Journal of Robotics, Networking and Artificial Life
Vol. 5(4); March (2019), pp. 228–231
DOI: https://doi.org/10.2991/jrnal.k.190220.003; ISSN 2405-9021 print; 2352-6386 online
https://www.atlantis-press.com/journals/jrnal

Research Article

An fNIRS Study of Brain State during Letter and Category
Fluency Tasks
Akane Onishi1, Hiroshi Furutani1,2, Tomoyuki Hiroyasu1, Satoru Hiwa1,*Department of Biomedical Sciences and Informatics, Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto-fu 610-0394, Japan
Innovative Computing Research Center, Doshisha University, Kyotanabe-shi, Kyoto-fu 610-0394, Japan

12ARTICLE INFO
Article History
Received 16 October 2018
Accepted 10 December 2018
Keywords
Verbal fluency task
letter fluency task
category fluency task
fNIRS
brain activity

ABSTRACT
Verbal Fluency Tasks (VFT) can be categorized into the Letter Fluency Task (LFT) that evaluates the ability to generate words
starting with a particular letter, and the Category Fluency Task (CFT) that involves recall of words that belong to semantic
categories. In this study, we investigated the differences in brain activity during LFT and CFT in healthy subjects. Brain activity
in the frontal and the temporal region was measured using functional near-infrared spectroscopy with 40 channels. We observed
more prominent brain activity when performing LFT than CFT.

© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Functional Near-infrared Spectroscopy (fNIRS) is a technique thatmeasures metabolic changes in hemoglobin oxygen in vivo, usingnear-infrared light. fNIRS detects changes in concentrationof oxy-hemoglobin, deoxy-hemoglobin, total hemoglobin, andmeasures alteration in cerebral blood flow caused by neural activity[1,2]. Brain function measurement using fNIRS is noninvasive,has excellent temporal resolution, and is performed under routine
conditions. In recent times, fNIRS as a tool in advanced medicine
is attracting attention. fNIRS examination using Verbal Fluency
Task (VFT) is widely being used in diagnosis of depression symptoms. VFT is a task that requires to generate as many words withina category or starting with a given letter as possible within a timelimit. In the Letter Fluency Task (LFT), words are recalled fromparticular letter. In the Category Fluency Task (CFT), words arerecalled from semantic categories. The characteristics of brain
activity in VFT are different in depression, bipolar disorder andschizophrenia, therefore, diverse activating tasks are used for different disorders. It has been pointed out that VFT tests not only thesemantic memory related to vocabulary but also many cognitive
functions such as effective vocabulary search power, information
processing speed, and executive function [3]. There are several
studies on brain activity differences in LFT and CFT of healthy
subjects and patients with a mental illness [4–8]. However, only
a few studies compare LFT and CFT as such. In this study, brain
activities during LFT and CFT were compared in healthy subjects.
We hypothesized a difference in brain activity between LFT andCFT and therefore, the purpose of the current study was to invesCorresponding author. Email: shiwa@mail.doshisha.ac.jp*tigate the brain status of the two versions of the VFT over a wide
area of the frontal and bilateral temporal brain regions using 40
channels of fNIRS.

2. MATERIALS AND METHODS
2.1. Participants
Healthy subjects (average age 22.35 ± 0.93, 17 males, three females,right-handed) were recruited for this study. All the participants
performed LFT and CFT, and brain activity during each task wasmeasured using fNIRS. This study was approved by the Ethics
Committee of the Doshisha University.

2.2. Experiment Procedure
In the experiment, a block-design VFT (letter and category version)
was performed. The experimental design is shown in Figure 1.
It consisted of 60 s of pre-task rest followed by 60 s of task period.
Participants were initially instructed to watch a cross at the centerof the computer screen during the experiment. The task section
began with three tasks (a, ka, shi/animals, fruits, sports). This wasexplained to the participants by the computer as audible instructions. The content of the task was determined from the frequency
of use of letters and previous studies [8,9]. During LFT, participants
were instructed to generate as many words as possible startingwith the displayed letters, while during CFT, participants wereinstructed to generate as many words which would fit in the

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presented category. The two tasks were done consecutively and theparticipants were explained about the order at the beginning of theexperiment. The total number of correct words generated during
the task was recorded as an indicator of VFT performance.

and measurement data. All the calculations and modeling was doneusing the SPM-fNIRS toolbox.

2.3. NIRS Instrument

Brain activation analysis was performed with 11 fNIRS data.
Regions with maximum activity for LFT and CFT are represented
in Tables 1 and 2, respectively. Group analysis results are shown inTables 3 and 4. Brain activity observed during LFT performance
was higher than during CFT.

In this study, a 40-channel fNIRS device (LABNIRS, ShimadzuCorporation, Kyoto) was used. The distance between the irradiation
probe and the light receiving probe was set to 3.0 cm. To minimize
the artifacts, the participants were instructed to avoid voluntary movements. The experimental set-up is shown in Figure 2. The sampling
frequency was set at 37.037 Hz. After data acquisition, three-dimensional (3D) co-ordinates of each light source and detector were measured using a 3D digitizer (FASTRAK, Polhemus, Vermont, USA).
Subsequently, these were input to the Statistical Parametric Mapping(SPM)-fNIRS toolbox for spatial registration, and the layout of eachchannel and Montreal Neurological Institute co-ordinates was created.

2.4. Data Processing
Functional near-infrared spectroscopy data was band-pass filtered using 0.01–0.1 Hz. fNIRS data from nine participants wereexcluded due to a variation of 0.01 mM * cm in 0.1 s. fNIRS data
was band-stop filtered (physiological noise removal) using 0.12–
0.35 Hz, and with 0.7–1.5 Hz after artifact correction based on
moving standard deviation and spline interpolation. Next, trend
removal was performed using a high pass filter based on the discrete cosine transform set. General linear model analysis was usedfor extraction of the active regions in the brain. Using the hemodynamic response function, a blood flow alteration model was created as shown in Figure 3. Statistical significance in brain activity
between the two groups was examined using the regression coefficient obtained by regression analysis of blood flow alteration model

3. RESULTS

Figure 3 | Creation of blood flow alteration model.
Table 1 | Activated brain region (LFT) (FWE, p < 0.05)
RegionPrecentral L
Precentral R
Frontal Sup L
Frontal Sup R
Frontal Mid L
Frontal Mid R
Frontal Mid Orb L
Frontal Inf L
Frontal Inf R
Postcentral L
Postcentral R
SupraMarginal L
SupraMarginal R
Angular RTemporal Sup L
Temporal Sup R
Temporal Mid L
Temporal Mid R
Temporal Inf L

Number of subjects
101088108710109989788867

Table 2 | Activated brain region (CFT) (FWE, p < 0.05)
Figure 1 | Experimental design.

Figure 2 | Experimental set-up.

RegionPrecentral L
Precentral R
Frontal Sup L
Frontal Sup R
Frontal Mid L
Frontal Mid R
Frontal Inf L
Frontal Inf R
Postcentral L
Postcentral R
SupraMarginal L
SupraMarginal R
Temporal Sup L
Temporal Mid L
Temporal Inf L

Number of subjects
777677777666666

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4. DISCUSSION

5. CONCLUSIONIn this study, brain activation was investigated based on thechange in hemoglobin concentration measured during VFT
performance in healthy subjects. An extensive activation in thefrontal and temporal brain regions including Broca’s areas wasobserved during the VFT performance. In particular, LFT augmented the brain activation compared with CFT. During CFT,participants would search for words by utilizing the concept ofassociations such as already existing vehicles. On the other hand,in LFT, words were searched from the notion of phonology whichis rarely used in daily life. In other words, participants suppress
the recall of related words and perform complex word searches[10]. The results of this study suggest that LFT requires more activation of brain regions than CFT. As shown in Tables 1 and 2, four
regions are activated exclusively during LFT. The regions include
Angular Gyrus R (R-ANG), Middle Temporal Gyrus R (R-MTG),
Superior Temporal Gyrus R (R-STG), and Orbital Part of Inferior
Frontal Gyrus L (L-ORB) as shown in Figure 4. Amongst thesefour regions, ANG is located at the junction of the occipital, temporal, and parietal regions and is regarded as an important interface for communicating and integrating information with various
brain regions [11,12]. Several studies have associated ANG withmemory, meaning, memory consciousness, default mode network [13]. MTG, on the other hand, is activated with a more difficult phonological task and plays an important role in controlling
semantic search [14]. Co-activation of ANG and MTG observedduring LFT suggests their role in word searches that suppresses
related word remembrance.

Currently, several ongoing studies are investigating the differencein brain activity between LFT and CFT in healthy subjects andpatients with a mental illness. However, there are only a few studies that compare LFT and CFT as such. Therefore, in this study,
the brain activities during LFT and CFT were compared only inhealthy subjects. The results from 11 healthy subjects established
an increase in LFT activation compared to CFT. Our results suggestthat co-activation of ANG and MTG makes it possible to searchwords with related word recall suppressed.

Table 3 | Group analysis result (LFT) (FWE, p < 0.05)
RegionRolandic Oper L
Temporal Sup R
Frontal Inf Tri L
Frontal Inf Tri R
Table 4 | Group analysis result (CFT) (FWE, p < 0.05)
RegionFrontal Inf Tri R
Caudate R

Figure 4 | Brain regions activated only during LFT.

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Authors Introduction
Dr. Tomoyuki Hiroyasu

Ms. Akane Onishi
Akane Onishi is currently studying toward the bachelor’s degree inEngineering at Faculty of Life andMedical Sciences, Doshisha University,
Kyoto, Japan. Her research interests
include cognitive neuroscience, with aparticular focus on neuroimaging using
functional near-infrared spectroscopy.

Dr. Hiroshi Furutani

Tomoyuki Hiroyasu received theDoctor of Engineering from Waseda
University in 1998. He has been a
Professor of Life and Medical Sciences,Doshisha University, since 2008. His
research interests include the development of novel evolutionary algorithms,
medical image processing, medical
information systems, parallel computing, Grid, web systems,and Intelligent systems. Prof. Hiroyasu is a member of IEEE,
JSME, JSCES, IPSJ, SICE, and IEICE.

Dr. Satoru Hiwa
Hiroshi Furutani received PhD ofScience from Kyoto University in 1981.
He is currently working in Innovative
Computing Research Center at Doshisha
University as researcher. His researchinterests include bio-informatics, evolutionary computation and quantum
computing.

Satoru Hiwa received PhD in Enginee­ring from Doshisha University. Heis currently working in Faculty ofLife and Medical Sciences at Doshisha
Univer­sity, Kyoto, Japan, as an AssistantProfessor. His current research interests
include human brain mapping, noninvasive neuroimaging and computational
intelligence.