Resting-state functional network models for posttraumatic stress disorder

Tu, Joseph

doi:10.1152/jn.00705.2020

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Assuming that x, y 􏼈 􏼉 and z { } represent the input and output vectors of a two-input single-output generalized network, respectively [17,18], then the output of the generalized network can be expressed as follows:…”

Section: Two-input Single-output Generalized Network Modelmentioning

confidence: 99%

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Liu

Guo

2022

Mathematical Problems in Engineering

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Personalized interactive image recommendation has several issues, such as being slow or having poor recommendation quality. Therefore, we propose an image personalized recommendation algorithm (IPRA) using deep learning to improve the time and quality of personalized interactive image recommendations. First, the feature subimage is obtained and converted into a one-dimensional vector using the convolution neural network model. Single input and single output functional and dual input and single output generalized functional network model are integrated into the model to improve the learning ability of nonlinear mapping and avoid overfitting during the training process; second, a one-dimensional vector is clustered using the fuzzy k-means approach and then translated into hyperbolic space; Finally, the Poincare map model is used to map the updated vector, the transformed vector is mapped using the PM model, and the image information is fed back to the two-dimensional plane, and the image recommendation set is formed based on the ranking of similarity, and the visual recommendation is presented to the user. The results show that the size of the convolution kernel is 2 × 2, and the image one-dimensional vector clustering can be better completed. The optimal value of F1 is 0.92, and the optimal value of average time is 11 s. The image recommendation quality is better, and the image recommendation can be formed according to the photographic similarity, which has good application value.

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Section: Two-input Single-output Generalized Network Modelmentioning

confidence: 99%

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Liu

Guo

2022

Mathematical Problems in Engineering

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“…Graph theory models leveraging rs-fMRI data provide a comprehensive set of quantitative measures including network centrality that can be used to investigate global (network-wide) and local (network-specific) aspects of neural connectivity. For example, among participants with PTSD, the influence that a specific brain region has on system-wide information flow and integration as measured using neural centrality has previously been found to be reduced in hierarchical brain networks [ 16 – 18 ]. Centrality metric spatially characterizes the connectivity and contribution of each single brain region in dynamic network processes captured by function MRI data [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

et al. 2023

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World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have a high prevalence of chronic WTC-related post-traumatic stress disorder (WTC-PTSD). We investigated neural mechanisms underlying WTC-PTSD by applying eigenvector centrality (EC) metrics and data-driven methods on resting state functional magnetic resonance (fMRI). We identified how EC differences relate to WTC-exposure and behavioral symptoms. We found that connectivity differentiated significantly between WTC-PTSD and non-PTSD responders in nine brain regions, as these differences allowed an effective discrimination of PTSD and non-PTSD responders based solely on analysis of resting state data. Further, we found that WTC exposure duration (months on site) moderates the association between PTSD and EC values in two of the nine brain regions; the right anterior parahippocampal gyrus and the left amygdala (p = 0.010; p = 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, a dimensional measure of symptom severity was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Functional neuroimaging can provide effective tools to identify neural correlates of diagnostic and dimensional indicators of PTSD.

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“…Among PTSD subjects, centrality was significantly reduced in hierarchical brain networks. [15][16][17] Here, for the first time, we used centrality to better understand neural mechanisms underlying WTC-PTSD and to identify selective shifts in local brain areas that are associated with the WTC-exposure. Identification of local changes in neural activation among WTC responders with PTSD is crucial to advance intervention and may guide treatments protocols such as non-invasive brain stimulation, to modulate brain activity and elicit behavioral changes [18][19][20][21] .…”

mentioning

confidence: 99%

“…WTC-PTSD status was considered 'present' if criteria were reported in the past 3 months at the time of the interview 60 . PTSD symptom subdomains were measured using continuous subscales calculated using reported symptom severity in the SCID-IV for the following symptom domains: re-experiencing, avoidance, hyperarousal, and negative thoughts symptoms (scores ranging from , , , [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], respectively). Major depressive disorder (MDD) was assessed using the SCID-IV and the presence or absence of current (i.e., active in the past month) MDD diagnosis was determined.…”

mentioning

confidence: 99%

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

Invernizzi

Rechtman

Curtin

et al. 2022

Preprint

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World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have higher prevalence (23%) of persistent, clinically significant WTC-related post-traumatic stress disorder (WTC-PTSD). Here, we applied eigenvector centrality (EC) metrics and data driven methods on resting state functional magnetic resonance (fMRI) outcomes to investigate neural mechanisms underlying WTC-PTSD and to identify how EC shifts in brain areas relate to WTC-exposure and behavioral symptoms. Nine brain areas differed significantly and contributed the most to differentiate functional neuro-profiles between WTC-PTSD and non-PTSD responders. The association between WTC-exposure and EC values differed significantly between WTC-PTSD and non-PTSD in the right anterior parahippocampal gyrus and left amygdala (p= 0.010; p= 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, the index of PTSD symptoms was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Our understanding of functional changes in neural mechanisms underlying WTC-related PTSD is key to advance intervention and treatment.

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Resting-state functional network models for posttraumatic stress disorder

Cited by 5 publications

References 12 publications

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

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