scholarly journals Gradients of connectivity distance in the cerebral cortex of the macaque monkey

2018 ◽  
Author(s):  
Sabine Oligschläger ◽  
Ting Xu ◽  
Blazej M. Baczkowski ◽  
Marcel Falkiewicz ◽  
Arnaud Falchier ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Macaque Monkey ◽  
Spatial Layout ◽  
Primate Species ◽  
Spatial Proximity ◽  
Human Cortex ◽  
Functional Connections ◽  
Connectivity Distance

AbstractCortical connectivity conforms to a series of organizing principles that are common across species. Spatial proximity, similar cortical type, and similar connectional profile all constitute factors for determining the connectivity between cortical regions. We previously demonstrated another principle of connectivity that is closely related to the spatial layout of the cerebral cortex. Using functional connectivity from resting-state fMRI in the human cortex, we found that the further a region is located from primary cortex, the more distant are its functional connections with other areas of the cortex. However, it remains unknown whether this relationship between cortical layout and connectivity extends to other primate species. Here, we investigated this relationship using both resting-state functional connectivity as well as gold-standard tract-tracing connectivity in the macaque monkey cortex. For both measures of connectivity, we found a gradient of connectivity distance extending between primary and frontoparietal regions. As in the human cortex, the further a region is located from primary areas, the stronger its connections to distant portions of the cortex, with connectivity distance highest in frontal and parietal regions. The similarity between the human and macaque findings provide evidence for a phylogenetically conserved relationship between the spatial layout of cortical areas and connectivity.

scholarly journals Functional segregation of the human basal forebrain using resting state neuroimaging

2017 ◽  
Author(s):  
Ross D. Markello ◽  
R. Nathan Spreng ◽  
Wen-Ming Luh ◽  
Adam K. Anderson ◽  
Eve De Rosa
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Basal Forebrain ◽  
Nucleus Basalis ◽  
Functional Networks ◽  
List Type ◽  
Functional Connections ◽  
Diagonal Band ◽  
Hippocampal Complex

AbstractThe basal forebrain (BF) is poised to play an important neuromodulatory role in brain re-gions important to cognition due to its broad projections and complex neurochemistry. While significant in vivo work has been done to elaborate BF function in nonhuman rodents and primates, comparatively limited work has examined the in vivo function of the human BF. In the current study we used multi-echo resting state functional magnetic resonance imaging (rs-fMRI) from 100 young adults (18-34 years) to assess the potential segregation of human BF nuclei as well as their associated projections. Bottom-up clustering of voxel-wise functional connectivity maps yielded adjacent functional clusters within the BF that closely aligned with the distinct, hypothesized nuclei important to cognition: the nucleus basalis of Meynert (NBM) and the me-dial septum/diagonal band of Broca (MS/DB). Examining their separate functional connections, the NBM and MS/DB revealed distinct projection patterns, suggesting a conservation of nuclei-specific functional connectivity with homologous regions known to be anatomically innervated by the BF. Specifically, the NBM demonstrated coupling with a widespread cortical network as well as the amygdala, whereas the MS/DB revealed coupling with a more circumscribed net-work, including the orbitofrontal cortex and hippocampal complex. Collectively, these in vivo rs-fMRI data demonstrate that the human BF nuclei support functional networks distinct as-pects of resting-state functional networks, suggesting the human BF may be a neuromodulatory hub important for orchestrating network dynamics.HighlightsThe basal forebrain NBM and the MS/DB support two distinct functional networksFunctional networks closely overlap with known anatomical basal forebrainBasal forebrain networks are distinct from known resting-state functional networks

scholarly journals Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

scholarly journals Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency-bands and temporal scales

2020 ◽  
Author(s):  
Megan Godfrey ◽  
Krish D. Singh
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Fine Scale ◽  
Beta Band ◽  
Temporal Scales ◽  
Frequency Bands ◽  
Functional Connections ◽  
Connectivity Patterns ◽  
Oscillatory Coupling ◽  
Beta Frequency

AbstractRecent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity’) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We then compared these MRVE connectivity patterns to those derived using more standard amplitude-amplitude coupling measures, using methods designed to quantify the consistency of these patterns across participants.Using oscillatory amplitude envelope correlation (AEC), the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies’, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections and these showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band.It was also found that average mid-to fine scale variability within each region (fS ∼ 10-150Hz) negatively correlated with the region’s overall connectivity strength with other brain areas, as measured by fine scale MRVE correlation (fS ∼ 30-150Hz) and by alpha and beta band AEC. These findings suggest that local activity at frequencies fS ≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity.

scholarly journals High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains

2021 ◽  
Author(s):  
Mahdi Zarei ◽  
Dan Xie ◽  
Fei Jiang ◽  
Adil Bagirov ◽  
Bo Huang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Activity ◽  
Resting State ◽  
Cell Types ◽  
Functional Connections ◽  
Disease States ◽  
Highly Active ◽  
Artificial Neuronal Networks ◽  
Organizational Principle ◽  
Human Brains

Active neurons impact cell types with which they are functionally connected. Both activity and functional connectivity are heterogeneous across the brain, but the nature of their relationship is not known. Here we employ brain-wide calcium imaging at cellular resolution in larval zebrafish to record spontaneous activity of >12,000 neurons in the forebrain. By classifying their activity and functional connectivity into three levels (high, medium, low), we find that highly active neurons have low functional connections and highly connected neurons are of low activity. Intriguingly, deploying the same analytical methods on functional magnetic resonance imaging (fMRI) data from the resting state human brain, we uncover a similar relationship between activity and functional connectivity, that is, regions of high activity are non-overlapping with those of high connectivity. These findings reveal a previously unknown and evolutionarily conserved brain organizational principle that have implications for understanding disease states and designing artificial neuronal networks.

scholarly journals Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Connectivity Pattern ◽  
Head Motion ◽  
Connectivity Matrix ◽  
Normal Brain ◽  
Functional Connections ◽  
Human Connectome Project

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

scholarly journals Gradients of connectivity distance in the cerebral cortex of the macaque monkey

2018 ◽  
Vol 224 (2) ◽  
pp. 925-935 ◽  
Author(s):  
Sabine Oligschläger ◽  
Ting Xu ◽  
Blazej M. Baczkowski ◽  
Marcel Falkiewicz ◽  
Arnaud Falchier ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Macaque Monkey ◽  
Connectivity Distance

scholarly journals Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity

2020 ◽  
Vol 46 (5) ◽  
pp. 1210-1218 ◽  
Author(s):  
Yujiro Yoshihara ◽  
Giuseppe Lisi ◽  
Noriaki Yahata ◽  
Junya Fujino ◽  
Yukiko Matsumoto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
University Hospital ◽  
First Episode ◽  
Resting State Functional Connectivity ◽  
Functional Connections ◽  
The Relationship

Abstract Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier’s functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

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