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Cerebral hemodynamic response to generalized anxiety disorder.
Harandi, Ali Amini; Kimia, Negin; Medghalchi, Aida; Sharifipour, Ehsan; Pakdaman, Hossein; Siavoshi, Fatemeh; Barough, Siavash Shirzadeh; Esfandani, Akram; Hosseini, Mohammad Hossein.
Affiliation
  • Harandi AA; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: ali.amini.harandi@gmail.com.
  • Kimia N; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Medghalchi A; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Sharifipour E; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Pakdaman H; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Siavoshi F; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Barough SS; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Esfandani A; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Hosseini MH; Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Psychiatry Res Neuroimaging ; 333: 111654, 2023 08.
Article in En | MEDLINE | ID: mdl-37229961
ABSTRACT

BACKGROUND:

Generalized anxiety disorder (GAD) is the least studied among anxiety disorders. Therefore, we aimed to compare the cervical blood flow velocities using doppler ultrasonography in untreated chronic GAD patients and healthy individuals. MATERIAL AND

METHODS:

In this study, thirty-eight GAD patients were enrolled. And thirty-eight healthy volunteers were recruited as control participants. The common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) of both sides were explored. Also, we trained machine learning models based on cervical arteries characteristics to diagnose GAD patients.

RESULTS:

Patients with chronic untreated GAD showed a significant increase in peak systolic velocity (PSV) bilaterally in the CCA and the ICA (P value < 0.05). In GAD patients, the end-diastolic velocity (EDV) of bilateral CCA, VA, and left ICA was significantly decreased. The Resistive Index (RI) showed a significant increase in all patients with GAD. Moreover, the Support Vector Machine (SVM) model showed the best accuracy in identifying anxiety disorder.

CONCLUSION:

GAD is associated with hemodynamic alterations of extracranial cervical arteries. With a larger sample size and more generalized data, it is possible to make a robust machine learning-based model for GAD diagnosis.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carotid Artery, Internal / Carotid Artery, Common Type of study: Prognostic_studies Limits: Humans Language: En Journal: Psychiatry Res Neuroimaging Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carotid Artery, Internal / Carotid Artery, Common Type of study: Prognostic_studies Limits: Humans Language: En Journal: Psychiatry Res Neuroimaging Year: 2023 Document type: Article