Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Full text

ORIGINAL RESEARCH
published: 21 May 2020
doi: 10.3389/fnagi.2020.00141
Frontiers in Aging Neuroscience | www.frontiersin.org 1May 2020 | Volume 12 | Article 141
Edited by:
Woon-Man Kung,
Chinese Culture University, Taiwan
Reviewed by:
Stephane Perrey,
Université de Montpellier, France
Naimul Khan,
Ryerson University, Canada
*Correspondence:
Keum-Shik Hong
kshong@pusan.ac.kr
Received: 05 February 2020
Accepted: 27 April 2020
Published: 21 May 2020
Citation:
Yang D, Huang R, Yoo S-H, Shin M-J,
Yoon JA, Shin Y-I and Hong K-S
(2020) Detection of Mild Cognitive
Impairment Using Convolutional
Neural Network: Temporal-Feature
Maps of Functional Near-Infrared
Spectroscopy.
Front. Aging Neurosci. 12:141.
doi: 10.3389/fnagi.2020.00141
Detection of Mild Cognitive
Impairment Using Convolutional
Neural Network: Temporal-Feature
Maps of Functional Near-Infrared
Spectroscopy
Dalin Yang1, Ruisen Huang 1, So-Hyeon Yoo 1, Myung-Jun Shin 2, Jin A. Yoon 2,
Yong-Il Shin 3and Keum-Shik Hong 1
*
1School of Mechanical Engineering, Pusan National University, Busan, South Korea, 2Department of Rehabilitation Medicine,
Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan,
South Korea, 3Department of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National
University Yangsan Hospital, Yangsan-si, South Korea
Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer’s disease (AD),
which is considered the most common neurodegenerative disease in the elderly. Some
MCI patients tend to remain stable over time and do not evolve to AD. It is essential to
diagnose MCI in its early stages and provide timely treatment to the patient. In this study,
we propose a neuroimaging approach to identify MCI using a deep learning method and
functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and
nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop,
and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes
(1HbO) in the region of interest, 1HbO maps at 13 specific time points (i.e., 5, 10,
15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal
feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in
the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN)
was applied to identify the subjects into either MCI or HC, individually, after training the
CNN model with 1HbO maps and temporal feature maps above. Finally, we used the
5-fold cross-validation approach to evaluate the performance of the CNN. The results of
temporal feature maps exhibited high classification accuracies: The average accuracies
for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%.
Notably, the highest accuracy of 98.61% was achieved from the 1HbO slope map during
20–60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach
based on temporal feature maps is a promising diagnostic method for early detection of
MCI and can be used as a tool for clinical doctors to identify MCI from their patients.
Keywords: functional near-infrared spectroscopy (fNIRS), mild cognitive impairment (MCI), convolutional neural
network (CNN), temporal feature, brain map, N-back, Stroop, verbal fluency task

Yang et al. Detection of Mild Cognitive Impairment
INTRODUCTION
Alzheimer’s disease (AD) is authoritatively listed as the sixth
leading cause of death in the United States (US), and it is also the
fifth primary cause of death for those aged 65 years and above
(Taylor et al., 2017). Seven hundred thousand people aged 65
years and above in the US were estimated death based on AD
in 2019 (Hebert et al., 2013). As recently reported by Alzheimer
Association, it estimated 18.5 billion hours of assistance (valued
at $233.9 billion) was provided by the caregivers of people with
AD or other dementias (Alzheimer Association, 2019). It is
thought that AD starts at least 20 years before the symptoms
occur with small unnoticeable changes in the brain. Symptoms
arise because of the damaged nerve cells (neurons) related to
thinking, learning, and memory (Gordon et al., 2018). Symptoms
tend to grow over time and gradually start to interfere with
the ability of an individual to perform everyday activities until
death. AD is considered a progressive, irreversible neurological
brain disorder. Currently, no pharmacological treatment exists
that can decelerate or prevent the symptoms of AD (Alzheimer
Association, 2019). Many researchers suppose that the early stage
in the AD process, at either the mild cognitive impairment
(MCI) or preclinical stage, will be the most effective period for
future treatments to slow down or prevent the progression of
AD (Yiannopoulou and Papageorgiou, 2013). Thus, it is essential
to assess biomarkers (i.e., the indication of the medical state
observed from outside of patients; Strimbu and Tavel, 2010) for
identifying individuals who are in these early stages of the disease
and can receive appropriate treatment.
There are three categories of diagnostic biomarkers
for AD, which are named β-amyloid-Aβdeposits (A),
hyperphosphorylated tau aggregates (T), and neurodegeneration
or neuronal injury (N) (Jack et al., 2018). The ATN synopsis is
widely assessed through cerebrospinal fluid (CSF) or medical
imaging. Thus far, no evidence that supports the preeminence
of any biomarker over another (CSF vs. imaging) for the
diagnostic assessment of AD exists. The selection of biomarkers
typically relies on the cost, availability, and convenience of
tests (Khoury and Ghossoub, 2019). However, because medical
imaging can identify the different stages of the AD temporally
and anatomically, some researchers claim that the superiority of
medical imaging over the biofluid biomarkers mentioned above
(Márquez and Yassa, 2019).
Functional near-infrared spectroscopy (fNIRS) is a
non-invasive neuroimaging technique, which is used
to measure activation-induced changes in the cerebral
hemoglobin concentrations of oxyhemoglobin (1HbO)
and deoxyhemoglobin (1HbR) (Perrey, 2014; Shin and Im,
2018; Hong et al., 2020). The blood flow and oxygen metabolism
are induced by the neural activity in the neighboring capillary
network (Hong et al., 2014; Zafar and Hong, 2018; Ghafoor et al.,
2019). In comparison with the existing neuroimaging techniques
involving direct neural activation measurement methods such as
magnetoencephalography (MEG) and electroencephalography
(EEG) (Kumar et al., 2019), fNIRS offers the advantage of higher
spatial resolution and lower susceptibility to the movement
artifact (Naseer and Hong, 2015; Wilcox and Biondi, 2015; Hong
et al., 2018; Pfeifer et al., 2018). In contrast, other well-established
neuroimaging techniques are typically associated with the
metabolism of biochemical components during neural activity
and exist a limitation in terms of temporal resolution. These
techniques include positron emission tomography (PET),
single-positron emission computed tomography (SPECT), and
functional magnetic resonance imaging (fMRI) (Strangman et al.,
2002). In particular, because of the property requirement of the
radioactive isotopes, PET and SPECT do not allow continuous or
repeated measurements, a factor that also limits their application
in the cases of children and pregnant women (Irani et al.,
2007). Although fMRI is non-radiative and involves no risk, it
is physically constraining, is sensitive to movement artifacts,
exposes participants to an excessively noisy environment, and
is expensive (Ferrari and Quaresima, 2012). These features
render fMRI inappropriate for certain research and many clinical
applications (Santosa et al., 2014). In contrast, fNIRS is a novel
neuroimaging modality with the following advantages: it is
non-invasive, safe, less costly, portable, and tolerant of motion
artifacts (Perrey, 2008); it also has great temporal resolution
and moderate spatial resolution (Ghafoor et al., 2017; Zafar
and Hong, 2020). In addition, fNIRS is in progress to improve
the spatial and temporal resolutions with the development
of bundled-optodes configurations (Nguyen and Hong, 2016;
Nguyen et al., 2016), detection of the initial dip (Zafar and Hong,
2017; Hong and Zafar, 2018), and combination of adaptive
method (Iqbal et al., 2018; Hong and Pham, 2019; Pamosoaji
et al., 2019) to improve information transfer rate.
In the past decades, the fNIRS study of psychiatric or neural-
disorder patients highly depended on mass-univariate analytical
techniques such as statistical parametric mapping (Vieira
et al., 2017). Traditionally, the research studies compared the
hemodynamic response of a patient with that of healthy control
(HC) and determined neuroanatomical or neurofunctional
differences at the group level. Most AD/MCI detection studies
typically employed the 1HbO/1HbR (Jahani et al., 2017;
Perpetuini et al., 2017; Vermeij et al., 2017; Katzorke et al.,
2018; Yoon et al., 2019) and relative temporal features such as
the mean value, slope value, number of active channels, peak
location, skewness, and kurtosis (Yap et al., 2017; Li et al., 2018a),
and they determined the significant differences for comparison.
The straightforwardness and interpretability of this methodology
has led to considerable advances in our comprehension of the
neurological disorders. With the development of technology, the
following limitations of mass-univariate analytical techniques
have been revealed. (1) Statistical information is extracted
according to each region of interest (ROI) channel based on the
assumption that various brain regions perform independently.
In practice, this assumption is inconsistent with brain function
(Biswal et al., 2010). The network-level comparison explains the
neurological symptoms better than the focal-level comparison
(Mulders et al., 2015). (2) Statistical analysis cannot easily yield
individual diagnosis results (Vieira et al., 2017). Mass-univariate
techniques are suitable only for detecting differences between
groups. According to the evaluation conducted in our initial
study, the results of statistical analysis are not consistent with
the classification results (Yang et al., 2019). Thus, an effective
classification method based on fNIRS neuroimaging is crucial for
the detection of MCI in the clinical stage.
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Yang et al. Detection of Mild Cognitive Impairment
Deep learning (DL) has allowed significant progress in
the identification and classification of image patterns and is
considered a promising machine-learning methodology (Ravi
et al., 2017). Convolutional neural networks (CNNs), the
most broadly used DL architecture, have delivered excellent
performances in computer-aided prediction for neurological
disorders (Mamoshina et al., 2016; Tanveer et al., 2019). The
great success of CNNs in neural-image classification and analysis,
which evidences their strong image-classification ability (Cecotti
and Gräser, 2011; Ieracitano et al., 2018; Lin et al., 2018;
Waytowich et al., 2018; Oh et al., 2019), motivated us to develop
a CNN-based classification method for early-stage AD detection.
So far, there are not any discussions in the literature, which used
the DL method as an assistive tool for the diagnosis of early-stage
AD by fNIRS signals, except for our group.
In our initial investigation (Yang et al., 2019), we compared the
hemodynamic responses and statistical information between the
groups of MCI and HC: We evaluated the digital biomarkers (i.e.,
mean, slope, peak, kurtosis, and skewness) and image biomarkers
(i.e., t-map and connectivity map) for MCI identification. The
MCI group showed decreased 1HbO responses in comparison
with the HC group, which is consistent with the literature
(Vermeij et al., 2017; Katzorke et al., 2018). As digital biomarkers,
15 features (i.e., mean value of 1HbO for 5–65 s, mean value of
1HbR for 5–65 s, mean value of 1HbO for 5–25 s, mean value of
1HbR for 5–25 s, mean value of 1HbO for 0–peak time, slope of
1HbO for 5–15 s, slope of 1HbR for 5–15 s, slope of 1HbO for
20–60 s, slope of 1HbR for 20–60 s, slope of 1HbO for 60–70 s,
slope of 1HbR for 60–70 s, slope of 1HbO for 0–peak time, peak
time itself, skewness of 1HbO for 5–65 s, and kurtosis of 1HbO
for 5–65 s) were introduced for the statistical analysis and three
brain regions (i.e., left, middle, and right prefrontal brain regions)
were examined. Some of the features (e.g., mean value of 1HbO
for 5–65 s in the right prefrontal brain region with N-back task)
indicated a significant difference (p<0.05) between the MCI and
HC groups. For classification, linear discriminant analysis (LDA)
was used. The highest accuracy out of three mental tasks (i.e.,
N-back task, Stroop task, and VFT) was 76.67% from N-back
and Stroop tasks, which were based on manually selected ROI
channels. Also, we evaluated the t-map and connectivity map as
image biomarkers. The CNN result based on t-maps of the N-
back task achieved the best performance of 90.62%. Based upon
these findings, the conclusion was that image biomarkers like t-
map or connectivity map provide a better classification accuracy
than digital biomarkers. Motivated on this, we will investigate
whether the combined digital biomarkers on a given space (i.e.,
mean-value image in a specified time interval, or slope-value
image in a specific time interval, etc.) can provide an improved
classification accuracy than the t-map result obtained in the
previous work.
In this study, we investigated 63 types of neural images based
on temporal (3 types), spatial (39 types), and temporal-spatial (21
types) features of fNIRS signals, which were acquired based on
three mental tasks—the N-back, Stroop, and verbal fluency tasks
(VFT)—for the early detection of AD via a CNN. The temporal
features refer to the raw 1HbO in time series, neuroimaging in
the spatial domain refers to the brain map generated at specific
time points, and the temporal-spatial features represent temporal
features (mean value, slope value, skewness, and kurtosis) in the
spatial domain. To the best of the author’s knowledge, this is the
first fNIRS neuroimaging study integrating digital biomarkers in
a spatial domain, in which the diagnosis performance for early
AD detection via a DL approach has been explored.
METHODS
Figure 1 presents a diagram of the proposed system. fNIRS data
were acquired while the subjects were performing the three
aforementioned mental tasks. After the signal preprocessing, the
ROI channels were selected for the subsequent steps. In the
FIGURE 1 | Systematic diagram of the proposed system.
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Yang et al. Detection of Mild Cognitive Impairment
feature-extraction step, the raw concentration changes in HbO,
neuroimaging at a specific time point (i.e., 5, 10, 15, 20, 25,
30, 35, 40, 45, 50, 55, 60, and 65 s) in the spatial domain and
neuroimaging of temporal features (mean value of 1HbO for
5–65 s, mean value of 1HbO for 5–25 s, slope of 1HbO for 5–
15 s, slope of 1HbO for 20–60 s, slope of 1HbO for 60–70 s,
skewness of 1HbO for 5–65 s, and kurtosis of 1HbO for 5–
65 s) in the spatial domain were generated for training the CNN
model separately. Finally, 5-fold cross-validation was employed
to assess the performance of the CNN model trained by the
features mentioned above.
Participants
In this study, 15 MCI patients (1 male and 14 females) and 9 HCs
(2 males and 7 females) were recruited from the Pusan National
University Hospital (Busan, South Korea). All 24 subjects are
right-handed, able to communicate in Korean, similar ages,
and educational backgrounds. The mental health state of each
participant was assessed using three criteria: the Korean-mini-
mental state examination (K-MMSE) (Han et al., 2008), the
Seoul Neuropsychological Screening Battery (Ahn et al., 2010),
and magnetic resonance imaging (MRI) data. Table 1 shows
the summarized demographic information for 24 participants,
comprising age (mean ±SD), gender, educational background
(mean ±SD), statistical information, and K-MMSE scores (mean
±SD). The experiment was performed consistently with the
TABLE 1 | Demographic information of participants.
Characteristics MCI (n=15) HC (n=9) p-valuea
Gender (Male/Female) 1/14 2/7 0.44
Education [years] 11.2 (±4.81) 10.56 (±2.88) 0.36
Age [years] 69.27 (±7.09) 68.33 (±4.69) 0.36
K-MMSE Score 25.13 (±2.33) 27.22 (±1.98) 0.49
K-MMSE, Korea Mini-Mental State Examination.
aTwo sample t-test with a significant level of 0.05.
approval of the Pusan National University Institutional Review
Board (General Assembly of the World Medical Association,
2013). All the subjects were provided with a comprehensive
explanation of the whole experimental contents before the start of
the experiment. After the introduction, they were asked to write
consent agreeing of the test.
Experimental Paradigm
As shown in Figure 2, the experiment comprised three mental
task sections, where each section consisted of three trials. In this
study, the N-back task was used to assess working memory (Kane
et al., 2007). The ability to inhibit cognition was evaluated by the
Stroop task. This suppression occurs when the other attribute of
the same stimulus simultaneously effects during the processing of
a stimulus (McVay and Kane, 2009; Scarpina and Tagini, 2017).
The performance of semantic verbal fluency task indicated the
ability of the vocabulary size, lexical access speed, updating, and
inhibition for each subject (Shao et al., 2014).
Participants were asked to sit on a comfortable chair and were
directed to avoid movement. Each task trial took 60 s, and a 30 s
rest was given between tasks. First, the subjects enjoyed a 10 m
resting state before a task-based experiment section began. Then,
they performed the 2-back version of the N-back task wherein
a digital number between one and nine was randomly showed
on the screen. When the current number matched the second
to last number previously displayed on display, the participants
were instructed to press the keyboard. The subjects were then
asked to execute the Stroop task. The Korean-color word Stroop
test (K-CWST) was utilized in this study. The participants were
requested to read the color of letters within a limited time.
Those letters were written by four different colors, i.e., red, blue,
yellow, and black, respectively. Finally, the subjects executed
the semantic VFT by generating as many words as possible
within 1 min; the words should relate to the given semantic
category. The amount of information of participants, which can
be retrieved based on the categorization and memorial source of
text during the limited time, were measured during this task.
FIGURE 2 | Experiment paradigm for various mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) during the examination.
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Yang et al. Detection of Mild Cognitive Impairment
FIGURE 3 | (A) The placement of Emitter and detector, (B) channel configuration with FpZ as a reference point based on the 10–20 international system.
fNIRS Data Acquisition
The data utilized in this study were acquired by NIRSIT
(OBELAB Inc., Rep. of Korea), which is a near-infrared
multi-channel continuous wave system using a sampling rate
of 8.138 Hz. The wavelengths employed for detecting two
chromophores (i.e., oxygenated hemoglobin and deoxygenated
hemoglobin) were 780 and 850 nm, respectively. A total
of 24 emitters and 32 detectors, the placements of which
are illustrated in Figure 3A, were used to measure the
neural activation of the prefrontal cortex comprehensively.
In total, 48 channels were selected for covering the entire
prefrontal cortex. The channel configuration, illustrated in
Figure 3B, was set up in accordance with the international
10–20 EEG system with the reference point FpZ. The
pairs of emitter and detector (one channel) were placed 30
mm apart.
fNIRS Data Pre-processing
The modified Beer-Lambert law was utilized to convert the
optical densities to 1HbO and 1HbR (Sassaroli and Fantini,
2004). The converted signals passed 4th-order Butterworth
low- and high-pass filters (i.e., cutoff frequencies: 0.001 and
0.1 Hz, respectively) to remove physiological noise, i.e., cardiac
noise−1 Hz, respiration−0.25 Hz, and Mayer signal−0.1 Hz
(Naseer et al., 2016; Khan and Hong, 2017; Liu et al., 2018;
Nguyen et al., 2018). In accordance with our previously published
evaluation results (Yang et al., 2019) and the relevant literature
(Hoshi, 2007), it was observed that 1HbO is more sensitive
and dependable than 1HbR. Besides, 1HbO shows a stronger
correlation with the fMRI BOLD response than 1HbR (Cui
et al., 2011; Li et al., 2018a). Therefore, 1HbO signals were
used because their signal-to-noise ratio was higher than that of
1HbR signals.
As the limitation of the spatial resolution (as compared to
fMRI), the ROIs—the areas that are active during the mental
task—must be estimated. ROI analyses have been widely as a
means of testing prior hypotheses above brain function in fMRI
and PET areas; they enhance the statistical power as compared
to entire brain area analyses and facilitate comparisons through
multiple participants (Mitsis et al., 2008). In this study, the
ROI was defined by the weighting factor (t-value) between the
desired hemodynamic response function (dHRF) and the fNIRS
measurement. The measurement (y) can be represented by the
linear relationship of the dHRF with the coefficients and the
error (ε), as shown in Equation (1). The dHRF was generated
by convoluting the canonical hemodynamic response function
(using two gamma functions) with the stimulation duration
(i.e., the 60 s task and 30 s rest period). The t-value (t) was
calculated using the robustfit function of MATLABTM. The
null hypothesis is β1=0, and SE represents standard error.
ROI channels (i.e., activated channel) were selected when the
calculated t-value was higher than the critical t-value (tcrt =
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Yang et al. Detection of Mild Cognitive Impairment
FIGURE 4 | Overview of (A) spatial features and (B) temporal-spatial features using the hemodynamic response of 1HbO.
1.9632). The critical t-value was computed by the degree of
freedom of the signals and statistical significance (p<0.05 for
two-sided tests).
y=1dHRFβ0
β1+ε, (1)
t=β0
SE (β),β1
SE (β). (2)
Feature Extraction
In this study, the extracted features were divided into three
categories: temporal, spatial, and temporal-spatial features.
Temporal features referred to the raw 1HbO in the time
series and were considered to contain the concentration
change in 1HbO with time. The spatial feature describes
neuroimaging at the specific time points. In this study, we
selected 13 time points (i.e., 5, 10, 15, 20, 25, 30, 35,
40, 45, 50, 55, 60, and 65 s) to create the neural image
for comparison purposes. The spatial feature indicates the
neural activation at a specific time point in the spatial
domain (prefrontal brain cortex). Figure 4A illustrates an
example of neuroimaging at the 15 s time point. In this
study, the selected time windows between 5 and 65 s were
considered the effect of initial time delay (3–5 s) during the
hemodynamic response.
The temporal-spatial feature expresses the temporal
information (mean value of 1HbO for 5–65 s, mean value
of 1HbO for 5–25 s, slope of 1HbO for 5–15 s, slope of 1HbO
for 20–60 s, slope of 1HbO for 60–70 s, skewness of 1HbO for
5–65 s, and kurtosis of 1HbO for 5–65 s) in the spatial domain
as shown in Figure 5. In other words, they display the values of
specific time points according to the channel placement in the
prefrontal cortex. Figure 4B illustrates the neuroimaging of the
slope map for the period between 5 and 15 s. The mean value
distinguishes the difference in the neural activation of the MCI
and HC. Since the initial peak time of the hemodynamic response
typically occurs during the time windows of the first 20 s, the
time interval of 5–25 s was chosen. The slope features, i.e., the
slope maps of 5–15 s, 20–60 s, and 60–70 s, were selected based
on the characteristic from three intervals of the hemodynamic
response: the initial increasing, plateau, and final period of
1HbO, i.e., 5–15 s, 20–60 s, and 60–70 s, respectively. The slope
indicates the difference in speed of activation between two
groups, MCI and HC. Lastly, the difference in the asymmetry
and the point of the probability distribution were measured
by skewness (i.e., from 5 to 65 s) and kurtosis (i.e., from 5
to 65 s). These measurements are intended to investigate the
overall difference of hemodynamic responses between MCI
patients and HCs. All temporal information (mean, slope,
skewness, and kurtosis) was determined by utilizing functions
of mean,polyfit,skewness, and kurtosis, respectively, based on
the MATLABTM.
Convolutional Neural Network
CNN is a special type of feedforward neural network. It takes
advantage of local spatial coherence in the input, which allows
the model to include fewer weights because of the parameter-
sharing strategy (Cecotti and Gräser, 2011; Kim and Choi,
2019; Oh et al., 2019). In addition, CNN can learn features
automatically from the input images by adjusting the parameters
to minimize classification errors (Trakoolwilaiwan et al., 2017;
Liu and Stathaki, 2018; Moon et al., 2018). Typically, CNN
comprises convolutional, activation, pooling, and fully connected
layers (Yi et al., 2018; Kim et al., 2019). Convolutional layers are
the crucial component of CNN. Suppose the input is Xwith the
2-dimensional image (h×w), and the weight matrices (called
kernels) have the size (k1×k2), the local input region Xican be
converted to feature map (Yj) as shown in Equation (3), and size
is y1×y2.
Yj=f XXi•Kj+βj!, (3)
where (•) denotes the convolution operator, and βjis the bias
term. One feature map (Yj) would be generated based on the
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Yang et al. Detection of Mild Cognitive Impairment
FIGURE 5 | Time interval distribution of the temporal features (i.e., mean, slope, skewness, and kurtosis) for temporal-spatial neural images generation.
sharing parameters of the j-th kernel with stride s. Thus, the size
of the feature map can be calculated by using:
y1=h−k1+2×p
s+1 , (4)
y2=w−k2+2×p
s+1 , (5)
where prefers to the parameter of zero padding. This parameter
is applied to keep the size of the output and input the
same by padding the input edges with zeros. The activation
layers are utilized after the convolutional layer. Typically,
a non-linear transfer function called rectified linear units
(ReLu) is widely used to achieve a better performance in
regard to generalization and learning time (Yarotsky, 2017;
Ieracitano et al., 2018). The function is shown in Equation
(6). Thus, the feature map transfers the negative activation to
be zero.
f(x)=max (o,x). (6)
There are two options in the pooling layer—average pooling and
maximum pooling—that are used to reduce the resolution of the
input feature map. As discussed in the literature (Sun et al., 2017),
the effectiveness of maximum pooling is significantly superior
to average pooling because of the ability to capture invariant
features and better generalization performance. For this reason,
we also employed maximum pooling in this study. The output (z1
×z2) of the pooling layer is as follows:
z1=y1−p1
sp
+1, (7)
z2=y2−p2
sp
+1, (8)
where spis the stride of maximum pooling, and p1=p2
represents the pooling size. Drop-out is applied for improving
the CNN performance and avoiding overfitting. In this layer, the
input and output are the same size. It is randomly initialized
to turn the on or off of the corresponding neuron of the
CNN at the beginning of the training iteration. As in the
standard DL method, each neuron of a fully connected layer
is connected with the previous layer. Since this is the issue of
two group classification, there are two neurons for the last fully
connected layer.
The architecture of the proposed CNN model contains four
layers that two convolutional layers and two fully connected
layers—as shown in Figure 6. In order for the input size to be
consistent, the input neural image size is set to 200 ×200. The
number of kernels is eight; the kernel size is 4 ×4; the size of
the pooling area is 2 ×2; the value of the stride is 1. There were
128 neurons in the first fully connected layer with the activation
function of ReLu. The loss function employed is categorical
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Yang et al. Detection of Mild Cognitive Impairment
FIGURE 6 | Convolutional neural network architecture used during the study for mild cognitive impairment and healthy control classification.
TABLE 2 | Number of ROI channels of each subject for three mental tasks (i.e., N-back task, Stroop task, and verbal fluency task).
Subject N-back task Stroop task Verbal fluency task
Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 Trial 3
1 17 23 29 31 33 36 39 27 37
2 32 26 19 23 19 15 28 32 32
3 36 33 39 32 34 35 19 32 31
4 23 31 18 12 12 18 6 23 20
5 10 37 32 20 9 8 11 16 18
6 21 22 16 12 25 22 27 34 24
7 16 30 21 13 38 23 40 37 27
8 15 15 25 9 17 16 19 31 29
9 27 27 20 21 33 22 18 11 17
10 35 10 30 38 7 24 40 41 24
11 40 36 35 39 33 38 28 35 39
12 31 28 28 19 30 21 11 22 8
13 44 31 35 37 35 24 3 22 34
14 26 22 28 38 10 6 28 22 31
15 35 1 30 36 25 13 3 17 30
16 23 8 11 19 30 21 25 17 25
17 8 16 9 20 14 15 22 29 27
18 47 26 41 8 17 20 42 39 40
19 28 40 27 41 15 14 21 19 15
20 23 14 1 31 30 30 1 27 46
21 16 28 38 20 3 3 33 28 28
22 40 2 2 6 10 12 10 30 31
23 30 43 21 13 30 18 20 24 31
24 21 31 47 36 48 24 36 35 43
Total 1,826 1,609 1,867
cross-entropy. Adam optimization was used to select the adaptive
learning rate and the parameters during gradient descent.
In this study, the CNN model may have suffered from an
overfitting problem because of the limitation of the sample data.
We employed 5-fold cross-validation to decrease the influence of
this problem on the experiment results. The data were randomly
divided into 5-folds. One subsample fold was selected to test the
performance of the trained model, and the remaining 4-folds
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Yang et al. Detection of Mild Cognitive Impairment
were set to train the CNN model. This process was reiterated five
times to ensure that each subsample was utilized as a validation
set once.
RESULTS
Hemodynamic Response and Behavioral
Result
As shown in Table 1, the statistical analysis of the behavioral
measurement (i.e., the averaged K-MMSE score) was performed
by two independent sample t-test with a significant level of 0.05.
The result (p=0.49) presents a negative correlation between the
behavioral state and the real subject mental state. In this study,
we analyzed 3,456 fNIRS channels (i.e., 24 subjects ×3 trials ×
48 channels) for each task. As shown in Table 2, the total number
of selected ROI channels (activated channels) was 1,826 (N-back
task), 1,609 (Stroop task), and 1,867 (VFT). The percentage of
activation/deactivation was calculated by dividing the number of
the ROI channels by the total number of channels. Therefore,
the percentages of activated patterns are 52.83% (N-back task),
46.56% (Stroop task), and 54.02% (VFT), respectively.
Figure 7 summarizes the averages and standard deviations
(STDs) of the hemodynamic response from the ROI channels
of the patients with MCI and the HCs during various mental
tasks—N-back task, Stroop task, and VFT. The solid lines refer
to the mean of the 1HbO, and shaded areas represent the STD
of 1HbO among the subjects. To compare the unique patterns
(i.e., an increase or a decrease) of the hemodynamic response
of the HCs and the MCI individuals in the N-back task (MCI:
solid magenta line and HC: solid green line), Stroop task (MCI:
solid red line and HC: solid blue line), and VFT task (MCI: solid
cyan line and HC: solid yellow line), respectively, we applied
two independently sampled t-tests. The results indicate that the
average hemodynamic response of MCI patients is significantly
lower than that of HCs in the N-back task (p<0.001) and VFT
task (p<0.001). In the Stroop task, the average hemodynamic
change in MCI individuals appears to be similar to that of
HCs (p=0.06825).
Neural Images in Spatial and
Temporal-Spatial Domain
The results of the neuroimaging that was conducted based on
the concentration change in oxygen-hemoglobin of the specific
time point (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and
65 s), are presented in Figure 8. The neural activation slightly
changed over time in the entire prefrontal cortex. It is also easily
observed that the MCI group displays a lower neural activation
than the HCs in the three mental tasks. Figure 9 illustrates the
neuroimaging created by the temporal features (i.e., mean values
from 5 to 65 s and from 5 to 25 s, slopes from 5 to 15 s, from
20 to 60 s, and from 60 to 70 s, skewness from 5 to 65 s, and
kurtosis from 5 to 65 s) in the spatial domain. As compared to
Figure 8, the patterns of the neural images of temporal features in
the spatial domain area differ; for example, the neural images in
Figure 8 are highly correlated, and the neural images generated
by each temporal feature in Figure 9 have their individual
FIGURE 7 | Temporal feature of the 1HbO for three mental tasks (i.e., N-back
task, Stroop task, and verbal fluency task) for mild cognitive impairment and
healthy control groups, respectively.
characteristics. Interestingly, the neural images generated by the
mean values also display the same patterns as characteristics
generated by the specific time points. Among the three mental
tasks, the VFT task shows the highest neural activation pattern
in the HC group. The lowest neural activation is shown by the
Stroop task in the MCI group. In addition, the neural images at
5 s among the six groups show lower neural firing than the neural
images at 10 s and at other time points. In contrast, neural images
at 60 s and previous time points show higher neural firing than
those at 65 s.
CNN Results for Classification of Neural
Images
The input dataset of the temporal feature contained 24 (subjects)
×3 (trials) ×ROI channels. In the neural images in the spatial
and temporal-spatial domain cases, each category had a dataset
of size 24 (subjects) ×3 (trials). To verify the CNN’s capability
to classify MCI individuals and HCs, we utilized the standard
metrics (accuracy, recall, precision, and F1-score) (Powers, 2011;
Lin et al., 2018) to assess the results. Their definitions are
Accuracy =TP +TN
TP +TN +FP +FN , (9)
Recall =TP
TP +FN , (10)
Precision =TP
TP +FP , (11)
F1score =2×Precision ×Recall
Precision +Recall , (12)
where TP, TN, FP, and FN represent the true positive, true
negative, false positive, and false negative, respectively. In this
study, TP indicated the number of MCI patients correctly
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Yang et al. Detection of Mild Cognitive Impairment
FIGURE 8 | Neuroimaging of specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) in spatial domain among three mental tasks (i.e., N-back
task, Stroop task, and verbal fluency task).
FIGURE 9 | Neuroimaging of temporal features (i.e., mean value from 5 to 65 s, mean value from 5 to 25 s, slope value from 5 to 15 s, slope value from 20 to 60 s,
slope value from 60 to 70 s, skewness value from 5 to 65 s, and kurtosis from 5 to 6 5 s) in spatial domain.
classified; TN is the number of HCs identified correctly; FP refers
to the number misclassified as MCI patients, and FN is the
number misclassified as HCs.
The CNN results for the dataset extracted by the temporal
domain are listed in Table 3. The average accuracy among the
three mental tasks is 80.15% with an STD of 3.95%, and the results
of recall, precision, and F1-score are 73.26% (4.10%), 64.23%
(6.63%), and 67.48% (5.40%), respectively. Stroop achieved a
higher accuracy rate than both the N-back task and VTF: 84.70%.
Tables 4–6depicts the CNN’s performance for neural images
at various time points in the spatial domain of the N-back
task, Stroop task, and VFT, respectively. CNN’s classification
performance is divided into four different categories, as
mentioned previously: accuracy (%), recall (%), precision (%),
and F1-score (%). Furthermore, for each category, the results
for specific time points are shown in the first column. Table 4
represents CNN classification results of the N-back task. For the
N-back task, the accuracy rate ranges from 65.28 to 93.06%, recall
from 55.66 to 60%, precision from 41.11 to 86.43%, and F1-score
from 46.34 to 87.83%. The classification result at the 15 s time
point shows the best performance. The average accuracy rate
is 82.59%. In the Stroop task case (shown in Table 5), the best
performance appeared at the 60 s time point. Interestingly, this
is consistent with the results for the hemodynamic response in
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Yang et al. Detection of Mild Cognitive Impairment
TABLE 3 | Convolutional neural network results of the temporal features among
three mental tasks (i.e., N-back task, Stroop task, and verbal fluency task).
Task Accuracy (%) Recall (%) Precision (%) F1-score (%)
N-back task 77.90 70.00 58.95 63.22
Stroop task 84.70 77.87 71.67 73.55
VFT 77.84 71.91 62.07 65.66
Average 80.15 73.26 64.23 67.48
STD 3.95 4.10 6.63 5.40
TABLE 4 | Convolutional neural network classification results of neuroimaging with
spatial features for the N-back task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
HbO map at 5 s 77.78 70.00 58.86 63.15
HbO map at 10 s 91.67 90.00 86.00 87.50
HbO map at 15 s 93.06 90.00 86.43 87.83
HbO map at 20 s 84.72 80.00 72.43 75.33
HbO map at 25 s 76.39 70.00 58.43 62.83
HbO map at 30 s 77.78 70.00 58.43 62.83
HbO map at 35 s 77.78 70.00 58.43 62.83
HbO map at 40 s 91.67 90.00 86.00 87.50
HbO map at 45 s 77.78 70.00 58.43 62.83
HbO map at 50 s 93.06 90.00 86.43 87.83
HbO map at 55 s 84.72 80.00 72.43 75.33
HbO map at 60 s 65.28 55.66 41.11 46.34
HbO map at 65 s 81.94 78.00 71.00 73.36
Average 82.59 77.20 68.80 71.96
Figure 7, i.e., there is a peak at the 60 s time point in the case of
the Stroop task. The lower accuracies occur at 5 s (76.86%), 20 s
(75.43%), 35 s (78.57%), and 65 s (77.71%). The average accuracy
is 85.03%. In contrast, at the 60 s time point, the worst results
(65.28%) were obtained in the Stroop task. The higher accuracies
appear at the time points of 5 s (91.43%), 15 s (92.00%), and
50 s (92.00%) during the VFT task (shown in Table 6). These
results are in accordance with the hemodynamic response in
Figure 7 (solid yellow line and cyan solid line). The average
accuracy is 82.20%. Among the results of the three mental tasks,
the four verifying factors are always congruent. This means that
when the accuracy rate is higher, the values of the corresponding
recall, precision, and F1-score are also higher. For instance,
when the highest accuracy is 98.57%, the highest recall (98.89%),
precision (98.33%), and F1-score (98.50%) also appear for the
same features.
The CNN classification results of neuroimaging of temporal
features in the spatial domain are shown in Tables 7–9. The first
column of “mean map (5:65 s),” “mean map (5:25 s),” “slope map
(5:15 s),” “slope map (20:60 s),” “slope map (60:70 s),” “kurtosis
map (5:65 s),” and “skewness map (5:65 s)” represents the neural
map generated based on a mean value of 5–65 s, mean value of
5–25 s, slope value of 5–15 s, slope value of 20–60 s, slope value of
60–70 s, kurtosis of 5–65 s, and skewness of 5–65 s, respectively.
In comparison, in the results of neural imaging of spatial features,
TABLE 5 | Convolutional neural network classification results of neuroimaging with
spatial features for the Stroop task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
HbO map at 5 s 76.86 70.00 58.43 62.83
HbO map at 10 s 90.57 88.89 84.33 86.00
HbO map at 15 s 83.43 78.89 70.76 73.83
HbO map at 20 s 75.43 68.89 56.76 61.33
HbO map at 25 s 84.86 80.00 72.43 75.33
HbO map at 30 s 92.00 90.00 86.00 87.50
HbO map at 35 s 78.57 70.00 59.28 63.48
HbO map at 40 s 84.86 80.00 72.43 75.33
HbO map at 45 s 84.00 80.00 72.00 75.00
HbO map at 50 s 85.71 80.00 72.86 75.65
HbO map at 55 s 92.86 90.00 86.43 87.83
HbO map at 60 s 98.57 98.89 98.33 98.50
HbO map at 65 s 77.71 70.00 58.86 63.15
Average 85.03 80.43 72.99 75.83
TABLE 6 | Convolutional neural network classification results of neuroimaging with
spatial features for the VFT task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
HbO map at 5 s 91.43 88.89 84.76 86.33
HbO map at 10 s 77.71 70.00 58.86 63.15
HbO map at 15 s 92.00 90.00 86.00 87.50
HbO map at 20 s 70.57 60.00 45.29 50.98
HbO map at 25 s 77.71 70.00 58.86 63.15
HbO map at 30 s 84.86 80.00 72.43 75.33
HbO map at 35 s 77.71 70.00 58.86 63.15
HbO map at 40 s 78.57 70.00 59.29 63.48
HbO map at 45 s 84.86 80.00 72.43 75.33
HbO map at 50 s 92.00 90.00 86.00 87.50
HbO map at 55 s 78.57 70.00 59.29 63.48
HbO map at 60 s 84.86 80.00 72.43 75.33
HbO map at 65 s 77.71 70.00 58.86 63.15
Average 82.20 76.07 67.18 70.60
the average accuracies are higher for all three mental tasks (i.e.,
N-back task: 89.46%, Stroop task: 88.00%, and VFT: 90.37%) as
shown in Tables 7–9, respectively. In the N-back task, the highest
accuracy (98.61%) occurred in the slope map from 20 to 60 s,
and the lowest accuracy appeared in the mean map (5–25 s). The
highest accuracy is 98.57% in the slope map (5–15 s) during the
Stroop task, and the lowest accuracy is 77.71% in the slope map
(20–60 s). For the VFT task, the accuracy range is from 84.86%
(mean map during 5–65 s and skewness map) to 98.57% (mean
map 5–25 s).
DISCUSSION
The objective of the study was to investigate the neuroimaging
biomarkers and select the possible candidate biomarkers for
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Yang et al. Detection of Mild Cognitive Impairment
TABLE 7 | Convolutional neural network classification results of neuroimaging with
temporal-spatial features for the N-back task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
Mean map (5:65 s) 84.72 80.00 72.43 75.33
Mean map (5:25 s) 76.39 70.00 58.43 62.83
Slope map (5:15 s) 91.67 90.00 86.00 87.50
Slope map (20:60 s) 98.61 98.89 98.33 98.50
Slope map (60:70 s) 92.86 90.00 86.42 87.83
Skewness map (5:65 s) 91.67 90.00 86.00 87.50
Kurtosis map (5:65 s) 90.28 88.89 84.33 86.00
Average 89.46 86.83 81.71 83.64
TABLE 8 | Convolutional neural network classification results of neuroimaging with
temporal-spatial features for the Stroop task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
Mean map (5:65 s) 92.86 90.00 86.43 87.83
Mean map (5:25 s) 91.43 88.89 84.76 86.33
Slope map (5:15 s) 98.57 98.89 98.33 98.50
Slope map (20:60 s) 77.71 70.00 58.86 63.15
Slope map (60:70 s) 85.71 80.00 72.86 75.65
Skewness map (5:65 s) 84.86 80.00 72.43 75.33
Kurtosis map (5:65 s) 83.43 78.00 71.43 73.69
Average 87.80 83.68 77.87 80.07
TABLE 9 | Convolutional neural network classification results of neuroimaging with
temporal-spatial features for the VFT task.
Features Accuracy (%) Recall (%) Precision (%) F1-score (%)
Mean map (5:65 s) 84.86 80.00 72.43 75.33
Mean map (5:25 s) 98.57 98.89 98.33 98.50
Slope map (5:15 s) 92.86 90.00 86.43 87.83
Slope map (20:60 s) 85.71 80.00 72.86 75.65
Slope map (60:70 s) 92.86 90.00 86.43 87.83
Skewness map (5:65 s) 92.86 90.00 86.43 87.83
Kurtosis map (5:65 s) 84.86 80.00 72.43 75.33
Average 90.37 86.98 82.19 84.04
the early detection of AD. To attain this goal, we examined
neural images that were generated based on 3 temporal features,
13 spatial features, and 7 temporal-spatial features for training
the CNN model, respectively. Finally, we suggest the use
of two temporal-spatial features (mean map, slope map) for
identification of MCI patients due to the high classification
accuracy (90.37%, the averaged accuracy of VFT, Table 9).
Especially, the slope map from 20 to 60 s with the N-back
task achieved the highest accuracy of 98.61%, see Table 7,
Slope map (20:60 s). It is the first study to assess neural
images obtained by fNIRS signals for early AD detection.
Furthermore, the results obtained for MCI detection constitute
the highest diagnosis performance in fNIRS areas. Interpretable,
non-invasive, reliable, low cost, and portable biomarkers are
always the necessary tools for the identification of patients with
MCI symptoms. The computer-aided neural imaging method
could provide a novel direction for the clinical diagnosis
of MCI.
fNIRS, a novel non-invasive neuroimaging modality, has
proven its worth during the last decade, especially in the
healthcare industries (Khan et al., 2018; Hong and Yaqub,
2019). The first article on the use of fNIRS for MCI detection
appeared in 2006. This paper proposed that MCI patients
show a decreased 1HbO in the right parietal cortex during
the VFT (Arai et al., 2006), and it was the first study to
suggest fNIRS as a potential tool for screening AD/MCI. After
7 years, more related papers were published. One of the articles
proved that the difference in 1HbO of MCI individuals and
HCs could also be measured in the prefrontal cortex (Doi
et al., 2013). In addition, the 1HbO during the resting state
(Viola et al., 2013) and N-back task (Niu et al., 2013) also
presents signs of neurodegeneration in the MCI group. In 2014,
abnormal metabolisms of MCI were observed by some clinical
research groups (Babiloni et al., 2014; Liu et al., 2014), such
as the hypercapnia effect, global brain hypoperfusion, oxygen
hypometabolism, and neurovascular decoupling. Later, the
verification of neurodegeneration was also extended to the other
cerebral brain regions, i.e., prefrontal cortex (Uemura et al., 2016;
Yeung et al., 2016b; Vermeij et al., 2017) inferior frontotemporal
cortex (Katzorke et al., 2018), and lateral prefrontal cortex
(Marmarelis et al., 2017). Meanwhile, some researchers (Yeung
et al., 2016a; Yap et al., 2017; Li et al., 2018a,b,c; Zeller et al.,
2019) started to explore reliable biomarkers, i.e., complexity,
number of activated channels, mean value of 1HbO, time to
reach peak value, and slope, and lateralization hyperactivation
patterns. Interestingly, neurodegeneration symptoms similar to
those mentioned above could be repeated. By virtue of these
studies, novel directions for understanding the neurological
information of MCI symptoms better were provided. Moreover,
the relative results also proved that fNIRS is a promising tool for
detecting the difference between MCI individuals and HCs. In
this study, the hemodynamic responses (shown in Figure 7) of
1HbO during three tasks, the N-back task, the Stroop task, and
VFT, were consistent with the result of the researches (Yap et al.,
2017; Li et al., 2018b) related to AD/MCI detection. Interestingly,
the percentages of the activated channels (i.e., N-back: 52.83%,
Stroop: 46.56%, and VFT: 54.02%) were similar to Mandrick et al.
(2013), in which the percentage of activation in the prefrontal
cortex during the mental and motor tasks was 51%. However, the
provision of a diagnostic decision based on the group difference
presents a challenge, because a high STD exists between the
two groups.
As mentioned above, researchers prefer to observe the
hemodynamic response in time series. Typically, the fluctuation
of concentration changes in oxygenated hemoglobin provides
evidence for differences in metabolism between the two groups.
However, this technique suffers from at least two limitations.
(1) Poor robustness: Because of the disturbance of the noise
and some intrinsic physiological causes, there is a high
possibility that some channels will not be activated or fully
activated by noise (Birn, 2012; Wald and Polimeni, 2017).
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Yang et al. Detection of Mild Cognitive Impairment
To a large extent, the affected channel would influence the
fluctuation of 1HbO/1HbR. (2) Loss of information from
the brain network: According to the recent literature (Fornito
and Harrison, 2012), the neural disorder is associated with
subtle abnormalities distributed throughout the brain. Studies
have implied that the neurodegeneration arises from disordered
interaction in the connected neural system rather than in the
focal channel (Breakspear and Jirsa, 2007). Thus, neglecting
the spatial (network level) domain would lead to a significant
loss in terms of understanding better the symptom of neural
disorders. This comparison was also evaluated in our previous
publication (Yang et al., 2019). As our initial investigated
indicated, the digital biomarkers, which were obtained based
on ROI channels, showed lower accuracy than the image
biomarkers (network level). Similarly, the difference of the
hemodynamic response for the MCI and HC groups is easy
to observe in the spatial domain, which is demonstrated in
Figures 8,9.
To meet the demand for clinical diagnoses, it is crucial
to converting the neural images into a form that allows an
interpretable clinical decision (Martinez-Murcia et al., 2018).
With the development of machine/deep learning methods,
researchers have started to utilize machine/deep learning
for identifying AD/MCI, because the natural images and
brain images have similarities (Vieira et al., 2017). In the
recent literature (Ieracitano et al., 2018; Ju et al., 2019), it
was claimed that deep learning methods (e.g., CNN) would
present a superiority for diagnosis for AD/MCI by using
EEG and fMRI signals. Also, in our initial fNIRS study
(Yang et al., 2019), we compared the statistical analysis,
LDA, and CNN for identifying MCI patients from HCs.
The results are consistent with the EEG (Ieracitano et al.,
2018) and fMRI studies (Ju et al., 2019) that deep learning
methods have a better performance than LDA/statistical
analysis. Therefore, in this study, we employed the CNN and
evaluated the temporal, spatial, temporal-spatial biomarkers for
MCI diagnosis.
One of the first studies (Gupta et al., 2013) in which a
CNN was applied to structural MRI data achieved classification
accuracy rates of 94.7% for AD vs. HC and 86.4% for MCI vs.
HC. Thus far, with the development of DL, the classification
accuracy has reached a high accuracy for MRI (98/99%) (Khagi
et al., 2019), EEG (98.4%) (Amezquita-Sanchez et al., 2016),
and fMRI (97%) (Hojjati et al., 2018). According to the results
shown in Tables 4–9, the average classification accuracy (i.e.,
N-back task: 89.46%, Stroop task: 88.00%, and VFT: 90.37%)
in the temporal and spatial domain could prove that fNIRS is
also a promising diagnostical modality. In comparison to our
previous study, which utilized the image biomarkers (t-map and
connectivity map) for MCI identification with fNIRS signals
(Yang et al., 2019). Our current classification accuracy (highest
accuracy: 98.61%) is further improved, and more reliable image
biomarkers (e.g., mean map and slope map) are provided for the
clinical diagnosis.
Among the three mental tasks, there was no significant
difference based on the CNN classification performance. In
comparison with the results of the mental task, the feature
selection seems considerably more informative. The performance
yielded by the temporal-spatial feature is superior to that yielded
by the temporal and spatial features. According to our results, the
temporal feature (i.e., the slope map between 5 and 15 s) always
showed a good performance. The possible reason is the lower
hemodynamic response of people with MCI during the initial
stage of the task, which in turn explains why the slope value of
the HC group increases faster than that of the MCI group. To
reach a more reliable and precise decision, we suggest utilizing a
combination of features.
Although the present study has proposed and evaluated
the imaging biomarkers for MCI detection with the fNIRS
signals using the CNN method (i.e., the highest accuracy was
98.61%), some limitations need be mentioned. First, the fNIRS
signals were measured only from the prefrontal cortex, since
the benefit of no hair in the prefrontal region can minimize the
scattering and attenuation effects. A different result with different
biomarkers might also be observed from other brain regions
(e.g., the parietal cortex). Derosière et al. (2014) have shown
that the parietal cortex revealed a better classification accuracy
than the prefrontal cortex for attention state classification.
Therefore, a combination of both prefrontal and parietal cortices
will provide an improved classification accuracy for MCI case
too. Second, the exact location of the FpZ reference point in
the International 10–20 System might not have been observed
consistently, because the fNIRS device (NIRSIT, OBELAB Inc.,
Republic of Korea) had fixed emitter-detector distances, and
the head shapes of individual subjects were not the same. In
addition, data augmentation might be another way to avoid CNN
overfitting issues. In this study, we used two drop out layers to
overcome the overfitting problem, and the loss plot showed that
the overfitting did not appear. The data augmentation method
will be considered in our future work to deal with the overfitting
issue. For the investigation of the spatial feature, we randomly
selected 13 specific time points as features for conducting the
comparison. Because of the limitation of the computation, it
is difficult to list all the time points (i.e., 8.138 Hz ×90 s =
732 points) and conduct the process as mentioned above. This
limitation could be compensated by using a real-time analysis
system or dividing more time segments by a shorter time window
(i.e., 5–15 s). Likewise, in the temporal-spatial features, the time
point selection also presents a challenge. In general, a neural
image based on the first 15 s always yields a good accuracy level.
Driving more time points in the time windows between 5 and 15 s
would like to be done in future work to reduce the processing
time. In the real clinical application, the biggest challenge
is data training: However, once the model gets appropriately
trained, the system can be used as a reliable tool for diagnosing
MCI patients.
CONCLUSION
In this study, we highlighted the feasibility of using fNIRS
for the early detection of AD by using neural imaging based
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Yang et al. Detection of Mild Cognitive Impairment
on the temporal, spatial, and temporal-spatial features. This
systematically analyzed results indicate that neural imaging
of the combined temporal and spatial features (i.e., the
average accuracy of N-back: 89.46%, Stroop: 87.80%, and
VFT: 90.37%) produces a more reliable performance than
those when using temporal and spatial features separately.
In particular, the slope map (20–60 s) during the N-back
task achieved the highest accuracy of 98.61%. In the Stroop
task and VFT case, the maximum accuracy is 98.57% by
the slope map (5–15 s) and mean map (5–25 s). Besides
that, all the mental tasks could achieve a good accuracy
(>90%) within the time windows (5–15 s). This finding
provides the possibility to use the short time windows for
early detection of the AD. Conclusively, our results indicate
that the CNN-aided temporal-spatial neuroimaging method
could assist the clinical diagnosis of MCI. Additionally, the
classification performance based on the spatial neural image
also provides a possibility of reducing the diagnosis time in
future studies.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Pusan National University Institutional Review
Board. The patients/participants provided their written informed
consent to participate in this study.
AUTHOR CONTRIBUTIONS
DY conducted the data analysis and wrote the first draft of
the manuscript. RH and S-HY participated in the initial data
analysis. M-JS and JY interviewed the participants and managed
the processes related to experimentation and interventions. Y-IS
designed the initial experimental paradigm. K-SH suggested the
theoretical aspects, corrected the manuscript, and supervised all
the process from the beginning. All authors have approved the
final manuscript.
FUNDING
This work was supported by the National Research Foundation
(NRF) of Korea under the auspices of the Ministry of
Science and ICT, Republic of Korea (Grant No. NRF-
2017R1A2A1A17069430).
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Conflict of Interest: 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.
Copyright © 2020 Yang, Huang, Yoo, Shin, Yoon, Shin and Hong. This is an open-
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Frontiers in Aging Neuroscience | www.frontiersin.org 17 May 2020 | Volume 12 | Article 141