Network analysis of cognitive, oculomotor and speech parameters in schizophrenia

Шмуклер, А. Б.; Kosytuk, G P; Латанов, А. В.; Sidorova, M Yu; Anisimov, V.; Zakharova, Natalia; Karyakina, Maria; Reznik, A. M.; Соколов, А В; Spektor, Valery A.; Sukhachevskii, I S; Churikova, M A

doi:10.17116/jnevro202012006254

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Network analysis that enables a comprehensive assessment of the existing disorders to be performed, with determination of interrelations between individual signs and their dynamic changes, is one such approach. The early results of these studies are promising, 39 and further development of this area with analysis of extensive findings and an evaluation of a wide spread of parameters is required.…”

Section: Discussionmentioning

confidence: 99%

The Evolution of Approaches to Schizophrenia Diagnostics: from Kraepelin to ICD-11

Шмуклер

Борисович²

2021

Consortium Psychiatricum

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This article presents the evolution of views on schizophrenia diagnostics over the course of 150 years, beginning from the pre-Kraepelin period and ending with concepts developed in recent decades. Consideration is given to the merits and demerits of contemporary official classifications (DSM-5 and ICD-11) as well as to alternative approaches, particularly in relation to scientific research, and their prospects for development. Special attention is paid to the Research Domain Criteria Project (RDoC) of the National Institute of Mental Health (NIMH). Another promising area discussed in this paper relates to network analysis as a method for the investigation of psychotic disorders, particularly schizophrenia.

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Section: Discussionmentioning

confidence: 99%

The Evolution of Approaches to Schizophrenia Diagnostics: from Kraepelin to ICD-11

Шмуклер

Борисович²

2021

Consortium Psychiatricum

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“…56 , 57 , 58 , 59 In general, the task is to carry out a network analysis of the identified disorders. 60 , 61 , 62 …”

Section: Discussionmentioning

confidence: 99%

“…[56][57][58][59] In general, the task is to carry out a network analysis of the identified disorders. [60][61][62] Thus, the research carried out at the present time preserves the tradition of an integrated clinical and social approach, on the basis of which organizational models of psychiatric services are proposed. An important advantage of this approach is the applicability of its results in healthcare practice, that is, the translational nature of scientific studies.…”

Section: Care System For Special Patient Groupsmentioning

confidence: 99%

Comprehensive Clinical and Social Research at the Moscow Research Institute of Psychiatry: Translation into Clinical Practice

Шмуклер¹,

Борисович²,

Movina³

et al. 2021

Consortium Psychiatricum

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The article is devoted to the work of the Moscow Research Institute of Psychiatry to improve psychiatric care for patients with psychotic disorders. An important feature of this work was an integrated approach, in which the clinical picture of the disease was assessed in close connection with the patient's personal and psychological characteristics, social conditions of his life, therapeutic opportunities, rehabilitation potential and organizational structure of care. The article reflects the results of many years of work of the department of outpatient psychiatry and the organization of psychiatric care under the guidance of Professor I.Ya. Gurovich. The results of scientific research carried out by the staff of the institute in a traditional humanistic manner are presented. The translational nature of the research is emphasized by its inextricable link with clinical and social approaches. As a result of many years of work, a concept was developed to provide assistance to various groups of patients, starting with the first manifestations of the disease and ending with cases of long-term chronic disorders with a pronounced level of social maladaptation. As a result, a whole spectrum of new organizational forms of psychiatric care was proposed, such as departments (clinics) of the first psychotic episode, medical rehabilitation departments, assertive community treatment departments, designed for the most difficult patients. These organizational forms were fixed in the regulatory documents of the Ministry of Health and Social Development of the Russian Federation. To date, the above departments have been established in psychiatric institutions in many regions of the Russian Federation. Further development of this area is associated with neurobiological research to identify complex biomarkers of psychotic spectrum disorders. Thus, the research carried out at the present time preserves the tradition of an integrated clinical and social approach to the study of mental disorders. It is shown that an important advantage of this approach is their translational nature.

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“…Evaluating the cognitive system via characteristics such as attention, perception, memory (functional and explicit), cognitive control, and speech behavior allows the characterization of mental disorders [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

et al. 2023

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Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC’s), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

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Network analysis of cognitive, oculomotor and speech parameters in schizophrenia

Cited by 6 publications

References 18 publications

The Evolution of Approaches to Schizophrenia Diagnostics: from Kraepelin to ICD-11

The Evolution of Approaches to Schizophrenia Diagnostics: from Kraepelin to ICD-11

Comprehensive Clinical and Social Research at the Moscow Research Institute of Psychiatry: Translation into Clinical Practice

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

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