Self‐reported navigation ability is associated with optic flow‐sensitive regions’ functional connectivity patterns during visual path integration

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.

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A sub+ cortical fMRI-based surface parcellation

10.1101/2019.12.20.883553 ◽

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.

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Body mass variability is represented by distinct functional connectivity patterns

10.31232/osf.io/b9ahw ◽

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.

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

10.1101/070524 ◽

2016 ◽

Cited By ~ 1

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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Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽

10.7554/elife.32696 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

Ruedeerat Keerativittayayut ◽

Ryuta Aoki ◽

Mitra Taghizadeh Sarabi ◽

Koji Jimura ◽

Kiyoshi Nakahara

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Large Scale ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Memory Encoding ◽

Graph Metrics ◽

Connectivity Patterns ◽

Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

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