Arguments for the biological and predictive relevance of the proportional recovery rule.

The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values - an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery.

Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

Importance: Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population. Objective: To identify future suicide attempt risk factors in the general population using a data-driven machine learning approach including more than 2500 questions from a large, nationally representative survey of US adults. Design, Setting, and Participants: Data came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). NESARC is a face-to-face longitudinal survey conducted with a national representative sample of noninstitutionalized civilian population 18 years and older in the US. The cumulative response rate across both waves was 70.2% resulting in 34 653 wave 2 interviews. A balanced random forest was trained using cross-validation to develop a suicide attempt risk model. Out-of-fold model prediction was used to assess model performance, including the area under the receiver operator curve, sensitivity, and specificity. Survey design and nonresponse weights allowed estimates to be representative of the US civilian population based on the 2000 census. Analyses were performed between May 15, 2019, and June 10, 2020. Main Outcomes and Measures: Attempted suicide in the 3 years between wave 1 and wave 2 interviews. Results: Of 34 653 participants, 20 089 were female (weighted proportion, 52.1%). The weighted mean (SD) age was 45.1 (17.3) years at wave 1 and 48.2 (17.3) years at wave 2. Attempted suicide during the 3 years between wave 1 and wave 2 interviews was self-reported by 222 of 34 653 participants (0.6%). Using survey questions measured at wave 1, the suicide attempt risk model yielded a cross-validated area under the receiver operator characteristic curve of 0.857 with a sensitivity of 85.3% (95% CI, 79.8-89.7) and a specificity of 73.3% (95% CI, 72.8-73.8) at an optimized threshold. The model identified 1.8% of the US population to be at a 10% or greater risk of suicide attempt. The most important risk factors were 3 questions about previous suicidal ideation or behavior; 3 items from the 12-Item Short Form Health Survey, namely feeling downhearted, doing activities less carefully, or accomplishing less because of emotional problems; younger age; lower educational achievement; and recent financial crisis. Conclusions and Relevance: In this study, after searching through more than 2500 survey questions, several well-known risk factors of suicide attempt were confirmed, such as previous suicidal behaviors and ideation, and new risks were identified, including functional impairment resulting from mental disorders and socioeconomic disadvantage. These results may help guide future clinical assessment and the development of new suicide risk scales.

Impact of sleep on complicated grief severity and outcomes.

BACKGROUND: Complicated grief (CG) is characterized by persistent, impairing grief after losing a loved one. Little is known about sleep disturbance in CG. Baseline prevalence of subjective sleep disturbance, impact of treatment on sleep, and impact of mid-treatment sleep on CG and quality of life outcomes were examined in adults with CG in secondary analyses of a clinical trial. METHODS: Patients with CG (n = 395, mean age =53.0; 78% female) were randomized to CGT+placebo, CGT+citalopram (CIT), CIT, or placebo. Subjective sleep disturbance was assessed by a grief-anchored sleep item (Pittsburgh Sleep Quality Index: PSQI-1) and a four-item sleep subscale of the Quick Inventory of Depressive Symptomatology (QIDS-4). Sleep disturbance was quantified as at least one QIDS-4 item with severity ≥2 or grief-related sleep disturbance ≥3 days a week for PSQI-1. Outcomes included the Inventory of Complicated Grief (ICG), Work and Social Adjustment Scale (WSAS), and Clinical Global Impressions Scale. RESULTS: Baseline sleep disturbance prevalence was 91% on the QIDS-4 and 46% for the grief-anchored PSQI-1. Baseline CG severity was significantly associated with sleep disturbance (QIDS-4: p = .015; PSQI-1: p = .001) after controlling for comorbid depression and PTSD. Sleep improved with treatment; those receiving CGT+CIT versus CIT evidenced better endpoint sleep (p = .027). Mid-treatment QIDS-4 significantly predicted improvement on outcome measures (all p < .01), though only WSAS remained significant after adjustment for mid-treatment ICG (p = .02). CONCLUSIONS: Greater CG severity is associated with poorer sleep beyond PTSD and depression comorbidity. Additional research including objective sleep measurement is needed to optimally elucidate and address sleep impairment associated with CG.

Changes in typical beliefs in response to complicated grief treatment.

BACKGROUND: Prolonged grief disorder (PGD) is a new diagnosis in the 11th edition of the International Classification of Diseases, estimated to affect 1 in 10 bereaved people and causing significant distress and impairment. Maladaptive thoughts play an important role in PGD. We have previously validated the typical beliefs questionnaire (TBQ), which contains five kinds of thinking commonly seen in PGD: protesting the death, negative thoughts about the world, needing the person, less grief is wrong, and grieving too much. The current paper examines the role of maladaptive cognition as measured by the TBQ in PGD and its change with treatment. METHODS: Among participants in a multisite clinical trial including 394 adults, we examined (a) the relationship between maladaptive thoughts at baseline and treatment outcomes, (b) the relationship between maladaptive thoughts and suicidality at baseline and posttreatment, and (c) the effect of treatment with and without complicated grief therapy (CGT) on maladaptive thinking. RESULTS: TBQ scores were associated with treatment outcomes and were strongly related to suicidal thinking before and after treatment. TBQ scores showed significantly greater reduction in participants who received CGT with citalopram versus citalopram alone (adjusted mean standard error [SE] difference, -2.45 [0.85]; p = .004) and those who received CGT with placebo versus placebo alone (adjusted mean [SE] difference, -3.44 [0.90]; p < .001). CONCLUSIONS: Maladaptive thoughts, as measured by the TBQ, have clinical and research significance for PGD and its treatment.

Gestational Age is Dimensionally Associated with Structural Brain Network Abnormalities Across Development.

Prematurity is associated with diverse developmental abnormalities, yet few studies relate cognitive and neurostructural deficits to a dimensional measure of prematurity. Leveraging a large sample of children, adolescents, and young adults (age 8-22 years) studied as part of the Philadelphia Neurodevelopmental Cohort, we examined how variation in gestational age impacted cognition and brain structure later in development. Participants included 72 preterm youth born before 37 weeks' gestation and 206 youth who were born at term (37 weeks or later). Using a previously-validated factor analysis, cognitive performance was assessed in three domains: (1) executive function and complex reasoning, (2) social cognition, and (3) episodic memory. All participants completed T1-weighted neuroimaging at 3 T to measure brain volume. Structural covariance networks were delineated using non-negative matrix factorization, an advanced multivariate analysis technique. Lower gestational age was associated with both deficits in executive function and reduced volume within 11 of 26 structural covariance networks, which included orbitofrontal, temporal, and parietal cortices as well as subcortical regions including the hippocampus. Notably, the relationship between lower gestational age and executive dysfunction was accounted for in part by structural network deficits. Together, these findings emphasize the durable impact of prematurity on cognition and brain structure, which persists across development.

Linked dimensions of psychopathology and connectivity in functional brain networks.

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology - mood, psychosis, fear, and externalizing behavior - are associated (r = 0.68-0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.

Diminished Cortical Thickness Is Associated with Impulsive Choice in Adolescence.

Adolescence is characterized by both maturation of brain structure and increased risk of negative outcomes from behaviors associated with impulsive decision-making. One important index of impulsive choice is delay discounting (DD), which measures the tendency to prefer smaller rewards available soon over larger rewards delivered after a delay. However, it remains largely unknown how individual differences in structural brain development may be associated with impulsive choice during adolescence. Leveraging a unique large sample of 427 human youths (208 males and 219 females) imaged as part of the Philadelphia Neurodevelopmental Cohort, we examined associations between delay discounting and cortical thickness within structural covariance networks. These structural networks were derived using non-negative matrix factorization, an advanced multivariate technique for dimensionality reduction, and analyzed using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. We found that impulsive choice, as measured by greater discounting, was most strongly associated with diminished cortical thickness in structural brain networks that encompassed the ventromedial prefrontal cortex, orbitofrontal cortex, temporal pole, and temporoparietal junction. Furthermore, structural brain networks predicted DD above and beyond cognitive performance. Together, these results suggest that reduced cortical thickness in regions known to be involved in value-based decision-making is a marker of impulsive choice during the critical period of adolescence.SIGNIFICANCE STATEMENT Risky behaviors during adolescence, such as initiation of substance use or reckless driving, are a major source of morbidity and mortality. In this study, we present evidence from a large sample of youths that diminished cortical thickness in specific structural brain networks is associated with impulsive choice. Notably, the strongest association between impulsive choice and brain structure was seen in regions implicated in value-based decision-making; namely, the ventromedial prefrontal and orbitofrontal cortices. Moving forward, such neuroanatomical markers of impulsivity may aid in the development of personalized interventions targeted to reduce risk of negative outcomes resulting from impulsivity during adolescence.

Quantitative assessment of structural image quality.

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.