scholarly journals Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

2018 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Nils Inge Landrø ◽  
Rune Jonassen ◽  
Tobias Kaufmann ◽  
Aldo Cordova-Palomera ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Total Sample ◽  
Individual Variability ◽  
Functional Brain Imaging ◽  
Depression Symptoms ◽  
Imaging Data ◽  
Functional Brain ◽  
Symptom Profiles ◽  
Connectivity Patterns

AbstractBackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.MethodsWe assessed symptoms of depression and anxiety using Beck’s Depression and Beck’s Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.ResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.ConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional 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 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.

scholarly journals Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia

2019 ◽  
Vol 13 ◽  
Author(s):  
Philippe Albouy ◽  
Anne Caclin ◽  
Sam V. Norman-Haignere ◽  
Yohana Lévêque ◽  
Isabelle Peretz ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Developmental Disorders ◽  
Functional Brain Imaging ◽  
Imaging Data ◽  
Functional Brain ◽  
Congenital Amusia ◽  
Brain Imaging Data

scholarly journals Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

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 Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

2022 ◽  
Vol 19 (1) ◽  
pp. 509
Author(s):  
Fangyuan Tian ◽  
Hongxia Li ◽  
Shuicheng Tian ◽  
Chenning Tian ◽  
Jiang Shao
Keyword(s):  
Functional Connectivity ◽  
Coal Mine ◽  
Brain Connectivity ◽  
Clustering Coefficient ◽  
Mine Safety ◽  
Functional Near Infrared Spectroscopy ◽  
Functional Brain ◽  
Unsafe Behavior ◽  
Brain Functional Connectivity ◽  
Mine Workers

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

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