Comparative Primate Connectomics

2018 ◽  
Vol 91 (3) ◽  
pp. 170-179 ◽  
Author(s):  
James K. Rilling ◽  
Martijn P. van den Heuvel
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Tool Use ◽  
False Negative ◽  
Structural Connectivity ◽  
Cell Counting ◽  
Primate Species ◽  
Future Research ◽  
Ecological Niches ◽  
Human Specific

A connectome is a comprehensive map of neural connections of a species nervous system. While recent work has begun comparing connectomes across a wide breadth of species, we present here a more detailed and specific comparison of connectomes across the primate order. Long-range connections are thought to improve communication efficiency and thus brain function but are costly in terms of energy and space utilization. Methods for measuring connectivity in the brain include measuring white matter volume, histological cell counting, anatomical tract tracing, diffusion-weighted imaging and tractography, and functional connectivity in MRI. Comparisons of global white matter connectivity suggest that larger primate brains are less well connected than smaller primate brains, but that humans have more connections than expected for our cortical neuron number, which may be concentrated in the prefrontal cortex. Although there is significant overlap in structural connectivity between humans and nonhuman primates, human-specific connections are found in cortical areas involved with language, imitation, and tool use. Similar to structural connectivity, there is also widespread overlap between humans and macaques in resting state functional connectivity. However, there are again a number of human-specific connections in cortical regions involved in language, tool use, and empathy. Comparative connectomics also offers the opportunity to detect specializations of connectivity in other primate species besides humans. Future research should capitalize on the ability of diffusion tractography to measure connectivity in postmortem brains that could expand the representation of species beyond humans, chimpanzees, and rhesus macaques, and facilitate identification of connectivity-based adaptations to different social and ecological niches. This work will require careful attention to establishing cortical homologies across species and to improving tractography methods to limit detection of false-positive and false-negative connections. Finally, it will be important to attempt to establish the functional significance of variation in connectivity profiles by examining how these covary with behavior and cognition both across and within species.

Altered Resting-State Functional and White Matter Tract Connectivity in Stroke Patients With Dysphagia

2013 ◽  
Vol 28 (3) ◽  
pp. 260-272 ◽  
Author(s):  
Shasha Li ◽  
Zhenxing Ma ◽  
Shipeng Tu ◽  
Muke Zhou ◽  
Sihan Chen ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Resting State ◽  
Structural Connectivity ◽  
White Matter Tract ◽  
Stroke Patients ◽  
Hemispheric Stroke ◽  
Swallowing Dysfunction ◽  
The Mean

Background. Swallowing dysfunction is intractable after acute stroke. Our understanding of the alterations in neural networks of patients with neurogenic dysphagia is still developing. Objective. The aim was to investigate cerebral cortical functional connectivity and subcortical structural connectivity related to swallowing in unilateral hemispheric stroke patients with dysphagia. Methods. We combined a resting-state functional connectivity with a white matter tract connectivity approach, recording 12 hemispheric stroke patients with dysphagia, 12 hemispheric stroke patients without dysphagia, and 12 healthy controls. Comparisons of the patterns in swallowing-related functional connectivity maps between patient groups and control subjects included ( a) seed-based functional connectivity maps calculated from the primary motor cortex (M1) and the supplementary motor area (SMA) to the entire brain, ( b) a swallowing-related functional connectivity network calculated among 20 specific regions of interest (ROIs), and ( c) structural connectivity described by the mean fractional anisotropy of fibers bound through the SMA and M1. Results. Stroke patients with dysphagia exhibited dysfunctional connectivity mainly in the sensorimotor-insula-putamen circuits based on seed-based analysis of the left and right M1 and SMA and decreased connectivity in the bilateral swallowing-related ROIs functional connectivity network. Additionally, white matter tract connectivity analysis revealed that the mean fractional anisotropy of the white matter tract was significantly reduced, especially in the left-to-right SMA and in the corticospinal tract. Conclusions. Our results indicate that dysphagia secondary to stroke is associated with disruptive functional and structural integrity in the large-scale brain networks involved in motor control, thus providing new insights into the neural remodeling associated with this disorder.

scholarly journals Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity

2021 ◽  
Author(s):  
Josh Neudorf ◽  
Shaylyn Kress ◽  
Ron Borowsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Functional Connectivity ◽  
Brain Research ◽  
Structural Connectivity ◽  
Model Performance ◽  
Future Research ◽  
Individual Level ◽  
Human Connectome Project ◽  
Deep Learning Model

AbstractAlthough functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

scholarly journals Using structural connectivity to augment community structure in EEG functional connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 761-787 ◽  
Author(s):  
Katharina Glomb ◽  
Emeline Mullier ◽  
Margherita Carboni ◽  
Maria Rubega ◽  
Giannarita Iannotti ◽  
...  
Keyword(s):  
Community Structure ◽  
White Matter ◽  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Source Analysis ◽  
Volume Conduction ◽  
Functional Relationships ◽  
The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

scholarly journals 3046 Reduced structural and functional connectivity in infants with prenatal opioid exposure

2019 ◽  
Vol 3 (s1) ◽  
pp. 52-52
Author(s):  
Stephanie Merhar ◽  
Adebayo Braimah ◽  
Traci Beiersdorfer ◽  
Brenda Poindexter ◽  
Nehal Parikh
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Behavioral Problems ◽  
Resting State ◽  
Maternal Education ◽  
Structural Connectivity ◽  
Resting State Functional Connectivity ◽  
Small Pilot Study ◽  
Sensorimotor Network

OBJECTIVES/SPECIFIC AIMS:. This study aims to understand the effects of prenatal opioid exposure on structural and functional connectivity in the neonatal brain. Our central hypothesis is that infants with prenatal opioid exposure will have decreased structural and functional connectivity as compared to non-exposed controls. Our overarching goal is to improve neurodevelopmental and behavioral outcomes in infants with prenatal opioid exposure. METHODS/STUDY POPULATION:. Infants with prenatal opioid exposure were recruited from 2 birth hospitals in our area. Control infants were recruited from the larger community. Infants underwent MRI between 4-6 weeks of age in the Cincinnati Children’s Hospital Imaging Research Center. MRI sequences included 3D structural T1 and T2-weighted imaging, resting state functional connectivity MRI, and multi-shell DTI (36 directions at b=800 and 68 directions at b=2000). Tract-based spatial statistics (TBSS) was used to identify differences in fractional anisotropy (a measure of white matter integrity) between groups. Group independent component analysis was used to identify differences in resting-state networks between groups RESULTS/ANTICIPATED RESULTS:. There were 5 subjects enrolled in the study with evaluable imaging, 3 infants with prenatal opioid exposure and 2 unexposed controls. Structural MRI was normal in all cases. Infants with prenatal opioid exposure had reduced structural connectivity as measured by fractional anisotropy (FA) in the genu and splenium of the corpus callosum as compared with controls. The orange/red color represents areas in which the FA of the opioid-exposed group was lower than controls and green represents the white matter skeleton common to both groups. Infants with prenatal opioid exposure also had significantly reduced within-network functional connectivity strength (z-transformed partial correlation coefficient 0.358 vs 0.199, p = 0.03) in the sensorimotor network as compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT:. In this small pilot study, both structural and functional connectivity were reduced in opioid-exposed infants compared with controls. This data suggests that differences in structural and functional connectivity may underlie the later developmental and behavioral problems seen in opioid-exposed children. These findings must be validated in a larger population with correction for confounding factors such as maternal education

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 Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study

2016 ◽  
Vol 46 (13) ◽  
pp. 2771-2783 ◽  
Author(s):  
C. Wang ◽  
F. Ji ◽  
Z. Hong ◽  
J. S. Poh ◽  
R. Krishnan ◽  
...  
Keyword(s):  
At Risk ◽  
White Matter ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Salience Network ◽  
White Matter Microstructure ◽  
Close Proximity ◽  
And Diffusion

BackgroundSalience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression.MethodWe examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis.ResultsARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years.ConclusionsOur findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.

scholarly journals New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

scholarly journals Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality Based on Structural Connectivity

2021 ◽  
Author(s):  
Josh Neudorf ◽  
Shaylyn Kress ◽  
Ron Borowsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Functional Connectivity ◽  
Brain Research ◽  
Structural Connectivity ◽  
Model Performance ◽  
Future Research ◽  
Ideal Model ◽  
Network Method ◽  
Deep Learning Model

AbstractAlthough functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. The most successful recent whole-brain methods have managed to account for 36% of the variance in functional connectivity based on structural connectivity. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 81% of the variance in functional connectivity, more than double that of the previous best model, and 99% of the variance in functional centrality. Regions of particular importance to the model’s performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

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

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