Article
Psychiatry
Annarita Barone, Michele De Prisco, Benedetta Altavilla, Camilla Avagliano, Raffaele Balletta, Elisabetta Filomena Buonaguro, Mariateresa Ciccarelli, Luigi D'Ambrosio, Sara Giordano, Gianmarco Latte, Marta Matrone, Federica Milandri, Danilo Notar Francesco, Licia Vellucci, Andrea de Bartolomeis
Summary: This study used a machine learning approach to explore the relationship between Treatment Resistant Schizophrenia (TRS) and the five-factor model of the Positive and Negative Syndrome Scale (PANSS). Through the random forest algorithm, it was found that patients with higher scores in disorganization, positive, and excitement symptoms were more likely to be classified as TRS. A multivariate logistic regression model further confirmed that only the disorganization factor was significantly associated with TRS.
JOURNAL OF PSYCHIATRIC RESEARCH
(2022)
Article
Clinical Neurology
Hans Kirschner, Matthew R. Nassar, Adrian G. Fischer, Thomas Frodl, Gabriela Meyer-Lotz, Soeren Froboese, Stephanie Seidenbecher, Tilmann A. Klein, Markus Ullsperger
Summary: Deficits in reward learning are core symptoms in many mental disorders. This study investigates the neuro-computational mechanisms behind these impairments and explores whether they are shared across forms of psychopathology. The findings suggest that reduced trial-by-trial learning dynamics reflect a common deficit in both depression and schizophrenia, but also identify disorder-specific learning deficits.
Review
Genetics & Heredity
Jiangbo Ying, Qian Hui Chew, Roger S. McIntyre, Kang Sim
Summary: Treatment-resistant schizophrenia (TRS) is difficult to treat and has a negative impact on patients' quality of life. Clozapine is effective for TRS but has side effects. This review summarizes the current genetic factors associated with TRS, clozapine resistance, and side effects. Further research is needed to identify risk genes and understand the interactions between genes and relevant clinical factors in TRS treatment.
Article
Health Care Sciences & Services
Hong Seok Oh, Bong Ju Lee, Yu Sang Lee, Ok-Jin Jang, Yukako Nakagami, Toshiya Inada, Takahiro A. Kato, Shigenobu Kanba, Mian-Yoon Chong, Sih-Ku Lin, Tianmei Si, Yu-Tao Xiang, Ajit Avasthi, Sandeep Grover, Roy Abraham Kallivayalil, Pornjira Pariwatcharakul, Kok Yoon Chee, Andi J. Tanra, Golam Rabbani, Afzal Javed, Samudra Kathiarachchi, Win Aung Myint, Tran Van Cuong, Yuxi Wang, Kang Sim, Norman Sartorius, Chay-Hoon Tan, Naotaka Shinfuku, Yong Chon Park, Seon-Cheol Park
Summary: This study developed an effective model for predicting the augmented use of clozapine with ECT in patients with schizophrenia using a machine learning algorithm. The study revealed that Asian patients treated with a combination of clozapine and ECT were more likely to be female, inpatients, have a longer duration of illness, and have a higher prevalence of negative symptoms and social or occupational dysfunction.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Clinical Neurology
Chuanjun Zhuo, Yong Xu, Haibo Wang, Chunhua Zhou, Jian Liu, Xiaocui Yu, Hailin Shao, Hongjun Tian, Tao Fang, Qianchen Li, Jiayue Chen, Shuli Xu, Xiaoyan Ma, Weiliang Yang, Cong Yao, Bo Li, Anqu Yang, Yuhui Chen, Guoyong Huang, Chongguang Lin
Summary: Clozapine-induced metformin-resistant prediabetes/diabetes is common in the early-treatment resistance subtype of treatment-resistant schizophrenia and is associated with poor clinical efficacy of clozapine.
JOURNAL OF AFFECTIVE DISORDERS
(2021)
Article
Psychiatry
Matthew Bracher-Smith, Elliott Rees, Georgina Menzies, James T. R. Walters, Michael C. O'Donovan, Michael J. Owen, George Kirov, Valentina Escott-Price
Summary: This study assessed the value of machine learning in predicting schizophrenia and found that it did not outperform logistic regression in terms of prediction accuracy. However, different machine learning approaches yielded risk scores that were associated with different schizophrenia-related traits.
SCHIZOPHRENIA RESEARCH
(2022)
Article
Neurosciences
Long-Biao Cui, Ya-Juan Zhang, Hong-Liang Lu, Lin Liu, Hai-Jun Zhang, Yu-Fei Fu, Xu-Sha Wu, Yong-Qiang Xu, Xiao-Sa Li, Yu-Ting Qiao, Wei Qin, Hong Yin, Feng Cao
Summary: This study demonstrates the use of radiomics approach with multiple thalamic features to identify schizophrenia patients and predict early treatment response. The classification based on thalamus shows promising results for schizophrenia definition and treatment selection.
FRONTIERS IN NEUROSCIENCE
(2021)
Review
Neurosciences
Povilas Karvelis, Colleen E. Charlton, Shona G. Allohverdi, Peter Bedford, Daniel J. Hauke, Andreea O. Diaconescu
Summary: In this paper, the authors review studies that use brain activity data to predict treatment response in major depressive disorder (MDD) and discuss methodological differences and limitations. They highlight the potential of theory-driven generative modeling and suggest improvements in model interpretability and generalizability.
NETWORK NEUROSCIENCE
(2022)
Article
Engineering, Biomedical
Caroline L. Alves, Thaise G. L. de O. Toutain, Joel Augusto Moura Porto, Patricia Maria de Carvalho Aguiar, Eduardo Ponde de Sena, Francisco A. Rodrigues, Aruane M. Pineda, Christiane Thielemann
Summary: This study presents a rigorous approach using machine learning and deep learning techniques to automate the diagnosis of schizophrenia. By analyzing functional magnetic resonance imaging and electroencephalogram datasets, the researchers established a model that achieved excellent classification results. The findings demonstrate that the topology and dynamics of brain networks in individuals with schizophrenia differ from those without the disorder, and EEG measurements outperformed complex networks in capturing the brain alterations associated with schizophrenia.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Carla Barros, Carlos A. Silva, Ana P. Pinheiro
Summary: The complexity and heterogeneity of schizophrenia symptoms present challenges for objective diagnosis, but early detection can lead to improved treatment outcomes. Recent research has focused on neurobiological mechanisms and biomarkers for schizophrenia, as well as utilizing machine learning techniques for classification. Further clinical validation and research are necessary for the development of effective EEG-based models and interventions.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Computer Science, Information Systems
Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore
Summary: Computer-aided diagnosis systems using neuroimaging techniques assist in the early diagnosis of mental disorders such as schizophrenia. Advances in AI have allowed for the analysis and interpretation of neuroimaging data to detect and classify various mental illnesses. This study examines AI methods for the automated diagnosis of schizophrenia using EEG, structural MRI, and functional MRI, as well as different datasets and techniques used for pre-processing the images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
J. Ruiz de Miras, A. J. Ibanez-Molina, M. F. Soriano, S. Iglesias-Parro
Summary: This study evaluated the use of machine learning techniques in diagnosing schizophrenia and proposed a processing pipeline using resting state EEG data. The results showed that complexity measures had a high ability to differentiate between patients and healthy controls, and the proposed pipeline allowed standard machine learning algorithms to efficiently classify schizophrenia patients.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Chemistry, Multidisciplinary
Lena A. Hofmann, Steffen Lau, Johannes Kirchebner
Summary: This study used machine learning algorithms to explore inpatient aggression in offender patients with schizophrenia spectrum disorders. Support vector machines outperformed other algorithms, and negative behavior, rule-breaking, PANSS score, poor impulse control, and impulsivity were identified as predictive variables. This study underscores the importance of mental illness and its association with antisocial behavior.
APPLIED SCIENCES-BASEL
(2022)
Editorial Material
Psychology, Clinical
Daniel S. Barron
Summary: The clinical interview, as a data gathering procedure in psychiatry, lacks a standardized definition compared to vital signs. Different psychiatrists have their own approach to conducting clinical interviews. This diversity in interviewing techniques can lead to varied examination results and treatment recommendations even within the same clinical setting, highlighting the lack of standardization and operationalization in the evaluation process.
PSYCHOLOGICAL MEDICINE
(2021)
Article
Psychiatry
Martina Sonnweber, Steffen Lau, Johannes Kirchebner
Summary: This study aims to differentiate between violent and non-violent offenders with schizophrenia spectrum disorder using machine learning algorithms and identified ten key factors that influence violent offending in this population. The findings contribute to the development of preventive and therapeutic strategies to reduce violence prevalence.
COMPREHENSIVE PSYCHIATRY
(2021)
Article
Anesthesiology
Ahmad Khodayari-Rostamabad, Soren S. Olesen, Carina Graversen, Lasse P. Malver, Geana P. Kurita, Per Sjogren, Lona L. Christrup, Asbjorn M. Drewes
Letter
Anesthesiology
Soren S. Olesen, Ahmad Khodayari-Rostamabad, Carina Graversen, Asbjorn M. Drewes
Article
Clinical Neurology
Ahmad Khodayari-Rostamabad, Carina Graversen, Lasse P. Malver, Geana P. Kurita, Lona L. Christrup, Per Sjogren, Asbjorn M. Drewes
CLINICAL NEUROPHYSIOLOGY
(2015)
Article
Clinical Neurology
Maryam Ravan, Gary Hasey, James P. Reilly, Duncan MacCrimmon, Ahmad Khodayari-Rostamabad
CLINICAL NEUROPHYSIOLOGY
(2015)
Article
Endocrinology & Metabolism
Jens B. Frokjaer, Carina Graversen, Christina Brock, Ahmad Khodayari-Rostamabad, Soren S. Olesen, Tine M. Hansen, Eirik Softeland, Magnus Simren, Asbjorn M. Drewes
JOURNAL OF DIABETES AND ITS COMPLICATIONS
(2017)
Article
Clinical Neurology
Ahmad Khodayari-Rostamabad, James P. Reilly, Gary M. Hasey, Hubert de Bruin, Duncan J. MacCrimmon
CLINICAL NEUROPHYSIOLOGY
(2013)
Proceedings Paper
Computer Science, Artificial Intelligence
Miao Sun, Tony X. Han, Ming-Chang Liu, Ahmad Khodayari-Rostamabad
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2016)
Meeting Abstract
Neurosciences
Gary Hasey, Ahmad Khodayari-Rostamabad, James Reilly, Duncan MacCrimmon, Hubert DeBruin
BIOLOGICAL PSYCHIATRY
(2015)
Proceedings Paper
Engineering, Biomedical
Ahmad Khodayari-Rostamabad, Carina Graversen, Soren S. Olesen, Lasse P. Malver, Geana P. Kurita, Per Sjogren, Lona L. Christrup, Asbjorn M. Drewes
2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2014)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Ahmad Khodayari-Rostamabad, James P. Reilly, Gary M. Hasey
2013 3RD INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION IN NEUROIMAGING (PRNI 2013)
(2013)
Proceedings Paper
Engineering, Biomedical
Maryam Ravan, Duncan MacCrimmon, Gary Hasey, James P. Reilly, Ahmad Khodayari-Rostamabad
2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2012)
Meeting Abstract
Neurosciences
Maryam Ravan, James P. Reilly, Gary M. Hasey, Duncan J. MacCrimmon, Ahmad Khodayari-Rostamabad
BIOLOGICAL PSYCHIATRY
(2012)
Meeting Abstract
Neurosciences
Gary M. Hasey, Ahmad Khodayari-Rostamabad, James P. Reilly, Hubert de Bruin, Duncan MacCrimmon
BIOLOGICAL PSYCHIATRY
(2012)
Proceedings Paper
Engineering, Biomedical
Ahmad Khodayari-Rostamabad, James P. Reilly, Gary M. Hasey, Hubert deBruin, Duncan MacCrimmon
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2011)
Meeting Abstract
Neurosciences
Gary Hasey, Ahmad Khodayari-Rostamabad, James P. Reilly, Hubert de Bruin, Duncan MacCrimmon
BIOLOGICAL PSYCHIATRY
(2011)
Article
Clinical Neurology
Jaakko Vallinoja, Timo Nurmi, Julia Jaatela, Vincent Wens, Mathieu Bourguignon, Helena Maenpaa, Harri Piitulainen
Summary: The study aimed to assess the effects of lesions related to spastic diplegic cerebral palsy on functional connectivity. Using multiple imaging modalities, the researchers found enhanced functional connectivity in the sensorimotor network of individuals with spastic diplegic cerebral palsy, which was not correlated with hand coordination performance.
CLINICAL NEUROPHYSIOLOGY
(2024)
Article
Clinical Neurology
Francesca Ginatempo, Nicola Loi, John C. Rothwell, Franca Deriu
Summary: This study comprehensively investigated sensorimotor integration in the cranial-cervical muscles of healthy adults and found that the integration of sensory inputs with motor output is profoundly influenced by the type of sensory afferent involved and the functional role played by the target muscle.
CLINICAL NEUROPHYSIOLOGY
(2024)