scholarly journals Assessment of Disrupted Brain Structural Connectome in Depressive Patients With Suicidal Ideation Using Generalized Q-Sampling MRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Vincent Chin-Hung Chen ◽  
Chun-Ju Kao ◽  
Yuan-Hsiung Tsai ◽  
Man Teng Cheok ◽  
Roger S. McIntyre ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Suicidal Ideation ◽  
Theoretical Analysis ◽  
Brain Structures ◽  
Healthy Controls ◽  
Global Integration ◽  
Structural Variations ◽  
Topological Parameters ◽  
Graph Theoretical Analysis ◽  
Depressed Patients

Suicide is one of the leading causes of mortality worldwide. Various factors could lead to suicidal ideation (SI), while depression is the predominant cause among all mental disorders. Studies have shown that alterations in brain structures and networks may be highly associated with suicidality. This study investigated both neurological structural variations and network alterations in depressed patients with suicidal ideation by using generalized q-sampling imaging (GQI) and Graph Theoretical Analysis (GTA). This study recruited 155 participants and divided them into three groups: 44 depressed patients with suicidal ideation (SI+; 20 males and 24 females with mean age = 42, SD = 12), 56 depressed patients without suicidal ideation (Depressed; 24 males and 32 females with mean age = 45, SD = 11) and 55 healthy controls (HC; nine males and 46 females with mean age = 39, SD = 11). Both the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values were evaluated in a voxel-based statistical analysis by GQI. We analyzed different topological parameters in the graph theoretical analysis and the subnetwork interconnections in the Network-based Statistical (NBS) analysis. In the voxel-based statistical analysis, both the GFA and NQA values in the SI+ group were generally lower than those in the Depressed and HC groups in the corpus callosum and cingulate gyrus. Furthermore, we found that the SI+ group demonstrated higher global integration and lower local segregation among the three groups of participants. In the network-based statistical analysis, we discovered that the SI+ group had stronger connections of subnetworks in the frontal lobe than the HC group. We found significant structural differences in depressed patients with suicidal ideation compared to depressed patients without suicidal ideation and healthy controls and we also found several network alterations among these groups of participants, which indicated that white matter integrity and network alterations are associated with patients with depression as well as suicidal ideation.

scholarly journals Mapping Brain Microstructure and Network Alterations in Depressive Patients with Suicide Attempts Using Generalized Q-Sampling MRI

2021 ◽  
Vol 11 (3) ◽  
pp. 174
Author(s):  
Vincent Chin-Hung Chen ◽  
Chun-Ju Kao ◽  
Yuan-Hsiung Tsai ◽  
Roger S. McIntyre ◽  
Jun-Cheng Weng
Keyword(s):  
Statistical Analysis ◽  
White Matter ◽  
Theoretical Analysis ◽  
Depressive Disorder ◽  
Suicide Attempts ◽  
Healthy Controls ◽  
Graph Theoretical Analysis ◽  
Depressed Patients ◽  
Structural Differences ◽  
History Of

Depressive disorder is one of the leading causes of disability worldwide, with a high prevalence and chronic course. Depressive disorder carries an increased risk of suicide. Alterations in brain structure and networks may play an important role in suicidality among depressed patients. Diffusion magnetic resonance imaging (MRI) is a noninvasive method to map white-matter fiber orientations and provide quantitative parameters. This study investigated the neurological structural differences and network alterations in depressed patients with suicide attempts by using generalized q-sampling imaging (GQI). Our study recruited 155 participants and assigned them into three groups: 44 depressed patients with a history of suicide attempts (SA), 56 depressed patients without a history of suicide attempts (D) and 55 healthy controls (HC). We used the GQI to analyze the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values in voxel-based statistical analysis, topological parameters in graph theoretical analysis and subnetwork connectivity in network-based statistical analysis. GFA indicates the measurement of neural anisotropy and represents white-matter integrity; NQA indicates the amount of anisotropic spins that diffuse along fiber orientations and represents white-matter compactness. In the voxel-based statistical analysis, we found lower GFA and NQA values in the SA group than in the D and HC groups and lower GFA and NQA values in the D group than in the HC group. In the graph theoretical analysis, the SA group demonstrated higher local segregation and lower global integration among the three groups. In the network-based statistical analysis, the SA group showed stronger subnetwork connections in the frontal and parietal lobes, and the D group showed stronger subnetwork connections in the parietal lobe than the HC group. Alternations were found in the structural differences and network measurements in healthy controls and depressed patients with and without a history of suicide attempt.

scholarly journals Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes?

2019 ◽  
Author(s):  
Oren Civier ◽  
Robert Elton Smith ◽  
Chun-Hung Yeh ◽  
Alan Connelly ◽  
Fernando Calamante
Keyword(s):  
Statistical Analysis ◽  
Theoretical Analysis ◽  
Threshold Level ◽  
Brain Organization ◽  
Graph Theoretical Analysis ◽  
Human Connectome Project ◽  
Significant Difference ◽  
Topological Measures ◽  
Using Data ◽  
The Stability

ABSTRACTRecent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs’ weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest~70-90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies.Declarations of interestnone.

Graph theoretical analysis of tau burden and the functional connectome in Alzheimers disease

2019 ◽  
Author(s):  
A Weller ◽  
GN Bischof ◽  
P Schlüter ◽  
N Richter ◽  
J Kukolja ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Alzheimers Disease ◽  
Graph Theoretical Analysis ◽  
Functional Connectome

scholarly journals Phosphodiesterase 8A to discriminate in blood samples depressed patients and suicide attempters from healthy controls based on A-to-I RNA editing modifications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Salvetat ◽  
Fabrice Chimienti ◽  
Christopher Cayzac ◽  
Benjamin Dubuc ◽  
Francisco Checa-Robles ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Rna Editing ◽  
Whole Blood ◽  
Healthy Controls ◽  
Major Depressive ◽  
Suicide Attempters ◽  
Blood Samples ◽  
Depressed Patients ◽  
The Brain

AbstractMental health issues, including major depressive disorder, which can lead to suicidal behavior, are considered by the World Health Organization as a major threat to global health. Alterations in neurotransmitter signaling, e.g., serotonin and glutamate, or inflammatory response have been linked to both MDD and suicide. Phosphodiesterase 8A (PDE8A) gene expression is significantly decreased in the temporal cortex of major depressive disorder (MDD) patients. PDE8A specifically hydrolyzes adenosine 3′,5′-cyclic monophosphate (cAMP), which is a key second messenger involved in inflammation, cognition, and chronic antidepressant treatment. Moreover, alterations of RNA editing in PDE8A mRNA has been described in the brain of depressed suicide decedents. Here, we investigated PDE8A A-to-I RNA editing-related modifications in whole blood of depressed patients and suicide attempters compared to age-matched and sex-matched healthy controls. We report significant alterations of RNA editing of PDE8A in the blood of depressed patients and suicide attempters with major depression, for which the suicide attempt took place during the last month before sample collection. The reported RNA editing modifications in whole blood were similar to the changes observed in the brain of suicide decedents. Furthermore, analysis and combinations of different edited isoforms allowed us to discriminate between suicide attempters and control groups. Altogether, our results identify PDE8A as an immune response-related marker whose RNA editing modifications translate from brain to blood, suggesting that monitoring RNA editing in PDE8A in blood samples could help to evaluate depressive state and suicide risk.

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