Improving processing speed in adolescents at clinical high risk for psychosis with the Specific COgnitive REmediation plus Surround (SCORES) intervention: Study protocol.

AIM: Recent preventative approaches with young people at clinical high risk for psychosis (CHR-P) have focused on the remediation of the cognitive deficits that are readily apparent and predictive of future illness. However, the small number of trials using cognitive remediation with CHR-P individuals have reported mixed results. The proposed 2-phased study will test an innovative internet-based and remotely-delivered Specific COgnitive REmediation plus Surround (or SCORES) intervention that targets early processing speed deficits in CHR-P adolescents aged 14-20 years old. METHODS: In the first R61 phase, a single-arm 2-year proof of concept study, 30 CHR-P individuals will receive SCORES for 10 weeks (4 h per week/40 h total) with a midpoint assessment at 20 h (5 weeks) to demonstrate target engagement and identify the optimal dose needed to engage the target. The Go/No-Go criteria to move to the R33 phase will be processing speed scores improving by a medium effect size (Cohen's d ≥ .6). The proposed package includes a set of complimentary support surround procedures to increase enjoyment and ensure that participants will complete the home-based training. In the second R33 phase, a 3-year pilot study, we will replicate target engagement in a new and larger sample of 54 CHR-P individuals randomized to SCORES (optimized dose) or to a video game playing control condition. In addition, the R33 phase will determine if changes in processing speed are associated with improved social functioning and decreasing attenuated positive symptoms. The support surround components of the intervention will remain constant across phases and conditions in the R33 phase to firmly establish the centrality of processing speed training for successful remediation. CONCLUSIONS: The SCORES study is a completely virtual intervention that targets a core cognitive mechanism, processing speed, which is a rate-limiting factor to higher order behaviours and clinical outcomes in CHR-P adolescents. The virtual nature of this study should increase feasibility as well improve the future scalability of the intervention with considerable potential for future dissemination as a complete treatment package.

Functional phenotypes in schizophrenia spectrum disorders: defining the constructs and identifying biopsychosocial correlates using data-driven methods.

Functional impairments contribute to poor quality of life in schizophrenia spectrum disorders (SSD). We sought to (Objective I) define the main functional phenotypes in SSD, then (Objective II) identify key biopsychosocial correlates, emphasizing interpretable data-driven methods. Objective I was tested on independent samples: Dataset I (N = 282) and Dataset II (N = 317), with SSD participants who underwent assessment of multiple functioning areas. Participants were clustered based on functioning. Objective II was evaluated in Dataset I by identifying key features for classifying functional phenotype clusters from among 65 sociodemographic, psychological, clinical, cognitive, and brain volume measures. Findings were replicated across latent discriminant analyses (LDA) and one-vs.-rest binomial regularized regressions to identify key predictors. We identified three clusters of participants in each dataset, demonstrating replicable functional phenotypes: Cluster 1-poor functioning across domains; Cluster 2-impaired Role Functioning, but partially preserved Independent and Social Functioning; Cluster 3-good functioning across domains. Key correlates were Avolition, anhedonia, left hippocampal volume, and measures of emotional intelligence and subjective social experience. Avolition appeared more closely tied to role functioning, and anhedonia to independent and social functioning. Thus, we found three replicable functional phenotypes with evidence that recovery may not be uniform across domains. Avolition and anhedonia were both critical but played different roles for different functional domains. It may be important to identify critical functional areas for individual patients and target interventions accordingly.

A few theoretical results for Laplace and arctan penalized ordinary least squares linear regression estimators.

Two new nonconvex penalty functions - Laplace and arctan - were recently introduced in the literature to obtain sparse models for high-dimensional statistical problems. In this paper, we study the theoretical properties of Laplace and arctan penalized ordinary least squares linear regression models. We first illustrate the near-unbiasedness of the nonzero regression weights obtained by the new penalty functions, in the orthonormal design case. In the general design case, we present theoretical results in two asymptotic settings: (a) the number of features, p fixed, but the sample size, n → ∞ , and (b) both n and p tend to infinity. The theoretical results shed light onto the differences between the solutions based on the new penalty functions and those based on existing convex and nonconvex Bridge penalty functions. Our theory also shows that both Laplace and arctan penalties satisfy the oracle property. Finally, we also present results from a brief simulations study illustrating the performance of Laplace and arctan penalties based on the gradient descent optimization algorithm.

Examining the Effectiveness of a Digital Media Campaign at Reducing the Duration of Untreated Psychosis in New York State: Results From a Stepped-wedge Randomized Controlled Trial.

BACKGROUND AND HYPOTHESIS: Longer duration of untreated psychosis (DUP) predicts worse outcomes in First Episode Psychosis (FEP). Searching online represents one of the first proactive step toward treatment initiation for many, yet few studies have informed how best to support FEP youth as they engage in early online help-seeking steps to care. STUDY DESIGN: Using a stepped-wedge randomized design, this project evaluated the effectiveness of a digital marketing campaign at reducing DUP and raising rates of referrals to FEP services by proactively targeting and engaging prospective patients and their adult allies online. STUDY RESULTS: Throughout the 18-month campaign, 41 372 individuals visited our website, and 371 advanced to remote clinical assessment (median age = 24.4), including 53 allies and 318 youth. Among those assessed (n = 371), 53 individuals (14.3%) reported symptoms consistent with psychotic spectrum disorders (62.2% female, mean age 20.7 years) including 39 (10.5%) reporting symptoms consistent with either Clinical High Risk (ie, attenuated psychotic symptoms; n = 26) or FEP (n = 13). Among those with either suspected CHR or FEP (n = 39), 20 (51.3%) successfully connected with care. The campaign did not result in significant differences in DUP. CONCLUSION: This study highlights the potential to leverage digital media to help identify and engage youth with early psychosis online. However, despite its potential, online education and professional support alone are not yet sufficient to expedite treatment initiation and reduce DUP.

A simple illustration of interleaved learning using Kalman filter for linear least squares.

Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis.

OBJECTIVE: Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker. METHODS: In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design. RESULTS: The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems. CONCLUSIONS: This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.

BACKGROUND: Delirium is a critically underdiagnosed syndrome of altered mental status affecting more than 50% of older adults admitted to hospital. Few studies have incorporated speech and language disturbance in delirium detection. We sought to describe speech and language disturbances in delirium, and provide a proof of concept for detecting delirium using computational speech and language features. METHODS: Participants underwent delirium assessment and completed language tasks. Speech and language disturbances were rated using standardized clinical scales. Recordings and transcripts were processed using an automated pipeline to extract acoustic and textual features. We used binomial, elastic net, machine learning models to predict delirium status. RESULTS: We included 33 older adults admitted to hospital, of whom 10 met criteria for delirium. The group with delirium scored higher on total language disturbances and incoherence, and lower on category fluency. Both groups scored lower on category fluency than the normative population. Cognitive dysfunction as a continuous measure was correlated with higher total language disturbance, incoherence, loss of goal and lower category fluency. Including computational language features in the model predicting delirium status increased accuracy to 78%. LIMITATIONS: This was a proof-of-concept study with limited sample size, without a set-aside cross-validation sample. Subsequent studies are needed before establishing a generalizable model for detecting delirium. CONCLUSION: Language impairments were elevated among patients with delirium and may also be used to identify subthreshold cognitive disturbances. Computational speech and language features are promising as accurate, noninvasive and efficient biomarkers of delirium.

Decreased inpatient psychiatric admissions with telepsychiatry use during the COVID-19 pandemic.

Objective: This study examines the quality of care provided through telepsychiatry by comparing psychiatric hospitalization rates among patients receiving in-person psychiatric care prior to the COVID-19 pandemic with rates among patients receiving virtual psychiatric care during the COVID-19 pandemic. Methods: Mental health-related hospitalization rates among patients enrolled in a large academic hospital's outpatient psychiatry programs between March 1, 2018 and February 28, 2022 were retrospectively analyzed. Four time periods were created, spanning March 1 to February 28 of the following year. Demographic and clinical data were collected from the electronic health record, and descriptive statistics were calculated. Change in hospitalization rate between time periods was evaluated using McNemar's test. Results: In the 2018 time period, 7.38% of all enrolled patients were hospitalized, compared to 7.70% hospitalized in the 2019 period, 5.74% in the 2020 period, and 5.38% in the 2021 period. Patients enrolled in both the 2018 and the 2019 periods saw no difference in hospitalization rate between the 2 years (2.93% in 2018, 2.83% in 2019; p = 0.830); patients enrolled in both 2019 and 2020 saw significantly lower hospitalization rates in 2020 (5.47% in 2019, 4.58% in 2020; p = 0.022); and patients enrolled in both 2020 and 2021 saw no difference (3.34% in 2020, 3.23% in 2021; p = 0.777). Conclusion: Psychiatric hospitalization rates significantly decreased between the 2019 and the 2020 periods, suggesting a decrease in admissions associated with adoption of telepsychiatry. Future research should differentiate the roles played by telepsychiatry and COVID-19-related factors in reducing hospitalization rates during the pandemic.

Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features.

BACKGROUND AND HYPOTHESIS: Quantitative acoustic and textual measures derived from speech ("speech features") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross-diagnostic latent factors for speech disturbance with relevance for SSD and computational modeling. STUDY DESIGN: Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N = 334), including SSD (n = 90). Speech features were quantified using an automated pipeline for brief recorded samples of free speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. The relationships between factor scores and computational speech features were examined for 202 of the participants. STUDY RESULTS: We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs SSD groups. CONCLUSIONS: We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.

Relapse prevention through health technology program reduces hospitalization in schizophrenia.

BACKGROUND: Psychiatric hospitalization is a major driver of cost in the treatment of schizophrenia. Here, we asked whether a technology-enhanced approach to relapse prevention could reduce days spent in a hospital after discharge. METHODS: The Improving Care and Reducing Cost (ICRC) study was a quasi-experimental clinical trial in outpatients with schizophrenia conducted between 26 February 2013 and 17 April 2015 at 10 different sites in the USA in an outpatient setting. Patients were between 18 and 60 years old with a diagnosis of schizophrenia, schizoaffective disorder, or psychotic disorder not otherwise specified. Patients received usual care or a technology-enhanced relapse prevention program during a 6-month period after discharge. The health technology program included in-person, individualized relapse prevention planning with treatments delivered via smartphones and computers, as well as a web-based prescriber decision support program. The main outcome measure was days spent in a psychiatric hospital during 6 months after discharge. RESULTS: The study included 462 patients, of which 438 had complete baseline data and were thus used for propensity matching and analysis. Control participants (N = 89; 37 females) were enrolled first and received usual care for relapse prevention followed by 349 participants (128 females) who received technology-enhanced relapse prevention. During 6-month follow-up, 43% of control and 24% of intervention participants were hospitalized (χ2 = 11.76, p<0.001). Days of hospitalization were reduced by 5 days (mean days: b = -4.58, 95% CI -9.03 to -0.13, p = 0.044) in the intervention condition compared to control. CONCLUSIONS: These results suggest that technology-enhanced relapse prevention is an effective and feasible way to reduce rehospitalization days among patients with schizophrenia.

Potential application of elastic nets for shared polygenicity detection with adapted threshold selection.

Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis.

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.

Outcomes During and After Early Intervention Services for First-Episode Psychosis: Results Over 5 Years From the RAISE-ETP Site-Randomized Trial.

To examine long-term effects of early intervention services (EIS) for first-episode psychosis, we compared Heinrichs-Carpenter Quality of Life (QLS) and Positive and Negative Syndrome Scale (PANSS) scores and inpatient hospitalization days over 5 years with data from the site-randomized RAISE-ETP trial that compared the EIS NAVIGATE (17 sites; 223 participants) and community care (CC) (17 sites; 181 participants). Inclusion criteria were: age 15-40 years; DSM-IV diagnoses of schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, or psychotic disorder not otherwise specified; first psychotic episode; antipsychotic medication taken for ≤6 months. NAVIGATE-randomized participants could receive NAVIGATE from their study entry date until NAVIGATE ended when the last-enrolled NAVIGATE participant completed 2 years of treatment. Assessments occurred every 6 months. 61% of participants had assessments conducted ≥2 years; 31% at 5 years. Median follow-up length was CC 30 months and NAVIGATE 38 months. Primary analyses assumed data were not-missing-at-random (NMAR); sensitivity analyses assumed data were missing-at-random (MAR). MAR analyses found no significant treatment-by-time interactions for QLS or PANSS. NMAR analyses revealed that NAVIGATE was associated with a 13.14 (95%CI:6.92,19.37) unit QLS and 7.73 (95%CI:2.98,12.47) unit PANSS better improvement and 2.53 (95%CI:0.59,4.47) fewer inpatient days than CC (all comparisons significant). QLS and PANSS effect sizes were 0.856 and 0.70. NAVIGATE opportunity length (mean 33.8 (SD = 5.1) months) was not associated (P = .72) with QLS outcome; duration of untreated psychosis did not moderate (P = .32) differential QLS outcome. While conclusions are limited by the low rate of five-year follow-up, the data support long-term benefit of NAVIGATE compared to community care.

Metabolic disturbances, hemoglobin A1c, and social cognition impairment in Schizophrenia spectrum disorders.

Social cognitive impairments are core features of schizophrenia spectrum disorders (SSD) and are associated with greater functional impairment and decreased quality of life. Metabolic disturbances have been related to greater impairment in general neurocognition, but their relationship to social cognition has not been previously reported. In this study, metabolic measures and social cognition were assessed in 245 participants with SSD and 165 healthy comparison subjects (HC), excluding those with hemoglobin A1c (HbA1c) > 6.5%. Tasks assessed emotion processing, theory of mind, and social perception. Functional connectivity within and between social cognitive networks was measured during a naturalistic social task. Among SSD, a significant inverse relationship was found between social cognition and cumulative metabolic burden (ß = -0.38, p < 0.001) and HbA1c (ß = -0.37, p < 0.001). The relationship between social cognition and HbA1c was robust across domains and measures of social cognition and after accounting for age, sex, race, non-social neurocognition, hospitalization, and treatment with different antipsychotic medications. Negative connectivity between affect sharing and motor resonance networks was a partial mediator of this relationship across SSD and HC groups (ß = -0.05, p = 0.008). There was a group x HbA1c effect indicating that SSD participants were more adversely affected by increasing HbA1c. Thus, we provide the first report of a robust relationship in SSD between social cognition and abnormal glucose metabolism. If replicated and found to be causal, insulin sensitivity and blood glucose may present as promising targets for improving social cognition, functional outcomes, and quality of life in SSD.

Antenatal Antidepressant Prescription Associated With Reduced Fetal Femur Length but Not Estimated Fetal Weight: A Retrospective Ultrasonographic Study.

PURPOSE/BACKGROUND: Antidepressants are among the most frequently prescribed medications during pregnancy and may affect fetal weight. Associations between antenatal antidepressant use and ultrasonographic measures of fetal development have rarely been examined. We hypothesized that the prescription of an antenatal antidepressant would be associated with lower estimated fetal weight (EFW). METHODS/PROCEDURES: A retrospective analysis of routine ultrasonographic data extracted from electronic medical records was performed on a cohort of pregnant women with psychiatric diagnoses and grouped according to the presence of an antenatal antidepressant prescription (n = 32 antidepressant-prescribed and n = 44 antidepressant prescription-free). After stratifying for gestational age, comparisons included 13 ultrasonographic parameters, frequency of oligohydramnios and polyhydramnios and growth deceleration, and maternal serum protein markers assessed per routine care, including α-fetoprotein, free ß-human chorionic gonadotropin, and unconjugated estriol levels, using t tests, nonparametric and Fisher tests, and effect sizes (ESs) were computed. FINDINGS/RESULTS: No statistically significant EFW differences between groups at any time point were detected (P > 0.05). Antenatal antidepressant prescription was associated with lower femur length at weeks 33 to 40 (P = 0.046, ES = 0.75) and greater left ventricular diameter at weeks 25 to 32 (P = 0.04, ES = 1.18). No differences for frequency of oligohydramnios or polyhydramnios or growth deceleration were observed (P > 0.05). We did not detect group differences for maternal proteins (P > 0.05). IMPLICATIONS/CONCLUSIONS: Our evidence suggested a lack of association between antenatal antidepressant prescription and lower EFW but indicated an association with lower femur length and greater left ventricular diameter in mid-late gestation. Future research should examine the clinical implications of these findings.

Exploring the relationship between emotion and cognitive load types during patient handovers.

Cognitive Load Theory has emerged as an important approach to improving instruction in the health professions workplace, including patient handovers. At the same time, there is growing recognition that emotion influences learning through numerous cognitive processes including motivation, attention, working memory, and long-term memory. This study explores how emotion influences the cognitive load experienced by trainees performing patient handovers. From January to March 2019, 693 (38.7%) of 1807 residents and fellows from a 24-hospital health system in New York city completed a survey after performing a handover. Participants rated their emotional state and cognitive load. The survey included questions about features of the learner, task, and instructional environment. The authors used factor analysis to identify the core dimensions of emotion. Regression analyses explored the relationship between the emotion factors and cognitive load types. Two emotion dimensions were identified representing invigoration and tranquility. In regression analyses, higher levels of invigoration, tranquility, and their interaction were independently associated with lower intrinsic load and extraneous load. The interaction of invigoration and tranquility predicted lower germane load. The addition of the emotion variables to multivariate models including other predictors of cognitive load types significantly increased the amount of variance explained. The study provides a model for measuring emotions in workplace learning. Because emotion appears to have a significant influence on cognitive load types, instructional designers should consider strategies that help trainees regulate emotion in order to reduce cognitive load and improve learning and performance.

Evidence for validity for the Cognitive Load Inventory for Handoffs.

CONTEXT: Patient handovers remain a significant patient safety challenge. Cognitive load theory (CLT) can be used to identify the cognitive mechanisms for handover errors. The ability to measure cognitive load types during handovers could drive the development of more effective curricula and protocols. No such measure currently exists. METHODS: The authors developed the Cognitive Load Inventory for Handoffs (CLIH) using a multi-step process, including expert interviews to enhance content validity and talk-alouds to optimise response process validity. The final version contained 28 items. From January to March 2019, we administered a cross-sectional survey to 1807 residents and fellows from a large health care system in the USA. Participants completed the CLIH following a handover. Exploratory factor analysis of data from one-third of respondents identified high-performing items; confirmatory factor analysis of data from the remaining sample assessed model fit. Model fit was evaluated using the comparative fit index (CFI) (>0.90), Tucker-Lewis index (TFI) (>0.80), standardised root mean square residual (SRMR) (<0.08) and root mean square of error of approximation (RMSEA) (<0.08). RESULTS: Participants included 693 trainees (38.4%) (231 in the exploratory study and 462 in the confirmatory study). Eleven items were removed during exploratory factor analysis. Confirmatory factor analysis of the 16 remaining items (five for intrinsic load, seven for extraneous load and four for germane load) supported a three-factor model and met criteria for good model fit: the CFI was 0.95, TFI was 0.93, RMSEA was 0.074 and SRMR was 0.07. The factor structure was comparable for gender and role. Intrinsic, extraneous and germane load scales had high internal consistency. With one exception, scale scores were associated, as hypothesised, with postgraduate level and clinical setting. CONCLUSIONS: The CLIH measures three types of cognitive load during patient handovers. Evidencefor validity is provided for the CLIH's content, response process, internal structure and association with other variables. This instrument can be used to determine the relative drivers of cognitive load during handovers in order to optimize handover instruction and protocols.

Detecting time lag between a pair of time series using visibility graph algorithm.

Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.

Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm.

Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.

Correction to: graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.

The original version of the article contained an error in the electronic supplementary material. The caption of the figure in the electronic supplementary material was omitted.