Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

BACKGROUND

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

METHODS

Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months.

RESULTS

Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.

CONCLUSIONS

Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

POS1127 USE OF ARTIFICIAL INTELLIGENCE AND CLOUD-BASED INFRASTRUCTURE TO IMPROVE THE SPEED AND ACCURACY OF ELIGIBILITY READS IN OSTEOARTHRITIS TRIALS

Background

The Kellgren-Lawrence grading (KLG) system is used in clinical trials of osteoarthritis (OA) to define the structural severity of the disease as part of patient eligibility assessment. However, the use of KLG system has proven to be challenging due to considerable inter-reader variability[1], [2], which may result in recruitment of sub-optimal patient cohort or delays in patient recruitment.

Objectives

The objective of this study was to determine the impact of an AI-assisted, cloud-based data management system on the rate of adjudication and the speed of OA patient recruitment.

Methods

A total of 3855 bilateral fixed-flexion posteroanterior radiographs of the tibiofemoral joints from global multi-centre trials were included in this study. Two experienced readers performed an initial KLG assessment of both knees; the adjudication was performed by a third experienced reader. A cloud-based imaging data management system was deployed, the readers could access the data simultaneously and adjudication was automatically triggered.

We quantified the adjudication rate and the distribution of disagreements in KLG scores provided by the initial readers. Furthermore, the delay in delivery time of the KLG reports to the recruiting site was recorded.

Results

48% (1836) of the initial reads required adjudication. Approximately 70% of the disagreements affected the conventional KLG 2-3 inclusion range of OA clinical trials. Use of the cloud-based data management allowed 41% of the reports to be delivered within 24 hours, if no adjudication was required vs an average of 5 days as estimated based on the readers’ prior experience.

Table 1 provides details on the distribution of disagreements resulting in adjudication reads. Figure 1 shows the delivery time for KLG with and without adjudication.

Table 1.

Distribution of disagreements of initial reads resulting in an adjudication read being triggered.

Disagreement triggering AdjudicationNumber of CasesPercentageKLG 0 – 171327.8%KLG 1 – 282031.9%KLG 2 – 348218.7%KLG 3 – 444617.4%Other1104.3%Total2569100%Figure 1.

Time to deliver eligibility report

Nehrer et al. showed that assisting the readers with AI generated KLG scoring reports, the agreement rate between readers for KLG assessment increased by 21% [2]. Adding to this, 30% of the adjudications (stemming from KLG 0 –1 disagreements between readers) could be avoided when using AI generated reports.

Conclusion

We assess the rate of adjudication and speed of reporting of KLG of data from multi-centre OA clinical trials. Future work is planned to assess the effect of AI-assisted OA grading systems within our cloud-based data management system on reader agreement and recruitment speed in global clinical trials.

References

[1]D. J. Hunter et al., “OARSI Clinical Trials Recommendations: Knee imaging in clinical trials inosteoarthritis,” Osteoarthritis and Cartilage, vol. 23, no. 5. W.B. Saunders Ltd, pp. 698–715, 2015. doi: 10.1016/j.joca.2015.03.012.

[2]S. Nehrer et al., “Automated Knee Osteoarthritis Assessment Increases Physicians’ Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative,” Cartilage, vol. 13, no. 1_suppl, pp. 957S-965S, Dec. 2021, doi: 10.1177/1947603519888793.

Disclosure of Interests

Olga Kubassova Grant/research support from: Takeda, Lilly, Abbvie, Pfizer, Mikael Boesen Speakers bureau: Lilly, Novartis, Abbvie, Pfizer, Cristiano Pereira da Costa: None declared, Julia O’Lynn: None declared, Andrew Patterson: None declared, Dimitri Kessler: None declared

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers

Background

Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process.

Objective

The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk–prediction models in clinical practice. The specific goals are to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk–prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider.

Methods

We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff.

Results

Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk–prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings.

Conclusions

Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning–based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds

Convergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values < 0.05 FDR-corrected for multiple comparisons. Higher P factor scores were associated with hypoconnectivity within default network and hyperconnectivity between default network and multiple control networks. Among connections within these 28 significant cells, the P factor was predominantly associated with "attenuating" effects (67%; pPERMUTATION < 0.0002), i.e., reduced connectivity at neurotypically positive connections and increased connectivity at neurotypically negative connections. These results demonstrate that the general factor of psychopathology produces attenuating changes across multiple networks including default network, involved in spontaneous responses, and control networks involved in cognitive control. Moreover, they clarify mechanisms of transdiagnostic risk for psychopathology and invite further research into developmental causes of distributed attenuated connectivity.

It's quality and quantity: the effect of the amount of comments on online suicidal posts

Every day, individuals post suicide notes on social media asking for support, resources, and reasons to live. Some posts receive few comments while others receive many. While prior studies have analyzed whether specific responses are more or less helpful, it is not clear if the quantity of comments received is beneficial in reducing symptoms or in keeping the user engaged with the platform and hence with life. In the present study, we create a large dataset of users' first r/SuicideWatch (SW) posts from Reddit (N=21,274), collect the comments as well as the user's subsequent posts (N=1,615,699) to determine whether they post in SW again in the future. We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments -as a measure of social support- increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful. On the contrary, we find that receiving more comments increases the likelihood a user will post in SW again. We discuss how receiving more comments is helpful, not by permanently relieving symptoms since users make another SW post and their second posts have similar mentions of suicidal ideation, but rather by reinforcing users to seek support and remain engaged with the platform. Furthermore, since receiving only 1 comment -the most common case- decreases the likelihood of posting again by 14% on average depending on the time window, it is important to develop systems that encourage more commenting.

Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis

AIMS

To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.

METHODS

Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3-4, 6-8, and 9-12 months post-baseline and remission at 3-4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/.

RESULTS

There was no evidence of an association between age and prognosis before or after adjusting for depressive 'disorder characteristics' that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3-4 months post-baseline per-5-year increase in age = 0(95% CI: -0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3-4 months or 9-12 months post-baseline, but men had worse prognoses at 6-8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6-8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive 'disorder characteristics' and employment status (12.23% (-1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive 'disorder characteristics' and all available confounders.

CONCLUSION

Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive 'disorder characteristics' in clinic may be important.

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials

BACKGROUND

Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.

METHOD

Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.

RESULTS

Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.

CONCLUSION

The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

We present the first results of the Fermilab National Accelerator Laboratory (FNAL) Muon g-2 Experiment for the positive muon magnetic anomaly a_{μ}≡(g_{μ}-2)/2. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency ω_{a} between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ω[over ˜]_{p}^{'} in a spherical water sample at 34.7 °C. The ratio ω_{a}/ω[over ˜]_{p}^{'}, together with known fundamental constants, determines a_{μ}(FNAL)=116 592 040(54)×10^{-11} (0.46 ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both μ^{+} and μ^{-}, the new experimental average of a_{μ}(Exp)=116 592 061(41)×10^{-11} (0.35 ppm) increases the tension between experiment and theory to 4.2 standard deviations.

Increased Functional Segregation Related to the Salience Network in Unaffected Siblings of Youths With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

Although there are frequent reports of shared neurofunctional and neurostructural alterations among probands with attention-deficit/hyperactivity disorder (ADHD) and their unaffected siblings, there is little knowledge regarding whether abnormalities in the resting-state functional connectivity of ADHD probands is also expressed in unaffected siblings, or whether this unaffected (but at-risk) cohort manifests distinct patterns.

METHOD

We used a multivariate connectome-wide association study examining intrinsic functional connectivity with resting-state functional magnetic resonance imaging (MRI) in a sample (aged 8-17 years) of medication-naive ADHD probands (n = 56), their unaffected siblings (n = 55), and typically developing (TD) youths (n = 106).

RESULTS

ADHD probands showed, relative to TD youths, increased connectivity between the default-mode network (DMN) and task-positive networks. Relative to ADHD and TD groups, respectively, unaffected siblings showed increased connectivity within the salience network and reduced connectivity between the DMN and salience network. No shared alterations in functional connectivity among ADHD probands and their unaffected siblings were identified. These findings were largely confirmed by complementary pairwise connectomic comparisons. However, the main connectivity differences between ADHD and unaffected siblings were not replicated in a tightly age- and sex-matched subsample (20 proband-sibling pairs and 60 TD youths).

CONCLUSION

Our findings suggest that increased functional segregation related to the attention networks, especially the salience (ventral attention) system, may be a potential feature of at-risk siblings who remain unaffected by ADHD expression. Further replications are needed in other larger and sex-matched samples.

CLINICAL TRIAL REGISTRATION INFORMATION

Structural and Functional Connectivity of Frontostriatal and Frontoparietal Networks as Endophenotypes of ADHD; https://clinicaltrials.gov/; NCT01682915.

The Mars 2020 Engineering Cameras and Microphone on the Perseverance Rover: A Next-Generation Imaging System for Mars Exploration

The Mars 2020 Perseverance rover is equipped with a next-generation engineering camera imaging system that represents an upgrade over previous Mars rover missions. These upgrades will improve the operational capabilities of the rover with an emphasis on drive planning, robotic arm operation, instrument operations, sample caching activities, and documentation of key events during entry, descent, and landing (EDL). There are a total of 16 cameras in the Perseverance engineering imaging system, including 9 cameras for surface operations and 7 cameras for EDL documentation. There are 3 types of cameras designed for surface operations: Navigation cameras (Navcams, quantity 2), Hazard Avoidance Cameras (Hazcams, quantity 6), and Cachecam (quantity 1). The Navcams will acquire color stereo images of the surface with a 96 ∘ × 73 ∘ field of view at 0.33 mrad/pixel. The Hazcams will acquire color stereo images of the surface with a 136 ∘ × 102 ∘ at 0.46 mrad/pixel. The Cachecam, a new camera type, will acquire images of Martian material inside the sample tubes during caching operations at a spatial scale of 12.5 microns/pixel. There are 5 types of EDL documentation cameras: The Parachute Uplook Cameras (PUCs, quantity 3), the Descent stage Downlook Camera (DDC, quantity 1), the Rover Uplook Camera (RUC, quantity 1), the Rover Descent Camera (RDC, quantity 1), and the Lander Vision System (LVS) Camera (LCAM, quantity 1). The PUCs are mounted on the parachute support structure and will acquire video of the parachute deployment event as part of a system to characterize parachute performance. The DDC is attached to the descent stage and pointed downward, it will characterize vehicle dynamics by capturing video of the rover as it descends from the skycrane. The rover-mounted RUC, attached to the rover and looking upward, will capture similar video of the skycrane from the vantage point of the rover and will also acquire video of the descent stage flyaway event. The RDC, attached to the rover and looking downward, will document plume dynamics by imaging the Martian surface before, during, and after rover touchdown. The LCAM, mounted to the bottom of the rover chassis and pointed downward, will acquire 90 ∘ × 90 ∘ FOV images during the parachute descent phase of EDL as input to an onboard map localization by the Lander Vision System (LVS). The rover also carries a microphone, mounted externally on the rover chassis, to capture acoustic signatures during and after EDL. The Perseverance rover launched from Earth on July 30th, 2020, and touchdown on Mars is scheduled for February 18th, 2021.

A novel cardiac signal processing system for electrophysiology procedures: early insights from the pure ep 2.0 study

Background

Intracardiac electrogram data remain one of the primary diagnostic inputs guiding complex ablation procedures. However, the technology to collect, process, and display intracardiac signals has remained relatively unchanged for the past two decades.

Purpose

We test a new platform, the PURE EP™ 2.0 system (PEP; BioSig Technologies) for signal processing and display.

Methods

Identical electrocardiographic and intracardiac signal data were recorded during 15 AF ablation procedures from the PEP system, the signal recording system, and the 3D mapping system (Figure). The collected signals underwent blinded, controlled evaluation by three independent electrophysiologist reviewers to determine whether the PEP signals are a viable alternative to conventional sources and if it provides additional or clearer diagnostic information. Reviewers were asked to record the quality of each signal sample on a scale of 1–10 and select a rationale for their rating in a dropdown menu. Each paired signal rating was collected and unblinded for the analysis. If the reviewer rated the samples in the set within 1 point of each other, the PEP sample was deemed equivalent to the control. Using a 2+1 statistical method, the ratings from the three reviewers were then compared looking for at least two positive reviews for each PEP sample.

Results

Based on the ratings for each pair of signals, a cumulative total of 29 PEP signals out of 34 (85.3%) were rated as statistically equivalent or better for this dataset. In 35.5% of samples, the reviewers selected PEP because “more signal components were visible”.

Conclusion

The PURE EP 2.0 system is able to produce reliable and high-quality signals when compared to available standard of care systems. Further studies with larger dataset across multiple sites are needed to validate these results.

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): BioSig Technologies

Emotion expression and regulation in three cultures: Chinese, Japanese, and American preschoolers' reactions to disappointment

There are strong cultural norms for how emotions are expressed, yet little is known about cultural variations in preschoolers' outward displays and regulation of disappointment. Chinese, Japanese, and American preschoolers' (N = 150) displays of emotion to an undesired gift were coded across both social and nonsocial contexts in a "disappointing gift" paradigm. Generalized estimating equations revealed that, regardless of culture, when children received a disappointing gift, they showed more positive expressions of emotion ("fake smile") in social contexts (in the presence of unfamiliar and familiar examiners) relative to when they were alone, suggesting that preschool-aged children are able to mask their disappointment with positive displays. However, children's emotion expressions varied across both cultures and contexts. American children were more positively and negatively expressive than Japanese children and were more negatively expressive than Chinese children. Chinese and Japanese preschoolers verbally reported more negative emotions but showed more neutral expressions than American preschoolers when receiving the disappointing gift. In addition, across different contexts of the task, there were subtle differences in how Chinese and Japanese children regulated their emotional expressions, with Chinese children showing similar levels of neutral expressions (e.g., "poker face") across different contexts in the task. Thus, our findings highlight the importance of understanding cultural meanings and practices underlying emotion development during early childhood.

Altered resting-state functional connectivity in adolescents is associated with PTSD symptoms and trauma exposure

Alterations in resting-state functional connectivity (rsFC) have been demonstrated in Posttraumatic Stress Disorder (PTSD). However, such reports have primarily focused on adult participants, whereas findings in adolescents with PTSD are mixed and not entirely consistent with the adult literature. Here, we examined rsFC in a non-treatment seeking adolescent sample with posttraumatic stress symptoms (PTSS; n = 59) relative to asymptomatic controls (n = 226). We also examined differences between trauma-exposed and non-exposed control subgroups (TEC n = 73 and Non-TEC n = 153) to examine alterations associated with more general trauma exposure. Finally, we compared the PTSS and TEC groups, to confirm that the reported alterations in PTSS were not driven by trauma exposure. Using a seed-based approach, we examined connectivity of default-mode (DMN) and salience (SN) networks, where alterations have been previously reported. Results suggest that PTSS are associated with less within-DMN connectivity and greater SN-DMN connectivity, as well as altered connectivity with attention regions. Trauma exposure is associated with greater within-SN connectivity. Additionally, we report findings from exploratory connectome-based analysis, which demonstrate a number of topological alterations within DMN in the PTSS group. Overall, our findings replicate prior reports of altered rsFC in PTSD and extend them to non-treatment seeking, trauma-exposed adolescents, who did or did not report PTSS. They specifically highlight SN-DMN desegregation, lower within-DMN and greater within-SN connectivity, as well as altered connectivity with attention regions, in trauma-exposed adolescents. Future research is required to confirm that adolescents with diagnosed PTSD have similar/exacerbated connectivity patterns.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

NETWORK CLASSIFICATION WITH APPLICATIONS TO BRAIN CONNECTOMICS

While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.

Prioritising physical and psychological symptoms: what are the barriers and facilitators to the discussion of anxiety in the primary care consultation?

Background

Anxiety is under-recorded and under-treated in the UK and is under-represented in research compared with depression. Detecting anxiety can be difficult because of co-existing conditions. GPs can be reluctant to medicalise anxiety symptoms and patients can be reluctant to disclose them, for a variety of reasons. This research addresses the gap in evidence of real-life consultations of patients with anxiety and explores how physical and psychological symptoms are discussed and prioritised by patients and GPs in primary care consultations.

Methods

A mixed methods study using a baseline questionnaire, video-recorded primary care consultations and interview data with patients and GPs.

Results

Seventeen patients with anxiety symptoms (GAD-7 score ≥ 10) completed a questionnaire, had their consultation video-recorded and took part in a semi-structured interview. Four GPs were interviewed. The main themes that emerged from GP and patients accounts as barriers and facilitators to discussing anxiety mostly mirrored each other. The GP/patient relationship and continuity of care was the main facilitator for the discussion of anxiety in the consultation. The main barriers were: attribution of or unacknowledged symptoms; co-morbidities; and time constraints. GPs overcame these barriers by making repeat appointments and employing prioritising techniques; patients by choosing an empathetic GP.

Conclusions

The findings add to the evidence base concerning the management of anxiety in primary care. The findings suggest that the discussion around anxiety is a process negotiated between the patient and the GP influenced by a range of barriers and facilitators. Co-existing depression and health anxieties can mask anxiety symptoms in patients. Good practice techniques such as bringing back patients for appointments to foster continuity of care and understanding can help disclosure and detection of anxiety symptoms. Future research could investigate this longitudinally and should include a wider range of GPs practices and GPs.

Electronic supplementary material

The online version of this article (10.1186/s12875-019-0996-6) contains supplementary material, which is available to authorized users.

Resting state network modularity along the prodromal late onset Alzheimer's disease continuum

Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.

Anomalous network architecture of the resting brain in children who stutter

PURPOSE

We combined a large longitudinal neuroimaging dataset that includes children who do and do not stutter and a whole-brain network analysis in order to examine the intra- and inter-network connectivity changes associated with stuttering. Additionally, we asked whether whole brain connectivity patterns observed at the initial year of scanning could predict persistent stuttering in later years.

METHODS

A total of 224 high-quality resting state fMRI scans collected from 84 children (42 stuttering, 42 controls) were entered into an independent component analysis (ICA), yielding a number of distinct network connectivity maps ("components") as well as expression scores for each component that quantified the degree to which it is expressed for each child. These expression scores were compared between stuttering and control groups' first scans. In a second analysis, we examined whether the components that were most predictive of stuttering status also predicted persistence in stuttering.

RESULTS

Stuttering status, as well as stuttering persistence, were associated with aberrant network connectivity involving the default mode network and its connectivity with attention, somatomotor, and frontoparietal networks. The results suggest developmental alterations in the balance of integration and segregation of large-scale neural networks that support proficient task performance including fluent speech motor control.

CONCLUSIONS

This study supports the view that stuttering is a complex neurodevelopmental disorder and provides comprehensive brain network maps that substantiate past theories emphasizing the importance of considering situational, emotional, attentional and linguistic factors in explaining the basis for stuttering onset, persistence, and recovery.

The (non-)replicability of regulatory resource depletion: A field report employing non-invasive brain stimulation

Cognitive effort and self-control are exhausting. Although evidence is ambiguous, behavioural studies have repeatedly suggested that control-demanding tasks seem to deplete a limited cache of self-regulatory resources leading to performance degradations and fatigue. While resource depletion has indirectly been associated with a decline in right prefrontal cortex capacity, its precise neural underpinnings have not yet been revealed. This study consisted of two independent experiments, which set out to investigate the causal role of the right dorsolateral prefrontal cortex (DLPFC) in a classic dual phase depletion paradigm employing non-invasive brain stimulation. In Experiment 1 we demonstrated a general depletion effect, which was significantly eliminated by anodal transcranial Direct Current Stimulation to the right DLPFC. In Experiment 2, however, we failed to replicate the basic psychological depletion effect within a second independent sample. The dissimilar results are discussed in the context of the current 'replication crisis' and suggestions for future studies are offered. While our current results do not allow us to firmly argue for or against the existence of resource depletion, we outline why it is crucial to further clarify which specific external and internal circumstances lead to limited replicability of the described effect. We showcase and discuss the current inter-lab replication problem based on two independent samples tested within one research group (intra-lab).