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Najar D, Dichev J, Stoyanov D. Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry. J Clin Med 2024; 13:4363. [PMID: 39124630 PMCID: PMC11313617 DOI: 10.3390/jcm13154363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/14/2024] [Accepted: 07/23/2024] [Indexed: 08/01/2024] Open
Abstract
Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.
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Affiliation(s)
- Diyana Najar
- Faculty of Medicine, Medical University, 4002 Plovdiv, Bulgaria; (D.N.); (J.D.)
| | - Julian Dichev
- Faculty of Medicine, Medical University, 4002 Plovdiv, Bulgaria; (D.N.); (J.D.)
| | - Drozdstoy Stoyanov
- Department of Psychiatry, Medical University Plovdiv, 4000 Plovdiv, Bulgaria
- Research Institute & Strategic Research and Innovation Program for the Development of MU-PLOVDIV–(SRIPD-MUP), European Union-NextGenerationEU, 4002 Plovdiv, Bulgaria
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Stoyanov D, Paunova R, Dichev J, Kandilarova S, Khorev V, Kurkin S. Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale. World J Clin Cases 2023; 11:8458-8474. [PMID: 38188204 PMCID: PMC10768520 DOI: 10.12998/wjcc.v11.i36.8458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/10/2023] [Accepted: 12/05/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders. AIM To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD). METHODS 60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN. RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task. CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Rositsa Paunova
- Research Institute, Medical University, Plovdiv 4002, Bulgaria
| | - Julian Dichev
- Faculty of Medicine, Medical University, Plovdiv 4002, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University, Plovdiv 4002, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
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Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression. Diagnostics (Basel) 2022; 12:diagnostics12020469. [PMID: 35204560 PMCID: PMC8871050 DOI: 10.3390/diagnostics12020469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/18/2022] [Accepted: 02/08/2022] [Indexed: 01/29/2023] Open
Abstract
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.
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Stoyanov D. Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later. Front Psychiatry 2022; 13:999680. [PMID: 36304557 PMCID: PMC9595022 DOI: 10.3389/fpsyt.2022.999680] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022] Open
Abstract
Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at "bridging" the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology and Research Institute, Plovdiv Medical University, Plovdiv, Bulgaria
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Aryutova K, Paunova R, Kandilarova S, Todeva-Radneva A, Stoyanov D. Implications from translational cross-validation of clinical assessment tools for diagnosis and treatment in psychiatry. World J Psychiatry 2021; 11:169-180. [PMID: 34046313 PMCID: PMC8134869 DOI: 10.5498/wjp.v11.i5.169] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/17/2021] [Accepted: 03/31/2021] [Indexed: 02/06/2023] Open
Abstract
Traditional therapeutic methods in psychiatry, such as psychopharmacology and psychotherapy help many people suffering from mental disorders, but in the long-term prove to be effective in a relatively small proportion of those affected. Therapeutically, resistant forms of mental disorders such as schizophrenia, major depressive disorder, and bipolar disorder lead to persistent distress and dysfunction in personal, social, and professional aspects. In an effort to address these problems, the translational approach in neuroscience has initiated the inclusion of novel or modified unconventional diagnostic and therapeutic techniques with promising results. For instance, neuroimaging data sets from multiple modalities provide insight into the nature of pathophysiological mechanisms such as disruptions of connectivity, integration, and segregation of neural networks, focusing on the treatment of mental disorders through instrumental biomedical methods such as electro-convulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS). These methodologies have yielded promising results that have yet to be understood and improved to enhance the prognosis of the severe and persistent psychotic and affective disorders. The current review is focused on the translational approach in the management of schizophrenia and mood disorders, as well as the adaptation of new transdisciplinary diagnostic tools such as neuroimaging with concurrently administered psychopathological questionnaires and integration of the results into the therapeutic framework using various advanced instrumental biomedical tools such as ECT, TMS, tDCS and DBS.
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Affiliation(s)
- Katrin Aryutova
- Department of Psychiatry and Medical Psychology, Scientific Research Institute, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Scientific Research Institute, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Scientific Research Institute, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Scientific Research Institute, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Scientific Research Institute, Medical University of Plovdiv, Plovdiv 4002, Bulgaria
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Stoyanov D, Aryutova K, Kandilarova S, Paunova R, Arabadzhiev Z, Todeva-Radneva A, Kostianev S, Borgwardt S. Diagnostic Task Specific Activations in Functional MRI and Aberrant Connectivity of Insula with Middle Frontal Gyrus Can Inform the Differential Diagnosis of Psychosis. Diagnostics (Basel) 2021; 11:95. [PMID: 33435624 PMCID: PMC7827259 DOI: 10.3390/diagnostics11010095] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia-SCH-and second group with a depressive episode in the context of major depressive disorder or bipolar disorder-DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences-resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group-the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Zlatoslav Arabadzhiev
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Stefan Kostianev
- Department of Pathophysiology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Stefan Borgwardt
- Klinik für Psychiatrie und Psychotherapie, Universität zu Lübeck, 23538 Lübeck, Germany;
- Department of Psychiatry, University of Basel, 4001 Basel, Switzerland
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Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2021; 20:540-553. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 01/05/2023]
Abstract
Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy St Stoyanov
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
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Stoyanov D, Kandilarova S, Aryutova K, Paunova R, Todeva-Radneva A, Latypova A, Kherif F. Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis. Diagnostics (Basel) 2020; 11:E19. [PMID: 33374207 PMCID: PMC7823426 DOI: 10.3390/diagnostics11010019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 02/07/2023] Open
Abstract
Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (S.K.); (K.A.); (R.P.); (A.T.-R.)
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (S.K.); (K.A.); (R.P.); (A.T.-R.)
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (S.K.); (K.A.); (R.P.); (A.T.-R.)
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (S.K.); (K.A.); (R.P.); (A.T.-R.)
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (S.K.); (K.A.); (R.P.); (A.T.-R.)
| | - Adeliya Latypova
- Centre for Research in Neuroscience—Department of Clinical Neurosciences, CHUV—UNIL, 1010 Lausanne, Switzerland; (A.L.); (F.K.)
| | - Ferath Kherif
- Centre for Research in Neuroscience—Department of Clinical Neurosciences, CHUV—UNIL, 1010 Lausanne, Switzerland; (A.L.); (F.K.)
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Kandilarova S, Stoyanov D, Stoeva M, Latypova A, Kherif F. Functional MRI in Depression—Multivariate Analysis of Emotional Task. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00547-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Stoyanov D, Kandilarova S, Paunova R, Barranco Garcia J, Latypova A, Kherif F. Cross-Validation of Functional MRI and Paranoid-Depressive Scale: Results From Multivariate Analysis. Front Psychiatry 2019; 10:869. [PMID: 31824359 PMCID: PMC6886009 DOI: 10.3389/fpsyt.2019.00869] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 11/04/2019] [Indexed: 12/11/2022] Open
Abstract
Introduction: There exists over the past decades a constant debate driven by controversies in the validity of psychiatric diagnosis. This debate is grounded in queries about both the validity and evidence strength of clinical measures. Materials and Methods: The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). Results: We have been able to separate the two investigated clinical entities - schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. Following the individual and group MLM, we identified the three brain patterns that summarized all the individual variabilities of the individual brain patterns. Discussion: This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Javier Barranco Garcia
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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