Undergoing puberty ahead of peers (‘earlier pubertal timing’) is an important risk factor for mental health problems during early adolescence. The current study examined pathways between pubertal timing and mental health via connectivity of neural systems implicated in emotional reactivity and regulation (specifically corticolimbic connections) in 9- to 14-year-olds.

Research questions were examined in the Adolescent Brain Cognitive Development (ABCD) Study, a large population representative sample in the United States. Linear mixed models examined associations between pubertal timing and resting-state corticolimbic connectivity. Significant connections were examined as potential mediators of the relationship between pubertal timing and mental health (withdrawn depressed and rule-breaking) problems. Exploratory analyses interrogated whether the family environment moderated neural risk patterns in those undergoing puberty earlier than their peers.

Earlier pubertal timing was related to decreased connectivity between limbic structures (bilateral amygdala and right hippocampus) and the cingulo-opercular network, left amygdala and somatomotor (mouth) network, as well as between the left hippocampus and ventral attention network and visual network. Corticolimbic connections also mediated the relationship between earlier pubertal timing and increased withdrawn depressed problems (but not rule-breaking problems). Finally, parental acceptance buffered against connectivity patterns that were implicated in withdrawn depressed problems in those undergoing puberty earlier than their peers.

Findings highlight the role of decreased corticolimbic connectivity in mediating pathways between earlier pubertal timing and withdrawn depressed problems, and we present preliminary evidence that the family environment may buffer against these neural risk patterns during early adolescence.

Prior research has identified altered brain structure and function in individuals at risk for self-directed violence thoughts and behaviors. However, these studies have largely utilized healthy controls and findings have been inconsistent. Thus, this study examined differences in resting-state functional network connectivity among individuals with lifetime suicide attempt(s) v. lifetime self-directed violence thoughts alone.

Using data from the UK Biobank, this study utilized a series of linear regressions to compare individuals with lifetime suicide attempt(s) (n = 566) v. lifetime self-directed violence thoughts alone (n = 3447) on within- and between- network resting-state functional connectivity subnetworks.

There were no significant between-group differences for between-network, within-network, or whole-brain functional connectivity after adjusting for age, sex, ethnicity, and body mass index and performing statistical corrections for multiple comparisons. Resting-state network measures may not differentiate between individuals with lifetime suicide attempt(s) and lifetime self-directed violence thoughts alone.

Null findings diverge from results reported in smaller neuroimaging studies of suicide risk, but are consistent with null findings in other large-scale studies and meta-analyses. Strengths of the study include its large sample size and stringent control group. Future research on a wider array of imaging, genetic, and psychosocial risk factors can clarify relative contributions of individual and combined variables to suicide risk and inform scientific understanding of ideation-to-action framework.

Borderline personality disorder (BPD) is associated with altered activity in the prefrontal cortex (PFC) and amygdala, yet no studies have examined fronto-limbic circuitry in borderline adolescents and emerging adults. Here, we examined the contribution of fronto-limbic effective connectivity (EC) to the longitudinal stability of emotion-related impulsivity, a key feature of BPD, in symptomatic adolescents and young adults.

We compared resting-state EC in 82 adolescents and emerging adults with and without clinically significant borderline symptoms (n BPD = 40, ages 13–30). Group-specific directed networks were estimated amongst fronto-limbic nodes including PFC, ventral striatum (VS), central amygdala (CeN), and basolateral amygdala (BLA). We examined the association of directed centrality metrics with initial levels and rates of change in emotion-related impulsivity symptoms over a one-year follow-up using latent growth curve models (LGCMs).

In controls, ventromedial prefrontal cortex (vmPFC) and dorsal ACC had a directed influence on CeN and VS, respectively. In the BPD group, bilateral BLA had a directed influence on CeN, whereas in the healthy group CeN influenced BLA. LGCMs indicated that emotion-related impulsivity was stable across a one-year follow-up in the BPD group. Further, higher EC of R CeN to other regions in controls was associated with stronger within-person decreases in emotion-related impulsivity.

Functional inputs from BLA and vmPFC appear to play competing roles in influencing CeN activity. In borderline adolescents and young adults, BLA may predominate over CeN activity, while in controls the ability of CeN to influence BLA activity predicted more rapid reductions in emotion-related impulsivity.

A major obstacle in understanding and treating posttraumatic stress disorder (PTSD) is its clinical and neurobiological heterogeneity. To address this barrier, the field has become increasingly interested in identifying subtypes of PTSD based on dysfunction in neural networks alongside cognitive impairments that may underlie the development and maintenance of symptoms. The current study aimed to determine if subtypes of PTSD, based on normative-based cognitive dysfunction across multiple domains, have unique neural network signatures.

In a sample of 271 veterans (90% male) that completed both neuropsychological testing and resting-state fMRI, two complementary, whole-brain functional connectivity analyses explored the link between brain functioning, PTSD symptoms, and cognition.

At the network level, PTSD symptom severity was associated with reduced negative coupling between the limbic network (LN) and frontal-parietal control network (FPCN), driven specifically by the dorsolateral prefrontal cortex and amygdala Hubs of Dysfunction. Further, this relationship was uniquely moderated by executive function (EF). Specifically, those with PTSD and impaired EF had the strongest marker of LN-FPCN dysregulation, while those with above-average EF did not exhibit PTSD-related dysregulation of these networks.

These results suggest that poor executive functioning, alongside LN-FPCN dysregulation, may represent a neurocognitive subtype of PTSD.

The neuropeptide oxytocin is proposed as a promising therapy for social dysfunction by modulating amygdala-mediated social-emotional behavior. Although clinical trials report some benefits of chronic treatment, it is unclear whether efficacy may be influenced by dose frequency or genotype.

In a randomized, double-blind, placebo-controlled pharmaco-functional magnetic resonance imaging trial (150 male subjects), we investigated acute and different chronic (every day or on alternate days for 5 days) intranasal oxytocin (24 international units) effects and oxytocin receptor genotype-mediated treatment sensitivity on amygdala responses to face emotions. We also investigated similar effects on resting-state functional connectivity between the amygdala and prefrontal cortex.

A single dose of oxytocin-reduced amygdala responses to all face emotions but for threatening (fear and anger) and happy faces, this effect was abolished after daily doses for 5 days but maintained by doses given every other day. The latter dose regime also enhanced associated anxious-arousal attenuation for fear faces. Oxytocin effects on reducing amygdala responses to face emotions only occurred in AA homozygotes of rs53576 and A carriers of rs2254298. The effects of oxytocin on resting-state functional connectivity were not influenced by either dose-frequency or receptor genotype.

Infrequent chronic oxytocin administration may be therapeutically most efficient and its anxiolytic neural and behavioral actions are highly genotype-dependent in males.

The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS).

Using Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]. We also computed mass-univariate analyses in order to observe regional group differences [false-discovery-rate (FDR)-cluster corrected p < 0.05, k = 20].

We found that extracted features could predict accurately the classification of PTSD, PTSD + DS, and healthy controls, using both resting-state mALFF (91.63% balanced accuracy, p < 0.001) and amygdala complex connectivity maps (85.00% balanced accuracy, p < 0.001). These results were replicated using independent machine learning algorithms/cross-validation procedures. Moreover, areas weighted as being most important for group classification also displayed significant group differences at the univariate level. Here, whereas the PTSD + DS group displayed increased activation within emotion regulation regions, the PTSD group showed increased activation within the amygdala, globus pallidus, and motor/somatosensory regions.

The current study has significant implications for advancing machine learning applications within the field of psychiatry, as well as for developing objective biomarkers indicative of diagnostic heterogeneity.