The Institute for Implementation Science Scholars (IS-2) is a dissemination and implementation (D&I) science training and mentoring program. A key component of IS-2 is collaborating and networking. To build knowledge on effective networking and mentoring, this study sought to 1) conduct a social network analysis to determine whether underrepresented scholars have equivalent levels of connection and 2) gain insights into the differences in networking among racial/ethnic subgroups of scholars.

Social network survey data were used to select participants based on number of collaborative connections (highest, lowest) and racial/ ethnic category (underrepresented, not underrepresented). Interviews were recorded, transcribed, and coded using an iterative process.

The sample consisted of eight highly networked scholars, eight less networked scholars, seven from underrepresented racial and ethnic groups, and nine from not underrepresented groups. Qualitative data showed a lack of connection, reluctance to network, and systematic issues including institutional biases as possible drivers of group differences. In addition, scholars provided suggestions on how to overcome barriers to networking and provided insights into how IS-2 has impacted their D&I research and knowledge.

Underrepresented scholars have fewer network contacts than not underrepresented scholars in the IS-2 training program. It is imperative for leadership to be intentional with mentorship pairing, especially for underrepresented scholars. Future research might include interviews with program leaders to understand how network pairings are built to improve the mentorship experience.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental healthcare models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings.

This retrospective study involved analysis of headspace’s clinical minimum data set from young people accessing services in Australia between 1 July 2018 and 30 June 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors.

A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a ‘high complexity’ group, presenting with a variety of complexity factors, including severe disorders, a trauma history and psychosocial impairments. Two moderate complexity groups were identified representing ‘distress complexity’ and ‘psychosocial complexity’ (about 20% each). Compared with the ‘distress complexity’ group, young people in the ‘psychosocial complexity’ group presented with a higher proportion of education, employment and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services.

The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental healthcare to ensure the availability of appropriate and timely support for all young people.

Panic disorder (PD) and agoraphobia (AG) are highly comorbid anxiety disorders with an increasing prevalence that have a significant clinical and public health impact but are not adequately recognized and treated. Although the current functional neuroimaging literature has documented a range of neural abnormalities in these disorders, primary studies are often not sufficiently powered and their findings have been inconsistent.

This meta-analysis aims to advance our understanding of the neural underpinnings of PD and AG by identifying the most robust patterns of differential neural activation that differentiate individuals diagnosed with one of or both these disorders from age-matched healthy controls.

We conducted a comprehensive literature search in the PubMed database for all peer-reviewed, whole-brain, task-based functional magnetic resonance imaging (fMRI) activation studies that compared adults diagnosed with PD and/or AG with age-matched healthy controls. Each of these articles was screened by two independent coding teams using formal inclusion criteria and according to current PRISMA guidelines. We then performed a voxelwise, whole-brain, meta-analytic comparison of PD/AG participants with age-matched healthy controls using multilevel kernel density analysis (MKDA) with ensemble thresholding (p<0.05-0.0001) to minimize cluster size detection bias and 10,000 Monte Carlo simulations to correct for multiple comparisons.

With data from 34 primary studies and a substantial sample size (N=2138), PD/AG participants, relative to age-matched healthy controls, exhibited a reliable pattern of statistically significant, (p<.05-0.0001; FWE-corrected) abnormal neural activation in multiple brain regions of the cerebral cortex and basal ganglia across a variety of experimental tasks.

In this meta-analysis we found robust patterns of differential neural activation in participants diagnosed with PD/AG relative to age-matched healthy controls. These findings advance our understanding of the neural underpinnings of PD and AG and inform the development of brain-based clinical interventions such as non-invasive brain stimulation (NIBS) and treatment prediction and matching algorithms. Future studies should also investigate the neural similarities and differences between PD and AG to increase our understanding of possible differences in their etiology, diagnosis, and treatment.

None Declared

Bipolar I disorder (BD-I) is a chronic and recurrent mood disorder characterized by alternating episodes of depression and mania; it is also associated with substantial morbidity and mortality and with clinically significant functional impairments. While previous studies have used functional magnetic resonance imaging (fMRI) to examine neural abnormalities associated with BD-I, they have yielded mixed findings, perhaps due to differences in sampling and experimental design, including highly variable mood states at the time of scan.

The purpose of this study is to advance our understanding of the neural basis of BD-I and mania, as measured by fMRI activation studies, and to inform the development of more effective brain-based diagnostic systems and clinical treatments.

We conducted a large-scale meta-analysis of whole-brain fMRI activation studies that compared participants with BD-I, assessed during a manic episode, to age-matched healthy controls. Following PRISMA guidelines, we conducted a comprehensive PubMed literature search using two independent coding teams to evaluate primary studies according to pre-established inclusion criteria. We then used multilevel kernel density analysis (MKDA), a well-established, voxel-wise, whole-brain, meta-analytic approach, to quantitatively synthesize all qualifying primary fMRI activation studies of mania. We used ensemble thresholding (p<0.05-0.0001) to minimize cluster size detection bias, and 10,000 Monte Carlo simulations to correct for multiple comparisons.

We found that participants with BD-I (N=2,042), during an active episode of mania and relative to age-matched healthy controls (N=1,764), exhibit a pattern of significantly (p<0.05-0.0001; FWE-corrected) different activation in multiple brain regions of the cerebral cortex and basal ganglia across a variety of experimental tasks.

This study supports the formulation of a robust neural basis for BD-I during manic episodes and advances our understanding of the pattern of abnormal activation in this disorder. These results may inform the development of novel brain-based clinical tools for bipolar disorder such as diagnostic biomarkers, non-invasive brain stimulation, and treatment-matching protocols. Future studies should compare the neural signatures of BD-I to other related disorders to facilitate the development of protocols for differential diagnosis and improve treatment outcomes in patients with BD-I.

None Declared

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent psychiatric condition that frequently originates in early development and is associated with a variety of functional impairments. Despite a large functional neuroimaging literature on ADHD, our understanding of the neural basis of this disorder remains limited, and existing primary studies on the topic include somewhat divergent results.

The present meta-analysis aims to advance our understanding of the neural basis of ADHD by identifying the most statistically robust patterns of abnormal neural activation throughout the whole-brain in individuals diagnosed with ADHD compared to age-matched healthy controls.

We conducted a meta-analysis of task-based functional magnetic resonance imaging (fMRI) activation studies of ADHD. This included, according to PRISMA guidelines, a comprehensive PubMed search and predetermined inclusion criteria as well as two independent coding teams who evaluated studies and included all task-based, whole-brain, fMRI activation studies that compared participants diagnosed with ADHD to age-matched healthy controls. We then performed multilevel kernel density analysis (MKDA) a well-established, whole-brain, voxelwise approach that quantitatively combines existing primary fMRI studies, with ensemble thresholding (p<0.05-0.0001) and multiple comparisons correction.

Participants diagnosed with ADHD (N=1,550), relative to age-matched healthy controls (N=1,340), exhibited statistically significant (p<0.05-0.0001; FWE-corrected) patterns of abnormal activation in multiple brains of the cerebral cortex and basal ganglia across a variety of cognitive control tasks.

This study advances our understanding of the neural basis of ADHD and may aid in the development of new brain-based clinical interventions as well as diagnostic tools and treatment matching protocols for patients with ADHD. Future studies should also investigate the similarities and differences in neural signatures between ADHD and other highly comorbid psychiatric disorders.

None Declared