scholarly journals Importance of self-connections for brain connectivity and spectral connectomics

2020 ◽  
Vol 114 (6) ◽  
pp. 643-651
Author(s):  
Xiao Gao ◽  
P. A. Robinson

AbstractSpectral analysis and neural field theory are used to investigate the role of local connections in brain connectivity matrices (CMs) that quantify connectivity between pairs of discretized brain regions. This work investigates how the common procedure of omitting such self-connections (i.e., the diagonal elements of CMs) in published studies of brain connectivity affects the properties of functional CMs (fCMs) and the mutually consistent effective CMs (eCMs) that correspond to them. It is shown that retention of self-connections in the fCM calculated from two-point activity covariances is essential for the fCM to be a true covariance matrix, to enable correct inference of the direct total eCMs from the fCM, and to ensure their compatibility with it; the deCM and teCM represent the strengths of direct connections and all connections between points, respectively. When self-connections are retained, inferred eCMs are found to have net inhibitory self-connections that represent the local inhibition needed to balance excitation via white matter fibers at longer ranges. This inference of spatially unresolved connectivity exemplifies the power of spectral connectivity methods, which also enable transformation of CMs to compact diagonal forms that allow accurate approximation of the fCM and total eCM in terms of just a few modes, rather than the full $$N^2$$ N 2 CM entries for connections between N brain regions. It is found that omission of fCM self-connections affects both local and long-range connections in eCMs, so they cannot be omitted even when studying the large-scale. Moreover, retention of local connections enables inference of subgrid short-range inhibitory connectivity. The results are verified and illustrated using the NKI-Rockland dataset from the University of Southern California Multimodal Connectivity Database. Deletion of self-connections is common in the field; this does not affect case-control studies but the present results imply that such fCMs must have self-connections restored before eCMs can be inferred from them.

2021 ◽  
Author(s):  
Mite Mijalkov ◽  
Giovanni Volpe ◽  
Joana B. Pereira

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by topological changes in large-scale functional brain networks. These networks are commonly analysed using undirected correlations between the activation signals of brain regions. However, this approach suffers from an important drawback: it assumes that brain regions get activated at the same time, despite previous evidence showing that brain activation features causality, with signals being typically generated in one region and then propagated to other ones. Thus, in order to address this limitation, in this study we developed a new method to assess whole-brain directed functional connectivity in patients with PD and healthy controls using anti-symmetric delayed correlations, which capture better this underlying causality. To test the potential of this new method, we compared it to standard connectivity analyses based on undirected correlations. Our results show that whole-brain directed connectivity identifies widespread changes in the functional networks of PD patients compared to controls, in contrast to undirected methods. These changes are characterized by increased global efficiency, clustering and transitivity as well as lower modularity. In addition, changes in the directed connectivity patterns in the precuneus, thalamus and superior frontal gyrus were associated with motor, executive and memory deficits in PD patients. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network changes occurring in PD compared to standard methods. This opens new opportunities for the analysis of brain connectivity and the development of new brain connectivity markers to track PD progression.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi135-vi135
Author(s):  
Ayan Mandal ◽  
Rafael Romero-Garcia ◽  
Jakob Seidlitz ◽  
Michael Hart ◽  
Aaron Alexander-Bloch ◽  
...  

Abstract Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers, and large-scale connectivity networks. Using lesion data from a total of 410 patients with high- and low-grade glioma, we identified – and replicated in an independent sample – three lesion covariance networks (LCNs), which reflect clusters of frequent glioma localization. These three LCNs overlapped with the anterior, posterior, and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal, and temporal cortices. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly coincided with multiple structural and functional connectivity networks, with LCN1 bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each LCN. These results build upon prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Cumulatively, our findings support a model wherein periventricular brain connectivity guides glioma development from the subventricular zone into distributed brain regions.


2020 ◽  
Vol 6 (5) ◽  
pp. eaay2739 ◽  
Author(s):  
Gabriel Castrillon ◽  
Nico Sollmann ◽  
Katarzyna Kurcyus ◽  
Adeel Razi ◽  
Sandro M. Krieg ◽  
...  

Transcranial magnetic stimulation (TMS) is a noninvasive method to modulate brain activity and behavior in humans. Still, stimulation effects substantially vary across studies and individuals, thereby restricting the large-scale application of TMS in research or clinical settings. We revealed that low-frequency stimulation had opposite impact on the functional connectivity of sensory and cognitive brain regions. Biophysical modeling then identified a neuronal mechanism underlying these region-specific effects. Stimulation of the frontal cortex decreased local inhibition and disrupted feedforward and feedback connections. Conversely, identical stimulation increased local inhibition and enhanced forward signaling in the occipital cortex. Last, we identified functional integration as a macroscale network parameter to predict the region-specific effect of stimulation in individual subjects. In summary, we revealed how TMS modulation critically depends on the connectivity profile of target regions and propose an imaging marker to improve sensitivity of noninvasive brain stimulation for research and clinical applications.


2020 ◽  
Author(s):  
ABID Y QURESHI ◽  
Jared A. Nielsen ◽  
Jorge Sepulcre

Abstract Background: The study of autism has been confounded by genetic and etiologic heterogeneity. The current study utilized a genetics-first approach to investigate the underlying neurobiology of autism by studying individuals with copy number variation at 16p11.2. Our aim was to investigate the prevailing theories of brain connectivity in autism – specifically, in regard to (1) distributed brain networks, (2) local and distant connectivity, and (3) functional lateralization. Methods: We analyzed resting-state functional connectivity MRI acquired in 26 carriers of a 16p11.2 deletion and 42 age-matched control participants. We also compared the functional connectivity metrics with measures of language ability, IQ, and social behavior.Results: We do not find widespread disruption of canonical large-scale networks in the deletion carriers. Nor do we find quantitative differences in the degree of local and distant connections. Instead, we discover unique connections in 16p11.2 deletion carriers that are not present in the control participants. Specifically, functional coupling between auditory cortex and regions of the default network is present only in the deletion carriers. In addition to the topographic shifts in functional connectivity, we observe reduced right hemispheric lateralization in the deletion carriers and less left hemispheric lateralization in individuals with poorer language ability.Conclusions: Links or connections between primary sensory areas and higher-order association areas violate fundamental large-scale circuit properties by functionally connecting brain regions specialized in local (hierarchical) processing with those that specialize in distant (parallel) processing. Aberrant hemispheric lateralization and connections between auditory and default networks may underlie difficulties with language and social interactions.


2013 ◽  
Vol 639-640 ◽  
pp. 1175-1179
Author(s):  
Yun Lei Fan ◽  
Yan Xiao ◽  
Yu Rong Guo ◽  
Tao Yuan

A networked structural laboratories system for evaluating the seismic performance of large-scale structure systems by seamlessly integrating geographically distributed experimental and computational substructures into a single test is described in this paper. It consists of an independent network based communication platform and various applications. The effectiveness of the proposed system is demonstrated by remote hybrid tests of a six-span bridge system at the Hunan University, Harbin Institute of Technology, Tsinghua University, China and the University of Southern California, USA. Successful application shows the system enables the shared use of testing resources by integrating single structural laboratories into a powerful and networked laboratory with advanced capability.


2021 ◽  
pp. 1-16
Author(s):  
Bridget M. Bertoldi ◽  
Emily R. Perkins ◽  
Catherine Tuvblad ◽  
Sofi Oskarsson ◽  
Mark D. Kramer ◽  
...  

Abstract The triarchic model was advanced as an integrative, trait-based framework for investigating psychopathy using different assessment methods and across developmental periods. Recent research has shown that the triarchic traits of boldness, meanness, and disinhibition can be operationalized effectively in youth, but longitudinal research is needed to realize the model's potential to advance developmental understanding of psychopathy. We report on the creation and validation of scale measures of the triarchic traits using questionnaire items available in the University of Southern California Risk Factors for Antisocial Behavior (RFAB) project, a large-scale longitudinal study of the development of antisocial behavior that includes measures from multiple modalities (self-report, informant rating, clinical-diagnostic, task-behavioral, physiological). Using a construct-rating and psychometric refinement approach, we developed triarchic scales that showed acceptable reliability, expected intercorrelations, and good temporal stability. The scales showed theory-consistent relations with external criteria including measures of psychopathy, internalizing/externalizing psychopathology, antisocial behavior, and substance use. Findings demonstrate the viability of measuring triarchic traits in the RFAB sample, extend the known nomological network of these traits into the developmental realm, and provide a foundation for follow-up studies examining the etiology of psychopathic traits and their relations with multimodal measures of cognitive-affective function and proneness to clinical problems.


1981 ◽  
Vol 24 (1) ◽  
pp. 151-151
Author(s):  
Lillian Glass ◽  
Sharon R. Garber ◽  
T. Michael Speidel ◽  
Gerald M. Siegel ◽  
Edward Miller

An omission in the Table of Contents, December JSHR, has occurred. Lillian Glass, Ph.D., at the University of Southern California School of Medicine and School of Dentistry, was a co-author of the article "The Effects of Presentation on Noise and Dental Appliances on Speech" along with Sharon R. Garber, T. Michael Speidel, Gerald M. Siegel, and Edward Miller of the University of Minnesota, Minneapolis.


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