ArticleMultivariate behavioral research
Unsupervised Classification During Time-Series Model Building.
Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 69 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
69 citing papers in PubMed.
- Functional connectivity subtypes during a positive mood induction: Predicting clinical response in a randomized controlled trial of ketamine for treatment-resistant depression.Journal of psychopathology and clinical science · 2025Trial
- Exploring Family Interactions: Heterogeneity in Family Specific Network Models.Family process · 2026Article
- Right hippocampal volume and attenuated psychotic symptoms distinguish subgroups of youth with common patterns of temporoparietal effective connectivity.Brain structure & function · 2026Article
- Characterizing Resting-State Frontoparietal-Amygdala Network Connectivity as a Potential Moderator of the Developmental Link Between Executive Functioning and Internalizing Symptoms: A Group Iterative Multiple Model Estimation-Based Approach.Biological psychiatry global open science · 2026Article
- Group, Subgroup and Person-Specific Longitudinal Associations Between Physical Activity and Affect in Individuals With and Without Depressive and Anxiety Disorders.International journal of methods in psychiatric research · 2026Article
- Connectivity between the central executive and salience networks normalizes with exposure-focused CBT in pediatric anxiety.medRxiv : the preprint server for health sciences · 2026Article
- The Speech Network in Childhood Stuttering: Differences in Functional Connectivity of the Planning and Motor Loops.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Acceptance and commitment therapy for moral injury: conceptualizing and intervening on moral injury through the lens of contextual behavioral science.Frontiers in psychiatry · 2026Article
- Transdiagnostic Network Mapping of Psychopathology in Daily Life: Rationale and Research Protocol.Clinical psychology in Europe · 2025Article
- Development of Self-Reported Reward Responsiveness and Inhibitory Control and the Role of Clinical and Neural Predictors.Journal of personality · 2025Article
- Article
- Relations of temporoparietal connectivity with neighborhood social fragmentation in youth at clinical high-risk for psychosis.Schizophrenia research · 2025Article
- Efficacy of a process-based, Mobile-delivered personalized CBT for anxiety disorders: Study protocol for a randomized controlled trial.Internet interventions · 2025Review
- Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study.JMIR formative research · 2025Article
- Group-to-individual generalizability and individual-level inferences in cognitive neuroscience.Neuroscience and biobehavioral reviews · 2025Review
- Unsupervised Model Construction in Continuous-Time.Structural equation modeling : a multidisciplinary journal · 2025Article
- Commentary on Serre et al. : Demonstrating the next era of addiction science.Addiction (Abingdon, England) · 2025Article
- Neural connectivity biotypes: predictors of clinical outcomes and improvement patterns of iTBS treatment in adolescents and young adults with depression.General psychiatry · 2025Article
- Examining Caregiver Practices During Adolescent Outpatient Alcohol Use and Co-Occurring Mental Health Treatment: Protocol for a Dyadic Ecological Momentary Assessment Study.JMIR research protocols · 2024Article
- Heterogeneity in suicide risk: Evidence from personalized dynamic models.Behaviour research and therapy · 2024Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.