations ◽  
Connectivity Patterns

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

Decoding sound categories based on whole-brain functional connectivity patterns

2018 ◽  
Vol 14 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Jinliang Zhang ◽  
Gaoyan Zhang ◽  
Xianglin Li ◽  
Peiyuan Wang ◽  
Bin Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Whole Brain ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity

scholarly journals Disentangling Mathematics from Executive Functions by Investigating Unique Functional Connectivity Patterns Predictive of Mathematics Ability

2019 ◽  
Vol 31 (4) ◽  
pp. 560-573 ◽  
Author(s):  
Kenny Skagerlund ◽  
Taylor Bolt ◽  
Jason S. Nomi ◽  
Mikael Skagenholt ◽  
Daniel Västfjäll ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Premotor Cortex ◽  
Mathematical Ability ◽  
Angular Gyrus ◽  
Intraparietal Sulcus ◽  
Supramarginal Gyrus ◽  
Connectivity Patterns ◽  
The Right

What are the underlying neurocognitive mechanisms that give rise to mathematical competence? This study investigated the relationship between tests of mathematical ability completed outside the scanner and resting-state functional connectivity (FC) of cytoarchitectonically defined subdivisions of the parietal cortex in adults. These parietal areas are also involved in executive functions (EFs). Therefore, it remains unclear whether there are unique networks for mathematical processing. We investigate the neural networks for mathematical cognition and three measures of EF using resting-state fMRI data collected from 51 healthy adults. Using 10 ROIs in seed to whole-brain voxel-wise analyses, the results showed that arithmetical ability was correlated with FC between the right anterior intraparietal sulcus (hIP1) and the left supramarginal gyrus and between the right posterior intraparietal sulcus (hIP3) and the left middle frontal gyrus and the right premotor cortex. The connection between the posterior portion of the left angular gyrus and the left inferior frontal gyrus was also correlated with mathematical ability. Covariates of EF eliminated connectivity patterns with nodes in inferior frontal gyrus, angular gyrus, and middle frontal gyrus, suggesting neural overlap. Controlling for EF, we found unique connections correlated with mathematical ability between the right hIP1 and the left supramarginal gyrus and between hIP3 bilaterally to premotor cortex bilaterally. This is partly in line with the “mapping hypothesis” of numerical cognition in which the right intraparietal sulcus subserves nonsymbolic number processing and connects to the left parietal cortex, responsible for calculation procedures. We show that FC within this circuitry is a significant predictor of math ability in adulthood.

scholarly journals A sub+ cortical fMRI-based surface parcellation

2019 ◽  
Author(s):  
John D. Lewis ◽  
Gleb Bezgin ◽  
Vladimir S. Fonov ◽  
D. Louis Collins ◽  
Alan C. Evans
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Life Span ◽  
Brain Volume ◽  
Subcortical Structures ◽  
Common Space ◽  
Cortical Parcellation ◽  
Connectivity Patterns ◽  
Deep Gray Matter ◽  
Volumetric Registration

AbstractBoth the cortex and the 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 mapping 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. We extend this surface-based approach to include also the subcortical deep gray-matter structures. Using the life-span data from the Enhanced Nathan Klein Institute - Rockland Sample, comprised of data from 590 individuals from 6 to 85 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that our extended functional parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels. We also show that our subcortical parcels align with known subnuclei. Further, we show that this parcellation is appropriate for use with data from other modalities; we generate cortical and subcortical white/gray contrast measures for this same dataset, and draw on the fact that areal differences are evident in the relation of white/gray contrast to age, to sex, to brain volume, and to interactions of these terms; we show that our extended functional parcellation provides an improved fit to the complexity of the life-span changes in the white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels. We provide our extended functional parcellation for the use of the neuroimaging community.

scholarly journals Body mass variability is represented by distinct functional connectivity patterns

2018 ◽  
Author(s):  
Jennifer R Sadler ◽  
Grace Elisabeth Shearrer ◽  
Kyle Stanley Burger
Keyword(s):  
Functional Connectivity ◽  
Body Mass ◽  
Visual Processing ◽  
Weight Status ◽  
Design Sensitivity ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Discordant Twins ◽  
Project Data ◽  
Discordant Twin

Understanding weight-related differences in functional connectivity provides key insight into neurocognitive factors implicated in obesity. Here, we sampled three groups from human connectome project data: 1) 47 pairs of BMI-discordant twins (n=94; average BMI-discordancy 6.7 3.1 kg/m2), 2) 47 pairs of gender and BMI matched BMI-discordant, unrelated individuals, and 3) 47 pairs of BMI-similar twins to test for body mass dependent differences in between network functional connectivity. Across BMI discordant samples, three networks appeared to be highly sensitivity to weight status; specifically, a network compromised of gustatory processing regions, a visual processing network, and the default mode network (DMN). Further, individuals with a lower BMI relative to their twin had stronger connectivity between striatal/thalamic and prefrontal networks (pFWE = 0.04) in the BMI-discordant twin sample. Cortical-striatal-thalamic networks underlie regulation of hedonically motivated behaviors. Stronger connectivity may facilitate increased regulation of decision-making when presented with highly rewarding, energy-dense foods. We also observed that individuals with a higher BMI than their twin had stronger connectivity between cerebellar and insular networks (pFWE = 0.04). Increased cerebellar-insula connectivity is associated with caloric deprivation and, in high BMI individuals, is associated compromised satiation signaling, thereby increasing risk for postprandial food intake. Connectivity patterns observed in the BMI-discordant twin sample were not see in a BMI-similar sample, providing evidence that the results are specific to BMI discordance. Beyond the involvement of gustatory and visual networks and the DMN, little overlap in results were seen between the two BMI-discordant samples. This may be a function of the higher study design sensitivity in the BMI-discordant twin sample, relative to the more generalizable results in the unrelated sample. These findings demonstrate that distinct connectivity patterns can represent weight variability, adding to mounting evidence that implicates atypical brain functioning with the accumulation and/or maintenance of elevated weight.

scholarly journals Intrinsic functional connectivity resembles cortical architecture at various levels of isoflurane anesthesia

2016 ◽  
Author(s):  
Felix Fischer ◽  
Florian Pieper ◽  
Edgar Galindo-Leon ◽  
Gerhard Engler ◽  
Claus C. Hilgetag ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Occipital Cortex ◽  
Amplitude Envelope ◽  
Cortical Areas ◽  
Temporal Properties ◽  
Connectivity Patterns ◽  
Different Depths ◽  
Amplitude Correlation

AbstractCortical activity patterns change in different depths of general anesthesia. Here we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from the ferret temporo-parieto-occipital cortex under various levels of isoflurane and determined the functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied by the emergence of burstsuppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalizing the functional connectivity patterns showed that the topographical structure remained similar across depths of anesthesia, resembling the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.

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