Optimizing the measurement of sample entropy in resting-state fMRI data.

Introduction: The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. Methods: We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. Results: When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. Discussion: We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.

Revisiting Associations Among Parent and Adolescent Religiosity and Early Adolescent Suicide Risk in the United States.

The contributions of religion to reduced suicide risk have been studied in adults and adolescents, though to our knowledge no comprehensive investigation has been conducted in early adolescents, at a time coinciding with emergence of suicide risk trajectories. In this largest study to date on this topic, we aimed to characterise the contributions of various measures of "private" and "public" religiosity to early adolescent suicide ideation (SI) and suicide attempt (SA) histories using information from a large, epidemiologically informed U.S. sample of adolescents (N = 7068; mean age = 12.89 years, 47% female) and their parents. In all youth, parent-reported adolescent religious importance was associated with reduced odds of SA (OR = 0.75, CI = 0.61-0.92, P = .005). Muslim youth were more likely (OR = 1.52, CI = 1.02-2.22, P = .033), and Catholic youth were less likely (OR = 0.80, CI = 0.67-0.95, P = .014), to report SI. A variety of sex differences were noted, with significant protective associations of adolescent self-reported religiosity on SI and SA, religious service attendance on SI, and religious importance on SI, in female-but not male-youth; and significant protective associations of religious importance on SA in male-but not female-youth. Against expectations, there was no evidence that parent religiosity moderated the link between youth religiosity and SI or SA. These results shed light on the roles of cultural and familial context in youth suicide risk, which may ultimately be targeted in screening and interventional approaches.

The Minnesota Medical Operations Coordination Center: A COVID-19 Statewide Response to Ensure Access to Critical Care and Medical-Surgical Beds.

BACKGROUND: COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity. RESEARCH QUESTION: Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges? STUDY DESIGN AND METHODS: The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey. RESULTS: From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences. INTERPRETATION: Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased.

Individual Risk Factors of PM2.5 Associated With Wintertime Mortality in Urban Patients With COPD.

BACKGROUND: Air pollution contributes to premature mortality, but potential impacts differ in populations with existing disease, particularly for individuals with multiple risk factors. Although COPD increases vulnerability to air pollution, individuals with COPD and other individual risk factors are at the intersection of multiple risks and may be especially susceptible to the effect of acute outdoor air pollution. RESEARCH QUESTION: What is the association between wintertime air pollution and mortality in patients with COPD and the modifying role of individual risk factors? STUDY DESIGN AND METHODS: This study evaluated 19,243 deceased veterans with prior COPD diagnosis who had resided in 25 US metropolitan regions (2016-2019). Electronic health records included patient demographic characteristics; smoking status; and comorbidities such as asthma, coronary artery disease (CAD), obesity, and diabetes. Using geocoded addresses, individuals were assigned wintertime fine particulate matter (particulate matter smaller than 2.5 µg in diameter [PM2.5]) and nitrogen dioxide air pollution exposures. Associations between acute air pollution and mortality were estimated by using a time-stratified case-crossover design with a conditional logistic model, and individual risk differences were assessed according to stratified analysis. RESULTS: A 1.05 (95% CI, 1.02-1.09) mortality risk was estimated for each 10 µg/m3 increase in daily wintertime PM2.5). Older patients and Black individuals displayed elevated risk. Obesity was a substantial air pollution-related mortality risk factor (OR, 1.11; 95% CI, 1.01-1.23), and the estimated risk for individuals with obesity plus CAD or obesity plus diabetes was 16% higher. INTERPRETATION: Wintertime PM2.5 exposure was associated with elevated mortality risk in people with COPD, but individuals with multiple comorbidities, notably obesity, had high vulnerability. Our study suggests that obesity, CAD, and diabetes are understudied modifiers of air pollution-related risks for people with existing COPD.

A Multilevel Examination of Cognitive Control in Adolescents With Nonsuicidal Self-injury.

Background: Nonsuicidal self-injury (NSSI), a transdiagnostic behavior, often emerges during adolescence. This study used the Research Domain Criteria approach to examine cognitive control (CC) with a focus on response inhibition and urgency relative to NSSI severity in adolescents. Methods: One hundred thirty-eight adolescents, assigned female sex at birth, with a continuum of NSSI severity completed negative and positive urgency measurements (self-report), an emotional Go/NoGo task within negative and positive contexts (behavioral), and structural and functional imaging during resting state and task (brain metrics). Cortical thickness, subcortical volume, resting-state functional connectivity, and task activation focused on an a priori-defined CC network. Eighty-four participants had all these main measures. Correlations and stepwise model selection followed by multiple regression were used to examine the association between NSSI severity and multiunit CC measurements. Results: Higher NSSI severity correlated with higher negative urgency and lower accuracy during positive no-inhibition (Go). Brain NSSI severity correlates varied across modalities and valence. For right medial prefrontal cortex and right caudate, higher NSSI severity correlated with greater negative but lower positive inhibition (NoGo) activation. The opposite pattern was observed for the right dorsolateral prefrontal cortex. Higher NSSI severity correlated with lower left dorsal anterior cingulate cortex (ACC) negative inhibition activation and thicker left dorsal ACC, yet it was correlated with higher right rostral ACC positive inhibition activation and thinner right rostral ACC, as well as lower CC network resting-state functional connectivity. Conclusions: Findings revealed multifaceted signatures of NSSI severity across CC units of analysis, confirming the relevance of this domain in adolescent NSSI and illustrating how multimodal approaches can shed light on psychopathology.

Sociodemographic effects on pandemic fatigue are multifaceted and context-specific: A longitudinal analysis of physical distancing adherence.

Background: Pandemic fatigue emerged early during the COVID-19 pandemic and remains a concern as new variants emerge and ongoing public health measures are needed to control them. A wide range of factors can affect pandemic fatigue, but empiric research indicating which may be most important to adherence in specific populations is lacking. Design & Methods: We conducted a longitudinal study of changes in physical distancing in two cohorts: adults living with children <18 years and adults ≥50 years old. Six types of non-work, non-household contacts were ascertained at six times from April to October 2020. We used generalized estimating equations Poisson regression to estimate the one-week change in contact rate and how this differed based on sociodemographic characteristics. Results: The rate of all contact types increased during the middle of the study period and decreased toward the end. Changes in contact rates over time differed according to several sociodemographic characteristics, including age, gender, race/ethnicity, education, household composition, and access to transportation. Furthermore, the factors influencing the rate of change in contact rates differed by the type or setting of the contact, for example contacts as a result of visiting another person's home versus during a retail outing. Conclusions: These results provide evidence for potential mechanisms by which pandemic fatigue has resulted in lower physical distancing adherence.

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease.

The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

Sex and age variations in the impact of puberty on cortical thickness and associations with internalizing symptoms and suicidal ideation in early adolescence.

PURPOSE: The childhood-to-adolescence transition is a notable period of change including pubertal development, neurodevelopment, and psychopathology onset, that occurs in divergent patterns between sexes. This study examined the effects of sex and puberty on cortical thickness (CT) in children and explored whether CT changes over time related to emergence of psychopathology in early adolescence. METHODS: We used longitudinal data (baseline ages 9-10 and Year 2 [Y2] ages 11-12) from the ABCD Study (n = 9985). Linear and penalized function-on-function regressions modeled the impact of puberty, as it interacts with sex, on CT. Focusing on regions that showed sex differences, linear and logistic regressions modeled associations between change in CT and internalizing problems and suicide ideation. RESULTS: We identified significant sex differences in the inverse relation between puberty and CT in fifteen primarily posterior brain regions. Nonlinear pubertal effects across age were identified in the fusiform, isthmus cingulate, paracentral, and precuneus. All effects were stronger for females relative to males during this developmental window. We did not identify associations between CT change and early adolescent clinical outcomes. CONCLUSION: During this age range, puberty is most strongly associated with regional changes in CT in females, which may have implications for the later emergence of psychopathology.

Psychiatric Diagnoses and Treatment in Nine- to Ten-Year-Old Participants in the ABCD Study.

Objective: Psychiatric disorders commonly emerge prior to adulthood. Identification and intervention may vary significantly across populations. We leveraged a large population-based study to estimate the prevalence of psychiatric disorders and treatments, and evaluate predictors of treatment, in children ages 9-10 in the United States. Method: We analyzed cross-sectional data from the Adolescent Brain Cognitive Developmental (ABCD) Study. The Computerized Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-COMP) was used to estimate clinical diagnoses, and the Child Behavior Checklist (CBCL) was used to assess internalizing and externalizing psychopathology. Parents reported on prescription medications and other mental health interventions. Prevalence rates of KSADS diagnoses and treatments were calculated. Logistic regression analyses estimated associations between clinical and sociodemographic predictors (sex at birth, race, ethnicity, income, education, urbanicity) and treatments. Results: The most common KSADS diagnoses were anxiety disorders, followed by attention deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder. ADHD and depression diagnoses predicted stimulant and antidepressant medication use, respectively. Bipolar and ADHD diagnoses also predicted antidepressant medications, outpatient treatment and psychotherapy. The odds of reporting specific treatments varied by sex, ethnic and racial identities, urbanicity, and income. Conclusion: Expected rates of KSADS-based psychiatric symptoms are present in the ABCD sample at ages 9-10, with treatment patterns broadly mapping onto psychopathology in expected ways. However, we observed important variations in reported treatment utilization across sociodemographic groups, likely reflecting societal and cultural influences. Findings are considered in the context of potential mental health disparities in U.S. children.

Multimodal assessment of sustained threat in adolescents with nonsuicidal self-injury.

Nonsuicidal self-injury (NSSI) is a common but poorly understood phenomenon in adolescents. This study examined the Sustained Threat domain in female adolescents with a continuum of NSSI severity (N = 142). Across NSSI lifetime frequency and NSSI severity groups (No + Mild NSSI, Moderate NSSI, Severe NSSI), we examined physiological, self-reported and observed stress during the Trier Social Stress Test; amygdala volume; amygdala responses to threat stimuli; and resting-state functional connectivity (RSFC) between amygdala and medial prefrontal cortex (mPFC). Severe NSSI showed a blunted pattern of cortisol response, despite elevated reported and observed stress during TSST. Severe NSSI showed lower amygdala-mPFC RSFC; follow-up analyses suggested that this was more pronounced in those with a history of suicide attempt for both moderate and severe NSSI. Moderate NSSI showed elevated right amygdala activation to threat; multiple regressions showed that, when considered together with low amygdala-mPFC RSFC, higher right but lower left amygdala activation predicted NSSI severity. Patterns of interrelationships among Sustained Threat measures varied substantially across NSSI severity groups, and further by suicide attempt history. Study limitations include the cross-sectional design, missing data, and sampling biases. Our findings highlight the value of multilevel approaches in understanding the complexity of neurobiological mechanisms in adolescent NSSI.

Quantifying Treatment-Related Fluctuations in CIDP: Results of the GRIPPER Study.

OBJECTIVE: The objective of this study was to explore the extent of IV immunoglobulin (IVIG) treatment-related fluctuations (TRFs) by using home collection of daily grip strength in patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and to use that information to develop evidence-based treatment optimization strategies. METHODS: This prospective observational study included 25 patients with well-defined CIDP. Participants recorded grip strength daily for 6 months. Disability and gait metrics were collected weekly. Serum immunoglobulin G levels were obtained at peak, trough, and midcycle IVIG intervals. Day-to-day grip strength changes <10% were considered random. To identify patients with TRFs, 3-day averaged grip strength was calculated on each consecutive day after an IVIG infusion. TRFs were defined as ≥10% 3-day averaged grip strength difference compared to the pre-IVIG baseline. RESULTS: Participants successfully recorded grip strength on all but 9% of recordable days. Twelve patients (48%) were classified as low/no fluctuaters and 13 (52%) as frequent fluctuaters. In the frequent fluctuating group, grip strength improved over 1 week and thereafter was relatively stable until the third week after infusion. Grip strength was significantly correlated with measures of disability. CONCLUSIONS: Grip strength collection by patients at home is reliable, valid, and feasible. A change in grip strength by ≥10% is a useful, practical, and evidence-based approach that may be used to identify clinically meaningful TRFs. From these data, we propose a treatment optimization strategy for patients with CIDP on chronic IVIG that may be applied to routine clinic care during both face-to-face and virtual video or telephone patient encounters. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT02414490.