Diagnosis of Mild Cognitive Impairment via Task-relevant Hemodynamic Responses

5th IFAC International Conference onAvailable online at www.sciencedirect.com Intelligent Control and Automation Sciences Belfast, Ireland, August 21-23, 5th IFACNorthern International Conference on 2019 Intelligent Control and Automation Sciences Belfast, Northern Ireland, August 21-23, 2019

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Diagnosis of Mild Cognitive Impairment via Task-relevant Hemodynamic Responses Diagnosis of Mild Cognitive Impairment viaKeum-Shik Task-relevant Hemodynamic Responses So-Hyeon Yoo. Hong* 

* So-Hyeon Keum-Shik Hong School of Mechanical Engineering, PusanYoo. National University, Busan 46241, Republic of Korea *  (e-mail: {bsh00156, kshong}@ pusan.ac.kr). Corresponding author School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea (e-mail: {bsh00156, kshong}@ pusan.ac.kr). *Corresponding author Abstract: Diagnosis of Alzheimer’s disease (AD) in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of AD. In this paper, we aim to Abstract: of Alzheimer’s disease (AD) in early stagetasks is important progressionnear-infrared of dementia diagnose Diagnosis MCI patients during semantic verbal fluency (SVFT)to prevent using functional in the aging society. Mild cognitive impairment (MCI) denotes an early stage of AD. In this paper, aim of to spectroscopy (fNIRS). To achieve our objective, t-values and correlations were calculated to find thewe region diagnose MCI patients during semantic verbal fluency tasks (SVFT) using functional near-infrared interest (ROI) channels and brain connectivities. From the ROI channels HbO data were averaged over subjects, spectroscopy (fNIRS). To achieve ourwere objective, t-values and correlations were calculated to find the region of features (mean, slope, and skewness) extracted for classification. Extracted features were labelled as two interestand (ROI) channels connectivities. From the ROI channels HbOand data were averaged over subjects, classes classified viaand twobrain classifiers, linear discriminant analysis (LDA) support vector machine (SVM). features (mean, slope, and skewness) were%extracted classification. features as two The classification accuracies were 69.23 for LDAfor and 73.07 % forExtracted SVM. The resultswere showlabelled that there are classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). significant differences of hemodynamic responses (HR) between MCI patients and healthy controls (HC). The classification accuracies were 69.23 % for LDA fNIRS and 73.07 for SVM. The show that there are Therefore, these results suggest a possibility of using as a % diagnostic tool forresults MCI patients. significant differences of hemodynamic responses (HR) between MCI patients and healthy controls (HC). Keywords: MCI, fNIRS,suggest Signal Medical Discrimination analysis © 2019, IFAC (International Federation of Automatic Control)as Hosting by Elsevier Allpatients. rights reserved. Therefore, these results aprocessing, possibility of usingsystem, fNIRS a diagnostic tool forLtd. MCI

Keywords: MCI, fNIRS, Signal processing, Medical system, Discrimination analysis  In these days, brain imaging technology has been introduced to  address this issue, including MRI and PET (Albert et al., 2011). 1. INTRODUCTION  In these days, brain used imaging technology has been introduced to MRI is commonly to visualize a brain structure of MCI address(i.e., this issue, including MRI and (Jack PET (Albert et al.,Frisoni 2011). Alzheimer disease 1.(AD) is the most common chronic patient volume of hippocampus) et al., 2010; INTRODUCTION commonly to visualize brain structure of brain MCI neurodegenerative brain disease, which is characterized by MRI et al.,is2010) and toused investigate task arelevant functional Alzheimer disease (AD) isDiagnosing the mostADcommon chronic (i.e., volume of hippocampus) (Jack et al., 2010; Frisoni gradual cognitive impairment. in the early stage patient connectivity (Li et al., 2009; Li et al., 2018; Sasaki, 2018). neurodegenerative brain disease, is characterized by Furthermore, et al., 2010) to and to investigate taskmetabolic relevant system, functional is clinically important because which the disease has already investigate the brain PETbrain was gradual cognitive impairment. Diagnosing stage used connectivity (LiMCI et al., 2009;from Li et 2018;of Sasaki, 2018). progressed when the pathological symptomsAD of in ADthe areearly revealed. to identify patients theal., images the deposition is clinically important because the diseaseforhas already Furthermore, to investigate theal., brain metabolic system, PET was Therefore, as soon as it is diagnosed, a treatment delaying its of amyloid beta (Klunk et 2004; Mintun et al., 2006; progressed when the pathological symptoms of AD are revealed. used to identify MCI patients from the images of the deposition progression can be started earlier. The mild cognitive Villemagne et al., 2013; Cohen & Klunk, 2014). In addition, Therefore, as(MCI) soon asisitknown is diagnosed, a treatment its of amyloid beta et al., by2004; Mintun al., 2006; impairment as an early stage for of delaying AD, which EEG was used for (Klunk MCI research recording the et post-synaptic progression can be state started earlier. cognitive potentials Villemagnewhich et al.,were 2013; Cohen & from Klunk, In addition, means an intermediate disorder with aThe moremild rapid cognition transmitted the2014). activated neurons impairment (MCI) known as an early due stage AD,that which EEG alpha, was used MCI research recording the Koenig post-synaptic declination than the iscognition declination to of aging can (i.e., betaforand delta waves)by(Jeong, 2004; et al., means state disorder withetaal., more rapidClinically, cognition potentials activated be seenaninintermediate the normal elderly (Petersen 1999). 2005) and which event were relatedtransmitted potentials from (ERP)the(Stam et al.,neurons 2007; declination than the cognition declination duethat to of aging alpha,etbeta and delta waves)MRI (Jeong, Koenig et al., the cognition declination of MCI is less than AD,that but can the (i.e., Waldemar al., 2007). However, and 2004; PET are not suitable be seen in the normal elderly (Petersen al., 1999). Clinically, and event potentials et al., 2007; possibility of progression to AD within 5etyears is higher than the 2005) for repeated uses related for clinical tools (ERP) due to (Stam strict measurement the cognition ofal., MCI is less than that early of AD, but the requirements, Waldemar et al., 2007). However, MRI and PET are notand suitable normal elderlydeclination (Petersen et 2001). Therefore, diagnosis size of device, low temporal resolution, high possibility of progression to ADimportant within 5 years higher than the for repeated uses for clinical tools duetemporal to strictresolution measurement of MCI can play as a very factoris in delaying or expense. Additionally, EEG has high and normal elderly (Petersen et al., 2001). Therefore, early diagnosis mobility, requirements, size of device, low temporal resolution, and high preventing the onset of AD. but limitation of spatial resolution, contaminated by of MCI can play as a very important factor in delaying or expense. Additionally, EEG has high (Li temporal resolution and electrical devices, and motion artifacts et al., 2018; Stuart et A clinical diagnosis ofAD. MCI is made by a patient care giver's mobility, but limitation of spatial resolution, contaminated by preventing the onset of al., 2018). histories on the patient’s behavior and neuropsychological electrical devices, and motion artifacts (Li et al., 2018; Stuart et A clinical diagnosis MCI is made byExamination a patient care giver's The fNIRS is one of brain imaging modalities that have been examinations such asof Mini-Mental State (MMSE) al., 2018). histories on theTombaugh patient’s & behavior and1992). neuropsychological (Petersen, 2004; McIntyre, Unfortunately, increasingly used (Naseer & Hong, 2015). The fNIRS uses two examinations such asrequire Mini-Mental State Examination (MMSE) or Themore fNIRS is one of wavelengths brain imaging(between modalities clinical evaluations experienced clinical professionals near-infrared 600that and have 1000been nm) (Petersen, 2004; Tombaugh & McIntyre, 1992). Unfortunately, increasingly used (Naseer & Hong, 2015). The fNIRS uses two and require a thorough clinical design, which results in to measure the changes in the concentrations of oxy-hemoglobin clinical evaluations require experienced clinical more near-infrared wavelengths(ΔHbR) (betweenassociated 600 and 1000 additional medical costs as well as subjectivity and professionals variability of or (ΔHbO) and deoxy-hemoglobin with nm) the and requireresults. a thorough clinical design, which results in to measure the changes in the concentrations of oxy-hemoglobin diagnostic To compensate the limitations of these metabolic activity of neurons in the cerebral cortex. Compared additionalresearch medicalon costs well as subjectivity and variability of (ΔHbO) (ΔHbR) with the methods, the as diagnosis of MCI has been studied from to MRI and and deoxy-hemoglobin PET, fNIRS is known forassociated its high temporal diagnostic results.such Toascompensate limitations of these metabolic activity of neurons in the cerebralofcortex. invasive methods cerebrospinalthe fluid (CSF) (Mattsson et resolution, harmlessness, low susceptibility motionCompared artefacts, methods, on mainly the diagnosis ofbiomarkers MCI has been studied from to MRI and is known for toits subjects high temporal al., 2013),research which are used as of AD, to brain mobility, low PET, cost, fNIRS and less constraint during invasive methods such as cerebrospinal fluid (CSF) (Mattsson et resolution, harmlessness, low susceptibility of motion artefacts, imaging techniques such as magnetic resonance imaging (MRI) experiment. Also, fNIRS has high spatial resolution and al., 2013), mainlyemission used as tomography biomarkers of AD, (Dubois to brain mobility, andaddition, less constraint subjects during (Jack et al.,which 1999),are positron (PET) robustness low than cost, EEG. In fNIRS cantoprovide functional imaging techniques such as magnetic resonance (MRI) experiment. fNIRSthehas high spatial and et al., 2007), and electroencephalography (EEG) imaging (Jeong, 2004). imaging by Also, measuring cerebral cortical resolution hemodynamic (Jack et al., 1999), positron emission tomography (PET) (Dubois responses robustness (HR), than EEG. In addition, fNIRSancanalternative provide functional which demonstrates imaging et al., 2007), and electroencephalography (EEG) (Jeong, 2004). imaging by measuring the2016). cerebral cortical hemodynamic modality (Nguyen & Hong, responses (HR), which demonstrates an alternative imaging modality (Nguyen & Hong, 2016). 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2019.09.134 Copyright © 2019 IFAC 158

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Table 1. Subject information Gender (male: female) Age K-MMSE score

MCI

HC

p-value

1:14

3:8

0.95

69.27 ± 7.09 25.13 ± 2.33

69.09 ± 5.11 27.22 ± 1.98

0.36 0.49

Fig. 2. 2.2

In these days, researchers have investigated whether HR is able to be compared in healthy control (HC) and MCI patients using fNIRS (Metzger et al., 2016; Yap et al., 2017). The previous fNIRS studies have researched brain activities during various tasks; working memory (Vermeij et al., 2017), memory encoding (Jahani et al., 2017; Moro et al., 2013), and verbal fluency task (Heinzel et al., 2015; Katzorke et al., 2017; Yap et al., 2017), etc. These results suggest that fNIRS can be used to detect the differences between MCI and HC in the early stage.

The SVFT is a task to generate as many words related with given semantic category as possible within limited time. The task measures how much information can be retrieved from the categorization and memory repository for the text for one minute.

2. METHODS Subjects

In this study, 15 MCI patients were recruited from the Pusan National University hospital and 15 HCs participated but 4 HCs’ data were discarded because the data quality was unreliable. All subjects had no history of cerebrovascular diseases or psychiatric disorder. MCI patients were already diagnosed by Korean MMSE (K-MMSE) (Cho et al., 2002), Seoul Neuropsychological Screening Battery (SNSB) (Ahn et al., 2010) and MRI from the neurology doctor. In Table 1, the information on all subjects are shown including age, gender, and K-MMSE scores. Before the experiment, each subject was fully informed about the purpose of the research and provided written informed consent. The entire experiment was approved by the Institute Review Board of Pusan National University Hospital and performed in accordance with the Declaration of Helsinki.

All subjects in each group were required to perform a session of SVFT that consisted of three word categories in each trial; each category lasts 20 sec, and therefore the total task period is 60 sec and followed by 30 sec rest (Fig. 2). Before a trial starts, subjects were given 30 sec to get ready and before the session, 5 minutes of resting time was provided. 3. DATA PROCESSING 3.1

Preprocessing

The fNIRS signal was processed using MATLABTM (2017a, MathWorks, USA). The fNIRS raw data was converted to concentration changes of HbO and HbR by modified BeerLambert’s law (Hiraoka et al., 1993). A 5th order Butterworth band-pass filter was applied to remove physiological noises (cardiac ~ 1 Hz, respiration ~0.25 Hz, Mayer signal ~0.1 Hz, etc.) and machine noise (Zafar & Hong, 2017) with cut-off frequency of 0.005 ~ 0.1 Hz (Liu & Hong, 2017; Santosa, Hong, & Hong, 2014). After that, the data was chopped for each trial. 3.2

Fig. 1.

Experimental design

A high-density fNIRS device (NIRSIT; OBELAB Co., Republic of Korea), which has 24 sources (laser diodes) and 32 detectors (total 204 channels including all short separation channels), was used to detect fNIRS signals on the prefrontal cortex at a sampling rate of 8.138 Hz. The NIRSIT is a wearable device that measures HbO and HbR using different absorption rates of nearinfrared lights through the cerebral cortex (Kwon et al., 2018). To measure HbO and HbR, two wavelengths (780 nm, 850 nm) were used. The distances between sources and detectors were 3 cm, total 48 channels were selected on the prefrontal cortex of each subject (Fig. 1). The NIRSIT and a tablet (Galaxy Tab; Samsung, Republic of Korea) were connected via WLAN communication, the data were collected in the tablet PC during the experiments.

In this study, we aim to verify the possibility of fNIRS to discriminate MCI patients from HCs. The semantic verbal fluency task (SVFT) was used as a means to examine the objective. To discriminate, the HR and activated brain regions were measured and the functional connectivity was calculated based on the measured fNIRS data. Furthermore, linear discriminant analysis (LDA) and support vector machine (SVM) have been used for classification.

2.1

Experimental paradigm.

Functional connectivity

Functional connectivity analysis was performed for the tasks in the experiment. The connection strength of the neuron group under the prefrontal cortex was expressed as a temporal correlation in local hemodynamics by calculating Pearson's correlation coefficient. Pearson's correlation coefficients were calculated to create a functional connectivity matrix between the desired set of channels. Rows and columns in these matrices

Channel configuration.

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represent channel numbers, but the element of the matrix was the correlation coefficients for the matching channel. Though weak correlation tends to make ambiguous the particularly strong and important connections, thus applying thresholds to represent fake connections in a functional and effective network that is often discarded (Rubinov & Sporns, 2010; Yaqub et al., 2018), so that in plotting images, a threshold value of correlation above the threshold was set to zero and then a value less than the threshold value. 3.3

Activation map

Verification of the cortical activation for a given stimulus is most important in the fNIRS data analysis (Hong & Nguyen, 2014). The desired hemodynamic response function (dHRF) was generated by convoluting stimulus patterns (i.e., 60 sec activation and 30 sec rest) and canonical hemodynamics response function. The t-value is defined as the ratio of the weighting factor to the dHRF (in fitting the measured data to the dHRF) to the standard error (Tak et al., 2015). A high t-value indicates that the signal is highly correlated with the dHRF. Before calculating t-values, HbO data were normalized and the first data point was made zero for each channel. Acquired t-values were represented in a map to illustrate the entire activation of the covered brain region.

Fig. 3. Resting state correlation map: (a) MCI, (b) HC, (c) MCI grey scale, (d) HC grey scale (cut-off value: 0.8 & -0.5). 4. RESULTS Fig. 3 illustrates the correlation coefficients of MCI and HC in the resting state. To avoid ambiguity, the high correlations over 0.8 returns 1 and the low correlations represented de-correlation under -0.5 returns -1 in the map. There are many decorrelated channels in the both group, but HC has more decorrelated channel than MCI, respectively. In addition, there is only one correlated channel in the MCI, but there are more correlation channels in the HC. This results show that MCI patients’ correlations in the PFC are remarkably less than that of HC.

3.4 Feature extraction and classification According to calculated t-values, the region of interest (ROI) is investigated which indicates a brain region in which the t-value is higher than the critical t-value (tcrt) (Hong et al., 2018; Naseer et al., 2016). In this study, tcrt was set to 1.9632 calculated as the degree of freedom of data (N - 1 = 730) and the level of statistical significance (p < 0.05 for two-sided tests). Signals from each ROI were collected for further analysis in feature selection and classification. This is because using only the relevant ROI (not all channels) signals improves the classification accuracy. The ROI channels for each trial of a given task were averaged by subjects. The mean, slope, and skewness values of the average HbO signal were then used as features (Khan et al., 2014; Naseer, Hong, & Hong, 2014). The dimension of features which is using for classification was 26 (the number of subjects) * 3 (the number of features). Extracted features were labelled as two classes and classified using LDA and SVM. LDA assumes data is normally distributed, and SVM generalizes the optimally separating hyperplane. In this paper, LDA which estimates with a diagonal covariance matrix and SVM which using linear kernel were used. We compared the two classifiers so that HR can be classified between MCI and HC. In this study, 10-fold cross validation was used to assess the classification accuracy (Hong & Santosa, 2016).

Fig. 4. SVFT correlation map: (a) MCI, (b) HC, (c) MCI grey scale, (d) HC grey scale (cut-off value: 0.8 & -0.5).

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The averaged HbO data of two groups extracted from the ROI cannels were plotted in Fig. 6. The black line represents MCI patients’ HbO, and the blue line represents HbO of HC. The red line represents the dHRF. According to Fig. 6., HC’s HbO value was higher in the SVFT. From these results, features are extracted by time windows (mean: 20 ~ 40 sec, slope: 1 ~ 5 sec, and skewness 5 ~ 55 sec). In Fig. 7, extracted features for each subject were plotted in the 3-D space. The features include the mean of HbO, mean of HbR and slope of HbO. The extracted features were classified into two groups. The classification accuracies were 69.23 % for LDA and 73.07 % for SVM. 6. CONCLUSIONS In this study, we aimed to find out significant differences between MCI patients and healthy controls by fNIRS. In the correlation map, correlation has increased from the resting state to SVFT. In addition, the changing of correlation was higher in the HC than MCI remarkably. This result means MCI patients’ prefrontal cortex are not connected as much as HC. Decreasing of brain connectivity suggests cognition loss. In the activation map, the activation in the Broca’s area was activated significantly higher in the HC. However, the BA 9 on the right hemisphere, which is involved in verbal fluency (Abrahams et. al., 2000), was activated higher in the MCI patients. This results show that brain regions were re-missioned because of dysfunction of Broca’s area by cognitive impairment.

Fig. 5. SVFT t-map: (a) MCI and (b) HC. Fig. 4 shows the correlations between channels in the PFC during the SVFT. Both groups have correlation changes with the SVFT compared with the resting state. However, the number of correlated channels during the SVFT are higher in the HC than MCI. This is more evident in Fig. 5, during SVFT, the pattern of brain activation appeared differently between two groups. The Broca’s area, well known as speech production (Chs. 11, 12, 14, 15, 42, 43, 47, and 48), was activated less in the case of MCI. However, Chs. 3, 6, and 21, correspond to Brodmann area (BA) 9, were activated higher in the MCI.

Finally, we could achieve over 70% of classification accuracy between MCI and HC with the SVFT. Therefore, it is possible to claim that the possibility of discriminating MCI and HC using fNIRS was verified by classification. Furthermore, it is possible to develop a MCI diagnostic system which is based on fNIRS. To establish an applicable system using fNIRS, we will focus on increasing the classification accuracies up to 90 % in the future studies.

Fig. 7. Extracted features in the 3-D space (blue circle: MCI, red star: HC). Fig. 6. Hemodynamic response (black: MCI, blue: HC)

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ACKNOWLEDGMENT 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 (Nos. NRF2017R1A2A1A17069430, NRF-2017 R1A4A1015627). REFERENCES Ahn, H.J., Chin, J., Park, A., Lee, B.H., Suh, M.K., Seo, S.W. & Na, D.L. (2010). Seoul neuropsychological screening battery-dementia version (SNSB-D): A useful tool for assessing and monitoring cognitive impairments in dementia patients. Journal of Korean medical science, 25(7), 1071-1076. Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., Gamst, A., Holtzman, D.M., Jagust, W.J., Petersen, R.C., Snyder, P.J., Carrillo, M.C., Thies, B. & Phelps, C.H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers & Dementia, 7(3), 270-279. Abrahams, S., Leigh, P.N., Harvey, A., Vythelingum, G.N., Grise, D. & Goldstein, L.H. (2000). Verbal fluency and executive dysfunction in amyotrophic lateral sclerosis (ALS). Neuropsychologia, 38, 734–747. Cho, B., Yang, J., Kim, S., Yang, D.W., Park, M. & Chey, J. (2002). The validity and reliability of a computerized dementia screening test developed in Korea. Journal of the Neurological Sciences, 203, 109-114. Cohen, A.D. & Klunk, W.E. (2014). Early detection of Alzheimer's disease using PiB and FDG PET. Neurobiology of Disease, 72, 117-122. Dronkers, N.F., Plaisant, O., Iba-Zizen, M.T. & Cabanis, E.A. (2007). Paul Broca's historic cases: high resolution MR imaging of the brains of Leborgne and Lelong. Brain, 130(5), 1432–1441, Dubois, B., Feldman, H.H., Jacova, C., Dekosky, S.T., Barberger-Gateau, P., Cummings, J., Delocourte, A., Galasko, D., Gauthier, S., Jicha, G., Meguro, K., O'Brien, J., Pasquier, F., Robert, P., Rossor, M., Solloway, S., Stern, Y., Visser, P.J. & Scheltens, P. (2007). Research criteria for the diagnosis of Alzheimer"s disease: Revising the NINCDSADRDA criteria. Lancet Neurology, 6(8), 734-746. Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P. & Thompson, P.M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6(2), 67-77. Heinzel, S., Metzger, F.G., Ehlis, A.C., Korell, R., Alboji, A., Haeussinger, F.B., Wurster, I., Brockmann, K., Suenkel, U., Eschweiler, G.W., Maetzler, W., Berg, D. & Fallgatter, A.J. (2015). Age and vascular burden determinants of cortical hemodynamics underlying verbal fluency. Plos One, 10(9). Hiraoka, M., Firbank, M., Essenpreis, M., Cope, M., Arridge, S.R., Vanderzee, P. & Delpy, D.T. (1993). A Monte-Carlo investigation of optical pathlength in inhomogeneous tissue

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