Evidence map›Paper›PMID 40562637›Full record

Observational studyInternational psychogeriatrics2025

Brain morphological characteristics predicting clinical response to selective serotonin reuptake inhibitors or cholinesterase inhibitors: A study of electronic medical records in patients with cognitive disorders.

Kengo Onda, Jill S Chotiyanonta, Hannah P Cowley, Yuto Uchida, Milap A Nowrangi, Roy Adams, Constantine G Lyketsos, Peter P Zandi, Kenichi Oishi

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in International psychogeriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kengo OndaDepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jill S ChotiyanontaDepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Hannah P CowleyResearch and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Yuto UchidaDepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Milap A NowrangiDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Roy AdamsDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Constantine G LyketsosDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Peter P ZandiDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Kenichi OishiDepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: koishi2@jhmi.edu.

Funding

Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Peter M. Abadir, Alexis Battle · 2021 to 2026
$31.2M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2020 to 2026
$29.3M
Machine learning-based methods for phenotyping dementia patients from electronic health record dataK25AG083064 · NIA · JOHNS HOPKINS UNIVERSITY · PI Roy Adams · 2023 to 2026
$561k
Forecasting response to pharmacological interventions in Alzheimer's and related dementias based on medical record and brain MRI characteristics: A precision medicine approachR21AG070404 · NIA · JOHNS HOPKINS UNIVERSITY · PI OISHI, KENICHI · 2020 to 2020
$450k
NIA NIH HHS K25 AG083064NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG073104NIA NIH HHS R21 AG070404
6 · The paper itself

Abstract

objectivesCurrent treatments for cognitive and neuropsychiatric symptoms in Alzheimer's disease and related dementias (ADRD), such as cholinesterase inhibitors (CEIs) and selective serotonin reuptake inhibitors (SSRIs), show inconsistent effectiveness, necessitating a personalized therapy approach. We aimed to develop predictive models using MRI-derived brain neuroanatomical features and clinical data to forecast responses to CEIs and SSRIs in ADRD patients. DESIGN AND

settingThis was a retrospective observational analysis of electronic health records (EHRs) and MRI data conducted within Johns Hopkins Medical Systems.

participantsCohort 1 comprised 179 patients prescribed CEIs or SSRIs for the first time with over a year of follow-up. Cohort 2 included 1244 patients with similar criteria to explore clinical characteristics linked to MRI features affecting treatment benefits. MEASURES: Medication efficacy was assessed via a Likert scale based on EMR descriptions. We quantified brain volumes across 280 anatomical areas on T1-weighted MRIs and applied an elastic net model after harmonizing volumes with the ComBat model. Predictive model efficacy was evaluated using receiver operating characteristic (ROC) analysis.

resultsPreserved volume in the nucleus basalis of Meynert correlated with better responses to both CEIs and SSRIs. Specific white matter volumes related to CEI benefits, while SSRI efficacy was linked to gray matter in the Nucleus Accumbens and frontal cortex. Area under ROC curve values were 0.82 for CEI and 0.75 for SSRI predictions. Older age and vascular factors were associated with reduced medication benefits.

conclusionsMRI-derived neuroanatomical features effectively predict medication responses in ADRD, potentially enabling more tailored treatment strategies.

Indexed as

Alzheimer DiseaseBrainCholinesterase InhibitorsCognition DisordersSelective Serotonin Reuptake InhibitorsAgedAged, 80 and overElectronic Health RecordsFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesTreatment OutcomeCholinesterase InhibitorsSelective Serotonin Reuptake InhibitorsAlzheimer's diseaseCholinesterase inhibitorElectronic health recordMagnetic resonance imagingPsychiatric symptomSelective serotonin reuptake inhibitorTreatment responseVascular dementia

Identifiers

PMID40562637
PMCPMC12895311

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Registered trials

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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.