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.

scholarly journals Geometry of inter-areal interactions in mouse visual cortex

2021 ◽  
Author(s):  
Ramakrishnan Iyer ◽  
Joshua H Siegle ◽  
Gayathri Mahalingam ◽  
Shawn Olsen ◽  
Stefan Mihalas
Keyword(s):  
Functional Connectivity ◽  
Linear Model ◽  
Piecewise Linear ◽  
Structural Connectivity ◽  
Source Area ◽  
Stimulus Presentation ◽  
Visual Coding ◽  
Cortical Areas ◽  
Sufficient Detail ◽  
Visual Cortical

The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual Coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.

scholarly journals Parcellation of the human amygdala using recurrence quantification analysis

2020 ◽  
Author(s):  
Krzysztof Bielski ◽  
Sylwia Adamus ◽  
Emilia Kolada ◽  
Joanna Rączaszek-Leonardi ◽  
Iwona Szatkowska
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Recurrence Quantification Analysis ◽  
Future Research ◽  
Valuable Insight ◽  
List Type ◽  
Brain Organization ◽  
Recurrence Quantification ◽  
Connectivity Patterns ◽  
Quantification Analysis

ABSTRACTSeveral previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has been neglected in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon’s entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.HighlightsA new method for parcellation of the human amygdala was developedThe ventrolateral and dorsomedial subdivisions of the amygdala were revealedThe two subdivisions correspond to the anatomically defined regions of the amygdalaThe two subdivisions differ with respect to values of entropyA new parcellation method provides novel information about amygdala subdivisions

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 Altered Functional Connectivity of the Primary Visual Cortex in Subjects with Amblyopia

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kun Ding ◽  
Yong Liu ◽  
Xiaohe Yan ◽  
Xiaoming Lin ◽  
Tianzi Jiang
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Visual Area ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Blurred Vision ◽  
Visual Areas ◽  
Connectivity Patterns ◽  
The Brain

Amblyopia, which usually occurs during early childhood and results in poor or blurred vision, is a disorder of the visual system that is characterized by a deficiency in an otherwise physically normal eye or by a deficiency that is out of proportion with the structural or functional abnormalities of the eye. Our previous study demonstrated alterations in the spontaneous activity patterns of some brain regions in individuals with anisometropic amblyopia compared to subjects with normal vision. To date, it remains unknown whether patients with amblyopia show characteristic alterations in the functional connectivity patterns in the visual areas of the brain, particularly the primary visual area. In the present study, we investigated the differences in the functional connectivity of the primary visual area between individuals with amblyopia and normal-sighted subjects using resting functional magnetic resonance imaging. Our findings demonstrated that the cerebellum and the inferior parietal lobule showed altered functional connectivity with the primary visual area in individuals with amblyopia, and this finding provides further evidence for the disruption of the dorsal visual pathway in amblyopic subjects.

scholarly journals Contribution of structural connectivity to MEG functional connectivity

2019 ◽  
Author(s):  
Anirudh Wodeyar ◽  
Ramesh Srinivasan
Keyword(s):  
Functional Connectivity ◽  
Graphical Models ◽  
Phase Synchronization ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Gamma Band ◽  
Amplitude Envelope ◽  
Common Input ◽  
Structural Networks ◽  
Envelope Correlation

AbstractStructural connectivity by axonal fiber bundles provides the substrate for transmission of action potentials across the brain. Functional connectivity in MEG signals is expected to arise from communication along structural connections. However, very little empirical evidence has been obtained to support this hypothesis. The main objective of this study is to use simulations and MEG data to directly evaluate the contribution of structural connectivity to MEG functional connectivity measures. Since axonal transmission is on a millisecond time scale we hypothesize that measures sensitive to phase synchronization in a frequency band, such as coherence, would have a closer relationship to structural connectivity than measures sensitive to slower time scales such as amplitude-envelope correlation. We estimate graphical models of MEG functional connectivity, i.e, the MEG effective connectivity, to reduce the influence of leakage effects and common input effects, and to explicitly model the contribution of structural connectivity to functional connectivity. Consistent with our hypothesis, networks defined by models of gamma band (> 30 Hz) coherence that incorporate phase information show the closest alignment to structural connectivity. However, at lower frequencies (1-30 Hz) there was better alignment between models of amplitude envelope correlation and structural connectivity. In simulations, summarizing network properties of graphical models using graph theoretic metrics provides a robust measure of the relationship between functional and structural connectivity that is preserved even at low signal to noise ratios. In MEG data, centrality of nodes in the gamma band networks more closely correspond to centrality of nodes in the structural networks than a direct comparison between edge weights.

scholarly journals Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

2021 ◽  
Author(s):  
Ronaldo V. Nunes ◽  
Marcelo Bussotti Reyes ◽  
Jorge F. Mejias ◽  
Raphael Y. de Camargo
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Brain Regions ◽  
Cortical Network ◽  
Ground Truth Data ◽  
Cortical Areas ◽  
Electrophysiological Measurements ◽  
Flow Of Information

AbstractInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Correlation (GPDC), provide estimates of the causal influence between areas. However, such methods have a limitation because their estimates depend on the number of brain regions simultaneously recorded. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale mouse cortical network. The model contains 19 cortical areas modeled using spiking neural populations, and directed weights for long-range projections were obtained from a tract-tracing cortical connectome. We show that the GPDC estimates correlate positively with structural connectivity. Moreover, the correlation between structural and directed functional connectivity is comparable even when using only a few cortical areas for GPDC estimation, a typical scenario for electro-physiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

scholarly journals Mapping hybrid functional-structural connectivity traits in the human connectome

2018 ◽  
Vol 2 (3) ◽  
pp. 306-322 ◽  
Author(s):  
Enrico Amico ◽  
Joaquín Goñi
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Main Task ◽  
Hybrid Functional ◽  
Entire Cohort ◽  
Human Connectome Project ◽  
Extended Approach ◽  
Visual Areas ◽  
Connectivity Patterns ◽  
Brain States

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.

Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung
Keyword(s):  
Functional Connectivity ◽  
Time Course ◽  
Language Skills ◽  
Brain Regions ◽  
Brain Areas ◽  
Daily Lives ◽  
Cortical Areas ◽  
Social Abilities ◽  
Behavioral Evidence ◽  
Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

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