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Cognitive Neuropsychiatry Volume 22, 2017 - Issue 5
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Articles

Ineffective initiation contributes to deficient verbal and non-verbal fluency in patients with schizophrenia

Paweł KrukowDepartment of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, PolandCorrespondence[email protected]
,
Michał HarciarekDivision of Clinical Psychology and Neuropsychology, Institute of Psychology, University of Gdańsk, Gdańsk, Poland
,
Justyna Morylowska-TopolskaDepartment of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
,
Hanna Karakuła-JuchnowiczDepartment of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
&
Kamil JonakDepartment of Biomedical Engineering, Lublin University of Technology, Lublin, Poland
Pages 391-406 | Received 28 Nov 2016, Accepted 12 Jul 2017, Published online: 26 Jul 2017

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Thomas J. Dinzeo, Virginia Culiañez Serna, Sherry D. Pujji & Eve M. Sledjeski. (2018) The relationship of categorical and phonological verbal fluency to negative schizotypy and social functioning in a non-clinical sample. Cognitive Neuropsychiatry 23:1, pages 43-57.
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Articles from other publishers (13)

Adam Domagała, Lucyna Domagała, Natalia Kopiś-Posiej, Michał Harciarek & Paweł Krukow. (2023) Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions. Frontiers in Psychiatry 14.
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Jing Zhang, Hui Yang, Wen Li, Yuanyuan Li, Jing Qin & Ling He. (2022) Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task. Frontiers in Neuroscience 16.
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Yasemin Sohtorik İlkmen & Ezgi Soncu Büyükişcan. (2022) Verbal Fluency Tests: Normative Data Stratified by Age and Education in an Istanbul Sample. Turkish Journal Of Neurology 28:2, pages 102-110.
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Monika Kaczorowska, Małgorzata Plechawska-Wójcik, Mikhail Tokovarov & Paweł Krukow. (2022) Automated Classification of Cognitive Workload Levels Based on Psychophysiological and Behavioural Variables of Ex-Gaussian Distributional Features. Brain Sciences 12:5, pages 542.
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Lingfang Yu, Hua Ni, Zenan Wu, Xinyu Fang, Yan Chen, Dandan Wang & Chen Zhang. (2021) Association of Cognitive Impairment With Anhedonia in Patients With Schizophrenia. Frontiers in Psychiatry 12.
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Paweł Krukow, Kamil Jonak, Cezary Grochowski, Małgorzata Plechawska-Wójcik & Hanna Karakuła-Juchnowicz. (2020) Resting-state hyperconnectivity within the default mode network impedes the ability to initiate cognitive performance in first-episode schizophrenia patients. Progress in Neuro-Psychopharmacology and Biological Psychiatry 102, pages 109959.
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Eric Josiah Tan, Erica Neill, Kiandra Tomlinson & Susan Lee Rossell. (2020) Semantic Memory Impairment Across the Schizophrenia Continuum: A Meta-Analysis of Category Fluency Performance. Schizophrenia Bulletin Open 1:1.
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Luisa Weiner, Nadège Doignon-Camus, Gilles Bertschy & Anne Giersch. (2019) Thought and language disturbance in bipolar disorder quantified via process-oriented verbal fluency measures. Scientific Reports 9:1.
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Robert Karpiński, Łukasz Jaworski, Józef Jonak & Przemysław Krakowski. (2019) Stress distribution in the knee joint in relation to tibiofemoral angle using the finite element method. MATEC Web of Conferences 252, pages 07007.
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Paweł Krukow, Michał Harciarek, Cezary Grochowski, Agata Makarewicz, Kamil Jonak & Hanna Karakuła-Juchnowicz. (2019) What specifically contributes to disturbed non-verbal fluency in patients with bipolar disorder: Ineffective performance initiation, slowed processing or lack of the execution strategy?. Psychiatry Research 271, pages 15-22.
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Paweł Krukow, Kamil Jonak, Hanna Karakuła-Juchnowicz, Arkadiusz Podkowiński, Katarzyna Jonak, Magdalena Borys & Michał Harciarek. (2018) Disturbed functional connectivity within the left prefrontal cortex and sensorimotor areas predicts impaired cognitive speed in patients with first-episode schizophrenia. Psychiatry Research: Neuroimaging 275, pages 28-35.
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Magdalena Borys, Malgorzata Plechawska-Wojcik, Pawel Krukow, Sara Barakate & Karim Hachmoud. (2017) Using machine learning models to classify user performance in the ruff figural fluency test from eye-tracking features. Using machine learning models to classify user performance in the ruff figural fluency test from eye-tracking features.
Magdalena Borys, Sara Barakate, Karim Hachmoud, Małgorzata Plechawska-Wójcik, Paweł Krukow & Marek Kamiński. (2017) Classification of user performance in the Ruff Figural Fluency Test based on eye-tracking features. ITM Web of Conferences 15, pages 02002.
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