A Computational Approach to Predict Clinical Features of PTSD: An fMRI and Machine Learning Study

被引：0
|
作者：
Vakili, Amin Zand [1 ]
Barredo, Jennifer [1 ]
Aiken, Emily [1 ]
Greenberg, Benjamin [1 ]
Carpenter, Linda [1 ]
Philip, Noah [1 ]
机构：
[1] Brown Univ, Alpert Med Sch, Providence, RI 02912 USA
来源：
NEUROPSYCHOPHARMACOLOGY | 2018年 / 43卷
关键词：
Post Traumatic Stress Disorder; Resting State Functional Connectivity; Machine Learning; fMRI Biomarkers;
D O I：
暂无
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
M27
引用
收藏
页码：S92 / S92
页数：1
相关论文
共 50 条
  • [31] Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition
    Pat, Narun
    Wang, Yue
    Bartonicek, Adam
    Candia, Julian
    Stringaris, Argyris
    CEREBRAL CORTEX, 2023, 33 (06) : 2682 - 2703
  • [32] Fear extinction learning improvement in PTSD after EMDR therapy: an fMRI study
    Rousseau, Pierre-Francois
    El Khoury-Malhame, Myriam
    Reynaud, Emmanuelle
    Boukezzi, Sarah
    Cancel, Aida
    Zendjidjian, Xavier
    Guyon, Valerie
    Samuelian, Jean-Claude
    Guedj, Eric
    Chaminade, Thierry
    Khalfa, Stephanie
    EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY, 2019, 10 (01):
  • [33] Using machine learning to predict processes and morphometric features of watershed
    Marzieh Mokarram
    Hamid Reza Pourghasemi
    John P. Tiefenbacher
    Scientific Reports, 13 (1)
  • [34] Using machine learning to predict processes and morphometric features of watershed
    Mokarram, Marzieh
    Pourghasemi, Hamid Reza
    Tiefenbacher, John P.
    SCIENTIFIC REPORTS, 2023, 13 (01):
  • [35] Using Machine Learning Techniques to Predict Esthetic Features of Buildings
    Aydin, Yusuf Cihat
    Mirzaei, Parham A.
    Hale, Jonathan
    JOURNAL OF ARCHITECTURAL ENGINEERING, 2021, 27 (03)
  • [36] Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach
    Hettige, Nuwan C.
    Thai Binh Nguyen
    Yuan, Chen
    Rajakulendran, Thanara
    Baddour, Jermeen
    Bhagwat, Nikhil
    Bani-Fatemi, Ali
    Voineskos, Aristotle N.
    Chakravarty, M. Mallar
    De Luca, Vincenzo
    GENERAL HOSPITAL PSYCHIATRY, 2017, 47 : 20 - 28
  • [37] Structural gray matter features and behavioral preliterate skills predict future literacy - A machine learning approach
    Beyer, Moana
    Liebig, Johanna
    Sylvester, Teresa
    Braun, Mario
    Heekeren, Hauke R. R.
    Froehlich, Eva
    Jacobs, Arthur M. M.
    Ziegler, Johannes C. C.
    FRONTIERS IN NEUROSCIENCE, 2022, 16
  • [38] Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach
    Sarkhosh, Seyed Mahdi Hosseini
    Shirzad, Nooshin
    Taghvaei, Mahdieh
    Tavangar, Seyed Mohammad
    Farhat, Sara
    Ebrahiminik, Hojat
    Hemmatabadi, Mahboobeh
    EUROPEAN RADIOLOGY, 2025,
  • [39] Development and Validation of Machine Learning Models to Predict Postoperative Delirium Using Clinical Features and Polysomnography Variables
    Ha, Woo-Seok
    Choi, Bo-Kyu
    Yeom, Jungyeon
    Song, Seungwon
    Cho, Soomi
    Chu, Min-Kyung
    Kim, Won-Joo
    Heo, Kyoung
    Kim, Kyung-Min
    JOURNAL OF CLINICAL MEDICINE, 2024, 13 (18)
  • [40] Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features
    Meti, Nicholas
    Saednia, Khadijeh
    Lagree, Andrew
    Tabbarah, Sami
    Mohebpour, Majid
    Kiss, Alex
    Lu, Fang-I
    Slodkowska, Elzbieta
    Gandhi, Sonal
    Jerzak, Katarzyna Joanna
    Fleshner, Lauren
    Law, Ethan
    Sadeghi-Naini, Ali
    Tran, William T.
    JCO CLINICAL CANCER INFORMATICS, 2021, 5 : 66 - 80
12345