{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"invariant_mode_decomposition_design__psychology","hypothesis_id":"H1","search_queries":["idiographic dynamic network analysis personalized psychotherapy treatment selection EMA module CBT","within-person symptom dynamics eigenvalues stability psychotherapy intervention selection","network control theory personalized intervention selection clinical time series psychotherapy","control systems engineering personalized mental health intervention ecological momentary assessment","\"modal controllability\" psychological intervention eigenvalues","\"unstable modes\" mental health dynamical system intervention psychotherapy","psychotherapy \"eigenvalue\" \"dynamic network\" intervention","patent personalized psychotherapy dynamic network treatment recommendation ecological momentary assessment"],"sources":[{"source_id":"C1","title":"On the Control of Psychological Networks","publisher":"PubMed Central / U.S. National Library of Medicine","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC9205512/","source_class":"PRIMARY_RESEARCH","claims_supported":["Introduces average and modal controllability to psychological-network research expressly to select intervention targets.","Uses clinical EMA data from people receiving treatment for complicated grief, with suicidal ideation as a focal outcome.","Defines modal controllability using the transition matrix's eigenvalues and eigenvectors and interprets modes as independent directions of system movement."]},{"source_id":"C2","title":"Formalizing Psychological Interventions Through Network Control Theory","publisher":"Scientific Reports / Philipps-Universität Marburg repository","url":"https://open.uni-marburg.de/server/api/core/bitstreams/006470d1-e57a-4fbc-86c0-65c62e38e272/content","source_class":"PRIMARY_RESEARCH","claims_supported":["Models person-level psychological interventions as perturbations of linear dynamic networks and relates intervention sensitivity and specificity to average and modal controllability.","Uses Dynamic Mode Decomposition with Control to estimate individual operators and intervention-input matrices.","Explicitly computes modal controllability from eigenvalues and eigenvectors, although the empirical setting concerns attitude interventions rather than psychotherapy."]},{"source_id":"C3","title":"Network Control Theory in Personalized Intervention Selection: A Proof-of-Concept Approach to Simulating Therapeutic Intervention Effects in Clinical Time-Series Data","publisher":"Psychotherapy Research / Taylor & Francis","url":"https://pubmed.ncbi.nlm.nih.gov/42137962/","source_class":"PRIMARY_RESEARCH","claims_supported":["Fits person-specific linear dynamic systems to pretreatment EMA from 20 patients with depression or anxiety.","Simulates 31 therapeutic interventions and evaluates them using controllability and cumulative impulse-response measures.","Directly advances dynamic-network modeling as a basis for personalized therapeutic-intervention selection while acknowledging idealized assumptions and lack of clinical validation."]},{"source_id":"C4","title":"Clinical Trial Protocol NCT06517589","publisher":"ClinicalTrials.gov","url":"https://cdn.clinicaltrials.gov/large-docs/89/NCT06517589/Prot_001.pdf","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["Specifies an 80-patient randomized comparison of process-based therapy and routine CBT.","Uses baseline EMA to construct idiographic dynamic networks, identifies central nodes, edges, self-loops, and feedback loops, and selects interventions matched to implicated change processes.","Continues daily EMA monitoring after treatment selection, placing personalized network review inside an active psychotherapy workflow."]},{"source_id":"C5","title":"Effectiveness of Network Analysis–Driven Personalized Digital Interventions Versus Standard Intervention for Depression: A Proof-of-Concept Pilot Randomized Controlled Trial","publisher":"Molecular Psychiatry / Springer Nature","url":"https://www.nature.com/articles/s41380-026-03467-w","source_class":"PRIMARY_RESEARCH","claims_supported":["Prospectively sequences up to nine digital intervention modules from core symptoms identified in person-specific networks estimated from ten-day EMA.","Randomized 62 participants between network-personalized and fixed module sequences.","Found a near-zero, statistically nonsignificant group-by-time difference, so existing evidence does not establish an outcome advantage for network-guided module sequencing."]},{"source_id":"C6","title":"A Process-Based Approach to Cognitive Behavioral Therapy: A Theory-Based Case Illustration","publisher":"Frontiers in Psychology","url":"https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.1002849/full","source_class":"PRIMARY_RESEARCH","claims_supported":["Documents CBT treatment planning based on an idiographic network of interacting processes rather than the largest symptom or diagnostic label.","Uses repeated personalized EMA and a person-level GIMME/VAR analysis to estimate contemporaneous and lagged relations and revisit treatment targets.","Shows that network-informed treatment planning, repeated model review, and mapping influential processes to therapeutic techniques are already articulated clinical practices."]},{"source_id":"C7","title":"US20250174337A1 — Artificial Intelligence-Based Personalized Predictive Treatment System","publisher":"USPTO via Google Patents","url":"https://patents.google.com/patent/US20250174337A1/en","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["Claims personalized selection and ongoing adjustment among psychotherapy, medication, and other treatments using EEG, assessments, patient factors, and daily-life monitoring.","Provides adjacent intellectual-property coverage for adaptive mental-health treatment planning, but its disclosed mechanism is similarity- and AI-based rather than an idiographic transition-eigenmode rule."]}],"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"Modal-controllability intervention targeting in psychological networks","similarity":"Already uses clinical EMA-derived psychological networks, eigenvalues and eigenvectors, modal controllability, outcome-specific objectives, and optimization of intervention targets rather than raw symptom magnitude.","remaining_difference":"It targets controllable symptoms or input sets and does not clearly filter locally unstable eigenmodes before mapping the most function-sensitive unstable mode to the next CBT module.","source_ids":["C1","C2"]},{"name":"Network-control simulation of personalized therapeutic interventions","similarity":"Estimates person-specific linear dynamics from clinical EMA, simulates many psychotherapy interventions, and ranks their expected effects for personalized selection.","remaining_difference":"The reported decision metrics are controllability and cumulative impulse response, not the proposed two-stage unstable-mode partition followed by functional-outcome sensitivity ranking.","source_ids":["C3"]},{"name":"Person-specific EMA network sequencing of treatment modules","similarity":"Directly converts person-specific EMA networks into individualized ordering of psychological-intervention modules and has already been evaluated in a randomized pilot.","remaining_difference":"Modules are sequenced from core symptom nodes rather than growing eigenmodes, and the comparator is a fixed sequence rather than largest-reported-symptom targeting.","source_ids":["C5"]},{"name":"Process-based CBT guided by idiographic dynamic networks","similarity":"Places dynamic-network interpretation between intensive measurement and intervention choice, targets coupled maintaining processes, and repeatedly monitors change during CBT.","remaining_difference":"Selection relies on central nodes, edges, loops, process evidence, and collaborative judgment rather than an explicit eigenvalue stability boundary plus module-level sensitivity calculation.","source_ids":["C4","C6"]}],"overlapping_components":["Within-patient intensive EMA or clinical time-series measurement","Person-specific VAR or linear state-transition modeling","Coupled symptom-and-process state representation","Eigenvalue/eigenvector-based modal controllability","Simulation or estimation of intervention effects","Outcome-specific intervention-target optimization","Mapping network targets to psychotherapy techniques or modules","Repeated monitoring and treatment-plan adaptation","Recognition of linearity, stationarity, measurement-burden, and estimation-validity limits"],"remaining_contrastive_claim":"No located source implements and prospectively tests a weekly CBT rule that first filters a patient's within-person transition eigenmodes by local instability, then ranks only those modes by the marginal sensitivity of functioning to candidate modules, and selects the module for the highest-ranked mode against a largest-symptom comparator.","claim_falsifier":"A pre-August-2-2026 paper, protocol, product manual, patent, trial record, or reproducible implementation showing that exact instability-filter-plus-functional-sensitivity sequence for CBT module selection—or evidence that its module choices are mathematically equivalent to an existing modal-controllability or network-control rule—would falsify the remaining contrastive claim.","problem_support":"MODERATE","recommendation":"RESEARCH","world_novelty_boundary":"This eight-query bounded search found substantial prior art for every major layer except the exact ordering of the decision rule: idiographic EMA networks, eigenmode/modal-controllability analysis, personalized intervention simulation, network-derived CBT targeting, module sequencing, and longitudinal adaptation are all documented. No dedicated standard or exact patent claim for unstable-mode-filtered CBT module choice was located, but this is neither a systematic review nor a freedom-to-operate search, and inaccessible or poorly indexed full texts could erase the narrow remaining distinction."}