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Stevanovic D, Nasic S, Doric A, Wentz E, Knez R. The Structure and Diagnostic Accuracy of the QbTest in Pediatric ADHD: A Retrospective Clinical Study. J Atten Disord 2023; 27:1296-1305. [PMID: 37199293 DOI: 10.1177/10870547231174035] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
OBJECTIVE The QbTest that combines a continuous performance task (CPT) with a motion-tracking system may help identify attention deficit hyperactivity disorder (ADHD). This study examined the structure and diagnostic ability of the QbTest in children and adolescents. METHOD Retrospective data from 1,274 children and adolescents were analyzed. The study assessed data on a principal component analysis (PCA), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The QbActivity component included the variables micro-events, distance, area, and time active; the QbImpulsivity included normalized commissions and commissions (anticipatory errors were added in a version for 6-12-year-olds only); and the QbInattention included omissions, reaction time, and reaction time variation. Sensitivity ranged between 22% and 50%, specificity 79% and 96%, PPVs 40% and 95%, and NPVs 24% and 66%. CONCLUSION The structure of the QbTest with three cardinal parameters and nine/ten CPT and motion analysis variables was supported. The diagnostic accuracy was found to be poor to moderate. Given that this is a retrospective study, the interpretation of diagnostic accuracy should be considered within this context.
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Affiliation(s)
- Dejan Stevanovic
- Clinic for Neurology and Psychiatry for Children and Youth, Belgrade, Serbia
- University of Gothenburg, Sweden
| | - Salmir Nasic
- University of Gothenburg, Sweden
- Skaraborgs Hospital, Skövde, Sweden
| | - Ana Doric
- University of Rijeka, Croatia
- IntechOpen Limited, Rijeka, Croatia
| | | | - Rajna Knez
- University of Gothenburg, Sweden
- Skaraborgs Hospital, Skövde, Sweden
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Park JY, Kim JW, Kang HJ, Choi W, Lee JY, Kim SW, Shin IS, Ahn Y, Jeong MH, Kim JM. Effect Modification of Cortisol on the Associations Between Obsessive-Compulsive Symptoms on Suicidality in Patients With Acute Coronary Syndrome. Psychiatry Investig 2023; 20:707-713. [PMID: 37559479 PMCID: PMC10460972 DOI: 10.30773/pi.2023.0085] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 08/11/2023] Open
Abstract
OBJECTIVE Obsessive-compulsive symptoms (OCS) and suicidal ideation (SI) are common in patients with acute coronary syndrome (ACS). This study investigated the associations of OCS and serum cortisol levels with SI, and further evaluated the possible modifying effects of cortisol on the associations between OCS and SI in acute and chronic phases of ACS. METHODS In total, 969 ACS patients were recruited from a tertiary university hospital in Korea within 2 weeks of disease onset and evaluated in terms of OCS (using the OCS dimension of the Symptom Checklist-90-Revised), serum cortisol levels, and SI (using the "suicidal thoughts" item of the Montgomery-Åsberg Depression Rating Scale). Covariates included sociodemographics, depression, vascular risk factors, and disease severity. After 1 year, 711 patients were re-evaluated in terms of SI. Logistic regression analysis was performed with adjustment for covariates. RESULTS Higher OCS was significantly associated with SI both at baseline and follow-up. Serum cortisol showed no such association, but modified the association between OCS and SI. That was the associations were significant only in the higher but not in the lower serum cortisol levels, with significant interaction terms after adjusted for relevant covariates. CONCLUSION Evaluating OCS and serum cortisol levels at the acute phase could improve the accuracy of clinical predictions of SI both in the acute and chronic phases of ACS.
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Affiliation(s)
- Jin-Young Park
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea
| | - Ju-Yeon Lee
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Youngkeun Ahn
- Department of Cardiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Myung Ho Jeong
- Department of Cardiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
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Paul AK, Bose A, Kalmady SV, Shivakumar V, Sreeraj VS, Parlikar R, Narayanaswamy JC, Dursun SM, Greenshaw AJ, Greiner R, Venkatasubramanian G. Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study. Front Psychiatry 2022; 13:923938. [PMID: 35990061 PMCID: PMC9388779 DOI: 10.3389/fpsyt.2022.923938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022] Open
Abstract
Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.
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Affiliation(s)
- Animesh Kumar Paul
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Anushree Bose
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Sunil Vasu Kalmady
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
- Canadian VIGOUR Centre, University of Alberta, Edmonton, AB, Canada
| | - Venkataram Shivakumar
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Vanteemar S Sreeraj
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Rujuta Parlikar
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Janardhanan C Narayanaswamy
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Serdar M Dursun
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | | | - Russell Greiner
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Ganesan Venkatasubramanian
- Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
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