Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

Pan, Nanfang; Wang, Song; Qin, Kun; Li, Lei; Chen, Ying; Zhang, Xun; Lai, Han; Suo, Xueling; Long, Yajing; Yu, Yifan; Ji, Shiyu; Raduà, Joaquim; Sweeney, John A.; Gong, Qiyong

doi:10.1016/j.bpsc.2022.06.003

Cited by 12 publications

(19 citation statements)

References 92 publications

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Section: Discussionmentioning

confidence: 99%

“…The DMN is also involved in affective empathy (Göttlich, Ye, Rodriguez-Fornells, Münte, & Krämer, 2017), which indicates the simulation of others' emotional experiences that resulted in empathic distress (Ashar, Andrews-Hanna, Dimidjian, & Wager, 2017). The execution of DMN during rest allows to construct discrete emotions of negative valence by conceptualizing affective sensations in the context of preceded experience (Pan et al, 2022;Satpute & Lindquist, 2019), and the deprivation of temporal coherence in DMN may impair the processing of generalizing across heterogeneous emotion features (Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012;Suo et al, 2022), all of which may further lead to distress symptoms. Specially, our findings of provincial within-DMN connectivity in the connectome demonstrated that the disruption of intrinsic synchronous coordination of DMN may be the predictors of subsequent pandemic-related distress, which is aligned with previous research studies that indicated its critical role as predictive biomarkers in individuals with major depressive disorder and posttraumatic stress disorder (Korgaonkar, Goldstein-Piekarski, Fornito, & Williams, 2020;Patriat, Birn, Keding, & Herringa, 2016).…”

Section: Connectome In the Network Levelmentioning

confidence: 99%

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Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

Pan

Qin

et al. 2022

Psychol. Med.

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Background Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults. Methods Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses. Results The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress. Conclusions Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

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Section: Discussionmentioning

confidence: 99%

Section: Connectome In the Network Levelmentioning

confidence: 99%

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

Pan

Qin

et al. 2022

Psychol. Med.

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“…A total of 151 healthy, right-handed Chinese university students (74/77 male/female, age 19–27 years), reporting no history of psychiatric or neurological diseases, participated in this study, which was part of a large ongoing project investigating the relation between brain and mental health [ 25 , 45 – 47 ]. All 151 participants underwent MRI scanning and completed paper-based questionnaires between October 2019 and January 2020 (T1, prior to the declaration of emergency state and city lockdown in China due to COVID-19).…”

Section: Methodsmentioning

confidence: 99%

“…To characterize the clusters identified from FCD/RSFC-behavior correlation analyses at a large-scale network level, we overlaid them onto 7 core networks [ 70 ]: default mode network [DMN], dorsal attention network [DAN], central executive network [CEN], affective network [AFN], somatomotor network [SMN], ventral attention network [VAN], and visual network [VN]; for details see https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011 . We evaluated the relative distribution (i.e., the number of overlapping voxels within a network divided by the identified cluster) to measure the similarity of the identified region to the large-scale networks [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

Psychological resilience mediates the protective role of default-mode network functional connectivity against COVID-19 vicarious traumatization

et al. 2023

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Vicarious traumatization (VT), a negative reaction to witnessing others’ trauma, has been experienced by some people during the COVID-19 pandemic, and can lead to mental health problems. This study aimed to identify functional brain markers of COVID-specific VT and explore the psychological mechanism underlying the brain-VT link. One hundred healthy participants underwent resting-state functional magnetic resonance imaging before the pandemic (October 2019–January 2020) and completed VT measurement during the pandemic (February–April 2020). Whole-brain correlation analysis based on global functional connectivity density (FCD) mapping revealed that VT was negatively correlated with FCD in the right inferior temporal gyrus (ITG) (i.e., the lower FCD in ITG, the worse the VT), identified by mapping onto known large-scale networks as part of the default-mode network (DMN). Resting-state functional connectivity (RSFC) analysis using ITG as seed found that VT was predicted by lower functional connectivity between ITG and other DMN regions including left medial prefrontal cortex, left orbitofrontal cortex, right superior frontal gyrus, right inferior parietal lobule and bilateral precuneus (i.e., the lower the ITG-DMN connectivity, the worse the VT). Mediation analyses suggested that psychological resilience served as a mediator in these associations of ITG FCD and ITG-DMN RSFC with VT. Our results provide novel evidence on the brain basis of VT and emphasize psychological resilience as an important link from DMN functional connectivity to COVID-specific-VT. This may facilitate public health interventions by helping identify individuals at risk of stress- and trauma-related psychopathologies.

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“…2 of 31 cortex (ACC) [10], bilateral amygdale and right parahippocampal gyrus [11,12]. On the other hand, they also showed an increase of grey matter volume and density in the bilateral praecuneus, right medium / paracingulate gyrus and posterior cingulate gyrus [7].…”

Section: Introductionmentioning

confidence: 99%

Two Grey–White Matter Circuits Separate Borderline Personality Disorder From Controls and Mediate the Relationship Between Specific Childhood Traumas and Symptoms. A mCCA+jICA and Random Forest Approach

Grecucci¹,

Dadomo²,

Lapomarda³

et al. 2023

Preprint

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Borderline Personality Disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study we applied for the first time an unsupervised machine learning approach known as mCCA+jICA, in combination with a supervised machine learning approach known as Random Forest, to possibly find covarying GM-WM circuits that separate BPD from controls and that are also predictive of this diagnosis. To this aim, we analyzed the structural images of patients with BPD and matched HCs. Results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in GM and WM circuits related to early traumatic experiences and specific symptoms.

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Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

Cited by 12 publications

References 92 publications

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

Psychological resilience mediates the protective role of default-mode network functional connectivity against COVID-19 vicarious traumatization

Two Grey–White Matter Circuits Separate Borderline Personality Disorder From Controls and Mediate the Relationship Between Specific Childhood Traumas and Symptoms. A mCCA+jICA and Random Forest Approach

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