Participants were randomly assigned into the rTMS or control group. Both groups participated in rTMS or sham stimulation protocol for 2 weeks, including five sessions every week. The baseline assessment occurred the day before the first stimulation session. The post-intervention assessment occurred 1 day after the final stimulation session. All participants had blood drawn at 8:00 am. Blood samples were collected to detect blood lipids and thyroid hormones. Participants were kept blind to the study hypothesis.

### rTMS Protocol

The rTMS was applied with the MagPro X100 stimulator (MagVenture) and figure-of-eight coil (MFC-B65). The right dorsolateral PFC (DLPFC) was the target site, which was defined as the F4 region of the international 10-20 system for electroencephalography. The motor threshold was determined before the stimulation. It was defined as the minimum stimulator output value required to generate contraction of the abductor pollicis brevis for at least 5 of 10 consecutive pulses, and was measured via EMG by means of the Biopac MP100, using a contraction threshold of 50 mV (Moscatelli et al., 2016a,b,c). In each session, 10 Hz of rTMS was applied at 90% motor threshold to the stimulation location. In each stimulation block, rTMS pulses were present for 2 s and absent for 28 s; there were 40 blocks in total. Subjects in the sham stimulation group received the same stimulation protocol applied in the same manner, except that the coil was held at an angle of 90◦ (Kim et al., 2012).

#### Output Measures

#### Hematological Examination

All subjects fasted prior to having blood drawn at 8:00 am before receiving rTMS intervention and the day after the intervention. Blood was drawn using right elbow flexion, routine disinfection and at about 10 ml for each patient. The venous blood was extracted into the coagulation vacuum tube and left at about 25◦C for 1 h. The serum was separated with 3000 rotations/min centrifugal force (LODZ-1.2, Beijing centrifuge factory) for 10 min and was kept in a −70◦C refrigerator. Before the experiment, serum samples were removed from the refrigerator and were re-dissolved in a water-bath at 37◦C water. After the second centrifugation, supernatant was used to measure the indexes. Throughout the whole process, the instrument maintained a good working state, and the quality was controlled in strict accordance with the reagent manual for testing.

#### Blood Lipids

CHO, TG, HDL-C and LDL-C were measured using the enzyme method. The reagents were sealed away from light, stored at 2–8◦C, and could not be inverted.

#### Thyroid-Related Hormones

T3, T4 and TSH were measured using the electrochemical luminescence method (Roche company cobas e601 automatic immunoassay). The reagents we used were: roche reagents T3 detection kit, Roche reagent T4 detection kit, and Roche reagent thyrotropin detection kit. Reagents were stored at 2–8◦C away from light and not inverted.

#### **T3 measurement steps (competition law principle)**

#### **T4 measurement steps (competition law principle)**

#### **TSH assay procedure (double antibody sandwich principle)**

### Data Analysis

The two-sample two-tailed Student's t-test was used to assess the baseline characteristics (age, years of education, scores of CMSS and MoCA) of participants in both groups, and the Chi-squared test was used to assess the gender difference between group. Paired sample t-test was used to examine the effect of rTMS/sham stimulation on the serum lipid metabolism activity, as well as on the endocrine activity related to the thyroid. All statistical analyses were conducted using SPSS 19.0 (IBM Corporation, Somers, NY, USA). The absolute effect size, Cohen's d (Cohen, 1988), were calculated to assess the effect of the rTMS intervention.

#### RESULTS

#### Demographic Characteristics

No significant differences at baseline were found between the two groups in age, gender, education, memory complaint and MoCA (p > 0.05), as shown in **Table 1**.

#### Effect of rTMS on Lipid Levels

The blood lipid levels between the two groups had no statistical difference at baseline (p > 0.05). As shown in **Table 2**, the CHO and TG levels were significantly lower after rTMS intervention (p < 0.05). Cohen's d for CHO was 0.54, and that for TG was 0.31. While in the sham group, no significant differences were found between baseline and post-treatment assessments (p > 0.05).

#### Effect of rTMS on Endocrine Activity Related to the Thyroid Gland

Serum thyroid hormone levels between the two groups had no statistical differences at baseline assessment (p > 0.05). As shown in **Table 3**, after rTMS intervention, the TSH and T4 levels were found to be significantly higher than those at baseline (p < 0.05). Cohen's d for T4 was 0.40, and that for TSH was 0.27. In the sham group, no significant differences were found between pre- and post-assessments (p > 0.05).

### DISCUSSION

In this study, 10 Hz of rTMS was applied to healthy older adults with normal baseline lipid levels. After 10 sessions of stimulation, CHO and TG levels were significantly decreased, accompanied by increased TSH and T4, compared with the baseline condition, while no significant differences were found in the control group.

#### TABLE 1 | Characteristics of repetitive transcranial magnetic stimulation (rTMS) and sham groups.

Note: CMSS, the Chinese Memory Symptoms Scale; MoCA, the Beijing Version of the Montreal cognitive assessment. aThe p value was obtained using a two-sample two-tailed t test. bThe p value was obtained using a two-tailed Pearson chi-square test. Data are shown as mean ± SD.

TABLE 2 | Comparison of lipid levels (mmol/L) between rTMS and sham groups.

Note: CHO, the total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol. ∗p < 0.05, two-tailed. Data are shown as mean ± SD.

Note: T3, triiodothyronine; T4, thyroxine; TSH, the thyroid-stimulating hormone. ∗p < 0.05, ∗∗p < 0.01, two-tailed. Data are shown as mean ± SD.

Our results indicate that rTMS may be effective for CVD risk factors. Previous studies confirmed the effect of rTMS on CV systems indexed by HR and HRV. In this study, we investigated the effect of rTMS on endocrine activity related to CVD risk factors and found that decreased serum lipid levels resulted from rTMS, suggesting rTMS influence on endocrine activity. In an animal study, Wang et al. (2013) explored the metabolic mechanism underlying the effects of rTMS. They observed that in mature mice, rTMS could reverse the metabolic abnormalities of cholesterol levels, to a degree similar to the young mice, showing that the rTMS could improve the metabolic profiles in PFC. Combining the present study with previous studies shows that rTMS could modulate the lipid metabolic activity associated with CVD.

The effect of rTMS on the serum lipid metabolic activity might result from its influence on the HPT axis. George et al. (1996) found that rTMS, applied over regions within the PFC, was associated with increases in serum TSH. Furthermore, Trojak et al. (2011) reported a significant increase in plasma TSH, above normal range, during low frequency rTMS (1 Hz) treatment. In addition, Osuch et al. (2009) demonstrated an increase in plasma T4 during treatment of anxiety disorders using low frequency rTMS. Thus, it can be inferred that rTMS may have significant effects on the pituitary-thyroid axis, which may potentially induce hyperthyroidism. Similar to previous studies, our study found significantly elevated TSH after rTMS, along with increased T4. Furthermore, in this study, we directly observed the alteration of serum lipid levels, suggesting that rTMS may influence the HPT axis and then affect the serum lipid levels.

Based on animal studies, we had confirmed that both normal aging (Wang et al., 2014) and pathological aging (Han et al., 2016; Wang J. et al., 2016; Shen et al., 2017) lead to metabolic abnormalities and that rTMS treatment could ameliorate metabolic abnormalities (Wang H. L. et al., 2016). For example, rTMS normalized prefrontal dysfunctions and cognitive-related metabolic profiling in aged mice (Wang H. L. et al., 2016). Besides, Lee et al. (2012) found that the effects of rTMS are related to changes in the brain lipids. In human studies, it also has been found that rTMS affects cortical metabolism (Bohning et al., 1999; Kimbrell et al., 2002). In the present study, we found that rTMS applied over the right DLPFC could alter the endocrine activity in normal aging adults. Combined evidence suggested that there is a pathway between the PFC and the hypothalamus, through which the PFC could modulate the endocrine activity.

As a non-invasive tool for the electrical stimulation of neural tissue (Barker et al., 1985), rTMS has the potential to modify excitability of the cerebral cortex at the stimulated site and at remote areas along functional anatomical connections (for a review, see Rossini et al., 2010). The PFC is linked to the thalamus (Alexander et al., 1986; Jones, 2007; Bolkan et al., 2017), and as a result, rTMS applied to the DLPFC might modulate the neural activity in the thalamus, which would then have an effect on the activity of the hypothalamus. The hypothalamus links the nervous system to the endocrine system, via the pituitary gland, in order to modulate the serum metabolic activity. It can be speculated that the PFC-thalamus pathway might play an important role in the present study to influence the HPT axis, and finally to modulate the serum metabolic activity of lipid levels.

Previous studies showed that CVD risk factors could be reduced with aerobic exercises. In a meta-analysis, Kodama et al. (2007) demonstrated that regular aerobic exercise could increase HDL-C level, which is associated with decreased risk of CVD (Maron, 2000). Alternative to physical exercise, in this study, the non-invasive rTMS is demonstrated as another promising tool for modulating CVD risk factors, specifically lipid levels, suggesting that rTMS is effective in minimizing CVD prevalence. Aerobic exercise may influence CV fitness, which results in changes to endocrine activity. While the effect of rTMS on endocrine activity might result from the altered excitability of the neuron, which may influence the signaling pathway of the HPT axis. This suggests that there is a pathway that underlies the transmission between electric signal and chemical signal. Future research on this issue is of great importance.

#### CONCLUSION

rTMS of the PFC is associated with increases in the TSH and T4 levels and decreases in the serum CHO and TG levels. rTMS might alter the serum lipid levels by modulating the activity of the HPT axis. It is a promising tool for the modulation of lipid metabolism in older adults, and it reduces the risk for AD. In future studies, more indexes related to lipid metabolism and

#### REFERENCES

the activity of the HPT axis need to be assessed to clarify the effect of rTMS on the risk for CVD and to examine the direct relationship between lipid metabolism and the activity of the HPT axis following rTMS.

#### AUTHOR CONTRIBUTIONS

WR designed the work and drafted the manuscript. JM collected and analyzed the data. JL guided the research project. ZZ and MW revised the work and agreed to be accountable for all aspects of the work.

PTSD: a preliminary study. J. Anxiety Disord. 23, 54–59. doi: 10.1016/j.janxdis. 2008.03.015

**Conflict of Interest Statement**: 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 © 2017 Ren, Ma, Li, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Divergent Roles of Vascular Burden and Neurodegeneration in the Cognitive Decline of Geriatric Depression Patients and Mild Cognitive Impairment Patients

Qing Ye, Fan Su, Liang Gong, Hao Shu, Wenxiang Liao, Chunming Xie, Hong Zhou, Zhijun Zhang and Feng Bai\*

Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China

Both geriatric depression and mild cognitive impairment (MCI) confer an increased risk for the development of dementia. The mechanisms underlying the development of cognitive impairment in geriatric depression patients remain controversial. The present study aimed to explore the association of cognitive decline with vascular risk, white matter hyperintensity (WMH) burden and hippocampal volume in both remitted geriatric depression (RGD) subjects and amnestic mild cognitive impairment (aMCI) subjects. Forty-one RGD subjects, 51 aMCI subjects, and 64 healthy elderly subjects underwent multimodal MRI scans and neuropsychological tests at both baseline and a 35-month follow-up. According to the changing patterns (declining or stable) of global cognitive function during the follow-up period, each group was further divided into a declining subgroup and a stable subgroup. The Framingham 10-year cardiovascular risk, WMH volume and hippocampal volume were measured to assess vascular pathology and neurodegeneration, respectively. The RGD declining group displayed a higher vascular risk and greater WMH volume than the RGD stable group, whereas no such difference was found in the aMCI subjects. In contrast, the aMCI declining group displayed a smaller left hippocampal volume than the aMCI stable group, whereas no such difference was found in the RGD subjects. Furthermore, greater increases in the WHM volume correlated with greater decreases in global cognitive function in the RGD declining group, and greater decreases in the left hippocampal volume correlated with greater decreases in global cognitive function in the aMCI declining group. In conclusion, the cognitive decline in RGD patients is associated with vascular burden, whereas the cognitive decline in aMCI patients is associated with neurodegeneration. These findings could contribute to a better understanding of the specific mechanisms of the development of dementia in each condition.

Keywords: cognitive decline, geriatric depression, hippocampal volume, mild cognitive impairment, vascular risk, white matter hyperintensity

#### Edited by:

Mohammad Amjad Kamal, King Abdulaziz University, Saudi Arabia

#### Reviewed by:

Christos Frantzidis, Aristotle University of Thessaloniki, Greece Panteleimon Giannakopoulos, Université de Genève, Switzerland

> \*Correspondence: Feng Bai<EMAIL_ADDRESS>
Received: 20 June 2017 Accepted: 17 August 2017 Published: 01 September 2017

#### Citation:

Ye Q, Su F, Gong L, Shu H, Liao W, Xie C, Zhou H, Zhang Z and Bai F (2017) Divergent Roles of Vascular Burden and Neurodegeneration in the Cognitive Decline of Geriatric Depression Patients and Mild Cognitive Impairment Patients. Front. Aging Neurosci. 9:288. doi: 10.3389/fnagi.2017.00288

## INTRODUCTION

Geriatric depression refers to a major depressive episode that develops in adults older than 60 years and is frequently accompanied by general impairments in physical health, global functioning, and quality of life. Geriatric depression often presents with cognitive deficits and confers an up to 50% increased risk for the development of dementia (Ownby et al., 2006; Diniz et al., 2013). Mild cognitive impairment (MCI) has been considered as a transitional state between normal aging and early Alzheimer's disease (AD), which is the most common form of dementia characterized by progressive cognitive impairment and behavioral deficits. MCI confers a high conversion rate to AD of 10–15% per year (Petersen et al., 1999). Interestingly, 11– 63% of elderly MCI patients exhibit accompanying depressive symptoms, and 18–55% of depressed patients develop cognitive deficits (Panza et al., 2010). The co-existence of MCI and depression confers over twice the risk for AD as MCI alone (Modrego and Ferrandez, 2004). In view of the distinct clinical link between geriatric depression and MCI, increasing attention has been paid to the convergence and divergence of the pathogeneses of geriatric depression and MCI.

While the amyloid beta (Aβ) pathology and subsequent neurodegeneration due to tau pathology play a major role in MCI and AD pathogenesis (Albert et al., 2011), a controversy has arisen over the mechanisms underlying the cognitive impairment in geriatric depression patients. Several studies have found that the cognitive impairment in geriatric depression patients is associated with the AD-related pathology, including Aβ deposition and hippocampal atrophy (O'Brien et al., 2004; Hou et al., 2012; Byun et al., 2016). Other findings suggest that the cognitive impairment in geriatric depression patients is due to vascular pathology that is frequently represented by the white matter hyperintensity (WMH) burden in the brain (Hickie et al., 1997; Murata et al., 2001). However, these studies only explored single biomarkers in isolation and lacked an overall view of AD-related pathology and vascular pathology. Interestingly, two recent studies measured both AD-related pathology and vascular burden in geriatric depression patients with and without MCI (Diniz et al., 2015; Byun et al., 2016). However, one study found that geriatric depression subjects with MCI display greater WMH burden than geriatric depression subjects with normal cognition, suggesting that vascular pathology is related to the cognitive impairment in geriatric depression subjects (Diniz et al., 2015). The other study demonstrated that AD processes contribute to the co-existence of geriatric depression and MCI (Byun et al., 2016). Both of these studies employed cross-sectional data. A great deal of studies have revealed that cognitive impairment is improved following the remission of depressive symptoms in a significant proportion of geriatric depression patients but not in all patients (Barch et al., 2012). Thus, measuring cognitive impairment in geriatric depression patients using cross-sectional data may have increased the heterogeneity and contributed to the divergence. However, few studies have explored and compared the relationships of cognitive impairment, AD-related pathology, and vascular pathology in both geriatric depression subjects and MCI subjects despite the close link between geriatric depression and MCI. The investigation of the convergence and divergence between the two conditions could deepen the understanding of the mechanisms underlying the development of dementia in each condition.

The present 35-month longitudinal study recruited remitted geriatric depression (RGD) subjects, amnestic mild cognitive impairment (aMCI, a subtype of MCI characterized by episodic memory deficits) subjects and healthy elderly subjects. Neuropsychological tests and multimodal MRI scans were performed at both baseline and a 35-month follow-up. According to the changing patterns (declining or stable) of global cognitive function during the follow-up period, the subjects were further divided into an RGD declining group, an RGD stable group, an aMCI declining group, an aMCI stable group, a control declining group, and a control stable group. Vascular risk, WMH volume, and hippocampal volume were explored to assess vascular pathology and neurodegeneration, respectively. The present study aimed to (i) explore and compare the associations of cognitive decline with vascular risk, WMH burden and hippocampal volume in RGD subjects and aMCI subjects and (ii) determine the behavioral significance of WMH burden and hippocampal atrophy.

### MATERIALS AND METHODS

#### Participants

As described in our previous study (Ye et al., 2017), 72 RGD patients, 87 aMCI patients, and 135 healthy elderly controls were recruited at the Affiliated ZhongDa Hospital, Southeast University. This study was performed in accordance with the recommendations of the Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee with written informed consent from all subjects. All subjects provided written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee. The participants were followed up for 35 months on average. A total of 41 RGD subjects, 51 aMCI subjects, and 64 controls were included in the present study. All of these subjects underwent multimodal MRI scans and neuropsychological tests at both baseline and follow-up. The reasons for the loss to follow-up of the other subjects included the development of neurological or other psychiatric diseases, relocation to other cities, death, and subjective unwillingness. According to the changing patterns (declining or stable) of global cognitive function during the follow-up period, the included subjects were further divided into the following six groups: RGD declining group (n = 18), RGD stable group (n = 23), aMCI declining group (n = 33), aMCI stable group (n = 18), control declining group (n = 21), and control stable group (n = 43).

#### Neuropsychological Assessments

Each subject underwent a standardized diagnostic evaluation that included demographic information, medical history, and examinations of the neurological and mental statuses. Global cognitive function was assessed with the Mini Mental State Examination (MMSE), a Clinical Dementia Rating (CDR) and a Mattis Dementia Rating Scale-2 (MDRS-2). The MDRS-2 is a reliable and valid psychometric instrument for detecting and staging dementia (Vitaliano et al., 1984; Smith et al., 1994; Green et al., 1995; Monsch et al., 1995). The MDRS is also effective for tracking cognitive decline over time (Salmon et al., 1990; Galasko et al., 2000; Gould et al., 2001). Subjects with an MDRS-2 score below 130 or a loss of more than five points during the followup period were defined as the "declining group" (Schmidt et al., 1994; Tard et al., 2015). Other subjects were defined as the "stable group." The mental statuses were assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Axis I Disorders (SCID-I), the Hamilton Depression Scale (HAMD), and the Self-Rating Depression Scale. All subjects underwent a neuropsychological battery test that included the Auditory-Verbal Learning Test-Delayed Recall (AVLT-DR), Rey-Osterrieth Complex Figure Test (CFT) with its 20-min Delayed Recall (CFT-DR), Trail Making Tests (TMT)-A and B, Stroop Color and Word Tests A, B, and C (Stroop A, B, and C), Verbal Fluency Test (VFT), Digital Span Test (DST), Semantic Similarity Test (Similarity), Digital Symbol Substitution Test (DSST), and Clock Drawing Test (CDT).

#### Inclusion and Exclusion Criteria

The inclusion and exclusion assessments were performed by two experienced neuropsychiatric physicians who administered a structured interview to subjects and their informants. The inclusion criteria for RGD subjects were as follows: (1) the age was >60 years; (2) all subjects had previously met the DSM-IV criteria for major depression disorder and had remitted for >6 months before enrollment; (3) the duration of illness was <5 years and the period of remaining anti-depressant medicationfree was >3 months before the assessment; and (4) the HAMD scores were <7 and MMSE scores were >24. The exclusion criteria were as follows: (1) primary neurological illness, including stroke or dementia; (2) another major psychiatric illness, including substance abuse or dependence; (3) history of electroconvulsive therapy; and (4) a medical illness that impaired cognitive function.

aMCI subjects were included according to the diagnostic criteria proposed by Petersen (2004) and others (Winblad et al., 2004), which was also described in our prior study (Ye et al., 2016). The inclusion criteria for the aMCI subjects were as follows: (1) subjective memory impairment corroborated by the subject and an informant; (2) objective memory performances documented by an AVLT-DR score ≤1.5 standard deviations from the age-adjusted and education-adjusted norms (the cutoff was ≤4 correct responses on 12 items for ≥8 years of education); (3) normal general cognitive function evaluated by an MMSE score ≥24; (4) a CDR of 0.5, with at least a 0.5 in the memory domain; (5) minimal or no impairment of routine daily life activities; and (6) the absence of dementia or insufficiency in meeting the National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association (NINCDS-ADRDA) and DSM-IV criteria for AD. The exclusion criteria were as follows: (1) a history of stroke (modified Hachinski score of >4), head injury, alcoholism, epilepsy, Parkinson's disease, major depression (excluded by a self-rating depression scale), or other psychiatric or neurological illness (excluded by case history and clinical assessment); (2) major medical illness (e.g., anemia, cancer, or thyroid dysfunction); (3) severe visual or hearing loss; and (4) T2-weighted MRI showing major infarction or other lesions (two experienced radiologists executed the scans).

Control subjects were required to have a CDR of 0, an MMSE score ≥26, and a delayed recall score >4 for those with ≥8 years of education. These participants also met the aforementioned exclusion criteria for aMCI.

### Apolipoprotein E (ApoE) Genotyping

To control for possible differences in hippocampal volume related to the ApoE ε4 allele (ApoE ε4), the most important genetic risk factor for sporadic AD, the status of ApoE ε4 was also assessed. Genomic DNA was extracted from 250 µL of EDTA-anticoagulated blood collected from each subject using a DNA direct kit (Tiangen, China). A polymerase chain reactionbased restriction fragment length polymorphism (PCR-RFLP) assay was employed to detect the rs7412 and rs429358 alleles, the haplotypes of which ultimately determine the ApoE genotype.

### Framingham 10-Year Cardiovascular Risk

The Framingham 10-year risk for developing general cardiovascular disease (including myocardial infarction, coronary death, heart failure, angina, stroke, transient ischemic attack, and peripheral artery disease) was calculated using gender, age, systolic blood pressure, treatment for hypertension, smoking, diabetes, and body mass index (D'Agostino et al., 2008). The Framingham 10-year cardiovascular risk was assessed for each subject at baseline.

#### Magnetic Resonance Imaging Procedures

The subjects were scanned using a Siemens Verio 3.0-T scanner (Siemens, Erlangen, Germany) with a 12-channel head coil at the Affiliated ZhongDa Hospital of Southeast University. A belt and foam pads were used to immobilize their heads to minimize head motion. High-resolution T1-weighted axial images covering the whole brain were obtained by a 3D-magnetization prepared rapid gradient-echo sequence: repetition time (TR) = 1,900 ms; echo time (TE) = 2.48 ms; flip angle (FA) = 9 ◦ ; acquisition matrix = 256 × 256; field of view (FOV) = 250 × 250 mm; thickness = 1.0 mm; gap = 0 mm, number of slices = 176. The T2 FLAIR axial images were obtained with the following parameters: TR = 8,000 ms; TE = 94 ms; FA = 150◦ ; acquisition matrix = 256 × 162; thickness = 5.0 mm; gap = 0 mm, number of slices = 20.

### WMH Segmentation and Quantification

Lesions were segmented by the lesion growth algorithm (Schmidt et al., 2012) as implemented in the LST toolbox version 2.0.15 (www.statistical-modelling.de/lst.html) for Statistical Parametric Mapping software (SPM12, http://www.fil.ion.ucl.ac.uk/spm). The algorithm first segments the T1 images into the three main tissue classes (cerebrospinal fluid, gray matter, and white matter). This information is then combined with the coregistered T2 FLAIR intensities to calculate lesion belief maps. By thresholding these maps with a pre-chosen initial threshold (κ = 0.30) an initial binary lesion map is obtained and is subsequently grown along voxels that appear hyperintense on the T2 FLAIR image. The result is a lesion probability map. It should be noted that the κ-value was determined by the visual inspection of the results by three experienced raters.

### Hippocampal Volume Assessment

As described in our previous study (Bai et al., 2009), hippocampal volume analysis was performed using the VBM8 toolbox for SPM12. First, the T1 images were normalized to the Montreal Neurological Institute (MNI) template using an affine and nonlinear spatial normalization and re-sampled to a voxel size of 1.5 × 1.5 × 1.5 mm. Second, the normalized images were segmented into cerebrospinal fluid, gray matter, and white matter segments according to MNI prior probability maps. Then, Jacobian modulation was applied to the segmented gray matter image, which could be incorporated to compensate for the effect of spatial normalization. Finally, the extracted gray matter set was smoothed with an 8-mm full width at half maximum Gaussian filter to reduce the effects of individual variation in gyral anatomy and to increase the signal-to-noise ratio. The hippocampus (left and right separately) was isolated using automated anatomical labeling implemented through the Resting State fMRI Data Analysis Toolkit 1.7 software (http:// restfmri.net/forum/index.php). Next, the hippocampal regions were interpolated to the same dimension, sizes, and origins with individual images. Finally, a mean volume index of all of the voxels of the hippocampal region (left and right) was computed for each subject. The hippocampal volume was obtained by multiplying the mean volume index by the number of voxels and the size of each voxel (1.5 × 1.5 × 1.5 mm).

### Statistical Analysis

#### Demographic and Neuropsychological Data

Mixed analysis of variance (ANOVA; with disease, cognitive change and time as fixed factors) and χ 2 -tests (applied for the comparisons of gender and ApoE ε4 status) were used to analyse the demographic data and neuropsychological data for statistically significant differences (P < 0.05). The individual raw scores of each cognitive test (except the MMSE and MDRS-2) were transformed into Z scores according to the following equation: Zi = ri−m S . Zi indicates the Z scores for the ith subject, ri indicates the raw score for the ith subject, m indicates the average score for each test for each group, and S indicates the standard deviation of the test scores for each group. The neuropsychological tests were grouped into four cognitive domains, including episodic memory, executive function, visuospatial function, and information processing speed. The composite Z score for each cognitive domain was obtained by averaging the Z scores of the relevant neuropsychological tests according to the following divisions: episodic memory (two tests, including the AVLT-DR and CFT-DR), executive function (five tests, including the TMT-B, Stroop C, VFT, DST-backward, and Similarity tests), visuospatial function (two tests, including the CFT and CDT), and information processing speed (four tests, including the DSST, TMT-A, Stroop A, and Stroop B). All statistical procedures were performed with the SPSS 19.0 software (SPSS, Inc., Chicago, IL, USA).

#### Vascular Risk, WMH Volume, and Hippocampal Volume Data

To improve the normal distribution of the Framingham 10-year cardiovascular risk data, a log10-transformation was applied to the raw data. Mixed ANOVA (with disease and cognitive change as fixed factors) was used to analyse the Framingham 10-year cardiovascular risk. Mixed analysis of covariance (ANCOVA; with disease, cognitive change, and time as fixed factors) was used to analyse the WMH volume and hippocampal volume, controlling for age, gender, and years of education. ApoE status was also treated as a covariate in the analysis of hippocampal volume. Post-hoc tests were performed to explore group differences in the ANOVA or ANCOVA. All statistical procedures utilized SPSS 19.0 software (SPSS, Inc., Chicago, IL, USA) with significance set at P < 0.05.

#### Correlative Analysis between Cognitive Impairment and WMH Volume or Hippocampal Volume

Correlation analyses were performed between the longitudinal changes of global cognitive function (MMSE and MDRS-2) and the longitudinal changes of WMH volume and hippocampal volume in RGD declining group and aMCI declining group. All statistical procedures for correlation analysis utilized SPSS 19.0 software (SPSS, Inc., Chicago, IL, USA) with significance of P < 0.05.

## RESULTS

### Demographic and Neuropsychological Data

As shown in **Table 1**, no significant differences in age or years of education were noted between the six groups. There were more females in the RGD stable group. A greater percentage of ApoE ε4 carriers were shown in the aMCI declining group. Significant main effects of disease were found on all cognitive tests. Compared with the control subjects, both the RGD subjects and aMCI subjects displayed poorer performances in all cognitive tests with the exception of the information processing speed for the RGD subjects. Significant main effects of cognitive change were found on the MDRS-2 scores and episodic memory scores. Subjects with declining cognitive function displayed poorer performances in these two tests than the subjects with stable cognitive function. Furthermore, a main effect of time revealed longitudinal decreases in the MDRS-2 scores and executive function scores in the whole sample during the follow-up period. Finally, there was no significant interaction of disease, cognitive change and time on the neuropsychological data (data on the interactions of any two factors are not shown).

### Framingham 10-year Cardiovascular Risk

As presented in **Table 2**, a significant interaction of disease and cognitive change was found in the Framingham 10 year cardiovascular risk. Post-hoc tests revealed that the RGD declining group displayed a higher risk than the RGD stable group, whereas no such difference was found within the control subjects or aMCI subjects (**Figure 1A**).

 <

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 <

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 E, ApoE; ANOVA, analysis of variance; MDRS-2, Mattis Dementia Rating Scale-2.

Apolipoprotein

#### WMH Volume

WMH lesions were segmented from T2 FLAIR images and T1 images in each subject (**Figure 1B**). As presented in **Table 2**, a significant main effect of disease on WMH volume was found. The RGD subjects exhibited greater WMH volume than the control subjects, whereas no significant difference was found between the aMCI subjects and the control subjects or between the aMCI subjects and the RGD subjects (**Figure 1C**). A significant main effect of time revealed that the whole sample displayed greater WMH volume at follow-up than at baseline (**Figure 1D**).

Notably, a significant interaction of disease and cognitive change was also demonstrated. Post-hoc tests as illustrated in **Figure 1E**, revealed the following: first, in the comparisons according to the cognitive change patterns (declining vs. stable), the RGD declining group displayed greater WMH volume than did the RGD stable group, whereas no such difference was found in the control subjects or aMCI subjects; and second, in the comparisons according to disease status (control vs. RGD vs. aMCI), the RGD declining group displayed greater WMH volume than did the control declining group.

#### Hippocampal Volume Left Hippocampal Volume

The mean volume index for the hippocampal region in each group was calculated by interpolating the hippocampus to the individual gray matter images segmented from the T1 images (**Figure 2A**). As presented in **Table 2**, a significant main effect of disease was found on the left hippocampal volume. The aMCI subjects had smaller left hippocampal volumes than did the control subjects, whereas no significant difference was found between the RGD subjects and the control subjects (**Figure 2B**). Furthermore, a significant interaction of disease and cognitive change was identified. Post-hoc tests revealed the following: first, in the comparisons according to cognitive change patterns (declining vs. stable), the aMCI declining group displayed smaller hippocampal volumes than did the aMCI stable group, whereas no such difference was found in the control subjects or RGD subjects; second, in the comparisons according to disease status (control vs. RGD vs. aMCI), the aMCI declining group had smaller hippocampal volumes than did the control declining group (**Figure 2C**).

#### Right Hippocampal Volume

As presented in **Table 2**, a significant interaction of disease and cognitive change was found in the right hippocampal volume. Post-hoc tests revealed the following, as illustrated in **Figure 2D**: first, in the comparisons according to cognitive change patterns (declining vs. stable), no significant difference was found in the control subjects, RGD subjects or aMCI subjects; second, in the comparisons according to disease status (control vs. RGD vs. aMCI), the aMCI declining group had smaller right hippocampal volumes than did the control declining group.

ANCOVA, Analysis of covariance; ANOVA, analysis of variance; CV, cardiovascular.

FIGURE 1 | (A) The interaction of disease and cognitive change on the Framingham 10-year cardiovascular risk. The RGD declining group displayed a higher risk than the RGD stable group, whereas no such difference was found within the control subjects or aMCI subjects. The bars are presented with the risk values [log10(%)]. The error bars represent the standard errors of the means of the risk values. (B) The segmentation of the WMH. The WMH lesions were segmented and quantified from T2 FLAIR images and T1 images. (C) The main effect of disease on WMH volume. The RGD subjects had greater WMH volumes than the control subjects. (D) The main effect of time on WMH volume. The whole sample displayed greater WMH volume at follow-up than at baseline. (E) The interaction of disease and cognitive change on WMH volume. The RGD declining group displayed greater WMH volume than the RGD stable group. The RGD declining group displayed greater WMH volume than the control declining group. The bars are presented with the WMH volumes. The error bars represent the standard errors of the means of the WMH volumes. \*P < 0.05. aMCI, Amnestic mild cognitive impairment; CV, cardiovascular; RGD, remitted geriatric depression; WMH, white matter hyperintensities.

#### Behavioral Significance of WHM Volume Changes and Hippocampal Volume Changes

As illustrated in **Figure 3**, in the RGD declining group, greater increases in WHM volume correlated with greater decreases in MMSE scores (r = −0.457, P = 0.049, two-tailed; **Figure 3A**). In the aMCI declining group, greater decreases in the left hippocampal volume correlated with greater decreases in the MDRS-2 scores (r = 0.362, P = 0.042, two-tailed; **Figure 3B**).

### DISCUSSION

From the integrated perspective of vascular pathology and neurodegeneration, the present longitudinal study is the first to explore the association of cognitive decline with vascular risk, WMH burden and hippocampal volume in both RGD subjects and aMCI subjects. The findings revealed that the cognitive decline in RGD subjects was associated with high vascular risk and high WMH burden, whereas the cognitive decline in aMCI subjects was associated with small hippocampal volume. These findings suggest that different pathogeneses may underline the cognitive decline in RGD patients and aMCI patients.

A strength of the present study is that the 35-month longitudinal data were used to define "cognitive decline" in RGD patients and aMCI patients. As mentioned above, cognitive impairment may be improved following the remission of depressive symptoms in a portion of the population (Barch et al., 2012). Similarly, there was also a great heterogeneity in the MCI patients, although MCI has been proposed as an intermediate state between normal aging and early AD. While many MCI subjects eventually develop dementia, others remain cognitively stable, and a small proportion may actually convert to cognitively normal subjects (McKhann et al., 2011; Villemagne et al., 2011). Thus, assessing cognitive impairment using cross-sectional data may display high heterogeneity, and the definition of "cognitive decline" based on the present 35-month longitudinal data could be more reliable. RGD or aMCI subjects with cognitive decline would have a much higher risk for dementia than subjects with stable cognition.

The other strength is that the present study provided an overall view of AD-related pathology and vascular pathology in both RGD patients and aMCI patients. Studies assessing a single biomarker could show a significant association of the biomarker with cognitive impairment more easily than those that

assess multiple biomarkers. As described above, previous studies that have assessed a single biomarker have demonstrated wide divergences of the association of cognitive decline with vascular burden and AD-related pathology in geriatric depression patients. A single biomarker may be associated with cognitive impairment; however, which biomarker has a major role has not been revealed by these studies. The present study explored vascular risk, WMH burden and hippocampal volume in both geriatric depression patients and aMCI patients. This overall view of AD-related pathology and vascular pathology could help to identify which pathology plays a major role in cognitive decline in each disorder.

The Framingham 10-year risk score provides an integrative perspective of multiple vascular risk factors (D'Agostino et al., 2008). In the present study, the RGD declining group had a much higher risk than did the RGD stable group. This is consentient with a previous study that demonstrated that a high score on the Framingham stroke risk scale is associated with cognitive impairment in elderly patients with major depressive disorder (Yeh et al., 2011). In contrast, the aMCI declining group and the aMCI stable group did not differ significantly in Framingham 10 year risk scores. Similarly, a 2.4-year follow-up study investigated the contribution of vascular burden to the progression of MCI to dementia and found no association between the Framingham Stroke Risk Profile scores and the progression to dementia (Clerici et al., 2012). A recent study explored the association between a vascular risk index and cognitive outcomes among MCI subjects and cognitively normal elderly subjects using linear regression models. Increased Framingham Stroke Risk Profile scores for MCI subjects only correlated with worse longitudinal episodic memory and not with the global cognition or other cognitive domains (Jefferson et al., 2015). Together with these findings, the present study highlighted the association of cognitive decline with vascular risk in geriatric depression by investigating the link in both RGD subjects and aMCI subjects.

The burden of small vessel cerebrovascular disease is well represented by WMH lesions on T2 FLAIR images. In the present study, greater WMH volume was found in the RGD subjects than in the control subjects. These findings support the vascular depression hypothesis in which cerebrovascular disease may predispose, precipitate, or perpetuate geriatric depression; this hypothesis is also known as "vascular depression" (Alexopoulos et al., 1997). In addition to geriatric depression, the cognitive decline in the RGD subjects was also related to WMH burden as suggested by the difference between the RGD declining group and the RGD stable group. The RGD declining group displayed greater WMH volume than did the RGD stable group, and greater increases in the WHM volume correlated with greater decreases of global function in the RGD declining group. This is consistent with previous findings that greater WMH burden is associated with cognitive deficits in geriatric depression patients (Hickie et al., 1997; Murata et al., 2001; Diniz et al., 2015). A significant advance of the present study is that the vascular burden was represented by both vascular risk and WMH volume. The present consistent findings in vascular risk and WMH volume convincingly relate the vascular burden to cognitive decline in geriatric depression patients. Indeed, increasing attention has been given to the concept of "disconnection syndromes" in which extensive ischemia and white matter lesions result in depression and cognitive deficits by disrupting corticalsubcortical or cortical-cortical connections (Alexopoulos, 2002).

However, the present study did not find a significant difference in WMH volume between the aMCI declining group and the aMCI stable group. A previous study also demonstrated that WMH is not associated with cognition in MCI subjects (Chen et al., 2006). However, other studies have demonstrated that greater WMH is associated with poor episodic memory and slow processing speed in MCI subjects (Fujishima et al., 2014; Lorius et al., 2015). Greater WMH is related to rapid cognitive decline in MCI subjects (Tosto et al., 2014). Furthermore, severe WMH also predicts the progression from MCI to dementia (Prasad et al., 2011; Kim et al., 2015). The divergences may be due to two reasons. First, the present follow-up period may not have been long enough to generate significant differences in WMH volume between the aMCI declining group and the aMCI stable group, although the aMCI declining group had moderately, but not significantly, greater WMH volumes than did the aMCI stable group. We will continue to follow up these subjects. Second, the analysis of WMH volume was performed in RGD subjects, aMCI subjects, and control subjects using an ANCOVA, with which it was more difficult to show differences than with the analyses used for the aMCI subjects alone or in the comparison between the aMCI subjects and the control subjects. Nevertheless, the present findings highlight a major role of vascular burden in the mechanisms underlying cognitive decline in RGD patients and suggest a secondary role of vascular burden in cognitive decline in aMCI patients.

Atrophy of the hippocampus, which is one of the earliest brain areas developing AD-related pathology, indicates the degree of neurodegeneration in MCI patients and serves as a well-established indicator for the early diagnosis of AD (Aisen et al., 2010; Jack et al., 2010). The present study revealed that the aMCI subjects had smaller hippocampal volumes than the controls, and the aMCI declining group had smaller hippocampal volumes than the aMCI stable group and/or control group. Furthermore, greater decreases in the left hippocampal volume correlated with greater decreases of global cognitive function in the aMCI declining group. In contrast, no significant difference in hippocampal volume was found between the RGD declining group and the RGD stable group. A handful of studies have explored the relationship between hippocampal volume and cognitive impairment in geriatric depression patients. Smaller hippocampal volumes are associated with impairments in global cognitive function (Sachs-Erisson et al., 2011), memory (O'Brien et al., 2004), and executive function (Hou et al., 2012) in geriatric depression patients. A recent study demonstrated that hippocampal volume is not associated with executive dysfunction, i.e., the common cognitive deficit in geriatric depression, although both smaller hippocampal volume and poor cognitive performance are displayed in depressed patients. The study suggested that the link between hippocampal volume and executive dysfunction may be indirect (Khan et al., 2015). The present data did not support the association between hippocampal volume and global cognitive function in RGD patients. These divergences may come from the heterogeneities of geriatric depression populations and methodologies, including statistical methods and the assessment and definition of cognitive impairment/decline.

Several limitations should be addressed. First, the present study did not measure Aβ deposition, which is a major hallmark of the pathological changes in AD. Increasing evidence suggests that vascular pathology interacts with Aβ pathology. Cerebral ischaemia promotes the accumulation of Aβ, and Aβ deposition in turn results in further decreases in cerebral blood flow (Popa-Wagner et al., 2015). The coexistence of vascular pathology and Aβ pathology is very common in dementia (Saito and Murayama, 2007; Schneider et al., 2009). Thus, although the present study demonstrated that different pathologies were associated with the cognitive decline in RGD patients and aMCI patients, there may also be convergent pathologies contributing to the cognitive decline in both conditions. Indeed, our previous study found both convergent and divergent patterns of microstructural integrity of white matter between RGD and aMCI subjects (Bai et al., 2012). Second, the range of the declines in the MDRS-2 scores in the RGD declining group, aMCI declining group and control declining group were not matched. Thus, the results of comparisons of WMH volume and hippocampal volume between these three groups should be treated with caution. Finally, the present study did not measure the Aβ pathology and tau pathology using PET imaging or cerebrospinal fluid analyses of Aβ and Tau. Some control subjects, especially those with cognitive decline, might also have AD-related pathology and could not serve as a control group. Some control declining subjects may improve their performance later on. Similarity, not all of aMCI subjects with cognitive decline would display AD-related pathology and ultimately develop dementia.

In conclusion, the present findings suggest that cognitive decline in RGD patients is associated with vascular burden, whereas the cognitive decline in aMCI patients is associated with neurodegeneration. These findings could contribute to a better understanding of specific mechanisms involved in the development of dementia in each condition, which may further provide novel strategies for the prevention and treatment of dementia.

### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee.

### AUTHOR CONTRIBUTIONS

FB: Study design, interpretation, data analysis, and manuscript writing. ZZ, HZ, and CX: Study design and interpretation. QY: Study design, data collection, analysis, and manuscript writing. FS, LG, HS, and WL: Data collection and analysis.

### FUNDING

This research was supported by the National Natural Science Foundation of China (No. 81671665); Jiangsu Provincial Key Medical Talents (No.ZDRCA2016085); Natural Science Foundation of Jiangsu Province (No. BK20160071); Six talent peaks project in Jiangsu Province (No. 2015-WSN-003); National High-tech R.D Program (863 Program) (No.2015AA020508).

### ACKNOWLEDGMENTS

We thank Haifeng Chen, Qin Xu, and Ying Cheng for their help in fMRI scanning.

### REFERENCES

**Conflict of Interest Statement:** 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 © 2017 Ye, Su, Gong, Shu, Liao, Xie, Zhou, Zhang and Bai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

#### *Edited by:*

*Mohammad Amjad Kamal, King Abdulaziz University, Saudi Arabia*

#### *Reviewed by:*

*Bo Zhou, Stanford University, United States Gianfranco Spalletta, Fondazione Santa Lucia (IRCCS), Italy*

#### *\*Correspondence:*

*Bing Zhang<EMAIL_ADDRESS>
*† Cofirst author: these authors have contributed equally to this work.*

#### *Specialty section:*

*This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology*

*Received: 06 June 2017 Accepted: 25 July 2017 Published: 15 August 2017*

#### *Citation:*

*Nie XL, Sun Y, Wan SR, Zhao H, Liu RY, Li XP, Wu SC, Nedelska Z, Hort J, Qing Z, Xu Y and Zhang B (2017) Subregional Structural Alterations in Hippocampus and Nucleus Accumbens Correlate with the Clinical Impairment in Patients with Alzheimer's Disease Clinical Spectrum: Parallel Combining Volume and Vertex-Based Approach. Front. Neurol. 8:399. doi: 10.3389/fneur.2017.00399*

*Xiuling Nie1†, Yu Sun1,2†, Suiren Wan1†, Hui Zhao3 , Renyuan Liu4 , Xueping Li4 , Sichu Wu4 , Zuzana Nedelska5,6, Jakub Hort 5,6, Zhao Qing4 , Yun Xu3 and Bing Zhang4 \**

*1 State Key laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China, 2 Institute of Cancer and Genetic Science, University of Birmingham, Birmingham, United Kingdom, 3Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 4Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China, 5 Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University in Prague, Motol University Hospital, Prague, Czechia, 6 International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic*

Deep gray matter structures are associated with memory and other important functions that are impaired in Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, systematic characterization of the subregional atrophy and deformations in these structures in AD and MCI still need more investigations. In this article, we combined complex volumetry- and vertex-based analysis to investigate the pattern of subregional structural alterations in deep gray matter structures and its association with global clinical scores in AD (*n* = 30) and MCI patients (*n* = 30), compared to normal controls (NCs, *n* = 30). Among all seven pairs of structures, the bilateral hippocampi and nucleus accumbens showed significant atrophy in AD compared with NCs (*p* < 0.05). But only the subregional atrophy in the dorsal–medial part of the left hippocampus, the ventral part of right hippocampus, and the left nucleus accumbens, the posterior part of the right nucleus accumbens correlated with the worse clinical scores of MMSE and MOCA (*p* < 0.05). Furthermore, the medial–ventral part of right thalamus significantly shrank and correlated with clinical scores without decreasing in its whole volume (*p* > 0.05). In conclusion, the atrophy of these four subregions in bilateral hippocampi and nucleus accumbens was associated with cognitive impairment of patients, which might be potential target regions of treatment in AD. The surface analysis could provide additional information to volume comparison in finding the early pathological progress in deep gray matter structures.

Keywords: Alzheimer's disease, mild cognitive impairment, deep gray matter structures, surface alteration, vertex analysis

### INTRODUCTION

Alzheimer's disease (AD) is the most prevalent form of age-related dementia (1, 2) and is usually preceded by a stage of cognitive decline. This clinical state of cognitive decline is conceptualized as mild cognitive impairment (MCI) (3, 4). Previous studies showed that MRI-based assessments of brain volume, such as hippocampal atrophy, can provided additional information for diagnosis of AD (5). Specifically deep gray matter structures, including bilateral nucleus accumbens (NAc), amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus, are associated with memory, emotional learning, shifting attention, and spatial working memory (6, 7), which were typically impaired in AD stage. Previous studies have focused on these regions and reported that there was atrophy on hippocampus and entorhinal cortex (8–12). Besides, it also suggested that other deep gray matter structures, like amygdala, putamen, and thalamus also showed significantly reduced volume, even in MCI (10, 13–15).

However, the subregions of deep gray matter structures may have different function and their impairment in AD may also have subregional specificity (9, 15–17). Therefore, it is very informative to explore the changes of deep gray matter structures on the subregional level in the AD and MCI. Previous studies have reported shape abnormalities in multiple deep gray matter structures, such as the dorsal–medial part of thalamus (15), the anterior hippocampus (9, 17), and the basolateral complex of amygdala (16, 17) in AD patients compared to controls. However, there is still lack of a systematic investigation of all deep gray matter structures.

In the current study, we unitized the automated software package, FMRIB's Integrated Registration and Segmentation Tool (FIRST) (16, 18, 19), which provided a powerful tool to analyze subregional atrophy and shape alterations within seven pairs of deep gray matter structures. The volumes of each deep gray matter structure and the vertex-wised distortion were compared among clinically normal controls (NCs) as well as MCI and AD patients. We hypothesized that the pattern of atrophy on subregional level would differ among the three mentioned groups, and the specific pattern of more profound atrophy or shape alterations would correlate with poorer scores of global clinical scores.

#### MATERIALS AND METHODS

#### Subjects

Ninety consecutive subjects were recruited from the memory clinic of Affiliated Drum Tower Hospital of Nanjing University Medical School. All subjects underwent a clinical evaluation using Mini-Mental State Examination (MMSE) (20) and Montreal Cognitive Assessment (MOCA) (21). Written consent was obtained from all subjects or their proxies after a detailed explanation of the study procedures, which was approved by the local Ethics Committee.

The MCI patients (*n* = 30) were diagnosed based on the following criteria (22): (a) memory complaint, preferably confirmed by an informant; (b) objective memory impairment, adjusted for age, and education; (c) normal or near-normal performance on general cognitive functioning and no or minimum impairment of daily life activities; and (d) not meeting the criteria for dementia. AD patients (*n* = 30) met NINCDS-ADRDA criteria (23). For comparison, we included NC (*n* = 30) who (a) had no cognitive complaints, (b) scored normally on global cognitive scales such as MMSE and MOCA, and (c) had no evidence of any structural abnormality on a conventional MRI scan.

Exclusion criteria were history of any significant medical, psychiatric, or neurological illness other than MCI or AD; history of brain injury; alcohol or drug abuse of alcohol; and missing clinical assessments.

#### MRI Acquisition

Images were acquired on a 3.0-T MR scanner (Achieva 3.0T TX dual Medical Systems; Philips Medical Systems, Eindhoven, Netherlands) using a three-dimensional turbo fast echo (3D-TFE) T1-weighted pulse sequence with TR = 9.7 ms, TE = 4.6 ms, flip angle = 8°, slice thickness = 1 mm, slice gap = 1 mm, FOV = 256 mm × 256 mm, and matrix = 192.

#### Image Processing

The flowchart of image processing is shown in **Figure 1**. MRI volume were processed using FMRIB Software Library (FSL, Version 5.0, http://www.fmrib.ox.ac.uk/fsl) (18, 24). Briefly, brain extraction was performed on 3D T1-weighted MR images using Brain Extraction Tool (25), which uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces (25).

FMRIB's Integrated Registration and Segmentation Tool (16) was applied to perform the segmentation and to measure volumes and vertexes in seven deep gray matter structures bilaterally, including NAc, amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. FIRST initially performed a two-stage linear registration using 12 degrees of freedom, and the registration was performed by FLIRT (26). In the first stage, an affine registration of the whole-head to a standard space template (MNI template), with 1 mm × 1 mm × 1 mm resolution, was implemented. In the second stage, a subcortical mask was used to exclude regions outside the deep gray matter structures. Finally, a boundary correction was used to determine whether boundary voxels belong to this structure or not.

Based on this deep gray matter segmentation, the volume of each region was first extracted. The boundary of each region was reconstructed into a vertex-based surface, and the vertex locations from each subject (at a corresponding anatomical point) were projected onto the surface normal of the average shape of all

the subjects. The projections were scalar values representing the signed, perpendicular distance from the average surface, where a positive value was outside the surface and a negative value was inside. Therefore, these scalar values of the vertex-based projection represented the shape information of each subjects and were used in the analysis of vertex-level group difference and to evaluate the shape deformation.

To reduce the influence of variation in head size, all volumes were scaled by normalized brain volume (NBV) estimated using SIENAX-FSL tool (24, 27). The segmentation result of each subject was visually checked and no error was observed. In the following statistical analysis, the volumes of each structure for each individual subject were corrected for NBV.

#### Statistical Analysis

#### Volume-Based Analysis

After calculating the volumes, we used SPSS v 21.0 to analyze the data, including two statistical methods: analysis of covariance (ANCOVA) and linear regression. To correct the differences in head size among individual participants, volumes of each structure were scaled by NBV using the following equation:

$$V\_{\text{stamlardf}} = \left(V\_{\text{roi}} / V\_{\text{NBV}}\right) \times 10^6$$

where *V*standard means the standardized volume corrected with NBV, *V*roi means the absolute volume of each segmented structure, and *V*NBV means NBV.

Analysis of covariance model was used to evaluate group-wise differences in volume measures between AD, MCI and NC, with controlling for age and gender. In this model, volumes of deep gray matter structures were included as dependent factors and diagnosis as independent factors. A correction for multiple comparisons was carried out using the false discovery rate (FDR) correction at *p* < 0.05. Subsequently, *post hoc* pair-wise comparisons were applied to identify between-group differences with *p*-value ≤0.05, which is considered as significant. Second, a linear regression model was applied to measure the associations between global clinical scores (MMSE and MOCA score) and volumes of each structure among all of the subjects. In the linear regression model, global clinical scores were included as the dependent variables and the volumes of deep gray matter structure were included as independent variables. Age, gender and NBV were included as covariates. A collinearity test was also performed to rule out multicollinearity between age and the volumes of deep gray matter structure.

#### Vertex-Based Analysis

To carry out the vertex analysis, we used FSL dedicated tools, generalized linear model (18) to design the statistical matrix and Randomize (28) to perform permutation inference. Two statistical models were also designed in the vertex analysis.

First, ANCOVA was applied to determine the subregional changes within the deep gray matter structures across AD, MCI, and NC groups. Second, we correlated local subregional changes with global clinical scores at *p*-value ≤0.05 across all subjects. Age and gender were taken into as covariates in both of the ANCOVA and correlational analyses. We applied the FDR (*q* = 0.05) to correct for the multiple comparisons. The MNI coordinates of the center of gravity (COG) (18) at subregional alterations were also calculated for a better awareness of the locations where subregional changes occurred.

#### RESULTS

#### Group Characteristics

Demographic and clinical characteristics of three groups are listed in **Table 1**. The AD, MCI, and NC groups did not differ in NBV (*p* = 0.052) and gender (*p* = 0.194), but differ in age (*p* = 0.028). As expected, the pathological alteration among groups led to the significant difference in the scores of MMSE and MOCA among patients with AD, MCI, and NC (*p* < 0.001).

#### Deep Gray Matter Structures Volumes in AD and MCI Groups

Group-wise differences in volumes of the deep gray matter structure are displayed in **Table 2**. Volumes of bilateral NAc and bilateral hippocampi (Hipp) were smaller in AD, compared to NC group (L\_Hipp: *p* = 0.001, R\_Hipp: *p* = 0.001, L\_NAc: *p* = 0.003, R\_NAc: *p* = 0.003). MCI had smaller left hippocampus (*p* = 0.020), right NAc (*p* = 0.010), and left putamen (*p* = 0.038) volumes, compared to the NC group. Only right hippocampus showed a smaller volume in AD, compared to MCI group (*p* = 0.012).

#### Associations between Volumes and Clinical Rating Scales

We did not find a multicollinearity among the selected variables: age and volumes of deep gray matter structures. After controlling age, gender, and NBV, the *p*-values and β-regression coefficient of the correlations between volumes of deep gray matter structures and the global clinical scores across all subjects are displayed in **Table 3**. The volumes of bilateral NAc, bilateral hippocampi, which also showed significant atrophy in AD, compared to NC, were significantly correlated with MMSE and MOCA scores (*p* ≤ 0.05). The volumes of other deep gray matter structures did not show significant correlations with clinical scores (*p* > 0.05).

*\*p* ≤ *0.05.*

*\*\*p* ≤ *0.01. Demographic data are presented as means* ± *SDs for continuous and proportions for categorical variables.*

*AD, Alzheimer's disease; MCI, mild cognitive impairment; NC, normal controls; NBV, normalized brain volume; MMSE, Mini-Mental State Examination; MOCA, Montreal Cognitive Assessment; M, male; F, female.*

Table 2 | Group-wise differences in the deep gray matter structures.

\**p* ≤ *0.05;* \*\**p* ≤ *0.01 using ANCOVA; FDR corrected for t-test and adjusted for normalized brain volume, age, and gender.*

*Significant across and between-group differences are in bold.*

*L, left; R, right; NAc, nucleus accumbens; Hipp, hippocampus; Put, putamen; Thal, thalamus.*

Table 3 | Association between volumes in deep gray matter structures and global clinical scores.

*Results are presented as* β*-coefficient (p-values) based on linear regression model. \*p* ≤ *0.05.*

*\*\*p* ≤ *0.01.*

### Vertex Analysis of Deep Gray Matter Structures

The results from vertex analysis are illustrated as probabilistic images that show the significant shape abnormalities within deep gray matter structures with 1 − *p* ≥ 0.95 (*p* ≤ 0.05) in **Figure 2**. After controlling for age and gender, the surface abnormalities were located within bilateral hippocampi and NAc in AD, compared to NC group, and only shape alterations in the right hippocampus were found in AD, compared to MCI group. Specifically, significant shape differences were detected in the ventral part of left NAc (left ventral NAc) and right hippocampus (right ventral hippocampus), posterior part of right NAc (right posterior NAc), the dorsal–medial part of left hippocampus (left dorsal–medial hippocampus), and medial–ventral part of right thalamus (right medial–ventral thalamus) in AD when compared to NC. Less surface alterations were found in left hippocampus, right NAc, and left putamen in MCI, compared to NC without correction for the multiple comparisons, while these alterations did not survive under FDR correction. **Figure 3** shows the location of shape abnormalities using MNI coordinates of COG (colored in yellow).

### Relationship between Regional Shape Alterations and Clinical Measures

**Figure 4** shows correlations between shape alterations of deep gray matter structures and clinical measures of MMSE and MOCA within the whole sample. MMSE scores were associated with shape abnormalities in bilateral hippocampi, left NAc, and right thalamus, whereas MOCA scores were associated with shape abnormalities in bilateral hippocampi, NAc, and thalami. The localization of subregional alterations and their correlation with MMSE and MOCA scores is shown in **Table 4**. It can be observed that the left dorsal–medial hippocampus, the right ventral hippocampus, the left ventral NAc, and the right medial–ventral thalamus significantly correlated with MMSE and MOCA, while the right posterior NAc and the medial–ventral part of the left thalamus only correlated with MOCA. Notably, compared with NC, these results are quite consistent with the atrophy regions found in AD groups, which are shown in **Figures 2** and **3**.

#### DISCUSSION

In this study, we assessed the pattern of subregional structural alterations in deep gray matter structures in AD and MCI patients. Compared with NC, we found that among all seven pairs of structures, the bilateral hippocampi and NAc showed significant atrophy in AD. But only the atrophy in the left dorsal-medial hippocampus, the right ventral hippocampus, the left ventral NAc and the right posterior NAc correlated with the worse global clinical scores. These results suggested that the atrophy of these four subregions in bilateral hippocampi and NAc was associated with clinical impairment in MCI and AD patients, which might be useful in diagnosis, evaluation and management of AD. Furthermore, compared with NC, the volume of bilateral thalami did not significantly decrease in AD patients, but the significant atrophy was observed in the right medial-ventral thalamus and correlated with global clinical scores by using surface analysis. These results implied that the surface analysis could provide additional information for volume comparison in finding the early pathological progress.

It is well known that hippocampus plays an important role in spatial, semantic, and episodic memory, which are generally impaired in AD and MCI patients (29–32). Numerous studies indicated that hippocampal atrophy contributes to memory impairment in patients with AD (33–38). Recent studies in AD found that obvious atrophy in hippocampal CA1, especially in MCI patients who converted to AD over time (37, 38). In the current study, we found that hippocampal atrophy and its

bilateral nucleus accumbens (NAc), right thalamus; AD compared to mild cognitive impairment (MCI) (B) in right hippocampus; MCI showed no significant alteration

compared to NC after FDR corrected (C), while there are alterations found in left hippocampus, right NAc, and left putamen before FDR corrected (D).

correlation with global clinical scores. Moreover, vertex analysis showed a alteration in the right ventral hippocampus and the left dorsal-medial hippocampus. This result is in line with a previous report that hippocampal atrophy is not homogeneous across the various hippocampal subregions during the progression of AD (5, 39). The dorsal hippocampus has been studied extensively for its significant role in spatial working memory, especially when processing the short-term spatial memory (40). Fanselow and Dong reported that the dorsal hippocampus performs primarily cognitive functions, while the ventral hippocampus relates to stress, emotion, and affect (41). Other studies also indicated that loss of neurogenesis in the dorsal and ventral hippocampus was associated with the impaired cognitive function (42), and the ventral hippocampus supported working memory for odor information (43). Experiments in rats showed that dorsal-medial hippocampus plays a role in flexible, adaptive behavior, and cognitive processing, and rats with dorsal-medial hippocampus lesions suffered significant working memory deficits (44). In line with the previous studies, the significant correlation between the dorsal/ventral hippocampal shape distortion and the global clinical scores in our study also emphasized the critical role of these two hippocampal subregions in cognitive impairment in AD process. Besides, the subregional level atrophy in hippocampi, which is slightly in MCI patients, but dramatically deteriorated in AD, further supporting the concept that AD is generally converted from MCI. Therefore, treatment on dorsal/ventral hippocampus may be effective to prevent the progression of AD. Drug treatment upon reduction of memory consolidation in rats with impaired dorsal hippocampus indicated that morphine may prevent impairment of memory consolidation (45). Yiu et al.

also reported that targeting CREB in the CA1 region of dorsal hippocampus may be a useful therapeutic strategy in treating humans with AD (46). The role of the ventral hippocampus in AD is still not clear. Since the stress management related to the ventral hippocampus is suggested as a useful intervention in early AD and MCI (47), the treatment on ventral hippocampus for AD still need to be explored. The measurement in hippocampal subregion might be useful in monitoring the effect of treatment or intervention on subregional level.

In addition to hippocampus, NAc participates in integrating the information involved in learning and executive function and is also related to the cognitive processing of aversion, motivation, pleasure, reward, and reinforcement learning (48, 49). NAc is part of the striatum and has close connections with both limbic structures of the hippocampus, the amygdala, and the prefrontal cortex. One previous research about late-onset Alzheimer's disease patients showed the significant reductions in NAc volumes (50). Some previous studies have shown that atrophy of NAc occurred in patients with AD (51, 52), while they failed to reveal its function in cognitive decline. In our study, our results were on par with these previous studies that the bilateral NAc were more atrophied in AD and MCI patients compared to NCs. Furthermore, we found the atrophy in NAc was correlated with MMSE and MOCA scores. These results suggested that in addition to hippocampus, the atrophy in bilateral NAc may also play an important role in clinical impairment in AD and MCI. The localized atrophy in NAc has been confirmed to be associated with apathy in Parkinson's disease, and the severity of apathy was correlated with morphological changes in this region (53). Comparably, the vertex analyses in our study found

changes occurred (marked with an arrow). COG, center of gravity. (A) AD-NC and (B) AD-MCI.

that the atrophy of NAc and its association with clinical scores were both constrained within the ventral and posterior aspects in AD. Our results suggested that the ventral/posterior parts of NAc may involve in AD progression. However, few studies have explored the function of NAc in subregional level, especially its functions in AD or MCI, which need to be investigated in the future. A previous study reported that the deep brain stimulation of NAc could be a new treatment strategies to addiction (54). In the future, treatment like deep brain stimulation on subregional level of NAc may be considered for intervention in AD patients.

Some studies have revealed that other deep gray matter structures, such as amygdala, basal nuclei, and thalamus, also suffer atrophy in AD patients (55). Shape changes of ventricle system were reported in ventricular regions adjacent to amygdala, caudate nucleus, and thalamus in AD patients (56). MRI study showed strongly reduced volumes of putamen and thalamus in AD patients and this atrophy may contribute to cognitive impairment (10). Volumes of caudate and thalamus in familial AD reduced at a presymptomatic stage (57). It is well recognized that thalamus is essential for generating attention (58) and is involved as a subcortical hub in many different neuronal pathways related to emotional, motivational and cognitive abilities that are impaired in AD (6). To our knowledge, few studies have investigated the subregional atrophy and its relationship with

(yellow color coded) between subregional atrophy and clinical scores (adjusted for age and gender; FDR corrected). Across the whole data cohort, correlations were observed (A) in hippocampi, left NAc, and right thalamus and MMSE scores; (B) in hippocampi, NAc, and thalami and MOCA scores.

Table 4 | Associations between subregional shape alterations and clinical measures (corresponding to the yellow areas in Figure 4).

global clinical scores in thalami. Significant bilateral volumetric and subregional atrophy in the dorsal–medial part of the thalamus in AD patients has already been reported (10, 15). Another study found the subregional atrophy in the medial part of bilateral thalami (59), but not explored the relationship with clinical scores. Our study revealed significant shape abnormalities in the right medial-ventral thalamus in AD patients, but not found smaller volumes in bilateral thalami. Furthermore, the atrophy in right medial-ventral thalamus significantly correlated with MMSE and MOCA, suggesting that some parts of thalamus may be more vulnerable in AD and MCI. Our results suggest a specific atrophy pattern of the thalamus in AD and MCI, and also indicated that vertex-based (shape) analysis may provide additional and valuable information to popular volumetric analysis.

The main limitation of our study is that, although we adjusted our analysis for age, gender, and NBV, relatively small group samples may have reduced the statistical power. Therefore, some counterintuitive results were also need to be further validated in the future. For example, the putamen volume was significantly different between MCI and NC, but not between AD and NC. Furthermore, this was a cross-sectional study, and so it was difficult to judge whether atrophy of specific deep gray matter structures was a primary or secondary phenomenon to the hippocampal or entorhinal cortex. Therefore, further and preferable longitudinal investigations are needed to confirm the relationship between clinical and cognitive decline and the subregional atrophy patterns.

## CONCLUSION

Based both on volumetric and vertex-based (shape) analysis of deep gray matter structures, this study showed that atrophy of deep gray matter structures in AD and MCI patients occurs at subregional level even when the volume of the whole given structure is not reduced. And this subregional atrophy within hippocampi and NAc correlates with worse global clinical scores. Atrophy measurement on subregional level may be useful in early diagnosis and management of AD patients or in evaluation of the treatment strategies.

## AUTHOR CONTRIBUTIONS

XLN, YS, and SRW contributed to this article equally, being responsible for the image procession, data analysis, and writing the article. HZ, RYL, XPL, and SCW are responsible for the MRI acquisition and data collection; ZN, JH, ZQ, and YX give advice on how to do the data analysis and modify the article. BZ put forward the research ideas and guide the whole study.

#### ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (91649116, 81571040, and 81471643), Jiangsu province key medical talents, "13th Five-Year" health promotion project of Jiangsu province (BZ.2016-2020), Social development project of science and technology project in Jiangsu Province (BE2016605), the National and Provincial postdoctoral project (BE179 and 1501076A, BZ), the key project of Nanjing Health Bureau

#### REFERENCES

(ZKX14027, BZ), Nanjing science and technology development program (NE179, BZ), Jiangsu Provincial Key Medical Discipline (Laboratory) (ZDXKA2016020, XY), and Jiangsu Provincial KeyMedical Discipline (Laboratory) (ZDXKA2016020). ZN and JH are supported by the project no. LQ1605 from the National Program of Sustainability II (MEYS CR) and ZN is supported by "Nadani" Foundation and Czech Alzheimer Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Conflict of Interest Statement:** 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 © 2017 Nie, Sun, Wan, Zhao, Liu, Li, Wu, Nedelska, Hort, Qing, Xu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Perspective Insights into Disease Progression, Diagnostics, and Therapeutic Approaches in Alzheimer's Disease: A Judicious Update

Arif Tasleem Jan1†‡, Mudsser Azam2‡, Safikur Rahman1 , Angham M. S. Almigeiti 1 , Duk Hwan Choi 1 , Eun Ju Lee1 , Qazi Mohd Rizwanul Haq2 and Inho Choi 1 \*

1 Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea, 2 Department of Biosciences, Jamia Millia Islamia, New Delhi, India

#### Edited by:

Ghulam Md Ashraf, King Abdulaziz University, Saudi Arabia

#### Reviewed by:

Cláudia Fragão Pereira, University of Coimbra, Portugal Victoria Campos-Peña, Instituto Nacional de Neurología y Neurocirugía, Mexico

> \*Correspondence: Inho Choi<EMAIL_ADDRESS>
† Present Address: Arif Tasleem Jan, School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah University, Rajouri, India

‡ These authors have contributed equally to this work.

Received: 19 June 2017 Accepted: 18 October 2017 Published: 01 November 2017

#### Citation:

Jan AT, Azam M, Rahman S, Almigeiti AMS, Choi DH, Lee EJ, Haq QMR and Choi I (2017) Perspective Insights into Disease Progression, Diagnostics, and Therapeutic Approaches in Alzheimer's Disease: A Judicious Update. Front. Aging Neurosci. 9:356. doi: 10.3389/fnagi.2017.00356 Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the progressive accumulation of β-amyloid fibrils and abnormal tau proteins in and outside of neurons. Representing a common form of dementia, aggravation of AD with age increases the morbidity rate among the elderly. Although, mutations in the ApoE4 act as potent risk factors for sporadic AD, familial AD arises through malfunctioning of APP, PSEN-1, and−2 genes. AD progresses through accumulation of amyloid plaques (Aβ) and neurofibrillary tangles (NFTs) in brain, which interfere with neuronal communication. Cellular stress that arises through mitochondrial dysfunction, endoplasmic reticulum malfunction, and autophagy contributes significantly to the pathogenesis of AD. With high accuracy in disease diagnostics, Aβ deposition and phosphorylated tau (p-tau) are useful core biomarkers in the cerebrospinal fluid (CSF) of AD patients. Although five drugs are approved for treatment in AD, their failures in achieving complete disease cure has shifted studies toward a series of molecules capable of acting against Aβ and p-tau. Failure of biologics or compounds to cross the blood-brain barrier (BBB) in most cases advocates development of an efficient drug delivery system. Though liposomes and polymeric nanoparticles are widely adopted for drug delivery modules, their use in delivering drugs across the BBB has been overtaken by exosomes, owing to their promising results in reducing disease progression.

Keywords: Alzheimer's disease, diagnostics, drugs, neurodegeneration, therapeutics

### INTRODUCTION

Alzheimer's disease (AD) is recognized as a disease of neurons and neuronal circuits. It arises as a result of progressive accumulation of β-amyloid fibrils (β-amyloid plaques) and abnormal forms of tau (tau tangles) within and outside of neurons (Jucker and Walker, 2013; Jaunmuktane et al., 2015). Approximately 46.8 million people over the age of 60 years have been diagnosed with AD worldwide (Prince et al., 2016). Though occurrence of the early onset of dementia is <1% per 4,000 individuals, the projected figure is estimated to be 131.5 million in 2050 (Prince et al., 2016). The projected increase in the prevalence of dementia is substantially higher in developing countries than in the USA and Europe, which already have a high proportion of older individuals in their populations. The Alzheimer's Association presented an estimate of 5.5 million people suffering from AD in the USA, and the incidence of AD in the USA is projected to be 7.7 millions in 2030 and 11–16 millions in 2050 (ADFF, 2016).

The pathology of AD begins well before symptom manifestation, with intracellular accumulation of neurofibrillary tangles that arise via abnormal tau protein phosphorylation and extracellular deposition of Aβ-plaques (Selkoe, 1994, 2001a,b). Interfering with neuronal communication, deposition of β-amyloid plaques causes neuronal death, while tau tangles Blocks transport of essentials into interior of the neurons. Characterized by progressive memory loss and cognitive impairment, advanced stage AD patients show symptoms ranging from neuron inflammation to neuron death. Various risk factors promote pathological changes well before the onset of clinical symptoms of AD. In addition to cardiovascular risk, studies suggest a significant contribution of lifestyle related factors such as obesity, diabetes, depression, smoking, and insufficient diet in dementia. This review presents an overview of AD with recent updates on epidemiology, factors that aggravate the disease, and a prospective insight into diagnostic markers and therapeutic options for disease treatment.

### GENETIC SUSCEPTIBILITY

AD represents one of the greatest health-care challenges of the twenty-first century. Its dependence on age and genetic background has resulted in its classification as either familial (FAD; showing early onset), which is observed in 5% of AD cases, or sporadic (SAD; showing late disease onset), which shows a high disease incidence rate. Though ApoE4 is a well-characterized risk factor in SAD, disease etiology in FAD is attributed to mutations of amyloid precursor protein (APP), presenilin1 (PS1), and presenilin 2 (PS2) genes (Goate et al., 1991; Levy-Lahad et al., 1995; Rogaev et al., 1995; Sherrington et al., 1996; Selkoe, 2001b). Accounting for 15% of total ApoE, APOE4 interferes with the clearance of Aβ from brain. Differing in amino acid substitutions at 112 and 158 positions, ApoE4 carries two arginines, while ApoE3 have two cysteines and ApoE2 has arginine and cysteine at these positions (Mahley and Huang, 2009). Attribution of APOE4 to AD is 50% in homozygous (Apo E4/E4) and 20– 30% in heterozygous condition with APOE3 (Genin et al., 2011).

Over the time, substantial evidences regarding accumulation of misfolded Aβ and tau tangles (so-called seeds of pathological consequences) in the brain of patients suffering from AD have been established (Karran et al., 2011). Evidential support of Aβ and tau involvement comes from FAD studies that report mutations in APP (Aβ precursor) and PS1&2 (catalytic γsecretase subunit; Karch and Goate, 2015; Ahmad et al., 2016). Having a critical role in the multi-causality of dementia (Boyle et al., 2013), mutations in APP enhance aggregation, while PSEN1&2 mutations cause less efficient APP processing, leading to longer and more hydrophobic Aβs (Scheuner et al., 1996; Chávez-Gutiérrez et al., 2012; Wong et al., 2013; **Table 1**). Though mutations in APP and PS1&2 accelerate generation of disease seeds, decreased Aβ clearance (Mawuenyega et al., 2010) and increased Aβ accumulation (Wahlster et al., 2013) enhances SAD.

### MITOCHONDRIAL STRESS IN AD

Performing vital biochemical functions, mitochondrial dysfunction significantly affects progression in the pathogenesis of AD (Swerdlow et al., 2014). Associated with the regulation of cellular metabolism, functional impairment of metabolic enzymes, in particular enzymes of the TCA cycle, causes reduction in the energy metabolism in brain (Huang et al., 2003; Bubber et al., 2005). Studies of the AD brain have revealed significant impairment in the functioning of pyruvate dehydrogenase complex (PDHC) and α-ketoglutarate dehydrogenase complex (KGDHC) enzymes, followed by impairment of isocitrate dehydrogenase (Huang et al., 2003; Bubber et al., 2005). Although, increased activity of succinate and malate dehydrogenases was observed, activities of the remaining four enzymes remains unaltered. Considering the high energy demand of neurons, a role of mitochondrial oxidative stress leading to energy imbalance appears to have a considerable effect on neurodegeneration.

Mitochondrial dysfunction, observed as altered mitochondrial DNA and increased cytochrome oxidase (COX) levels, indicates oxidative damage to the neurons of AD patients (Hirai et al., 2001; Nunomura et al., 2001; Moreira et al., 2006, 2007, 2009; Su et al., 2008). In concert with the amyloid hypothesis, altered APP processing increases Aβ deposition (Furukawa et al., 1996), whose aggregation causes oxidative stress through an increase in the production of H2O2 (Readnower et al., 2011). Inhibition of the mitochondrial electron transport chain (ETC) causes increased ROS production, which damages proteins, lipids, and nucleic acids, observed as increases in 8-hydroxy-2-deoxyguanosine (8-OHdG) and nitrotyrosine levels (Wang et al., 2005). Accumulation of Aβ in the synaptic mitochondria makes them susceptible to changes in synaptic Ca2+ as they have high levels of cyclophilin D (CypD; Sayre et al., 2005). Being a component of the mitochondrial permeability transition pore (mPTP), CypD translocation from matrix to mPTP increases interaction of CypD-mPTP with adenine nucleotide translocase resulting in opening of the pore and as such collapse of membrane potential, which ultimately leads to neuronal death (Juhaszova et al., 2004). An increase in the oxidative burden of mitochondria mediates activation of FoxO transcription factor (Kops et al., 2002; Fu and Tindall, 2008), which is associated with induction of SOD and catalase activity, as well as causing cell cycle arrest and cell death (Castellani et al., 2002). Increased ROS attenuation of the antioxidant arsenal of mitochondria alters the cellular redox state. Increased ROS levels act as an autophagy trigger and subjects mitochondria to mitophagy (Scherz-Shouval and Elazar, 2011).

TABLE 1 | Summary of mutations predicted in genes that have been associated with the occurrence of AD.

#### ENDOPLASMIC RETICULUM (ER) STRESS IN AD

Acting as a site of protein synthesis, disruption in the proteostasis causes accumulation of unfolded proteins in the ER lumen (Wu and Kaufman, 2006; Ron and Walter, 2007). To reduce abnormal protein aggregation, unfolded proteins are translocated to cytoplasm for degradation; a process referred as ER-associated protein degradation (ERAD; Hetz et al., 2011; Walter and Ron, 2011). Attainment of saturation in the ER's proteinfolding capacity elicits a dynamic signaling response referred as unfolded protein response (UPR; Lee, 2001; Ledoux et al., 2003). Recognition of unfolded proteins by stress sensors such as inositol requiring protein 1 (IRE1), protein kinase RNA (PKR) like ER kinase (PERK), and activating transcription factor 6 (ATF6) triggers downstream signaling via transcription factors (Hetz and Mollereau, 2014). PERK induces rapid translational attenuation by inhibition of the eukaryotic translational initiation factor 2α (eIF2α). PERK-mediated phosphorylation of eIF2α also favors translation of transcription factor ATF4, which is capable of controlling expression of genes related to amino acid metabolism, autophagy, and apoptosis. Increased phosphorylation of eIF2α in PS1 mutant knockout mice confirmed the inhibition of eIF2α phosphorylation by PS1 (Milhavet et al., 2002). Additionally, PS1-mediated abnormal processing of IRE1 disturbs UPR by interfering with the ER stress. Activation of IRE1 signaling induces splicing of Xbox binding protein 1 (XBP-1), which controls the expression of lipid synthesis, ER protein translocation, protein folding, and ERAD genes (Hetz et al., 2011; Walter and Ron, 2011). Polymorphism in the promoter region of XBP-1, which reduces its transcription, is considered as a risk factor for AD (Liu et al., 2013). Expression of GPR78 and GPR94 associated with the refolding of unfolded proteins attenuated in PS2-expressed cells is attributed to impaired IRE1 phosphorylation. Though protection against Aβ toxicity through enforced Xbp1 expression in a Drosophila melanogaster AD model is attributed to reduction in the release of Ca2+ from ER (Casas-Tinto et al., 2011), a similar effect in Caenorhabditis elegans AD model is correlated with augmented stress levels and enhanced autophagy (Safra et al., 2013). Nuclear translocation of ATF6 following protease cleavage activates ERAD genes and XBP-1. Moreover, ATF4, ATF6, and XBP-1 stimulate C/EBP homologous protein (CHOP) and its target growth arrest and DNA damage inducible 34 (GADD34), as well as pro-apoptotic components of the B cell lymphoma-2 (BCL2) family of proteins (Xu et al., 2005). Neurons expressing p-tau show enhanced UPR activation (Hoozemans et al., 2009; Abisambra et al., 2013). Excessive adaptive capacity of UPR triggers pro-apoptotic events through upregulation of the cell death genes such as caspase-12 (Szegezdi et al., 2003).

### AUTOPHAGY IN AD

Degradation of non-essential cellular components, such as misfolded and aggregated proteins, occurs through the autophagy-lysosomal system (ALS; Li et al., 2010; Murrow and Debnath, 2013). Activated by oxidative stress, nutrient starvation, etc., clearance of unwanted entities helps in restoring substrates for cellular remodeling (Ichimura and Komatsu, 2010; Li et al., 2010; Murrow and Debnath, 2013). Abundance of growth factors and cellular nutrients activates mTOR kinase, whereas a starvation state exerts inhibitory effect on mTOR kinase activity. The mTOR kinase-mediated phosphorylation of ATG13 prevents its association to Unc-51 like kinase (ULK), and recruitment of focal adhesion kinase family interacting protein of 200 kD (FIP200) inhibits autophagy, whereas inhibition of mTOR activates phosphatases that cause dephosphorylation of ATG13 (Kundu, 2011; Lee et al., 2012), thereby promotes autophagy. Although mTOR-dependent autophagy is prominent, there are reports of mTOR-independent autophagy mediated by (1) ATG5 and ATG7 via microtubule associated light chain 3-II (LC3-II; Nishida et al., 2009); (2) autophagic proteins Beclin1, Bcl-2, and ULK1 (Nishida et al., 2009; Shimizu et al., 2010); and (3) non-canonical signaling events involving Ca2+. In addition, Ca2+ has a prominent role in both canonical and non-canonical mTOR-independent autophagy (Cárdenas and Foskett, 2012; Decuypere et al., 2013). Deletion of Beclin-1 in AD mouse models has resulted in increased Aβ accumulation, while its overexpression leads to reduction in amyloid pathology (Pickford et al., 2008; Jaeger and Wyss-Coray, 2010). Though IP3 receptor-mediated Ca2+ signaling inhibits autophagy, an increase in the cytosolic Ca2+ level promotes autophagy (Criollo et al., 2007; Wang et al., 2008; Khan and Joseph, 2010; Vingtdeux et al., 2010). Some studies have linked PS mutation in FAD with neuronal dysfunction and apoptosis via Ca2+ dysfunction (Del Prete et al., 2014; Duggan and McCarthy, 2016).

Despite entrapping non-selective molecules, selective trapping of various molecules occurs through interaction of LC3-II with cargo-receptors such as nuclear dot protein-52 (NDP52), p62, and neighbor of BRAC1 (NRB1; Bjørkøy et al., 2005; Kirkin et al., 2009; Jo et al., 2014). Acting as an autophagic adaptor for degradation of toll/IL-1 receptor homology domain containing adaptor including IFN-β (TRIF) and tumor receptor associated factor-6 (TRAF6), a role of NDP-52 in docking of autophagosomes to T6BP, myosin VI, and optineurin for maturation has been reported (Inomata et al., 2012; Tumbarello et al., 2012). Interaction of NBR1 and p62 with ubiquitinated misfolded proteins and members of the ATG8 family makes them critical for autophagosome formation (Kabeya et al., 2000; Bjørkøy et al., 2005; Pankiv et al., 2007; Kirkin et al., 2009). PS1 mutations that cause a loss of lysosomal acidification ultimately lead to loss of lysosomal proteolytic activity (Lee et al., 2010). Disruption in the lysosomal function causes accumulation of autophagosomes containing protein aggregates, as observed in the AD brain. Studies indicate involvement of NDP-52 as a downstream facilitator of nuclear factor erythroid-2 related factor 2 (Nrf2)-mediated tau (phosphorylated) degradation (Jo et al., 2014). Reduction in the autophagy-aggravated AD pathology underlines the critical role of autophagy in the removal of Aβ aggregates (Cai et al., 2012; Di Domenico et al., 2014).

## BIOMARKERS OF AD

Cortical amyloid deposition of Aβ and phosphorylated tau (p-tau) are core biomarkers of AD in CSF (Blennow et al., 2010). With high accuracy diagnostics, specificity of the core repertoire of biomarkers is in the 80–90% range in the mild cognitive impairment stage of AD (Shaw et al., 2009; Visser et al., 2009). Despite this, assessment of the sensitivity of disease biomarkers reflects an imperfect criterion in the gold standard of clinical diagnosis of the disease because it fails to distinguish pathological changes such as plaque count between cognitively normal elderly persons and those with AD (Coart et al., 2015; Curtis et al., 2015). Changes in α-synuclein (α-syn), TDP43, and several vascular indicators, are other pathological markers in patients suffering from AD (Kovacs et al., 2013).

A step to increase the specificity and sensitivity of biomarkers has led to the screening of several different entities for use in the diagnosis of AD. One such biomarker involves Aβ oligomers (toxic forms of Aβ), which are associated with synaptic dysfunction (Overk and Masliah, 2014). Though increases in the Aβ oligomers are observed in AD, their limited occurrence in the CSF hinders their reliability in the diagnosis of AD (Hölttä et al., 2013; Yang et al., 2013). Neurogranin, a dendritic protein, is another synaptic biomarker candidate (Díez-Guerra, 2010). Increased amount of neurogranin in CSF of AD patients is correlated with the progression of mild cognitive impairment (Kvartsberg et al., 2015). Additionally, an increase in the level of presynaptic protein SNAP25 might indicate AD diagnosis (Brinkmalm et al., 2014). Of the other biomarkers, screening of CNS-specific protein candidates in blood also seems suitable; when sampling populations for AD. Combinations of proteins, lipids, and other molecules have been used in the assessment of AD (Henriksen et al., 2014; Mapstone et al., 2014). However, as they may be secreted in low amounts and are prone to degradation by plasma proteases, their suitability as biomarkers for AD is uncertain. Similarly, tau-imaging, which is commonly employed in drug trials to determine delay in the progression of AD may be another potentially suitable option for AD diagnosis.

### THERAPEUTIC APPROACHES TO COMBAT AD

The fact that critical care provides patients a better quality of life family care is considered as the mainstay in the treatment of AD. At present, efforts are being made on two fronts; toward developing disease modifying therapies that can uphold progression of the disease, and; developing drugs that can act as disease pathway blockers. As Aβ and tau hyper-phosphorylation are well-recognized hallmarks of AD (Lee et al., 2005; Querfurth and Laferla, 2010; Ahmad et al., 2017), current drug approaches are aimed at:

### Improved APP Processing via Inhibition and/or Activation of Enzymatic Machinery

To date, only five drugs have been approved by the USA FDA for treatment of cognitive manifestations of AD (**Table 2**). Of the different drug regimes, glutamate agonist memantine, and acetylcholine esterase (AchE) inhibitors such as rivastigmine, donepezil, and galantamine are employed in the treatment of the dementia phase of AD (Raina et al., 2008). Blocking the pathological stimulation of NMDA receptors, memantine protects neural cells from glutamate-mediated excitoxicity. AchE inhibitors cause temporary slowdown in cognitive function by decreasing activity of cholinesterase, leading to enhanced acetylcholine (Ach) levels and, thereby, brain functions (Godyn et al., 2016). As disruption of AchE has a direct correlation with NFT and Aβ deposits (Tavitian et al., 1993), AchE inhibitors stabilize cognitive performance and daily functioning in early dementia stages, whereas application of memantine provides benefits to patients suffering from moderate to severe dementia (Godyn et al., 2016). The fifth drug, tacrine is less prescribed due to its hepatotoxicity. Although these drugs provide symptomatic improvement in AD, benefits are generally short lived and with no effect on the pathogenic mechanism or on disease progression. Research into potential strategies is currently directed at screening bioactive compounds for their effect on enzyme inhibition, aggregation, prevention of Aβ formation, and upregulation in the removal of toxic Aβ.

### Immunotherapeutic Approaches to Existing Amyloids

Currently, the most attractive Aβ-directed approach, immunotherapy enlists both active (immune stimulation by vaccine for antibody production) and passive (injection of pre-prepared antibodies) immunization. Engaging both the cellular and humoral immune systems, active immunization is cost effective and ensures long-term high antibody titers. An active vaccination journey begins with AN1792, a fulllength Aβ1−42 peptide injected with the immune stimulant adjuvant QS21 (Panza et al., 2014). However, on observing meningoencephalitis among 6% of the enrolled AD patients, the phase II AN1792 trial was halted in 2002. Taking strong cognizance of AN1792 trials, larger studies were directed at testing passive immunotherapy by using human anti-Aβ monoclonal antibodies. At present various 2nd generation active Aβ vaccines are undergoing clinical trials; of particular note are CAD106 (Aβ1−6; Novartis), ACC001 (Aβ1−6; Janssen and Pfizer), ACI-24, V-950, and AD-02 (Aβ1−6; Alzforum, 2016). Compared to active immunization that leads to a polyclonal antibody response, target specificity (specific against monomeric Aβ, Aβ fibrils, and their carrier and transport proteins) in passive immunization has advantages both in preventing Aβ-aggregation and in promoting Aβ-clearance in anti-Aβ immunotherapy. The first monoclonal antibody was bapineuzumab directed against Aβ1−5 (Abushouk et al., 2017; Xing et al., 2017). Although it reduced Aβ in brain during phase II trials, it failed to achieve significant benefit in a phase III trial, leading to its termination in 2012. This was followed by trials of mAb m266, which showed enhanced Aβ clearance from brain to blood (Demattos et al., 2001; Dodart et al., 2002). Using am266 precursor, Solanezumab directed against mid-domain Aβ16−24 revealed dose-dependent increases in plasma Aβ, suggesting cleavage of insoluble species from senile plaques (Farlow et al., 2012; Han and Mook-Jung, 2014). Other Abs undergoing clinical trials include gantenerumab (showing binding specificity for Aβ plaques), crenezumab IgG4 mAb (showing binding specificity for Aβ oligomers, plaques, and fibrils that inhibit aggregation), GSK933766, and BAN2401 mAb (Alzforum, 2016). Reduction in Aβ production is also achieved by using inhibitors against secretases such as NIC5-15, Bryostatin-1, AZD3293, MK8931, and E2609 (Alzforum, 2016).

### Tau Centric Therapies

Tau centric therapies include the use of putative tau kinase inhibitors, microtubule stabilizers, and tau immunotherapy. Glycogen synthase kinase-3β (GSK-3β), the primary enzyme involved in tau phosphorylation, is considered the primary target for disease modification (Jaworski et al., 2011). As Aβ promotes GSK-3β activity, studies of GSK-3β inhibitors such as tideglusib and AZD1080 were pursued (King et al., 2014; Lovestone et al., 2015). Clinical trials of AZD1080 revealed nephrotoxicity (Eldar-Finkelman and Martinez, 2011), while the trials for tideglusib showed diminished clinical benefits. Microtubule stabilizers inhibit tau aggregation (Bulic et al., 2010). LMTC, a methylthionium chloride (MTC) derivative prevents tau interactions, thereby facilitating its clearance from the brain (Wischik et al., 2015). Other microtubule stabilizers include TPI207 and BMS241027 (epothilone-D). Immunotherapy for tau protein is directed toward prevention of NFT formation. Both AADvac1 (a synthetic tau derived peptide) and ACI-35 (liposome formulated based tau protein) are currently being evaluated for their effects on avoiding tau aggregation (Alzforum, 2016).

TABLE

2

Summary

of

therapeutic

options

available

in

the

treatment

of

AD.

#### Therapies to Combat Oxidative Stress

Oxidative stress due to reactive oxygen species (ROS), being a major player in neurodegeneration strategies, are currently being devised to combat its emergence at mitochondria (Federico et al., 2012; Yan et al., 2013; Kim et al., 2015). On the forefront, ferulic acid (FA), epigallocatechin-3-gallate, and nano formulation of naturally occurring curcumin were found exerting strong antioxidant, anti-inflammatory and amyloid disintegration properties (Rezai-Zadeh et al., 2005; Yang et al., 2005; Cheng et al., 2013; Sgarbossa et al., 2015; Cascella et al., 2017). Alleviating mitochondrial dysfunction under diseased conditions, peptide based strategies complemented with different drug molecules have shown positive results in overcoming the inefficiency of low antioxidant levels (Kumar and Singh, 2015). Of them, Szeto-Schiller (SS) peptides displaying a sequence motif that directs its accumulation inside mitochondria inhibit lipid peroxidation. Accumulation of SS31 on inner mitochondrial membrane prevents release of cyt-C (Szeto, 2008; Kumar and Singh, 2015). Similarly, accumulation of drugs such as MitoQ inside mitochondria increases its potential to neutralize free radicals several 100-folds than that attributed by natural antioxidants (Tauskela, 2007; Ross et al., 2008). Probucol, a drug that helps establishment of balance of mitochondal fission-fusion processes, also maintains AD mitochondria induced extracellular signal regulated kinase (ERK) activation (Champagne et al., 2003; Gan et al., 2014).

#### Autophagy Enhancer Therapy

As accumulation of the aggregated proteins enhances neurodegeneration, enhancement in the degenerative capacity via, autophagy inducers seems a good therapeutic approach. Their mode of action involves prevention in the accumulation of Aβ plaques and tau tangles via degradation of the aggregates. Of the available drugs, rapamycin acting as mTOR inhibitor was found ameliorating Aβ and tau pathies in AD mouse model (Caccamo et al., 2010; Rubinsztein et al., 2012). Latrepirdine stimulation of autophagy reduces Aβ neuropathy in mouse brain (Steele and Gandy, 2013). Metformin, a PP2A agonist, inhibition of TORC1 prevents hyperphosphorylation of tau protein (Kickstein et al., 2010). Other authophagy enhancers include reservatol and its analogs, RSVA314 and RSVA405, virally packaged BECN1 and its mimetics, nicotinamide, etc (Vingtdeux et al., 2010; Shoji-Kawata et al., 2013). Though, Beclin1 deficiency increases APP and Aβ levels, its overexpression in cultured neurons cause significant reduction in Aβ accumulation (Pickford et al., 2008). Additionally, PS1 and PS2 serving as a catalytic subunit of γ-secretase was found acting as autophagy modulators (Lee et al., 2010). Associated with the substrate cleavage, their knockdown was found exhibiting inefficiency in the clearance of protein aggregates. Cells deficient in PS1 was found having reduced levels of cargo shuttle protein p62, associated with the degradation of abnormal tau (Tung et al., 2014; Caccamo et al., 2017).

Failure of biologics that target amyloids to cross the bloodbrain barrier (BBB) indicates the necessity of development of an efficient drug delivery system. Liposomes and polymeric nanoparticles have shown promising results in delivering drugs and other therapeutic molecules (Ha et al., 2016); however, their use as a means to deliver drugs across the BBB has encountered roadblocks associated with biocompatibility and long-term safety. Persistent problems with low biocompatibility, restricted immune escape, circulation stability, and toxicity have led to a gradual shift of research toward the use of exosomes. Their long circulatory half-life, host biocompatibility, targetspecific drug delivery ability, and low toxicity have increased the interest in using exosomes to deliver drugs in several neurodegenerative diseases (Lai and Breakefield, 2012; Liu et al., 2016). As drug delivery through exosomes has shown promising results in the ongoing studies, their utilization in reducing disease progression may indicate their suitability in reducing progression of AD.

### CONCLUSION

AD progresses via aggregation and accumulation in the extracellular milieu of amyloid plaques and intraneuronal neurofibrillary tangles produced by p-tau. Although malfunctioning APP, PS-1 &-2 genes are considered the main culprits behind AD, mitochondrial dysfunction, ER stress and mitophagy significantly increase progression of the disease. Current research provides useful information on new targets and their utilization in designing novel inhibitors or drugs as part of attempts to achieve successful treatment of AD. It also studies different entities for employment as potent biomarkers in disease diagnosis and provides information on therapeutic suitability in the treatment of AD. Further studies on the use of exosomes as drug delivery vehicles are needed in order to reveal and reduce safety and ethical concerns. With increases in newly identified targets, studies pertaining to the design of drugs and other potent therapeutic molecules are needed in order to combat the progression of AD.

### AUTHOR CONTRIBUTIONS

IC, QH, and AJ: conceived the idea; AJ and MA: contributed to writing of the manuscript; AJ, SR, AA, DC, and EL: contributed to upgrading the contents and preparing the tables.

#### ACKNOWLEDGMENTS

Authors extend their thanks to colleagues for their criticism, which helped to improve the quality of this paper's contents and broaden its perspective to reach a broader audience. This work was supported by the Creative Economy Leading Technology Development Program through the Gyeongsangbuk-Do and Gyeongbuk Science & Technology Promotion Center of Korea (SF316001A).

## REFERENCES

in autophagosome membranes after processing. EMBO J. 19, 5720–5728. doi: 10.1093/emboj/19.21.5720

**Conflict of Interest Statement:** 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 © 2017 Jan, Azam, Rahman, Almigeiti, Choi, Lee, Haq and Choi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation

Guilin Meng1,2† , Xiulin Meng3† , Xiaoye Ma1 , Gengping Zhang4 , Xiaolin Hu5 , Aiping Jin1 , Yanxin Zhao1 \* and Xueyuan Liu1 \*

1Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China, 2School of Computer Science and Informatics, Indiana University, Bloomington, IN, United States, 3Houma People's Hospital, Linfen, China, 4Library of Tongji University, Shanghai, China, 5School of Life Sciences, Tsinghua University, Beijing, China

Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

Keywords: Alzheimer disease, BACE1, curcumin, ferulic acid, MMP2, STRING, text mining

### INTRODUCTION

Alzheimer's disease (AD) is a chronic neurodegenerative disease that usually progresses from short memory loss to dementia, and accounts for 50%–70% of dementia cases (Burns and Iliffe, 2009). According to the World Alzheimer Report (Prince, 2015), 46.8 million people worldwide are living with dementia, and this number is estimated to reach 131.5 million by 2050, which will result in an

#### Edited by:

Ghulam Md Ashraf, King Abdulaziz University, Saudi Arabia

#### Reviewed by:

Tarique Khan, Buck Institute for Research on Aging, United States Luigia Trabace, University of Foggia, Italy

#### \*Correspondence:

Yanxin Zhao<EMAIL_ADDRESS>Xueyuan Liu<EMAIL_ADDRESS>
†Co-first authors.

Received: 02 February 2018 Accepted: 11 May 2018 Published: 29 May 2018

#### Citation:

Meng G, Meng X, Ma X, Zhang G, Hu X, Jin A, Zhao Y and Liu X (2018) Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation. Front. Neuroinform. 12:31. doi: 10.3389/fninf.2018.00031

**220**

increasing burden on society and families. In addition, the cost of long-term care, home services, and non-professional caregivers is greater than the cost of direct medical care (Bullock, 2004; Winblad et al., 2016; Yokoyama et al., 2016).

Despite enormous financial and research investments, appropriate interventions to prevent the progress of AD are lacking (Iqbal and Grundke-Iqbal, 2011; Selkoe, 2013). Based on the failure of a number of novel AD drugs, investigators are increasingly convinced that AD is not a single but rather a multifactorial disease (Iqbal et al., 2013), and hence, drugs that target one node on the classical pathway have little effect on the AD disease network. Since AD is a multifactorial disease, drugs that modulate systemic or multiple targets are of interest.

Traditional Chinese medicine (TCM) compositions usually exert systemic impact and can be a source of drug repositioning efforts (Wang et al., 2011). TCM treatments are natural herbs discovered by the ancient Chinese and evolved through at least 3000 years of clinical practice. TCM is gaining increasing attention with the emergence of integrative and personalized medicine, characterized by pattern differentiation on individual variance and treatments based on natural herbal synergism (Wang and Wei, 2009). With the growing popularity and promising approach of TCM applicability, the ever-increasing demand for understanding the pharmacological mechanisms and potential drug efficacy are the major issues that need to be addressed.

In this study, we sought to shed light on TCM for AD. What typical TCM treatments could be effective for AD, and what are the underlying target-based mechanisms? How can we integrate systemic and target-based understandings of the disease and treatments (Cho et al., 2006)? With the overwhelming amount of biomedical knowledge recorded in texts, text mining is essential for identifying, extracting, managing, integrating and exploiting this information to discover new, hidden, or unsuspected information. Text mining is a computerbased discovery of new, previously unknown information, which automatically extracts information from different written resources (Ding et al., 2013), drawing on information retrieval, statistics, and computational linguistics. It has considerable potential for drug target discovery and re-labeling of existing drugs. Some typical proven drug repositioning cases are available for text mining, such as the beneficial effect of estrogen on human memory discovered by Smalheiser and Swanson (1996), thalidomide for treating acute pancreatitis extracted by Weeber et al. (2003), and the association of migraine with AMPA receptors identified using Litlinker (Yetisgen-Yildiz and Pratt, 2006).

Herein, we report an approach for finding an appropriate TCM for AD through the utilization of text-mining from literature database, exploring the underlying therapeutic mechanisms followed by searching for protein-protein interactions (PPI) using the STRING platform, and finally using the SHSY5Y-APP AD cell line model for validation.

#### MATERIALS AND METHODS

Our first aim was to select a TCM candidate from the extensive literature collection. The study workflow is shown in **Figure 1**.

### Data Collection and Extraction to Find a TCM Candidate for AD

First, we assembled an AD literature dataset by retrieving articles from PubMed using AD-related keywords: ''Alzheimer or Mild cognitive impairment or Dementia or Significant memory concern or Subjective memory complaint,'' a resource for extracting and defining TCM candidates. The TCM database, TCMID.v2.01 (Chen et al., 2006), was then utilized, which included names, stitch\_id, PubChem\_id, synonyms, formula, SMILES strings, and the source of the involved chemicals. The TCM terminologies, mentioned in the abstracts of the retrieved articles were then extracted using dictionarybased named entity recognition (i.e., simple word matching) provided by LingPipe1 . Using this method, the selection of the TCM for AD was retrieved in a relatively short duration.

Furthermore, we matched the PubChem ID of TCM and the origin ontology in the human metabolome database (HMDB; Southan et al., 2013) to determine the optimal TCM candidates. After focusing on a possible lead TCM candidate, we retrieved articles in the AD dataset, and extracted protein names that co-occurred with the candidate TCM using the dictionarybased entity recognition from NCBI protein list2 . This gave us with a list of possible proteins affected by the TCM candidate.

### Inferring Possible PPI (Li et al., 2017) Networks of the TCM Candidate Using STRING

STRING presents a specific and productive functional relationship between two proteins into a combined interaction confidence score, which is derived from the co-expression score, experimentally determined interaction score, and the automated text mining score. In this system, the automated text mining score is higher than or approximately equal to the experimentally determined interaction score since it is integrated from these scores. However, if the text mining score was lower than the experimentally determined interaction, there were two possibilities: (1) the experimentally determined interaction score was a false positive; (2) only a few studies are available related to these two proteins; however, experimental validation could be conducted.

We deposited the protein list mentioned above in the multiple protein column in the search webpage of STRING3 and acquired the PPI network after deleting results with co-expression scores >0 (already validated PPI), or experimentally determined interaction score × automated text mining score = 0 (little relevance). In the next step, we rearranged the PPI network according to text mining scores and obtained the top proteins in the network, which were most likely related to candidate mechanisms in AD.

#### Validation of Protein Expression and PPI in an AD Cell Line Model

In order to generate strong evidence not only in the data level, we validated the possible mechanisms mined from STRING using AD cell lines.

### Cell Culture and Treatment With TCM Candidate for AD

SHSY5Y-APP cells, a classic cell line for AD research, were a kind gift from Shanghai Jiao Tong University. The cells were cultured in MEM supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 units/mL penicillin, and 100 µg/mL streptomycin (Invitrogen, Carlsbad, CA, USA) at 95% humidity, 37◦C, and 5% CO2 in an incubator. The cells were passaged by trypsinization every 2–3 days. The SHSY5Y-APP cells were treated with different doses of the TCM candidate for 24 h. In the existing researches, the effective ferulic acid (FA) concentrations vary from 10 nM to 1 mM without toxic reactions in a variety of cell lines, in accordance with the point that FA is highly safe for daily and long-term consumption (Thakkar et al., 2015; Sompong et al., 2017; Zhang et al., 2017). In line with a previous study using the same cell line (Cui et al., 2013), micromolar (µM) was chosen as the unit for FA concentration and upgraded in steps of 0 µM, 15 µM, 30 µM and 60 µM.

#### Co-immunoprecipitation Assays (co-IP)

Cell lysates were centrifuged (10,000× g) at 4◦C for 15 min. Proteins were then immunoprecipitated with the relevant antibodies to determine interactions. The precleared Protein A/G Plus-Agarose beads (Merck KGaA, Darmstadt, Germany) were incubated with the immunocomplexes for 2 h and washed four times with phosphate-buffered saline. The immunoprecipitates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE; Merck KGaA, Darmstadt, Germany), followed by transfer to polyvinylidene difluoride (PVDF) membrane (Amresco, OH, USA). The antibody-antigen complexes were visualized using the UPV software according to the manufacturer's instructions. The immunoreactive bands were quantified to confirm the appropriate levels of proteins.

#### Western Blot

#### Preparation of Protein Samples

After TCM candidate exposure for 24 h, SHSY5Y-APP cells were washed with pre-cooled 4◦C PBS, and then the wash solution was discarded. The above procedure was repeated twice. PMSF was added to lyse the cells on ice with frequent shaking for 30 min. After lysis, the cells were scraped with a clean scraper, and then the cell debris and lysate were transferred and centrifuged at 12,000 rpm for 5 min at 4◦C. The supernatant after centrifugation was stored at −20◦C.

#### Determination of Protein Concentration

The standard BCA assay procedure was done as previously described (Huang et al., 2010). After blocking, the membranes were probed with the following primary antibodies (Cell

1http://alias-i.com/lingpipe/index.html

2https://www.ncbi.nlm.nih.gov/protein/

3https://string-db.org/

Signaling, Beverly, MA, USA) using different dilutions: rabbit anti-MMP2 (92 kDa, Abcam ab92539, 1:2000), rabbit anti-MMP9 (92 kDa, Abcam ab38898, 1:2000), rabbit anti-BACE1 (68 kDa, Abcam ab183612, 1:1000), rabbit anti-APP (87 kDa, Abcam ab15272, 1:600), and mouse anti-betaactin (42 kDa, Boster, BM0627, 1:200). All experiments were performed at least three times.

#### Electrophoresis

We prepared the 12% separation gel, 10% separation gel and 5% concentration gel. The prepared protein sample and the maker were added to 40 µg. After the sample was added, constant 80 V electrophoresis was performed until the bromophenol blue indicator was linear at the junction of the concentrated gel and the separation gel, and the pressure was changed to constant 120 V. This process took about 1.5 h. Next, we removed the gel and the target band according to the Marker. The PVDF membrane was soaked in methanol for several seconds and soaked in the electroporation buffer together with the filter paper. The transfer membrane conditions were as below: β-actin 200 mA 90 min, BACE1 200 mA 120 min, APP, MMP2 and MMP9 250 mA 120 min.

#### Immunoblotting and Analysis

The PVDF membrane was soaked in TBST containing 5% skimmed milk powder and shaken at room temperature for 2 h. We mixed the ECL reagent with the stable peroxidase solution in a ratio of 1:1, added the solution onto the PVDF membrane. X-ray film was placed in the solution, flushed, dried, scanned, and finally analyzed grayscale value with BandScan 5.0 software (NIH, USA). Statistical analysis was performed using SPSS 20.0 software (SPSS, Chicago, IL, USA). Quantitative data are presented as mean ± standard deviation (SD) of triplicates in an independent experiment that was repeated three times. Data were compared using Student's unpaired t-test for direct comparison between two-groups and the Tukey-Kramer test after a significant one-way analysis of variance (ANOVA), and F-test for multiple-group comparisons. P < 0.05 was considered as statistically significant.

### RESULTS

### Text Mining Using AD Literature and the TCM Database

We retrieved 195,882 articles from PubMed using AD-related keywords and assembled an AD article dataset.

After matching TCMID.v2.01 ingredients to the AD article database, we extracted a list of AD-related TCM ingredients with PubChem IDs, which was checked for origin ontology in HMDB.