Estimation of nonlinear measures of schizophrenia patients' EEG in emotional states

Akar, Saime Akdemir; Kara, Sadık; Latifoğlu, Fatma; Bilgiç, Vedat

doi:10.1016/j.irbm.2015.06.005

Cited by 7 publications

(6 citation statements)

References 51 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Akar, Kara, Latifoglu, et al, 2015a;Kim et al, 2000;Rockstroh et al, 1997) HFD (Raghavendra et al, 2009(Raghavendra et al, , 2009Sabeti et al, 2009) KFD (Akar, Kara, Latifoglu, et al, 2015b) LZC (Sabeti et al, 2009) CD (Jeong, Kim, Chae, et al, 1998;Jin et al, 2003) ApEn (Akar et al, 2016;Sabeti et al, 2009) Schizophrenia > Healthy Controls L1 (Roschke et al, 1995) MSE (Takahashi et al, 2010) LZC (Y. Li et al, 2008) ApEn (Thilakvathi et al, 2017) HFD (Raghavendra et al, 2009;Thilakvathi et al, 2017) Severe and acute > Mild schizophrenia CD (Koukkou et al, 1993) (Akar, Kara, Agambayev, et al, 2015) HFD (Akar, Kara, Agambayev, et al, 2015;Bachmann et al, 2018;Cukic et al, 2020;Mahato and Paul, 2019) DFA (values close to 1, J. S. Lee et al, 2007) SampEn (Cukic et al, 2020) LZC (Akar, Kara, Agambayev, et al, 2015;Bachmann et al, 2015;Y.…”

Section: Fundingmentioning

confidence: 99%

“…Diminished EEG complexity has been observed with several measures (see Table 1). This decrease in complexity, especially in the frontal areas of the brain (Akar, Kara, Latifoglu, & Bilgic, 2015a, 2015b; Jeong, Kim, Chae, et al, 1998; Kim et al, 2000; Raghavendra et al, 2009) may be interpreted as a reduction in information processing and inflexible neural networks (Kim et al, 2000). This is consistent with previous neuroimaging findings regarding hypofrontality (i.e., abnormal underactivation of frontal regions, Gattaz et al, 1992) in schizophrenia patients, which has been linked to the severity of negative symptoms, such as blunted affect, abulia and asociality (Wolkin et al, 1992).…”

Section: Brain Complexity In Psychology and Neurosciencementioning

confidence: 99%

See 1 more Smart Citation

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

Lau

Pham

Chen

et al. 2022

Eur J of Neuroscience

View full text Add to dashboard Cite

While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

show abstract

Section: Fundingmentioning

confidence: 99%

Section: Brain Complexity In Psychology and Neurosciencementioning

confidence: 99%

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

Lau

Pham

Chen

et al. 2022

Eur J of Neuroscience

View full text Add to dashboard Cite

show abstract

“…Regarding organic origin for social cognition insufficiency in schizophrenic persons; Akara SA et al (2015) monitor recorded EEG (Electroencephalogram) data to examine neural network in the left fronto-temporal area during emotional processing task [6]. They record EEG in 2 circumstances.…”

Section: Resultsmentioning

confidence: 99%

Social Cognition in Schizophrenia: A Review Study

Kamel¹,

AlQahtani²

2019

OJPsych

View full text Add to dashboard Cite

Social cognition includes all operations encountered in the process of perceiving, understanding & production of appropriate reactions while confronting with others. These include: social awareness, theory of mind, attributive style, and emotion processing. Social cognition skills have been recognized as a vital component in the rehabilitation process for schizophrenic persons, its strong contributors in social function among individual suffering from schizophrenia. The purpose of this review is to appraise various researches about social cognition and its correlates in addition to evaluating various cognitive interventions targeted toward improving social cognition function among patients group and to find the best techniques to ameliorate social cognitive deficits in schizophrenia. Databases were searched from the period from the year 2000 to October 2018. The search terms used were "Social cognition", emotional management program, schizophrenia. A total of 85 articles were identified and those who meet inclusion criteria including 27 articles. Finding indicates the efficacy of social intervention that targets cognitive aspect in functional abilities of schizophrenic patients and also emotion processing which are significant mediators of social performance aptitudes in patients group.

show abstract

“…In summary, as witnessed by the recent rise in related publications, the complexity analysis of EEG signals offers encouraging prospects for both research and clinical developments that are promising for furthering our understanding of normal and abnormal neurophysiological functioning. (Akar, Kara, Latifoglu, et al, 2015a;Kim et al, 2000;Rockstroh et al, 1997) HFD (Raghavendra et al, 2009(Raghavendra et al, , 2009Sabeti et al, 2009) KFD (Akar, Kara, Latifoglu, et al, 2015b) LZC (Sabeti et al, 2009) CD (Jeong, Kim, Chae, et al, 1998;Jin et al, 2003) ApEn (Akar et al, 2016;Sabeti et al, 2009) Schizophrenia > Healthy Controls L1 (Roschke et al, 1995) MSE (Takahashi et al, 2010) LZC (Y. Li et al, 2008) ApEn (Thilakvathi et al, 2017) HFD (Raghavendra et al, 2009;Thilakvathi et al, 2017) Severe and acute > Mild schizophrenia CD (Koukkou et al, 1993) (Akar, Kara, Agambayev, et al, 2015) HFD (Akar, Kara, Agambayev, et al, 2015;Bachmann et al, 2018;Cukic et al, 2020;Mahato and Paul, 2019) DFA (values close to 1, J. S. Lee et al, 2007) SampEn (Cukic et al, 2020) LZC (Akar, Kara, Agambayev, et al, 2015;Bachmann et al, 2015;Y.…”

Section: Discussionmentioning

confidence: 99%

Brain Entropy, Fractal Dimensions and Predictability: A Review of Complexity Measures for EEG in Healthy and Neuropsychiatric Populations

Lau¹,

Pham²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of (1) predictability and (2) regularity. We then synthesize complexity findings across different areas of psychological science, namely in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

show abstract

Estimation of nonlinear measures of schizophrenia patients' EEG in emotional states

Cited by 7 publications

References 51 publications

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

Social Cognition in Schizophrenia: A Review Study

Brain Entropy, Fractal Dimensions and Predictability: A Review of Complexity Measures for EEG in Healthy and Neuropsychiatric Populations

Contact Info

Product

Resources

About