Assisting Diagnostic Applications of Functional Near-Infrared Spectroscopy for Psychiatric disorders
Poster No:
2425
Submission Type:
Abstract Submission
Authors:
JongKwan Choi1, Yuna Kim2, Yun Seong Park2, Woojae Myung2
Institutions:
1OBELAB, Seoul, Korea, Republic of, 2Seoul National University Bundang Hospital, Bundang, Korea, Republic of
First Author:
Co-Author(s):
Introduction:
Major depressive disorder is known for its association with a high risk of persistent chronic symptoms that might cause modern social problems such as increase of suicide rates and reduction of economic outcomes. However, it is difficult to recognize early on patients' depressive symptoms and distinguish between major depressive disorder (MDD) patients and bipolar disorder (BP) patients, who require different treatment methods, through their behaviors and self-described reports. According to an fMRI and PET study [1], functional activation followed by cognitive stimulation of MDD and BP patients were different between normal. However, due to constraints of space and cost, conventional neuromonitoring tools are not widely used. In this study, we employed fNIRS technology due to its affordability and user-friendly nature, aiming to validate its potential as an adjunctive tool in distinguishing various psychiatric disorders.
Methods:
The MDD, BP and healthy control(HC) group participants were diagnosed using conventional methods, specifically the DSM-IV criteria, Hamilton Depression Rating Scale (HAMD), and Clinical Global Impression (CGI) score. Thirty healthy control group (HC) (ages: 32±11), thirty major depressive disorder group (MDD) (ages: 48±8) and thirty bipolar disorder group (BP) (ages: 28±7) were included. They performed verbal fluency task that was consisted of a 30 s pre-task period, 60 s task period, and a 70 s post-task period. During the pre- and post-task periods, participants were asked to say "ㅏ,ㅔ,ㅣ,ㅗ,ㅜ" (pronounce as "a,e,I,o,u") repeatedly. During the task period, they were instructed to generate as many words as possible, beginning with Korean letter as 'ㄱ', 'ㅅ' and 'o' (pronounce as "giek, siot, eong") for 20 s per letter [2].
The utilized fNIRS system is a NIRSIT (OBELAB Inc., Korea) described in figure 1 and it is composed by 24 lasers sources (780/850 nm) and 32 photo detectors with multiple source-detector spacing (1.5, 2.12, 3, 3.35 cm) and 48 channels implemented by 3cm separation were used. Detected signals were low pass filtered to decrease cardiovascular artifact and environmental noise. The hemodynamics responses extracted by modified beer-lambert law [3] and evaluated Integral and Centroid values to identify individuals with major diagnoses [4]. One-way ANOVA was performed between groups to evaluate significant different in each variables and analyses of variance (ANCOVA) to examine group differences of fNIRS features with age, education, and task performance.
The utilized fNIRS system is a NIRSIT (OBELAB Inc., Korea) described in figure 1 and it is composed by 24 lasers sources (780/850 nm) and 32 photo detectors with multiple source-detector spacing (1.5, 2.12, 3, 3.35 cm) and 48 channels implemented by 3cm separation were used. Detected signals were low pass filtered to decrease cardiovascular artifact and environmental noise. The hemodynamics responses extracted by modified beer-lambert law [3] and evaluated Integral and Centroid values to identify individuals with major diagnoses [4]. One-way ANOVA was performed between groups to evaluate significant different in each variables and analyses of variance (ANCOVA) to examine group differences of fNIRS features with age, education, and task performance.
·Region of interest frontal region (R1) and bilateral temporal region (R2)
Results:
One-way ANOVA analysis demonstrated significant differences between groups concerning age and years of education, indicating that MDD tended to be older than their BP and HC groups (F = 14.664, p < 0.005). In terms of task performance, In terms of task performance, the total number of valid words used during the task in the MDD and HC groups was similar and higher than the BP group (F = 5.622, p < 0.005). One-way ANOVA showed a significant difference among groups in the Integral Value (IV) and post-hoc analyses showed a significant difference between HC and each psychiatric disorder group (HC vs BP, HC vs MD; P < 0.05), although no significant distinctions were found between any two patient groups for CV but showed differences in a representative patterns of each groups.
·Average oxy-haemoglobin waveforms in the R1 and R2 regions in each groups
Conclusions:
This fNIRS study aims to explore the potential of fNIRS as a practical tool for distinguishing between healthy individuals and those diagnosed with major psychiatric disorders. It is demonstrated that utilizing the integral and centroid value extracted from hemoglobin changes have a potential in distinguishing individuals with mood disorders from healthy controls compared to distinguishing psychiatric disorders. These findings suggest that fNIRS has the potential to serve as a promising biomarker in the process of differentiating individuals with mood or psychosis disorders, marking a significant step towards tailoring treatments based on individual needs.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Classification and Predictive Modeling
Novel Imaging Acquisition Methods:
NIRS 1
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics
Keywords:
Anxiety
Cerebral Blood Flow
Cognition
Machine Learning
Near Infra-Red Spectroscopy (NIRS)
Psychiatric Disorders
1|2Indicates the priority used for review
Provide references using author date format
[2] Husain, S., Yu, R., Tang, TB. et al. Validating a functional near-infrared spectroscopy diagnostic paradigm for Major Depressive Disorder. Sci Rep 10, 9740 (2020).
[3] Delpy, D. T., Cope, M., van der Zee, P., Arridge, S., Wray, S., & Wyatt, J. S. (1988). Estimation of optical pathlength through tissue from direct time of flight measurement. Physics in Medicine & Biology, 33(12), 1433.
[4] Takizawa R, Fukuda M, Kawasaki S, Kasai K, Mimura M, Pu S, Noda T, Niwa S, Okazaki Y; Joint Project for Psychiatric Application of Near-Infrared Spectroscopy (JPSY-NIRS) Group. Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage. 2014 Jan 15;85 Pt 1:498-507