Analysis of brain connectivity patterns in autistic children during watching emotional faces

2016 ◽  
Author(s):  
Vida Mehdizadehfar ◽  
Farnaz Ghassemi ◽  
Ali Fallah
Keyword(s):  
Brain Connectivity ◽  
Autistic Children ◽  
Emotional Faces ◽  
Connectivity Patterns

Intrinsic functional brain connectivity patterns underlying enhanced interoceptive sensibility

2020 ◽  
Vol 276 ◽  
pp. 804-814 ◽  
Author(s):  
Xiaoqin Wang ◽  
Yafei Tan ◽  
Omer Van den Bergh ◽  
Andreas von Leupoldt ◽  
Jiang Qiu
Keyword(s):  
Brain Connectivity ◽  
Functional Brain ◽  
Connectivity Patterns

Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory

2007 ◽  
Vol 44 (6) ◽  
pp. 880-893 ◽  
Author(s):  
L. Astolfi ◽  
F. De Vico Fallani ◽  
F. Cincotti ◽  
D. Mattia ◽  
M. G. Marciani ◽  
...  
Keyword(s):  
Graph Theory ◽  
High Resolution ◽  
Brain Connectivity ◽  
Functional Brain ◽  
Connectivity Patterns ◽  
High Resolution Eeg

scholarly journals Abnormal Brain Connectivity Patterns in Adults with ADHD: A Coherence Study

PLoS ONE ◽  
2012 ◽  
Vol 7 (9) ◽  
pp. e45671 ◽  
Author(s):  
João Ricardo Sato ◽  
Marcelo Queiroz Hoexter ◽  
Xavier Francisco Castellanos ◽  
Luis A. Rohde
Keyword(s):  
Brain Connectivity ◽  
Connectivity Patterns ◽  
Abnormal Brain

scholarly journals Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Emiliano Santarnecchi ◽  
Chiara Del Bianco ◽  
Isabella Sicilia ◽  
Davide Momi ◽  
Giorgio Di Lorenzo ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Default Mode Network ◽  
Age Of Onset ◽  
Brain Connectivity ◽  
Disruptive Effect ◽  
Default Mode ◽  
Connectivity Patterns ◽  
The Impact

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients.

Structural ‘connectomic’ alterations in the limbic system of multiple sclerosis patients with major depression

2014 ◽  
Vol 21 (8) ◽  
pp. 1003-1012 ◽  
Author(s):  
Salvatore Nigro ◽  
Luca Passamonti ◽  
Roberta Riccelli ◽  
Nicola Toschi ◽  
Federico Rocca ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Major Depression ◽  
Diffusion Tensor ◽  
Negative Impact ◽  
Brain Connectivity ◽  
Strong Support ◽  
Imaging Data ◽  
Connectivity Patterns ◽  
The Right ◽  
Multiple Sclerosis Patients

Background: Major depression (MD) is a common psychiatric disorder in multiple sclerosis (MS). Despite the negative impact of MD on the quality of life of MS patients, little is known about its underlying brain mechanisms. Objective: We studied the whole-brain connectivity patterns that were associated with MD in MS. Alterations were mainly expected within limbic circuits. Methods: Diffusion tensor imaging data were collected in 20 MS patients with MD, 22 non-depressed MS patients and 16 healthy controls. We used deterministic tractography and graph analysis to study the white-matter connectivity patterns that characterized MS patients with MD. Results: We found that MD in MS was associated with increased local path length in the right hippocampus and right amygdala. Further analyses revealed that these effects were driven by an increased shortest distance between both the right hippocampus and right amygdala and a series of regions including the dorsolateral and ventrolateral prefrontal cortex, orbitofrontal cortex, sensory-motor cortices and supplementary motor area. Conclusion: Our data provide strong support for neurobiological accounts positing that MD in MS is mediated by abnormal ‘communications’ within limbic circuits. We also found evidence that MD in MS may be linked with connectivity alterations at the limbic-motor interface, a group of regions that translates emotions into survival-oriented behaviors.

scholarly journals How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
J. Toppi ◽  
F. De Vico Fallani ◽  
G. Vecchiato ◽  
A. G. Maglione ◽  
F. Cincotti ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Adjacency Matrix ◽  
Brain Connectivity ◽  
Small World ◽  
Connectivity Pattern ◽  
Edge Density ◽  
Statistical Validation ◽  
Common Procedure ◽  
Connectivity Patterns ◽  
Definition Of

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

scholarly journals Brain Connectivity Patterns in Children Linked to Neurocognitive Abilities

2020 ◽  
Author(s):  
Chandra Sripada ◽  
Mike Angstadt ◽  
Saige Rutherford ◽  
Aman Taxali ◽  
D. Angus Clark ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Spatial Reasoning ◽  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
General Factor ◽  
Multivariate Methods ◽  
Clinical Neuroscience ◽  
Psychological Traits ◽  
Connectivity Patterns ◽  
Objective Quantification

ABSTRACTThe development of objective brain-based measures of individual differences in psychological traits is a longstanding goal of clinical neuroscience. Here we show that reliable objective markers of children’s neurocognitive abilities can be built from measures of brain connectivity. The sample consists of 5,937 9- and 10-year-olds in the Adolescent Brain Cognitive Development multi-site study with high-quality functional connectomes that capture brain-wide connectivity. Using multivariate methods, we built predictive neuromarkers for a general factor of neurocognitive ability as well as for a number of specific cognitive abilities (e.g., spatial reasoning, working memory). Neuromarkers for the general neurocognitive factor successfully predicted scores for held-out participants at 19 out of 19 held-out sites, explaining over 14% of the variance in their scores. Neuromarkers for specific neurocognitive abilities also exhibited statistically reliable generalization to new participants. This study provides the strongest evidence to date that objective quantification of psychological traits is possible with functional neuroimaging.

scholarly journals Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

2021 ◽  
Vol 11 (9) ◽  
pp. 1167
Author(s):  
Victor B. Yang ◽  
Joseph R. Madsen
Keyword(s):  
Granger Causality ◽  
Epilepsy Surgery ◽  
Graph Algorithms ◽  
Rank Order ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Network Connectivity ◽  
Surgery Planning ◽  
Connectivity Patterns ◽  
Potential Applications

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

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