Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real-time fMRI neurofeedback

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

Background

At present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis.

Objectives

According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls).

Methods

We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors.

Results

All ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis.

Conclusion

In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

The Association between Default-mode Network Functional Connectivity and Childhood Trauma on the Symptom Load in Male Adults with Methamphetamine Use Disorder

Objective

: The relationship between adverse childhood experiences and methamphetamine use disorder (MUD) has been shown in previous studies; nevertheless, the underlying neural mechanisms remain elusive. Childhood trauma is associated with aberrant functional connectivity (FC) within the default-mode network (DMN). Furthermore, within the DMN, FC may contribute to impaired self-awareness in addiction, while cross-network FC is critical for relapse. We aimed to investigate whether childhood trauma was associated with DMN-related resting-state FC among healthy controls and patients with MUD and to examine whether DMN-related FC affected the effect of childhood trauma on the symptom load of MUD diagnosis.

Methods

: Twenty-seven male patients with MUD and 27 male healthy controls were enrolled and completed the Childhood Trauma Questionnaire. DMN-related resting-state FC was examined using functional magnetic resonance imaging.

Results

: There were 47.1% healthy controls and 66.7% MUD patients in this study with adverse childhood experiences. Negative correlations between adverse childhood experiences and within-DMN FC were observed in both healthy controls and MUD patients, while within-DMN FC was significantly altered in MUD patients. The detrimental effects of adverse childhood experiences on MUD patients may be attenuated through DMN-executive control networks (ECN) FC.

Conclusion

: Adverse childhood experiences were negatively associated with within-DMN FC in MUD patients and healthy controls. However, DMN-ECN FC may attenuate the effects of childhood trauma on symptoms load of MUD.

Sleep Deficits Inter-Link Lower Basal Forebrain-Posterior Cingulate Connectivity and Perceived Stress and Anxiety Bidirectionally in Young Men

BACKGROUND

The basal nucleus of Meynert (BNM), a primary source of cholinergic projections to the cortex, plays key roles in regulating the sleep-wake cycle and attention. Sleep deficit is associated with impairment in cognitive and emotional functions. However, whether or how cholinergic circuit, sleep, and cognitive/emotional dysfunction are inter-related remains unclear.

METHODS

We curated the Human Connectome Project data and explored BNM resting state functional connectivities (rsFC) in relation to sleep deficit, based on the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective reports of emotional states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated at a corrected threshold. We assessed the correlation between BNM rsFC, PSQI, and clinical measurements with Pearson regressions and their inter-relationships with mediation analyses.

RESULTS

In whole-brain regressions with age and alcohol use severity as covariates, men showed lower BNM rsFC with the posterior cingulate cortex (PCC) in correlation with PSQI score. No clusters were identified in women at the same threshold. Both BNM-PCC rsFC and PSQI score were significantly correlated with anxiety, perceived stress, and neuroticism scores in men. Moreover, mediation analyses showed that PSQI score mediated the relationship between BNM-PCC rsFC and these measures of negative emotions bidirectionally in men.

CONCLUSIONS

Sleep deficit is associated with negative emotions and lower BNM rsFC with the PCC. Negative emotional states and BNM-PCC rsFC are bidirectionally related through poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states.

Posttraumatic growth EEG neuromarkers: translational neural comparisons with resilience and PTSD in trauma-exposed healthy adults

Background: Supporting wellbeing beyond symptom reduction is necessary in trauma care. Research suggests increased posttraumatic growth (PTG) may promote wellbeing more effectively than posttraumatic stress disorder (PTSD) symptom reduction alone. Understanding neurobiological mechanisms of PTG would support PTG intervention development. However, most PTG research to-date has been cross-sectional data self-reported through surveys or interviews.Objective: Neural evidence of PTG and its coexistence with resilience and PTSD is limited. To advance neural PTG literature and contribute translational neuroscientific knowledge necessary to develop future objectively measurable neural-based PTG interventions.Method: Alpha frequency EEG and validated psychological inventories measuring PTG, resilience, and PTSD symptoms were collected from 30 trauma-exposed healthy adults amidst the COVID-19 pandemic. EEG data were collected using custom MNE-Python software, and a wireless OpenBCI 16-channel dry electrode EEG headset. Psychological inventory scores were analysed in SPSS Statistics and used to categorise the EEG data. Power spectral density analyses, t-tests and ANOVAs were conducted within EEGLab to identify brain activity differentiating high and low PTG, resilience, and PTSD symptoms.Results: Higher PTG was significantly differentiated from low PTG by higher alpha power in the left centro-temporal brain area around EEG electrode C3. A trend differentiating high PTG from PTSD was also indicated in this same location. Whole-scalp spectral topographies revealed alpha power EEG correlates of PTG, resilience and PTSD symptoms shared limited, but potentially meaningful similarities.Conclusion: This research provides the first comparative neural topographies of PTG, resilience and PTSD symptoms in the known literature. Results provide objective neural evidence supporting existing theory depicting PTG, resilience and PTSD as independent, yet co-occurring constructs. PTG neuromarker alpha C3 significantly delineated high from low PTG and warrants further investigation for potential clinical application. Findings provide foundation for future neural-based interventions and research for enhancing PTG in trauma-exposed individuals.

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

Structural equation modeling of treatment-related changes in neural connectivity for youth with PTSD

BACKGROUND

Previous studies suggest that improvement in symptoms of posttraumatic stress disorder (PTSD) is accompanied by changes in neural connectivity, however, few studies have investigated directional (effective) connectivity. The current study assesses treatment-related changes in effective connectivity in youth with PTSD undergoing Trauma-Focused Cognitive Behavioral Therapy (TF-CBT).

METHODS

Functional MRI scans before and after 16 weeks of TF-CBT for 20 youth with PTSD, or the same time interval for 20 healthy controls (HC) were included in the analysis. Structural equation modeling was used to model group differences in directional connectivity at baseline, and changes in connectivity from pre- to post-treatment.

RESULTS

At baseline, the PTSD group, relative to the HC group, had significantly greater connectivity in the path from dorsal cingulate to anterior cingulate and from dorsal cingulate to posterior cingulate corticies. From pre- to post-treatment, connectivity in these paths decreased significantly in the PTSD group, as did connectivity from right hippocampus to left superior temporal gyrus. Connectivity from the left amygdala to the lateral orbital frontal cortex was significantly lower in PTSD vs HC at baseline, but did not change from pre- to post-treatment.

CONCLUSION

Although based on a small sample, these results converge with previous studies in suggesting a central role for the dorsal cingulate cortex in PTSD symptoms. The direction of this connectivity suggests that the dorsal cingulate is the source of modulation of anterior and posterior cingulate cortex during trauma-focused cognitive behavioral therapy.

Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD

BACKGROUND

Alterations within large-scale brain networks-namely, the default mode (DMN) and salience networks (SN)-are present among individuals with posttraumatic stress disorder (PTSD). Previous real-time functional magnetic resonance imaging (fMRI) and electroencephalography neurofeedback studies suggest that regulating posterior cingulate cortex (PCC; the primary hub of the posterior DMN) activity may reduce PTSD symptoms and recalibrate altered network dynamics. However, PCC connectivity to the DMN and SN during PCC-targeted fMRI neurofeedback remains unexamined and may help to elucidate neurophysiological mechanisms through which these symptom improvements may occur.

METHODS

Using a trauma/emotion provocation paradigm, we investigated psychophysiological interactions over a single session of neurofeedback among PTSD (n = 14) and healthy control (n = 15) participants. We compared PCC functional connectivity between regulate (in which participants downregulated PCC activity) and view (in which participants did not exert regulatory control) conditions across the whole-brain as well as in a priori specified regions-of-interest.

RESULTS

During regulate as compared to view conditions, only the PTSD group showed significant PCC connectivity with anterior DMN (dmPFC, vmPFC) and SN (posterior insula) regions, whereas both groups displayed PCC connectivity with other posterior DMN areas (precuneus/cuneus). Additionally, as compared with controls, the PTSD group showed significantly greater PCC connectivity with the SN (amygdala) during regulate as compared to view conditions. Moreover, linear regression analyses revealed that during regulate as compared to view conditions, PCC connectivity to DMN and SN regions was positively correlated to psychiatric symptoms across all participants.

CONCLUSION

In summary, observations of PCC connectivity to the DMN and SN provide emerging evidence of neural mechanisms underlying PCC-targeted fMRI neurofeedback among individuals with PTSD. This supports the use of PCC-targeted neurofeedback as a means by which to recalibrate PTSD-associated alterations in neural connectivity within the DMN and SN, which together, may help to facilitate improved emotion regulation abilities in PTSD.

Electrophysiological signatures of resting state networks under new environmental conditions

It is assumed that cognitive processes are provided by the regulatory interactions of different brain networks. The three most stable resting state networks, among which the default mode network (DMN), the central executive network (CEN) and the salience network (SN) are considered to be the key neurocognitive networks for understanding higher cognitive functions. Peculiarities of changes in the connectivity of resting state networks of an individual entering a new environment and after a year of adaptation in this environment remain poorly studied. The aim of this study was to investigate the peculiarities of the connectivity of resting state networks calculated in EEG data in students right after moving to an unfamiliar environment and one year after moving. 128-channel EEGs were recorded in the resting state in 45 students (all men) aged from 18 to 29 years, who moved to the North region of Russia (Yakutsk, Republic of Sakha (Yakutia)). Resting state networks were calculated by the seed-based method. The subjects had increased SN connectivity with the sensorimotor cortex and the posterior node of DMN (posterior cingulate cortex and precuneus) in the condition when they were exposed to a new unfamiliar environment, compared to the condition after a year in the same environment. In general, the obtained data are consistent with the notion of increased SN functioning when encountering new significant stimuli and tasks, i.e. new environmental conditions, and the representation of SN as closely related to the function of homeostasis regulation according to organism's internal goals and environmental requirements.