Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia
Abstract The excitation/inhibition (E/I) ratio has been shown to be imbalanced in individuals diagnosed with autism (AT) or schizophrenia (SZ), relative to neurotypically developed controls (TD). However, the degree of E/I imbalance overlap between SZ and AT has not been extensively compared. In thi...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-07-01
|
| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03455-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332014838185984 |
|---|---|
| author | Lavinia Carmen Uscătescu Christopher J. Hyatt Jack Dunn Martin Kronbichler Vince Calhoun Silvia Corbera Kevin Pelphrey Brian Pittman Godfrey Pearlson Michal Assaf |
| author_facet | Lavinia Carmen Uscătescu Christopher J. Hyatt Jack Dunn Martin Kronbichler Vince Calhoun Silvia Corbera Kevin Pelphrey Brian Pittman Godfrey Pearlson Michal Assaf |
| author_sort | Lavinia Carmen Uscătescu |
| collection | DOAJ |
| description | Abstract The excitation/inhibition (E/I) ratio has been shown to be imbalanced in individuals diagnosed with autism (AT) or schizophrenia (SZ), relative to neurotypically developed controls (TD). However, the degree of E/I imbalance overlap between SZ and AT has not been extensively compared. In this project, we used resting state fMRI data to estimate the E/I ratio via the Hurst (H) exponent. Our main objectives were (1) to quantify group differences in the E/I ratio between TD, AT, and SZ, (2) to assess the potential of the E/I ratio for differential diagnosis, and (3) to verify the replicability of our findings in an independently acquired dataset. For each participant, we computed the Hurst exponent (H), an indicator of the E/I ratio, from the time courses of 53 independent components. Using a random forest classifier (RF), we ran a classification analysis using the larger of the two datasets (exploratory dataset; 519 TD, 200 AT, 355 SZ) to determine which of the 53 H would yield the highest performance in classifying SZ and AT. Next, taking the ten most important H based on the exploratory dataset, in combination with phenotypic information collected in the replication dataset (55 TD, 30 AT, 39 SZ), we used RF to compare the classification performance using five feature sets: (a) H only; (b) Positive and Negative Syndrome Scale (PANSS) and the Autism Diagnostic Observation Schedule (ADOS) only; (c) PANSS, ADOS, Bermond–Vorst Alexithymia Questionnaire (BVAQ), Empathy Quotient (EQ), and IQ; (d) H, PANSS and ADOS; (e) H, PANSS, ADOS, BVAQ, EQ and IQ. Classification performance using H only was higher in the exploratory dataset (AUC = 84%) compared to the replication dataset (AUC = 72%). In the replication dataset, the highest classification performance was obtained when combining H with PANSS, ADOS, BVAQ, EQ and IQ (i.e., model e; AUC = 83%). These results illustrate the added value of E/I to typical phenotypic data in differentiating AT and SZ. |
| format | Article |
| id | doaj-art-bb6454412ca94e6999e8fa4d302c6049 |
| institution | Kabale University |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj-art-bb6454412ca94e6999e8fa4d302c60492025-08-20T03:46:21ZengNature Publishing GroupTranslational Psychiatry2158-31882025-07-0115111210.1038/s41398-025-03455-8Using the excitation/inhibition ratio to optimize the classification of autism and schizophreniaLavinia Carmen Uscătescu0Christopher J. Hyatt1Jack Dunn2Martin Kronbichler3Vince Calhoun4Silvia Corbera5Kevin Pelphrey6Brian Pittman7Godfrey Pearlson8Michal Assaf9Olin Neuropsychiatry Research Center, Institute of LivingOlin Neuropsychiatry Research Center, Institute of LivingInterpretable AICentre for Cognitive Neuroscience & Department of Psychology, Paris-Lodron University of SalzburgTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityCentral Connecticut State University, Department of Psychological ScienceUniversity of Virginia, Department of NeurologyYale University, School of Medicine, Department of PsychiatryOlin Neuropsychiatry Research Center, Institute of LivingOlin Neuropsychiatry Research Center, Institute of LivingAbstract The excitation/inhibition (E/I) ratio has been shown to be imbalanced in individuals diagnosed with autism (AT) or schizophrenia (SZ), relative to neurotypically developed controls (TD). However, the degree of E/I imbalance overlap between SZ and AT has not been extensively compared. In this project, we used resting state fMRI data to estimate the E/I ratio via the Hurst (H) exponent. Our main objectives were (1) to quantify group differences in the E/I ratio between TD, AT, and SZ, (2) to assess the potential of the E/I ratio for differential diagnosis, and (3) to verify the replicability of our findings in an independently acquired dataset. For each participant, we computed the Hurst exponent (H), an indicator of the E/I ratio, from the time courses of 53 independent components. Using a random forest classifier (RF), we ran a classification analysis using the larger of the two datasets (exploratory dataset; 519 TD, 200 AT, 355 SZ) to determine which of the 53 H would yield the highest performance in classifying SZ and AT. Next, taking the ten most important H based on the exploratory dataset, in combination with phenotypic information collected in the replication dataset (55 TD, 30 AT, 39 SZ), we used RF to compare the classification performance using five feature sets: (a) H only; (b) Positive and Negative Syndrome Scale (PANSS) and the Autism Diagnostic Observation Schedule (ADOS) only; (c) PANSS, ADOS, Bermond–Vorst Alexithymia Questionnaire (BVAQ), Empathy Quotient (EQ), and IQ; (d) H, PANSS and ADOS; (e) H, PANSS, ADOS, BVAQ, EQ and IQ. Classification performance using H only was higher in the exploratory dataset (AUC = 84%) compared to the replication dataset (AUC = 72%). In the replication dataset, the highest classification performance was obtained when combining H with PANSS, ADOS, BVAQ, EQ and IQ (i.e., model e; AUC = 83%). These results illustrate the added value of E/I to typical phenotypic data in differentiating AT and SZ.https://doi.org/10.1038/s41398-025-03455-8 |
| spellingShingle | Lavinia Carmen Uscătescu Christopher J. Hyatt Jack Dunn Martin Kronbichler Vince Calhoun Silvia Corbera Kevin Pelphrey Brian Pittman Godfrey Pearlson Michal Assaf Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia Translational Psychiatry |
| title | Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia |
| title_full | Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia |
| title_fullStr | Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia |
| title_full_unstemmed | Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia |
| title_short | Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia |
| title_sort | using the excitation inhibition ratio to optimize the classification of autism and schizophrenia |
| url | https://doi.org/10.1038/s41398-025-03455-8 |
| work_keys_str_mv | AT laviniacarmenuscatescu usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT christopherjhyatt usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT jackdunn usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT martinkronbichler usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT vincecalhoun usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT silviacorbera usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT kevinpelphrey usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT brianpittman usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT godfreypearlson usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia AT michalassaf usingtheexcitationinhibitionratiotooptimizetheclassificationofautismandschizophrenia |