Poster No:
2373
Submission Type:
Abstract Submission
Authors:
Li-Ting Lin1, Albert Yang2
Institutions:
1National Yang Ming Chiao Tung University, Taipei, Taiwan, 2National Yang Ming Chiao Tung University, Taipei City, Taiwan
First Author:
Li-Ting Lin
National Yang Ming Chiao Tung University
Taipei, Taiwan
Co-Author:
Albert Yang
National Yang Ming Chiao Tung University
Taipei City, Taiwan
Introduction:
Violent issue in field of mental illness is worthy to pay attention, especially for becoming danger in society, e.g., harming others. Previous studies showed that some mental illness: schizophrenia, bipolar disorder have higher rates for aggression. However, it's hard to clarify how the mental symptoms affect on criminal behavior and there is lack of objective method for forensic psychiatry. Therefore, it's important to find the "biomarkers" for the interaction between mental illness and criminal behavior. Resting-state electroencephalogram (rs-EEG) is a non-invasive tool to measure brain signal and widely used in research and clinical use. And previous study showed that aggression behavior is associated with frontal, parietal, and temporal regions. In this study, we will focus on the highest rate for violent behavior in mental illness-schizophrenia spectrum disorder and aim to find the potential biomarkers or features for "criminal patients" and" aggression" based on re-EEG and focus on regions related to criminal behavior with non-linear analysis to contribute to clinical and practice.
Methods:
Closed eye periods in rs-EEG data (21 channel, EB neuro) of criminal cases and health people were collected from the forensic psychiatry databases and healthy people databases in the Taoyuan Psychiatric Center, Taiwan, including 22 criminal cases with violent crime: harming others with weapons or unarmed attack (age: 39.86 ± 11.15, 95.45% in males), 13 criminal cases with non-violent crime (age: 42.69 ± 9.05, 53.85% in males) and 16 healthy people (age: 40.25± 11.54, 25% in male). Before entering the formal analysis for rs-EEG, all data were conducted pre-processing to remove noise with bandpass filter, notch filter, median filtering and removing outliers. And each signal will be segment into several epochs with 10s (overlap 5s). There are 156 epochs in violent group, 65 epochs in non-violent group and 250 epochs in healthy control group. With these data, we conducted non-linear analysis: sample entropy, multiscale entropy for time series data in different brain regions to measure the complexity of EEG signal to figure out the complexity of brain signal in different group.
Results:
There is no significant difference in age among three group(p=.741). According to the analysis, we can find that there is difference in the complexity of time series in frontal, temporal, and parietal regions. To discuss the features for "criminal patients", there is lower complexity of brain signal in frontal and parietal regions and higher complexity of brain signal in temporal regions in first scale (sample entropy) in group of schizophrenics with criminal cases compared to healthy control. For the multiscale entropy, there is reverse performance for the complexity of brain signal in different scale in both frontal (after 4th scale) and parietal regions (after 2nd scale). Also, to discuss with the features for "aggression" in schizophrenia, non-violent crime groups showed highest complexity of brain signal (sample entropy) in all mentioned brain regions. For the multiscale entropy, there is higher complexity in temporal, parietal regions in 4th scale in violent group.
Conclusions:
Our findings supported previous studies findings-there are changes for physiological signals under the interaction of behavior and mental illness and offered an idea that the complexity of brain signal in different scale may be potential features to distinguish schizophrenics who may have violent behavior or illicit non-violent behavior. However, the meaning of the changes for complexity for brain signal in different brain regions should be explored more with clinical investigation.
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
EEG 1
Keywords:
Electroencephaolography (EEG)
Psychiatric Disorders
Other - crime
1|2Indicates the priority used for review
Provide references using author date format
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* Lindqvist, P. (1990). Schizophrenia and crime: a longitudinal follow-up of 644 schizophrenics in Stockholm. The British Journal of Psychiatry, 157(3), 345-350.
* Potegal, M. (2012). Temporal and frontal lobe initiation and regulation of the top-down escalation of anger and aggression. Behavioural brain research, 231(2), 386-395.
* Volavka, J. (2013). Violence in schizophrenia and bipolar disorder. Psychiatria danubina, 25(1), 0-33.
* Werhahn, J. E. (2021). Aggression subtypes relate to distinct resting state functional connectivity in children and adolescents with disruptive behavior. European child & adolescent psychiatry, 30, 1237-1249.