Dataset Information

Mapping PTSD symptoms to brain networks: a machine learning study.

ABSTRACT: Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N?=?50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R²) for total PCL-5 score was 0.29 and the R² for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, -0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p?=?0.030) as well as intrusion and avoidance scores (p?=?0.002 and p?=?0.034). It was not able to predict cognition and arousal scores (p?=?0.412 and p?=?0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.

SUBMITTER: Zandvakili A

PROVIDER: S-EPMC7303205 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Mapping PTSD symptoms to brain networks: a machine learning study.

Zandvakili Amin A Barredo Jennifer J Swearingen Hannah R HR Aiken Emily M EM Berlow Yosef A YA Greenberg Benjamin D BD Carpenter Linda L LL Philip Noah S NS

Translational psychiatry 20200618 1

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = ...[more]

PMID: 32555146

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Project description:Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods.We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process.The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Dataset Information

Mapping PTSD symptoms to brain networks: a machine learning study.

Publications

Mapping PTSD symptoms to brain networks: a machine learning study.

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