scholarly journals From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130529 ◽  
Author(s):  
Jinseop S. Kim ◽  
Marcus Kaiser
Keyword(s):  
Neural Networks ◽  
Caenorhabditis Elegans ◽  
Path Length ◽  
Clustering Coefficient ◽  
Neural Systems ◽  
Modular Organization ◽  
Characteristic Path Length ◽  
Fibre Tract ◽  
C Elegans ◽  
Diffusion Spectrum Imaging

The connectome, or the entire connectivity of a neural system represented by a network, ranges across various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether the connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of Caenorhabditis elegans and the fibre tract network of human brains obtained through diffusion spectrum imaging. We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of both C. elegans and human connectomes are higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution, and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in the C. elegans connectome or each region of interest in the human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared with the alternative arrangements. This implies that fewer genes are needed to encode for the organization of neural systems. While the first two findings show that the neural topologies are efficient in information processing, this suggests that they are also efficient from a developmental point of view. Together, these results show that neural systems are organized in such a way as to yield efficient features beyond those given by their modularity alone.

scholarly journals A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Cuibai Wei ◽  
Shuting Gong ◽  
Qi Zou ◽  
Wei Zhang ◽  
Xuechun Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Path Length ◽  
Brain Networks ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Glucose Intake ◽  
Item Scores ◽  
Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

scholarly journals Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Paul Y. Wang ◽  
Sandalika Sapra ◽  
Vivek Kurien George ◽  
Gabriel A. Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neural Systems ◽  
Graph Structure ◽  
Imaging Data ◽  
C Elegans ◽  
State Classification ◽  
Graph Neural Networks ◽  
Emergent Behaviors

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Disruption of Cortical Connectivity during Remifentanil Administration Is Associated with Cognitive Impairment but Not with Analgesia

2015 ◽  
Vol 122 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Ahmad Khodayari-Rostamabad ◽  
Søren S. Olesen ◽  
Carina Graversen ◽  
Lasse P. Malver ◽  
Geana P. Kurita ◽  
...  
Keyword(s):  
Reaction Time ◽  
Functional Connectivity ◽  
Path Length ◽  
Experimental Pain ◽  
Clustering Coefficient ◽  
Cognitive Test ◽  
Characteristic Path Length ◽  
Network Properties ◽  
Continuous Reaction ◽  
Continuous Reaction Time

Abstract Background: The authors investigated the effect of remifentanil administration on resting electroencephalography functional connectivity and its relationship to cognitive function and analgesia in healthy volunteers. Methods: Twenty-one healthy male adult subjects were enrolled in this placebo-controlled double-blind cross-over study. For each subject, 2.5 min of multichannel electroencephalography recording, a cognitive test of sustained attention (continuous reaction time), and experimental pain scores to bone-pressure and heat stimuli were collected before and after infusion of remifentanil or placebo. A coherence matrix was calculated from the electroencephalogram, and three graph-theoretical measures (characteristic path-length, mean clustering coefficient, and relative small-worldness) were extracted to characterize the overall cortical network properties. Results: Compared to placebo, most graph-theoretical measures were significantly altered by remifentanil at the alpha and low beta range (8 to 18 Hz; all P < 0.001). Taken together, these alterations were characterized by an increase in the characteristic path-length (alpha 17% and low beta range 24%) and corresponding decrements in mean clustering coefficient (low beta range −25%) and relative small-worldness (alpha −17% and low beta range −42%). Changes in characteristic path-lengths after remifentanil infusion were correlated to the continuous reaction time index (r = −0.57; P = 0.009), while no significant correlations between graph-theoretical measures and experimental pain tests were seen. Conclusions: Remifentanil disrupts the functional connectivity network properties of the electroencephalogram. The findings give new insight into how opioids interfere with the normal brain functions and have the potential to be biomarkers for the sedative effects of opioids in different clinical settings.

scholarly journals Interactions Between Aging and Alzheimer’s Disease on Structural Brain Networks

2021 ◽  
Vol 13 ◽  
Author(s):  
Zhanxiong Wu ◽  
Yunyuan Gao ◽  
Thomas Potter ◽  
Julia Benoit ◽  
Jian Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Path Length ◽  
Clustering Coefficient ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Structural Networks ◽  
Structural Brain

Normative aging and Alzheimer’s disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56–65, 66–75, and 71–85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

scholarly journals Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

2021 ◽  
Author(s):  
Yiran Wei ◽  
Chao Li ◽  
Zaixu Cui ◽  
Roxanne C. Mayrand ◽  
Jingjing Zou ◽  
...  
Keyword(s):  
Path Length ◽  
Clustering Coefficient ◽  
Survival Models ◽  
Characteristic Path Length ◽  
Network Construction ◽  
Topological Features ◽  
Structural Connectome ◽  
Newly Diagnosed Glioblastoma ◽  
Brain Parenchymal ◽  
Connectivity Strength

AbstractBackgroundGlioblastoma is characterized by extensive invasion into brain parenchymal tissue through white matter tracts. Systematically quantifying invasion, however, is limited by the conventional imaging tools, and could potentially be achieved by a structural connectome approach.MethodsTwo prospective patient cohorts of newly diagnosed glioblastoma were included for network construction. A fiber template was firstly derived by employing probabilistic tractography on healthy subjects. Through performing tract-based spatial statistics in patients and age-matched controls, the connectivity strength of each fiber was estimated in patients for network construction. Contrast-enhancing and non-enhancing tumors were segmented and overlaid to the network to identify connectome disruption in lesion and distant areas. The connectome disruption probabilities were calculated across all patients. Disruption indices and network topological features were examined using survival models.ResultsThe distant areas accounted for higher proportion of disruption than the contrast-enhancing tumor (16.8 ± 12.0% vs 5.8 ± 5.1%, P < 0.001). Compared to healthy controls, patient networks demonstrated lower clustering coefficient, but higher characteristic path length (each P < 0.001). Higher distant area disruption (HR = 1.43, P = 0.027) and characteristic path length (HR = 1.59, P = 0.031) were associated with worse survival, while higher clustering coefficient (HR = 0.59, P = 0.016) was associated with prolonged survival.ConclusionThe occult invasion in glioblastoma could be identified and quantified using structural connectome, which may confer benefits to precise patient management.

scholarly journals Functional Connectivity of EEG in Encephalitis during Slow Biphasic Complexes

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2978
Author(s):  
Giovanni Chiarion ◽  
Luca Mesin
Keyword(s):  
Functional Connectivity ◽  
Path Length ◽  
Inflammatory Diseases ◽  
Clustering Coefficient ◽  
Eeg Rhythms ◽  
Characteristic Path Length ◽  
Slowing Down ◽  
Eeg Functional Connectivity ◽  
Network Patterns ◽  
The Brain

The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients.

scholarly journals Shorter TR Combination with Finer Atlas Positively Modulate Topological Organization of Brain Networks: A Resting State fMRI Study

2020 ◽  
Author(s):  
Yan Zhang ◽  
QILI HU ◽  
Jiali Liang ◽  
Zhenghui Hu ◽  
Tianyi Qian ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Path Length ◽  
Brain Networks ◽  
Small World ◽  
Clustering Coefficient ◽  
High Temporal Resolution ◽  
Small World Networks ◽  
Planar Imaging ◽  
Characteristic Path Length ◽  
Topological Organization

Abstract BackgroundThe simultaneous multislice echo planar imaging technique can shorten the repetition time (TR) of blood oxygen level-dependent acquisition and thus acquires more information. However, little is known about the influence of higher temporal resolution on functional networks. Whether the topological organization of small-world networks is modulated in the multispectra at high temporal resolution is still unclear. Results: The network reconstruction based on the shorter TR and the finer atlas, showed significant (p<0.05, Bonferroni correction) increases in normalized clustering coefficient, small-worldness, clustering coefficient, local efficiency and global efficiency, and reductions in normalized characteristic path length and characteristic path length. ConclusionsThe shorter TR coupled with the finer atlas can positively modulate topological characteristics of brain networks. Although five multispectra present properties of small-world networks, the properties of the network in 0.082-0.1 Hz are weaker than those in 0.01-0.082 Hz. These findings provide new insights into the topological patterns of brain networks and have implications for the study of brain connectomes and their applications in brain disease.

scholarly journals Virtual Social Networks Online and Mobile Systems

2009 ◽  
Vol 5 (3) ◽  
pp. 233-253 ◽  
Author(s):  
Maytham Safar ◽  
Hussain Sawwan ◽  
Mahmoud Taha ◽  
Talal Al-Fadhli
Keyword(s):  
Social Networks ◽  
Mobile Technology ◽  
Path Length ◽  
Clustering Coefficient ◽  
Mobile Systems ◽  
Centrality Measures ◽  
The Internet ◽  
Characteristic Path Length ◽  
Mobile Industry ◽  
New Applications

Location-based applications are one of the most anticipated new segments of the mobile industry. These new applications are enabled by GPS-equipped phones (e.g., emergency applications, buddy finders, games, location-based advertising, etc.). These services are designed to give consumers instant access to personalized, local content of their immediate location. Some applications couple LBS with notification services, automatically alerting users when they are close to a pre-selected destination. With the advances in the Internet and communications/mobile technology, it became vital to analyze the effect of such technologies on human communications. This work studies how humans can construct social networks as a method for group communications using the available technologies. We constructed and analyzed a friends network using different parameters. The parameters that were calculated to analyze the network are the distribution sequence, characteristic path length, clustering coefficient and centrality measures. In addition, we built a PDA application that implements the concept of LBS using two system modules. In the first module, we have developed an application for entertainment purpose; an application program which enables end users to send their birth year and get their horoscope in return. The second part of the project was, to build an application, which helps people to stay in touch with their friends and family members (Find Friend). It helps users to find which of their buddies are within the same area they are in.

scholarly journals Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ke Song ◽  
Juan Li ◽  
Yuanqiang Zhu ◽  
Fang Ren ◽  
Lingcan Cao ◽  
...  
Keyword(s):  
Resting State ◽  
Path Length ◽  
Small World ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Control Group ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Brain Functional Connectivity ◽  
The Brain

Aim. This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory. Methods. A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t -test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis. Results. In the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes. Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, and cerebellum compared to the control group ( P < 0.05 ). Conclusion. Patients with ON show typical “small world” topology that differed from that detected in HC brain networks. The brain network in ON has a small-world attribute but shows reduced and abnormal connectivity compared to normal subjects and likely causes symptoms of cognitive impairment.

scholarly journals Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss

2021 ◽  
Vol 15 ◽  
Author(s):  
Yan Zou ◽  
Hui Ma ◽  
Bo Liu ◽  
Dan Li ◽  
Dingxi Liu ◽  
...  
Keyword(s):  
Hearing Loss ◽  
White Matter ◽  
Sensorineural Hearing Loss ◽  
Path Length ◽  
Sudden Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Clustering Coefficient ◽  
Healthy Controls ◽  
Characteristic Path Length ◽  
Topological Organization

Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p &lt; 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p &lt; 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.

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