DSM-5 specifies bulimia nervosa (BN) severity based on specific thresholds of compensatory behavior frequency. There is limited empirical support for such severity groupings. Limited support could be because the DSM-5’s compensatory behavior frequency cutpoints are inaccurate or because compensatory behavior frequency does not capture true underlying differences in severity. In support of the latter possibility, some work has suggested shape/weight overvaluation or use of single versus multiple purging methods may be better severity indicators. We used structural equation modeling (SEM) Trees to empirically determine the ideal variables and cutpoints for differentiating BN severity, and compared the SEM Tree groupings to alternate severity classifiers: the DSM-5 indicators, single versus multiple purging methods, and a binary indicator of shape/weight overvaluation.

Treatment-seeking adolescents and adults with BN (N = 1017) completed self-report measures assessing BN and comorbid symptoms. SEM Trees specified an outcome model of BN severity and recursively partitioned this model into subgroups based on shape/weight overvaluation and compensatory behaviors. We then compared groups on clinical characteristics (eating disorder symptoms, depression, anxiety, and binge eating frequency).

SEM Tree analyses resulted in five severity subgroups, all based on shape/weight overvaluation: overvaluation <1.25; overvaluation 1.25–3.74; overvaluation 3.75–4.74; overvaluation 4.75–5.74; and overvaluation ≥5.75. SEM Tree groups explained 1.63–6.41 times the variance explained by other severity schemes.

Shape/weight overvaluation outperformed the DSM-5 severity scheme and single versus multiple purging methods, suggesting the DSM-5 severity scheme should be reevaluated. Future research should examine the predictive utility of this severity scheme.

Mild cognitive impairment with Lewy bodies (MCI-LB) may be identified prospectively based on the presence of cognitive impairment and several core clinical features (visual hallucinations, cognitive fluctuations, parkinsonism, and REM sleep behavior disorder). MCI-LB may vary in its presenting features, which may reflect differences in underlying pathological pattern, severity, or comorbidity.

We aimed to assess how clinical features of MCI-LB accumulate over time, and whether this is associated with the rate of cognitive decline.

In this cohort study, 74 individuals seen with MCI-LB prospectively underwent repeated annual cognitive and clinical assessment up to nine years. Relationships between clinical features (number of core features present and specific features present) and cognitive change on the Addenbrooke’s Cognitive Examination–Revised (ACE-R) were examined with time-varying mixed models. The accumulation of core clinical features over time was examined with a multi-state Markov model.

When an individual with MCI-LB endorsed more clinical features, they typically experienced a faster cognitive decline (ACE-R Score Difference β = −1.1 [−1.7 to −0.5]), specifically when experiencing visual hallucinations (β = −2.1 [−3.5 to −0.8]) or cognitive fluctuations (β = −3.4 [−4.8 to −2.1]).

Individuals with MCI-LB typically acquired more clinical features with the passage of time (25.5% [20.0–32.0%] one-year probability), limiting the prognostic utility of baseline-only features.

The clinical presentation of MCI-LB may evolve over time. The accumulation of more clinical features of Lewy body disease, in particular visual hallucinations and cognitive fluctuations, may be associated with a worse prognosis in clinical settings.

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.

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

Multimorbidity, the presence of two or more health conditions, has been identified as a possible risk factor for clinical dementia. It is unclear whether this is due to worsening brain health and underlying neuropathology, or other factors. In some cases, conditions may reflect the same disease process as dementia (e.g. Parkinson's disease, vascular disease), in others, conditions may reflect a prodromal stage of dementia (e.g. depression, anxiety and psychosis).

To assess whether multimorbidity in later life was associated with more severe dementia-related neuropathology at autopsy.

We examined ante-mortem and autopsy data from 767 brain tissue donors from the UK, identifying physical multimorbidity in later life and specific brain-related conditions. We assessed associations between these purported risk factors and dementia-related neuropathological changes at autopsy (Alzheimer's-disease related neuropathology, Lewy body pathology, cerebrovascular disease and limbic-predominant age-related TDP-43 encephalopathy) with logistic models.

Physical multimorbidity was not associated with greater dementia-related neuropathological changes. In the presence of physical multimorbidity, clinical dementia was less likely to be associated with Alzheimer's disease pathology. Conversely, conditions which may be clinical or prodromal manifestations of dementia-related neuropathology (Parkinson's disease, cerebrovascular disease, depression and other psychiatric conditions) were associated with dementia and neuropathological changes.

Physical multimorbidity alone is not associated with greater dementia-related neuropathological change; inappropriate inclusion of brain-related conditions in multimorbidity measures and misdiagnosis of neurodegenerative dementia may better explain increased rates of clinical dementia in multimorbidity

Attentional impairments are common in dementia with Lewy bodies and its prodromal stage of mild cognitive impairment (MCI) with Lewy bodies (MCI-LB). People with MCI may be capable of compensating for subtle attentional deficits in most circumstances, and so these may present as occasional lapses of attention. We aimed to assess the utility of a continuous performance task (CPT), which requires sustained attention for several minutes, for measuring attentional performance in MCI-LB in comparison to Alzheimer’s disease (MCI-AD), and any performance deficits which emerged with sustained effort.

We included longitudinal data on a CPT sustained attention task for 89 participants with MCI-LB or MCI-AD and 31 healthy controls, estimating ex-Gaussian response time parameters, omission and commission errors. Performance trajectories were estimated both cross-sectionally (intra-task progress from start to end) and longitudinally (change in performance over years).

While response times in successful trials were broadly similar, with slight slowing associated with clinical parkinsonism, those with MCI-LB made considerably more errors. Omission errors were more common throughout the task in MCI-LB than MCI-AD (OR 2.3, 95% CI: 1.1–4.7), while commission errors became more common after several minutes of sustained attention. Within MCI-LB, omission errors were more common in those with clinical parkinsonism (OR 1.9, 95% CI: 1.3–2.9) or cognitive fluctuations (OR 4.3, 95% CI: 2.2–8.8).

Sustained attention deficits in MCI-LB may emerge in the form of attentional lapses leading to omissions, and a breakdown in inhibitory control leading to commission errors.

Obtaining complete and accurate information in recruitment registries is essential for matching potential participants to research studies for which they qualify. Since electronic health record (EHR) systems are required to make patient data available to external systems, an interface between EHRs and recruitment registries may improve accuracy and completeness of volunteers’ profiles. We tested this hypothesis on ResearchMatch (RM), a disease- and institution-neutral recruitment registry with 1357 studies across 255 institutions.

We developed an interface where volunteers signing up for RM can authorize transfer of demographic data, medical conditions, and medications from the EHR into a registration form. We obtained feedback from a panel of community members to determine acceptability of the planned integration. We then developed the EHR interface and performed an evaluation study of 100 patients to determine whether RM profiles generated with EHR-assisted adjudication included more conditions and medications than those without the EHR connection.

Community member feedback revealed that members of the public were willing to authenticate into the EHR from RM with proper messaging about choice and privacy. The evaluation study showed that out of 100 participants, 75 included more conditions and 69 included more medications in RM profiles completed with the EHR connection than those without. Participants also completed the EHR-connected profiles in 16 fewer seconds than non-EHR-connected profiles.

The EHR to RM integration could lead to more complete profiles, less participant burden, and better study matches for many of the over 148,000 volunteers who participate in ResearchMatch.

Neurodevelopmental challenges are the most prevalent comorbidity associated with a diagnosis of critical CHD, and there is a high incidence of gross and fine motor delays noted in early infancy. The frequency of motor delays in hospitalised infants with critical CHD requires close monitoring from developmental therapies (physical therapists, occupational therapists, and speech-language pathologists) to optimise motor development. Currently, minimal literature defines developmental therapists’ role in caring for infants with critical CHD in intensive or acute care hospital units.

This article describes typical infant motor skill development, how the hospital environment and events surrounding early cardiac surgical interventions impact those skills, and how developmental therapists support motor skill acquisition in infants with critical CHD. Recommendations for healthcare professionals and those who provide medical or developmental support in promotion of optimal motor skill development in hospitalised infants with critical CHD are discussed.

Infants with critical CHD requiring neonatal surgical intervention experience interrupted motor skill interactions and developmental trajectories. As part of the interdisciplinary team working in intensive and acute care settings, developmental therapists assess, guide motor intervention, promote optimal motor skill acquisition, and support the infant’s overall development.

Meta-analyses of functional magnetic resonance imaging (fMRI) studies have been used to elucidate the most reliable neural features associated with various psychiatric disorders. However, it has not been well-established whether each of these neural features is linked to a specific disorder or is transdiagnostic across multiple disorders and disorder categories, including mood, anxiety, and anxiety-related disorders.

This project aims to advance our understanding of the disorder-specific and transdiagnostic neural features associated with mood, anxiety, and anxiety-related disorders as well as to refine the methodology used to compare multiple disorders.

We conducted an exhaustive PubMed literature search followed by double-screening, double-extraction, and cross-checking to identify all whole-brain, case-control fMRI activation studies of mood, anxiety, and anxiety-related disorders in order to construct a large-scale meta-analytic database of primary studies of these disorders. We then employed multilevel kernel density analysis (MKDA) with Monte-Carlo simulations to correct for multiple comparisons as well as ensemble thresholding to reduce cluster size bias to analyze primary fMRI studies of mood, anxiety, and anxiety-related disorders followed by application of triple subtraction techniques and a second-order analysis to elucidate the disorder-specificity of the previously identified neural features.

We found that participants diagnosed with mood, anxiety, and anxiety-related disorders exhibited statistically significant (p < .05 – 0.0001; FWE-corrected) differences in neural activation relative to healthy controls throughout the cerebral cortex, limbic system, and basal ganglia. In addition, each of these psychiatric disorders exhibited a particular profile of neural features that ranged from disorder-specific, to category-specific, to transdiagnostic.

These findings indicate that psychiatric disorders exhibit a complex profile of neural features that vary in their disorder-specificity and can be detected with large-scale fMRI meta-analytic techniques. This approach has potential to fundamentally transform neuroimaging investigations of clinical disorders by providing a novel procedure for establishing disorder-specificity of observed results, which can be then used to advance our understanding of individual disorders as well as broader nosological issues related to diagnosis and classification of psychiatric disorders.

None Declared

Generalized anxiety disorder (GAD) is a highly prevalent mental illness that is associated with clinically significant distress, functional impairment, and poor emotional regulation. Primary functional magnetic resonance imaging (fMRI) studies of GAD report neural abnormalities in comparison to healthy controls. However, many of these findings in the primary literature are inconsistent, and it is unclear whether they are specific to GAD or shared transdiagnostically across related disorders.

This meta-analysis seeks to establish the most reliable neural abnormalities observed in individuals with GAD, as reported in the primary fMRI activation literature.

We conducted an exhaustive literature search in PubMed to identify primary studies that met our pre-specified inclusion criteria and then extracted relevant data from primary, whole-brain fMRI activation studies of GAD that reported coordinates in Talairach or MNI space. We then used multilevel kernel density analysis (MKDA) with ensemble thresholding to examine the differences between adults with GAD and healthy controls in order to identify brain regions that reached statistical significance across primary studies.

Patients with GAD showed statistically significant (α=0.05–0.0001; family-wise-error-rate corrected) neural activation in various regions of the cerebral cortex and basal ganglia across a variety of experimental tasks.

These results inform our understanding of the neural basis of GAD and are interpreted using a frontolimbic model of anxiety as well as specific clinical symptoms of this disorder and its relation to other mood and anxiety disorders. These results also suggest possible novel targets for emerging neurostimulation therapies (e.g., transcranial magnetic stimulation) and may be used to advance our understanding of the effects of current pharmaceutical treatments and ways to improve treatment selection and symptom-targeting for patients diagnosed with GAD.

None Declared

Functional magnetic resonance imaging (fMRI) has been used to identify the neural activity of both youth and adults diagnosed with major depressive disorder (MDD) in comparison to healthy age-matched controls. Previously reported abnormalities in depressed youth appear to mostly align with those found in depressed adults; however, some of the reported aberrant brain activity in youth has not been consistent with what is observed in adults, and to our knowledge there has not yet been a formal, quantitative comparison of these two groups. In addition, it is not known whether these observed differences between youth and adults with depression are attributable to developmental age or length-of-illness.

The aim of this study is to elucidate the similarities and differences in patterns of abnormal neural activity between adults and youth diagnosed with MDD and to then determine whether these observed differences are due to either developmental age or length-of-illness.

We used multilevel kernel density analysis (MKDA) with ensemble thresholding and triple subtraction to separately determine neural abnormalities throughout the whole brain in primary studies of depressed youth and depressed adults and then directly compare the observed abnormalities between each of those age groups. We then conducted further comparisons between multiple subgroups to control for age and length-of-illness and thereby determine the source of the observed differences between youth and adults with depression.

Adults and youth diagnosed with MDD demonstrated reliable, differential patterns of abnormal activation in various brain regions throughout the cerebral cortex that are statistically significant (p < .05; FWE-corrected). In addition, several of these brain regions that exhibited differential patterns of neural activation between the two age groups can be reliably attributed to either developmental age or length-of-illness.

These findings indicate that there are common and disparate patterns of brain activity between youth and adults with MDD, several of which can be reliably attributed to developmental age or length-of-illness. These results expand our understanding of the neural basis of depression across development and course of illness and may be used to inform the development of new, age-specific clinical treatments as well as prevention strategies for this disorder.

None Declared