*The Geomagnetic Field Transformants and Their Complexing with Data of Gravitational… DOI: http://dx.doi.org/10.5772/intechopen.111560*

In order to extract the maximum geological information, the following transformants of the observed geomagnetic field were calculated [3]: analytical signal, vertical and horizontal gradients of the anomalous magnetic field, the angle of the magnetic field gradient vector, high-frequency and low-frequency components of the magnetic field, autotracing of the anomaly axes ΔTa, position of singular points Euler (**Table 1**).

**Table 1.**

*Statistics on the values of transforms of the anomalous magnetic and gravitational, thermal, and radiogeochemical fields.*

In the Geosoft Oasis Montaj software, matrices were built (with a cell size of 250 250 m) using the "Bidirectional Line Gridding" algorithm.

The international analytical model IGRF was adopted as the normal geomagnetic field of the Earth.


**4. Conclusions**

In this study, we prepared a series of Ni-based catalysts for the in-situ HDO process of biomass-derived phenol to cyclohexane. The trimetallic 20Ni-5Co-5Cu/Al catalyst presented the highest activity compared to the other monometallic catalysts or bimetallic catalysts. The phenol conversion of 100% and the cyclohexane yield of 98.3% could be obtained in the in-situ HDO of phenol at 240 ◦C and 4 MPa initial N2 pressure. The high catalytic activity of the 20Ni-5Co-5Cu/Al catalyst could be attributed to the formation of Ni-Cu-Co alloy, which had the strong positive synergistic e ffects on the generation of hydrogen from the methanol aqueous-phase reforming and hydrodeoxygenation of phenol. The catalyst also showed excellent recyclability and exhibited good potential for upgrading the bio-oil to reduce the oxygen content by the in-situ hydrodeoxygenation.

**Author Contributions:** Conceptualization, H.X.; validation, H.X., X.G. and J.X.; writing—original draft preparation, H.X.; writing—review and editing, R.H.; funding acquisition, R.H.

**Funding:** Gratitude for the support from the National Natural Science Foundation of China (21276254, 21636006) and the Fundamental Research Funds for the Central Universities (GK201603051, GK201601005).

**Conflicts of Interest:** The authors declare no conflict of interest.


**7. References**

Final control voltage

900 *Overall Matching Time s* =

μ

Start Stop

New pacemaker tends to integrate a wireless telemetry system to allow home monitoring of the patient. The quality of service is strongly improved with an increase of safety, comfort and a reduction of cost. However, this challenge faces to a number of limitations like the need of low power high efficiency design, the degradation of the budget link while the antenna is immersed into the human body, etc. Indeed, it is demonstrated that the antenna impedance changes while immersed into human body causing mismatch of the antenna. To avoid antenna mismatch and reduction of the power efficiency of the radio link, we have proposed a new method to automatically match the antenna impedance to the front-end radio. This method operates in a single step to extract the antenna input impedance value exploited by a processor to match the antenna to the front-end radio both in transmission and reception. A demonstrator operating at the MICS 402-405 MHz frequency band was fabricated and an experimental set-up was presented. This prototype calibrates the system in less than 900μs with a 40MIPS clock processor to achieve a coefficient reflection S11 up to

The authors would like to thank ELA Medical (SORIN Group) for supporting this work.

[1] Haddad, S.A.P., Houben, R.P.M., Serdijin, W.A (2006). The evolution of pacemakers,

[2] Banbury, C.M. (1997). Surviving Technological Innovation in Pacemaker Industry 1959-

[3] Wheeler, H.A. (1975). Small Antennas, *IEEE Transactions on Microwave Theroy and*

1990, Garland Publishing Inc, ISBN<PHONE_NUMBER>

*Techniques,* Vol. AP-23, No. 4, pp. 462-469, July 1975

*IEEE Engineering in Medecine and Biology Magazine*, Vol. 25, Issue 3, pp. 38-48, Mars

TMatching

Initial control voltage

Fig. 26. Time Antenna calibration

**5. Conclusion**

**6. Acknowledgement**

**7. References**

2006

**Part 5**

**Animal Telemetry**


*3.1. Physicochemical Properties of Sorbents*

The physical and chemical properties of the three test sorbents, AC, SAC, and FeS, were summarized in Table 1. Because FeS is a non-porous mineral, its surface area and pore volume were much smaller than those of AC and SAC. Elemental analyses of AC and SAC showed that the oxygen and sulfur contents in SAC were increased after impregnation of sulfur on AC, hence SAC should be more favored for Hg2<sup>+</sup> uptake because sulfur-containing groups on SAC had high affinity towards mercuric sulfide formation. The oxygenated groups are also known beneficial for Hg adsorption. The increase in the oxygen content after sulfur impregnation could be due to the reaction of S with C to form vaporized CS2 evolved from the SAC surface.

**Table 1.** The physicochemical properties of sorbents.

\* SBET: specific BET surface area; SMicro: micropore area; VTotal: total pore volume; Vmicro: micropore volume.


**4.5 Patient-reported outcomes**

An important consideration in visual restoration is the extent to which the recipients judge the system to be beneficial in everyday life. The Functional Lowvision Observer Rated Assessment (FLORA) was developed by Geruschat et al. in order to evaluate the impact of partial restoration of ultra-low vision in subjects undergoing Argus II implantation. Initial results using the FLORA tool demonstrated that it was able to provide useful information about the everyday functional benefit of prosthesis-derived visual restoration, as well as identifying areas in which rehabilitation could be utilized to maximize subjective value. At 1 year, the assessment demonstrated an 80% reported positive effect, which dropped to 65% at 3 years. No patients reported a negative effect using this self-reporting tool [84, 85].

#### **Figure 7.**

*Subject performing prehension task (a) with live infrared motion capture of subject's hand (b). Credit: Luo et al. [82].*


*4.1.3 Glutathione perioxidases (GPx)*

Glutathione peroxidase (EC <IP_ADDRESS>) contains a single selenocysteine residue in each of the four identical subunits, which is important for enzyme activity. GPx (80 kDa) is an imperative intracellular enzyme that catalyzes H2O2 to water and lipid peroxides to their corresponding alcohols mainly in the mitochondria and sometimes in the cytosol. In mammals, there are five GPx isoenzymes. Though their expression is ubiquitous, the level of each isoform differs depending on their tissue type. Mitochondrial and cytosolic glutathione peroxidase (GPx1 or cGPx) reduces fatty acid hydroperoxides and H2O2 at the expense of glutathione [32].

GPx1 is the main ubiquitous selenoperoxidase present in most cells; found in the cytosolic, mitochondrial, and peroxisomal compartments. It is an important antioxidant enzyme required in the detoxification of H2O2 and lipid hydroperoxides and preventing DNA, protein and lipids damage by harmful accumulation of intracellular H2O2 [33]. GPx1 uses GHS as an obligate co-substrate in the reduction of H2O2 to water [32]. Phospholipid hydroperoxidase glutathione (PHGPX) is found in most tissues and can directly reduce the phospholipid hydroperoxides, fatty acid hydroperoxides, and cholesterol hydroperoxides that are produced in peroxidized membranes and oxidized lipoproteins [30].

GPx4 is found in both the cytosol and the membrane fraction, and is highly expressed in renal epithelial cells and tests. Cytosolic GPx2 or extracellular GPx3 is inadequately found in nearly all tissues except for the gastrointestinal tract and kidney. In recent, GPx5, a new kind, expressed particularly in mouse epididymis, is selenium independent [34].

Several studies underlined the clinical importance of GPx. In addition, GPx, especially GPx1, have been implicated in the progression and prevention of many frequent and complex diseases, including cancer and cardiovascular disease [34, 35].

#### *<IP_ADDRESS> Application*

GPx is an important antioxidant enzyme in the body. Glutathione (GHS), the master antioxidant, is important for GPx levels due to the closely linked relationship; GHS is a tripeptide that protects the cells against the negative effects of pollution and functions as the body's immune system booster. GHS plays an essential role in red blood cells to remain intact and protects white blood cells, which are responsible for the immune system. An antioxidant's role is specifically essential for the brain because it is sensitive to the presence of free radicals. To increase the body's protection from free radicals, it is imperative to combine certain antioxidants such as glutathione, vitamin C and E, Se, and GPx [3].

#### **4.2 Nonenzymatic antioxidants**

In previous decades, there has been increasing evidence that large amounts of antioxidants present in our diet contribute to the antioxidant defense system by preventing oxidative stress and specific human diseases. Phytochemicals, the plant-derived compounds, are one of the classes of the dietary factors, which play an essential role in functions of the body. Food materials contain a number of natural compounds reported to have antioxidant characteristics due to the presence

**11**

*Antioxidant Categories and Mode of Action DOI: http://dx.doi.org/10.5772/intechopen.83544*

*4.2.1 Glutathione*

*<IP_ADDRESS> Application*

*4.2.2 Vitamin E*

*<IP_ADDRESS> Application*

cal, cardiovascular, and physiological disorders [38].

glutathione transferase (GST) [42, 43].

in the onset of Parkinson's disease [44].

LDL, and prevent ischemic heart disease [49].

of hydroxyl groups in their structure. Synthetic and natural antioxidants prevent the oxidative damage to the most important macromolecules such as lipids, proteins, and nucleic acids found in human body through scavenging the free radicals formed in different biochemical processes [36]. These antioxidants consist of small molecules including vitamin C, E, and β-carotene or natural antioxidants such as flavonoids, tannins, coumarins, phenolics, and terpenoids [37]. Because of oxidative stress, the free radicals that have been produced react with lipids, proteins and nucleic acids and lead to stimulation of apoptosis, which causes various neurologi-

In addition to phytochemical antioxidants, which can protect the body from oxidative damage, there are other antioxidants for example polyphenols, lycopene, and lutein [39]. Even though there has been a considerable concentration on antioxidant function of phytochemicals for several years, it is distinguished that phytochemicals have nonantioxidant effects important for health such as cell signaling and gene expression [40].

Glutathione (γ-glutamyl-cysteinyl-glycine; GSH) is a tripeptide and is the most abundant intracellular antioxidant protecting normal cells from oxidative injury due to its role as a substrate of ROS scavenging enzymes. Glutathione is primarily present in its reduced form (GSH) in normal conditions, with only a small amount being found in the fully oxidized state (GSSG) [41]. Glutathione functions as a nonenzymatic antioxidant through free radical scavenging in cells and serves as a cofactor for several enzymes, include GPx, glutathione reductase (GR), and

Recently, there is a new era of therapeutic applications of glutathione through the association of decreased GSH levels with the common features of aging and a wide range of pathological conditions, including neurodegenerative disorders. Remarkably, depletion and alterations of GSH in its metabolism appear to be crucial

Vitamin E, C, and β-carotene are the main antioxidant vitamins for tissues against free radical damage. Vitamin E, a major lipid soluble antioxidant, functions as the most important membrane-bound antioxidant, neutralizing free radicals, and preventing oxidation of lipids within membranes [45]. Vitamin E is the free radical scavenger in the prevention of chronic diseases [46]. α-Tocopherol is the main form of vitamin E with antioxidant and immune functions. α-tocopherol has been revealed to be a more effective inhibitor of peroxynitrite-induced lipid peroxidation and inflammatory reactions [47]. *In vitro* tocotrienols have excellent antioxidant activity and have been proposed to restrain ROS more effectively than tocopherols [48].

The main function of vitamin E is to protect against lipid peroxidation through evidence suggesting that α-tocopherol and vitamin C function together in a cyclic type of process. It has been reported that vitamin E supplementation in hypercholesterolemic patients has shown to increase autoantibody levels against oxidized

#### *Antioxidant Categories and Mode of Action DOI: http://dx.doi.org/10.5772/intechopen.83544*

of hydroxyl groups in their structure. Synthetic and natural antioxidants prevent the oxidative damage to the most important macromolecules such as lipids, proteins, and nucleic acids found in human body through scavenging the free radicals formed in different biochemical processes [36]. These antioxidants consist of small molecules including vitamin C, E, and β-carotene or natural antioxidants such as flavonoids, tannins, coumarins, phenolics, and terpenoids [37]. Because of oxidative stress, the free radicals that have been produced react with lipids, proteins and nucleic acids and lead to stimulation of apoptosis, which causes various neurological, cardiovascular, and physiological disorders [38].

In addition to phytochemical antioxidants, which can protect the body from oxidative damage, there are other antioxidants for example polyphenols, lycopene, and lutein [39]. Even though there has been a considerable concentration on antioxidant function of phytochemicals for several years, it is distinguished that phytochemicals have nonantioxidant effects important for health such as cell signaling and gene expression [40].


Braak et al. (1999) demonstrated that alpha-synuclein-positive inclusions are present in the axons of PD patients, e.g., the intramedullary vagal axons from the dorsal motor vagal area, which itself contained many alpha-synuclein-positive Lewy bodies. Moreover, Iseki et al. (2001) found that ubiquitinpositive inclusions in the central nucleus of the amygdala of patient brains with dementia with Lewy bodies were also alpha-synuclein and tyrosine hydroxylase positive, suggesting that the degeneration of terminal axons of affected substantia nigra neurons were the source of the amygdala pathology in patients with dementia with Lewy bodies (Iseki et al., 2001). Consistent with this spreading model, the present study found that baseline brainstem FA WM values significantly correlated with both 1-year changes in FA WM and MD GM values. These findings indicate that DTI may serve as a valuable biomarker of both disease progression and, extrapolating from Spencer et al. (2017) preclinical study, also drug efficacy in studies of disease-modifying agents.

Burke and colleagues suggest that axonal damage in PD may occur prior to neuronal loss, as described in their "axonal dying back" model (Burke and O'Malley, 2013; Tagliaferro and Burke, 2016). The evidence for this claim derives primarily from various mouse models of PD [e.g., BAC, heterozygous null gene engrailed1 (En1), and Nurr1 transgenic mouse models], which all show axonal pathology prior to neuronal loss (for a review see Tagliaferro and Burke, 2016).

Correspondingly, in humans, circa 70% of nigrostriatal dopaminergic terminals are estimated to be lost at the timepoint of the PD diagnosis, while the level of neurodegeneration has only reached 30% of substantia nigra dopaminergic neurons (Cheng et al., 2010). Moreover, a recent study of Alzheimerrelated pathology suggests that DTI abnormalities may portend the downstream aggregation of pathological proteins: in a multi-model imaging study with MRI DTI and FTP tau PET, Jacobs et al. (2018) demonstrated that abnormal MD in the hippocampal cingulum bundle predicted tau accumulation in the downstream-connected posterior cingulate cortex 2 years later in amyloid positive older individuals. We note that as in synucleinopathies, intraneuronal pathologically aggregated tau proteins in Alzheimer's disease are likewise hypothesized to spread from neuron to neuron along WM tracts (Clavaguera et al., 2009). Taken together, these findings suggest that DTI may be used not only as an early marker of alpha-synuclein pathology, but of future upstream neuronal cell loss and downstream pathological alpha-synuclein aggregation.

The present study demonstrated baseline brainstem and cortical abnormalities in de novo PD WM and GM with DTI, as expected, and a propagation of these abnormalities within the first year of de novo PD to include large swathes of cortical regions. Importantly, both the FA and MD changes were functionally relevant, as evidenced by significant correlations with changes in MDS-UPDRS total scores. The patterns of cortical WM neurodegeneration in PD mimicked those described during normal aging, with stronger FA effects in frontal and stronger MD effects in parietal regions, which together suggest a selective vulnerability of these brain regions. In the context of preclinical PD models suggesting an early affectation of axons affected by synucleinopathy (i.e., prior to neuronal loss) (Cheng et al., 2010), the present findings indicate that DTI-based measures of WM and GM integrity may represent powerful early biomarkers of disease progression in de novo PD.

### REFERENCES

# DATA AVAILABILITY STATEMENT

The datasets analyzed for this study can be found in the PPMI data repository (http://www.ppmi-info.org/access-dataspecimens/download-data/).

### AUTHOR CONTRIBUTIONS

JD, FS, FB, and AB conceptualized the analysis plan. JD analyzed the data. KT and JD wrote the manuscript.

# FUNDING

KT, FS, FB, AB, and JD are current or former full-time employees of F. Hoffmann-La Roche, Basel, Switzerland. The authors received no specific funding for this work. F. Hoffmann-La Roche provided financial contribution in the form of salary for all authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

## ACKNOWLEDGMENTS

Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Briston-Myers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, and UCB.

**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 © 2018 Taylor, Sambataro, Boess, Bertolino and Dukart. This is an openaccess 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) and the copyright owner(s) 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.

# A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE

Trang T. Le1,2†, Rayus T. Kuplicki 1 \* † , Brett A. McKinney 2,3, Hung-Wen Yeh1 , Wesley K. Thompson4 , Martin P. Paulus 1 and Tulsa 1000 Investigators ‡

1 Laureate Institute for Brain Research, Tulsa, OK, United States, 2 Department of Mathematics, University of Tulsa, Tulsa, OK, United States, 3 Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States, 4 Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States

### Edited by:

Katja Franke, Universitätsklinikum Jena, Germany

### Reviewed by:

Guray Erus, University of Pennsylvania, United States Franziskus Liem, Universität Zürich, Switzerland

### \*Correspondence: Rayus T. Kuplicki

<EMAIL_ADDRESS>
†These authors have contributed equally to this work

‡Tulsa 1000 Investigators: Robin L Aupperle, Jerzy Bodurka, Yoon-Hee Cha, Justin S. Feinstein, Sahib S. Khalsa, Jonathan Savitz, W Kyle Simmons, Teresa A Victor

Received: 30 March 2018 Accepted: 21 September 2018 Published: 24 October 2018

### Citation:

Le TT, Kuplicki RT, McKinney BA, Yeh H-W, Thompson WK, Paulus MP and Tulsa 1000 Investigators (2018) A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE. Front. Aging Neurosci. 10:317. doi: 10.3389/fnagi.2018.00317 Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Keywords: BrainAGE, simulation, false positives, SVR, MRI, aging

# INTRODUCTION

Aging is a biological process that can affect behavioral and cognitive dimensions. Biological age as measured by telomere length deviates from an individual's chronological age as a result of environment, lifestyle, and genetics (Shammas, 2011). However, other measures of biological age that may be particularly relevant to psychopathology can involve structural and functional changes in the brain.

Several imaging modalities, including T1-weighted structural imaging (Franke et al., 2010), diffusion tensor imaging (Han et al., 2014; Lin et al., 2016), and functional MRI (Tian et al., 2016) have been used in conjunction with machine learning algorithms to predict an individual's age. Recently, integration of neuroimaging data of different feature types and across multiple modalities has been shown to improve age prediction (Erus et al., 2015; Liem et al., 2017; Gutierrez Becker et al., 2018). While the details vary according to modality, the general strategy has been to (1) extract image-related features, (2) build a model on a training set composed of healthy participants using these features to predict participant age, (3) apply that model to a testing set, producing a predicted age for each individual, (4) compute the difference between a participant's predicted age and chronological age (often referred to as Brain Age Gap Estimate, BrainAGE, or brain predicted age difference, brain-PAD), (5) test for relationships between other variables of interest and BrainAGE, and (6) make inferences about accelerated or delayed brain aging (Cole and Franke, 2017). Variables of interest have included physical fitness (Ritchie et al., 2017), physical activity (Steffener et al., 2016), cognitive impairment after traumatic brain injury (Cole et al., 2015), mortality risk in elderly participants (Cole et al., 2018), acute ibuprofen administration in healthy participants (Le et al., 2018), or status of various diseases and disorders such as diabetes (Franke et al., 2013), Alzheimer's disease (Gaser et al., 2013; Löwe et al., 2016), psychiatric disorders (Koutsouleris et al., 2014; Nenadic´ et al., 2017), and human immunodeficiency virus (Wilkins, 2017).

Support Vector Regression (SVR) with a radial kernel is a commonly used machine learning algorithm to predict age and compute BrainAGE (Franke et al., 2010), along with other methods such as Gaussian process and relevant vector regression (Drucker et al., 1997). The residual error of these age-predicting models, BrainAGE, is necessarily correlated with age, which results in an overestimation of the age of younger individuals and an underestimation of the age of older individuals. This is due to the fact that these algorithms, like all regression methods, are subject to the fundamental phenomenon of "regression toward the mean" (Galton, 1886). A theoretical basis for this phenomenon is presented in section Theoretical Basis for the Age-BrainAGE Correlation. In practice, the correlation between chronological age and BrainAGE is visually evident in many figures of chronological vs. predicted age (Franke et al., 2010; Cole et al., 2018). While most studies involving BrainAGE have not discussed the age-BrainAGE correlation, some have accounted for this correlation by using predicted age as the primary outcome, which is similar to the correction we propose (Erus et al., 2015; Habes et al., 2016).

The age-BrainAGE correlation may affect the apparent relationship between BrainAGE and variables of interest when these other variables are also related to age. In the clinical neuroscience domain, for example, we may be interested in covariates including physiological variables such as body composition or psychological measures of mood or testing performance, some of which have clear relationships with age. In this study, we examine the detectability of multiple covariate effects in both real and simulated data. Using real data, we characterized relationships between BrainAGE, age, and other variables of interest. Then, we generated a known "ground truth" with characteristics similar to what we observed in real data. In our simulation model, age has a direct effect on the variables of interest, which may in turn affect simulated imaging features. We include both linear and nonlinear effects at each level.

The goals of the current study are: (1) to highlight the universal correlation between chronological age and BrainAGE in theory and practice and (2) develop a general framework for simulating age-dependent data that can be used to investigate the effect of the age-BrainAGE correlation in subsequent analyses. One of the challenges of determining the best practices for using BrainAGE in statistical modeling is related to the fact that variables of interest may be related to age, but not directly related to accelerated or delayed brain aging. In that case, spurious relationships with BrainAGE may be observed. Our results strongly support the proposal that models including BrainAGE as an independent variable should be adjusted for chronological age as well.

### METHODS

We begin with a theoretical explanation for regression toward the mean and the concurrent correlation between the residuals and observed values for any regression. Then, we show in our own data the relationships between chronological age, BrainAGE, and other covariates of interest as a basis for the parameters in our simulations. Finally, we describe a simulation approach to generate data with a comparable age effect on brain image features and show how the age-BrainAGE correlation can contribute to observed relationships, even when the simulated independent variables do not associate with imaging features. The R scripts for simulation and analysis are publicly available on the GitHub repository https://github.com/ lelaboratoire/BrainAGE-simulation.

### Theoretical Basis for the Age-BrainAGE Correlation

### Regression Toward the Mean

Consider n data points yi , xi , i = 1, . . . , n used to fit a simple linear regression y = α + βx + ε. Least-square estimation leads to

$$
\hat{\beta} = r\_{\infty} \frac{s\_{\mathcal{Y}}}{s\_{\mathcal{X}}}, \qquad \hat{\alpha} = \overline{\mathcal{y}} - \hat{\beta}\overline{\mathcal{x}},
$$

where rxy is the Pearson correlation between x and y, sx and sy are the standard deviation, respectively. Substituting the formulas

$$\frac{\hat{\boldsymbol{\gamma}} - \overline{\boldsymbol{\gamma}}}{s\_{\boldsymbol{\gamma}}} = r\_{\boldsymbol{\gamma}\boldsymbol{\gamma}} \left( \frac{\boldsymbol{x} - \overline{\boldsymbol{x}}}{s\_{\boldsymbol{\gamma}}} \right).$$

In this setting, regression toward the mean refers to the phenomenon that the standardized predicted value of y is closer to its mean than that of x to its mean for any imperfect correlation, −1 < rxy < 1. The weaker the correlation, the greater the extent of regression toward the mean. For perfect correlations ( rxy  = 1), the standardized distance between the predicted value in y to its mean equals that of x to its mean and there is no regression toward the mean. The implication for BrainAGE is that the age of younger individuals tends to be overestimated and the age of older individuals tends to be underestimated.

### Partition of Variance or Analysis of Variance (ANOVA)

In the general setting y = f (X) + ε, where X can be any dimension and f (·) can be any regression model, the variance of y is partitioned into a part that can be explained by X, and a part due to random error: σ 2 y = σ 2 X + σ 2 ε . Then

$$\begin{aligned} \text{Cov}\left(\mathbf{y}, \mathbf{f}\left(\mathbf{X}\right)\right) &= \sigma\_{\mathbf{X}}^2, \text{Cov}\left(\mathbf{y}, \varepsilon\right) = \sigma\_{\varepsilon}^2\\ \text{Corr}\left(\mathbf{y}, \mathbf{f}\left(\mathbf{X}\right)\right) &= \frac{\sigma\_{\mathbf{X}}^2}{\sqrt{\sigma\_{\mathbf{X}}^2 + \sigma\_{\varepsilon}^2}\sqrt{\sigma\_{\mathbf{X}}^2}} = \frac{\sigma\_{\mathbf{X}}}{\sqrt{\sigma\_{\mathbf{X}}^2 + \sigma\_{\varepsilon}^2}},\\ \text{Corr}\left(\mathbf{y}, \varepsilon\right) &= \frac{\sigma\_{\varepsilon}}{\sqrt{\sigma\_{\mathbf{X}}^2 + \sigma\_{\varepsilon}^2}} \end{aligned}$$

For yˆ = ˆ f (X), y = ˆ f (X) + ˆε and

$$\text{Corr}\left(\boldsymbol{\jmath}, \boldsymbol{\hat{f}}\left(\mathbf{X}\right)\right) = \frac{\hat{\sigma}\_{\mathbf{X}}}{\sqrt{\hat{\sigma}\_{\mathbf{X}}^{2} + \hat{\sigma}\_{\varepsilon}^{2}}},\\\text{Corr}\left(\boldsymbol{\jmath}, \boldsymbol{\hat{\varepsilon}}\right) = \frac{\hat{\sigma}\_{\boldsymbol{\varepsilon}}}{\sqrt{\hat{\sigma}\_{\mathbf{X}}^{2} + \hat{\sigma}\_{\varepsilon}^{2}}}$$

where σˆ 2 X = Var ˆ f (X) = Var yˆ and σˆ 2 ε = Var εˆ .

Thus, Corr y, εˆ > 0 unless ˆ f (X) predicts y perfectly with σˆε = 0. The correlation formulas suggest that the correlation between residual and y decreases with the correlation between y and yˆ, i.e., prediction accuracy of ˆ f (X). **Supplementary Figure 1** illustrates this phenomenon using a simple simulation where y was a function of x plus random normal noise. As the noise decreases (and fit increases), the correlation between y and the residuals decreases as well.

In the context of BrainAGE, the goal is to find ˆ f (·) that best predicts chronological age (y) using brain measures as X, and BrainAGE is computed as −ˆε = ˆy − y. Because ˆ f (X) never predicts chronological age perfectly, BrainAGE remains correlated with age. When BrainAGE is used as the response variable in subsequent analyses to make inferences on a covariate Z, it is important to check whether Z is associated with chronological age. If Z is not associated with chronological age, then one may simply evaluate the bivariate association between BrainAGE and Z. On the other hand, if Z is associated with both chronological age and BrainAGE, chronological age may confound the relationship between BrainAGE and Z (Elwood, 1992) and should be taken into account. Confounding effects can be addressed at study design (e.g., randomization and matching) or in statistical analysis [e.g., stratification of the confounder or including the confounder as a covariate (Pourhoseingholi et al., 2012)]. For example, Franke et al. (2010) considered a variable Z that represents two groups (ill vs. healthy) and selected two groups of individuals with similar chronological age (so Z is not associated with chronological age) to compare their BrainAGE. In the current work, we include chronological age as a covariate and evaluate this approach in the context of BrainAGE.

### Empirical Data

We used two separate datasets to illustrate the correlation between BrainAGE and chronological age and the effect this can have on associations with covariates of interest. All data were collected at the Laureate Institute for Brain Research between 2009 and 2017. All protocols were approved by Western Institutional Review Board (www.wirb.com). Participants signed written informed consent and received financial compensation for their participation.

### Training Dataset

Structural MRI data were collected from 475 healthy volunteers (mean age ± sd = 30.5 ± 10.3 years; age range = 18–60 years; 259 female) between 2009 and 2017. Each participant was scanned in a 3T GE MR750 whole body scanner. Scans were acquired using axial T1-weighted MP-RAGE sequences with a 24 cm FOV, 256 × 256 acquisition matrix, 8-degree flip angle and 0.9375 × 0.9375 mm in-plane resolution with no gap. Other parameters varied within the following ranges: 5.736 to 6.292 ms TR, 1.896–2.104 ms TE, 0.9–1.2 mm slice thickness, with either an 8- (General Electric, Milwaukee, WI) or 32- (Nova Medical Inc., Wilmington MA) channel phased array coil. Healthy neuropsychiatric status was assessed using either the MINI-international Neuropsychiatric Interview (Sheehan et al., 1998) or the Structured Clinical Interview for DSM-IV (First et al., 2002).

### Testing Dataset

Structural MRI data were collected from 489 (mean age ± sd = 34.6 ± 10.6 years; age range = 18–56 years; 312 female) participants as part of Tulsa 1000, a longitudinal observational study including people with mood/anxiety, substance use, eating disorders, and healthy controls. Inclusion criteria for the participant populations were Patient Health Questionnaire ≥10, Overall Anxiety Severity and Impairment Scale ≥8, Drug Abuse Screening Test >3, or SCOFF ≥2. Exclusion criteria included a history of significant brain trauma, neurological disorders, change in medication within 6 week prior to scanning, bipolar disorder, and schizophrenia. Scanning parameters for this dataset were: 24 cm FOV, 256 × 256 acquisition matrix, 186 axial slices, 0.9 mm slice thickness with no gap, TR/TE = 5/2.012 ms, using an 8-channel phased array coil (General Electric, Milwaukee, WI). Testing and training sets differed on mean age (t = 6.2, p < 0.0001, mean difference 4.2 years) and sex composition (χ 2 = 8.2, p = 0.004).

All participants in the testing dataset also underwent an intense battery of assessments including self-report, clinical interviews, neuropsychological testing, and body composition analysis. For full details, please see (Victor et al., 2018). From these, we selected 154 measures, which were used to illustrate the normal range of correlations with age and how these can affect the relationship between BrainAGE and covariates of interest.

### Image Processing

All images in both the testing and training sets were processed using Freesurfer version 6.0.0 (Dale et al., 1999) in order to produce gray/non-gray matter masks. Then, using a procedure similar to Franke (Franke et al., 2010) but implemented in AFNI, all gray matter masks were transformed to MNI space via affine transformation, smoothed with an 8 mm gaussian kernel, and downsampled to 8 × 8 × 8 mm voxels. This produced a set of 3,707 voxels per participant, with the value at each voxel representing the fraction of that voxel comprised of gray matter.

R (version 3.2.2) and R package caret (version 6.0.76) were used to fit a support vector regression (SVR) model with radial basis functions. The ε (tolerance margin) was fixed at and cost parameters were tuned using 5 repeats of 10-fold cross validation in the training set. The hyperparameter space was sampled using a grid search that fixed ε at 0.000145 and allowed cost to vary from 0.25 to 4,096. The final best model (cost = 2) was then applied to the testing set to produce one predicted age for each participant. BrainAGE was taken to be predicted age minus chronological age.

Additionally, we define the Brain Age Gap Estimate Residualized (BrainAGER) to be the residual of the regression of BrainAGE on age to remove the remaining linear bias of age. This way, we have a measure of deviation from expected age that is linearly uncorrelated with chronological age.

### Simulation

To investigate the effect of the age-BrainAGE correlation on subsequent modeling results, we simulated hierarchical correlation structures among brain features, chronological age and covariates using a generative biological model (**Figure 1**). We then generated two groups of independent variables. Within each group of variables, some are dependent on age and others are not. One group was used in the simulation of neuroimaging features, while the other was not. We randomly split the data set into two subsets, trained SVR on the training set and computed BrainAGE on the testing set. On the testing set, we conducted linear regressions of BrainAGE on all independent variables, both with and without chronological age. With 1,000 replications, we assessed the significance of the contribution from the independent variables by examining the distribution of the resulting p-values.

### Model Definition

A realistic simulation model should capture the properties of normal age-related brain volumetric data, such as brain region-dependent changes and nonlinear chronological age dependence (Fjell et al., 2013). A realistic simulation should also include the ability to generate age-dependent deviations from the normal population and age-dependent covariates that may influence BrainAGE nonlinearly. We consider a biological causal path model and develop a novel age-basis-function approach for simulating BrainAGE data with covariates (**Figure 1** and **Supplementary Figure 2**).

Denoting age by A, we assumed an underlying (unobserved) biological process represented by m functions of age, denoted as fm (A), which we referred to as age basis functions (ABFs). Here, without a function space defined, the term "basis" is used loosely to indicate the elementary functions that can be combined linearly to form any variable of interest y:

$$\mathbf{y} = \sum\_{\mathbf{m}=1} \mathbf{w}\_{\mathbf{m}} \mathbf{f}\_{\mathbf{m}}(\mathbf{A}) + \epsilon. \tag{1}$$

In this study, we implemented three monotone decreasing ABFs that can generate a wide range of non-linear functions (**Supplementary Figure 3**), and used these ABFs to simulate covariates of interest and the features extracted from an imaging modality.

### **Simulating covariates**

A covariate of interest Zj for participant i with chronological age Ai was generated by

$$Z\_{ij} = \sum\_{m=1}^{3} \alpha\_{m\not\!j} f\_m(A\_i) \; + \; \epsilon\_{ij} \tag{2}$$

where αmj is a covariate-specific weight and the covariate-specific error ǫij∼N 0, σ 2 j denotes a Gaussian noise with mean 0 and standard deviation σj .

### **Simulating imaging modality**

The proportional gray-matter volume for voxel k of a participant i with chronological age Ai was generated by

$$\mathbf{v\_{ik}} = \sum\_{\mathbf{m}=1}^{3} \mathbf{w\_{mik}} \mathbf{f\_{m}}(\mathbf{A\_{i}}) \, \, + \, \epsilon\_{\mathbf{i}} \tag{3}$$

or, in short, vik = f (Ai) + ǫi , where ǫi represents Gaussian noise with mean 0 and standard deviation σν . This setting allows capturing within-participant correlations (4b) and spatial dependence within participants (4c):

$$\text{Var}\left(\mathbf{v}\_{\text{ik}}\right) = \text{Var}\left(\mathbf{f}\left(\mathbf{A}\_{\text{i}}\right)\right) + \sigma\_{\text{v}}^{2} \tag{4a}$$

$$\text{Cov}\left(\mathbf{v}\_{\text{ik}}, \mathbf{v}\_{\text{i'k}}\right) = \text{Cov}\left(\mathbf{f}\left(\mathbf{A}\_{\text{i}}\right), \mathbf{f}\left(\mathbf{A}\_{\text{i'}}\right)\right) + \sigma\_{\text{v}}^2 \tag{4b}$$

$$\text{Cov}\left(\mathbf{v}\_{\text{ik}}, \mathbf{v}\_{\text{ik}'}\right) = \text{Var}\left(\mathbf{f}\left(\mathbf{A}\_{\text{i}}\right)\right) \tag{4c}$$

Note that the weight function wmik (Ai) allows the weights of ABFs to vary across individuals and volumes, and as a function of an individual's chronological age.

To further make the imaging modality dependent on some covariates, we let

$$
\omega\_{mik} = \omega\_{mk} + D\_{\text{i}} \tag{5}
$$

particular individual i, the ABFs are combined to create volume k's gray matter proportion vik (orange, black, and blue arrows) and age-dependent covariates of interest, Zij(A) with a different set of coefficients αj . (B) Some of the Zij are then fed back into the wik when generating volume vik , which leads to two levels of age association between covariate and BrainAGE. (C) Proportional gray matter volume (volumetric data) generated from non-linear combinations of ABFs. (D) Predicted-age and BrainAGE computed from simulated volumetric data and simulated chronological age with Support Vector-based regression; (E) Test for association between BrainAGE and covariates of interest.

where wmk is the population mean weight for ABF fm at voxel k, and the participant level departures Di depends on the first q variables (covariates):

$$\mathbf{D}\_{\mathbf{i}} = \boldsymbol{\chi} \sum\_{\mathbf{j}=1}^{q} \mathbf{Z}\_{\mathbf{i}\mathbf{j}} \mathbf{(A}\_{\mathbf{i}} \mathbf{} \tag{6}$$

Other measurable variables, Zj>q, do not contribute to the weights deviation. In addition to the age-related imaging features that are generated from the ABFs, we also added 25% "background" features that do not correlate with age. Other parameters such as standard deviation of the noise ǫ were chosen with the objective of yielding R 2 and MAE values that closely match our empirical results when the volumetric features were used as inputs to the support vector regression (SVR) model to estimate chronological age. Nevertheless, the choice of parameters and even the simulation design matrix do not affect the overall improvement in the regression that includes age as an explanatory variable from the regression without age.

Finally, we carried out linear regressions of the covariates of interest on BrainAGE, with and without including age as an explanatory variable in the model. Over 100 replications, we assessed the detectability of the covariates as significant contributors to BrainAGE by examining their p-value distributions. In the ideal case, we should detect relationships between BrainAGE and covariates Z ′ j s.

### Simulation Steps

In steps 3 and 4, we simulated 16 covariate types in each of 1,000 replicate data sets (**Supplementary Table 1**). The 16 variables were simulated by using all 8 possible combinations of the three

FIGURE 2 | Similar out-of-sample R 2 when applying SVR to predict age as well as negative correlation between BrainAGE and chronological age between T1000 data and simulated data. (a,b) Chronological age vs. predicted age in the testing dataset, with a mean absolute error (MAE) of 4.78 years and R 2 = 0.65 in (a) and MAE = 5.15, R 2 = 0.841 in (b). Overlaying black 45-degree line and blue regression line showed regression toward the mean. (c,d) Chronological age vs. BrainAGE (r = −0.63). Negative correlation between BrainAGE and chronological age indicates younger participants tend to have positive BrainAGE and old participants tend to have negative BrainAGE. (e,f) After removing the linear trend in (b,c), there is no relationship between age and BrainAGER (r = 0.001). BrainAGER has an expected value of 0, regardless of chronological age.

age basis functions. Half of them contributed to the weights wmik (A), which consequently affected the gray matter density. For example, Z2 and Z10 were both derived from only the linear basis function f1, but Z10 does not influence the aging.

Additionally, the complete simulation procedure was carried out for two scenarios: one with relatively large and another with relatively small effects of the covariates on BrainAGE. This was achieved by modifying the constant γ in Equation (3) so that, in one case, the final weights wmik have a larger fold change on the original weights. In particular, the fold change is computed as

$$FC = \frac{\overline{w}\_{mk}}{\overline{w}\_{mk}} = \frac{w\_{mk} + \overline{D}\_{mk}}{w\_{mk}},\tag{7}$$

where Dmik is the average of Dmik(A) across all ages.

FIGURE 3 | Relationship between age-covariate correlation and the difference in measured correlation. The difference between using BrainAGE and BrainAGER depends on the age-covariate relationship. (A) Covariate-BrainAGER correlations as a function of the covariate-BrainAGE correlation, with points colored according to the Age-Covariate correlation. The 45-degree line is shown, and covariates more strongly related to age are further from the line. (B) The squared difference in r between using BrainAGE and BrainAGER as a function of the variance explained by age.

The last column contains the direct correlation between each covariate and age (rage). For BrainAGE, where age is not considered there are 17 covariates with FDR adjusted p < 0.05 and for BrainAGER, which is residualized on age, there are six covariates with adjusted p < 0.05. Cells with p < 0.05 are bold.

# RESULTS

# Empirical

### Covariate Correlations With Age

Observed Pearson correlations between age and the 154 clinical variables ranged from −0.33 (PROMIS physical function) to 0.29 (waist circumference) (**Supplementary Figure 4**). Because any confounding effect of the correlation between age and covariates of interest is likely to be worse with larger correlations, we focused on simulated covariates that correlated with age with an r of up to 0.3.

### Age Prediction Accuracy and Bias

After fitting on the training dataset, SVR achieved a mean absolute error of 4.84 years and explained 64% of the variance in age in the testing dataset (**Figure 2a**). This is comparable to the cross-validated performance on the training set, where MAE was 5.1 years and R 2 was 0.59. The correlation between age and predicted age was 0.82. On the other hand, regression toward the mean lead to a negative relationship between age and BrainAGE (r = −0.63, **Figure 2c**). After removing the linear trend as shown in **Figure 2c**, we observed no relationship between age and BrainAGER (r = 0.001, **Figure 2e**). More explicitly, BrainAGE had a positive expected value at low chronological age and a negative expected value at high chronological age, while BrainAGER has an expected value of 0 regardless of actual age.

### Relationships Among Age-Covariate, Covariate-BrainAGE, and Covariate-BrainAGER Correlations

In order to investigate the effect that the correlation between BrainAGE and chronological age can have on the conclusions of an imaging study, we computed the correlations between each of the covariates and age, BrainAGE and BrainAGER. Larger age-covariate correlations lead to larger differences in measured correlation between that covariate and BrainAGER or BrainAGE (**Figure 3A**, colored points far from the 45◦ line). When age did not correlate with a covariate, BrainAGE, and BrainAGER tended to give similar results (gray points, near the 45◦ line). When age positively correlated with covariates (e.g., BMI), BrainAGER gave more positive values, and when age negatively correlated with covariates (e.g., PROMIS physical function), BrainAGER yields more negative values. Similarly, the greater the variance explained by age, the greater the squared difference in r between using BrainAGE or BrainAGER (**Figure 3B**).

**Table 1** shows the top 22 variables that are significantly correlated with either BrainAGE or BrainAGER after FDR correction for 154 tests. Notably, 17 variables were related to BrainAGE, and the strongest relationships were among variables strongly correlated with age, including body composition (percent body fat r = −0.2, percent body water r = 0.2, percent dry lean mass r = 0.2) and sensation seeking (r = 0.18). BrainAGER was only significantly correlated with six variables including waist to hip ratio (r = 0.15), color naming scaled (r = −0.15), and lean body mass (r = 0.17).

### Simulation

### Negative Correlation Between BrainAGE and Chronological Age in Simulated MRI Data

We set the parameters of our simulation algorithm to achieve realistic characteristics of experimental data, such as correlation distribution between volumes and chronological age and the negative correlation between computed BrainAGE and chronological age. This negative correlation was also present in previous models such as with Gaussian Process Regression (Cole et al., 2018) and Relevant Vector Regression (Franke et al., 2010). Simulated results closely mirrored empirical results. The simulated testing data had MAE of 4.58 years and R 2 of 0.71 (**Figure 2b**). In our simulated data, we observed an overestimation of younger participant's ages and an underestimation of older participant's ages (**Figure 2d**). After removing the effect of age on BrainAGE, simulated BrainAGER had an expected value of 0 regardless of actual age (**Figure 2f**).

### Reduction of False Discoveries in Regression That Include Age as Explanatory Variable

In the linear models regressing BrainAGE on the 16 covariates of interest with simulated large effect sizes (FC = 1.255), we observed the following: when age was not included as an explanatory variable, many age-related covariates were shown to have statistically significant association with BrainAGE (**Figures 4A,C**), even when they did not contribute to the weights that made up the neuroimaging features (**Figure 4**, **orange** boxplots above the horizontal). These false positives (FP) were simply the result of the relationship between these covariates and chronological age that are part of the BrainAGE's defining formula. Moreover, several covariates that were simulated to contribute to the brain structure volumes had p-values on average above 0.05 (**Figure 4**, **blue** boxplots below the horizontal).

When age was included in the regression as an extra explanatory variable, the significance increased (p-values decreased) for all variables that were generated to have an association with the imaging features, even variables that were already detected in the previous regression without age (**Figures 4B,D**). Further, the decrease in significance (increase in p-values) for unrelated covariates indicated a significant decrease in the number of false positives. Variation in the p-values across covariates came from their different (linear and nonlinear) age dependencies and effects on volumetric variation. In other words, the real "significance" of a covariate depended on from which age basis functions it was generated and how it affected the brain features (w1k , w2k , or w3k ). Simulations with a smaller effect size (FC = 1.170, **Figures 4C,D**) showed a similar effect, though attenuated, for covariates that were contributors to wmk. The positive rate (true and false) across 100 replications is quantified in **Supplementary Table 2**. Values in this table represent the portion of each boxplot above the horizontal line, which is the TP rate for covariates that had an influence on imaging features and FP rates for covariates that did not.

## DISCUSSION

This study aims to highlight the relationship between chronological age and BrainAGE and its transitive effect on the relationship between BrainAGE and covariates of interest that are also related to age. We propose a solution to this problem: either use BrainAGER, or in the simple case of posthoc linear regression, use chronological age as a covariate in subsequent analyses. We developed a simulation framework to generate data with complex, but known, relationships between the original imaging features, age, and a set of covariates that may also be related to age. Then, we were able to quantify the effect that accounting for age has on the ability to detect actual and spurious correlations with covariates in subsequent analyses.

Our main findings can be separated into three parts: analytical, empirical, and simulated data results. The analytical results provide a theoretical basis for the age-BrainAGE correlation, and the analyses using real and simulated data demonstrate

differing age dependencies (linear and nonlinear) and effects on volumetric variation. Blue boxes are variables that have a direct (TRUE) effect on BrainAGE, orange boxes are variables that do not have a direct effect on BrainAGE (FALSE), and this effect is relatively large in the top (A,B) and small in the bottom (C,D) plots. Boxplots on the left (A,C)do not use age as an explanatory variable and models on the right (B,D) include age as an explanatory variable. "Significance" was measured by –log(p). Horizontal line is at –log(0.05).

this effect in practice. For the empirical data, there were three main findings: (1) many variables that may be of interest are correlated with age with Pearson coefficients of up to r = 0.3, (2) BrainAGE is strongly negatively correlated with chronological age (r = −0.63 in our dataset), (3) BrainAGER provides a measure of deviation between predicted and actual age that is not dependent on age, and has substantially different correlations with covariates that are correlated with age when compared to BrainAGE.

Since it is unknown which covariates are actually related to premature aging, we then developed a simulation framework to generate synthetic data. Simulated data showed: (1) similar characteristics to actual data when used to train and test a model on separate datasets, and (2) increased detectability of true positives and decreased occurrence of false positives when accounting for the age-covariate relationship, with this being modulated by the size of the simulated effect on physiology.

Based on our observations in both real and simulated data, we recommend that the relationship between chronological age and BrainAGE should be accounted for. The two methods proposed in this study are either: (1) regress age on BrainAGE, producing BrainAGER, which is centered on 0 regardless of a participant's actual age or (2) include age as a regressor when doing follow-up analyses. In fact, these two methods will produce the same coefficients in the case of linear regression, with slightly larger t-statistics in the second case. The advantage of using BrainAGER is simplicity and generalizability; it could be used as the dependent variable in any arbitrary model, rather than being confined to simple linear regression. While the focus of this study is not to show specific correlates of premature aging, it is worth noting that 17 variables significantly correlated to BrainAGE whereas only 6 were related to BrainAGER, with 1 variable (PROMIS Alcohol Negative Consequences) overlapping between the two sets (**Table 1**). Thus, accounting for the age-BrainAGE relationship results in a vastly different set of positive findings and would lead to a remarkably different interpretation of these data. More explicitly, not correcting the age-BrainAGE correlation would lead to an extensive set of spurious results in this dataset.

# Limitations

There are a few cases where the age-BrainAGE correlation is not relevant. When comparing two groups with matched age, any differences in BrainAGE are not likely to be caused by the relationship with age. When the individuals being examined are in a restricted age range, there is not likely to be much contribution from the age-BrainAGE correlation. Also, when the variable of interest is not related to age, removing the effect of age makes almost no difference (**Figure 3B**). However, when these cases are not true, our findings suggest that we should include age as an explanatory variable in a final model that aims to detect association of brain anomalies with covariates of interest.

The magnitude of the age-BrainAGE correlation is directly related to the accuracy of the prediction model. The fact that the residuals are correlated with observed values is a characteristic of regression in general, regardless of the specific data domain, and has a theoretical basis described in section Theoretical Basis for the Age-BrainAGE Correlation. Several factors may decrease the model performance on our testing set, and thereby increase the age-BrainAGE correlation. Specifically, the distribution of age ranges in our samples is non-uniform, which may lead to more weight being given to the middle of the distribution. There are substantial differences between the testing and training sets we used including age, sex, and diagnosis. It may therefore be possible to improve model performance on the testing set by subsampling the training set to have a more uniform distribution of ages and to match the testing set on several factors. However, model performance is already comparable across testing and training sets (R 2 of 0.59 and MAE of 5.1 years, compared to 0.64 and 4.84) and is comparable with what has been previously reported.

Although the simulation was carefully designed and executed, because of the model's complexity, we have not fully explored all scenarios with different simulation parameters. However, we have identified effect size as the most important parameter and showed how it influenced the results. When varying other parameters, we still observed a reduction in the number of false positives when age is included as an explanatory variable in the final regression (results not shown). Moreover, while determining the parameters, we aimed to obtain realistic patterns as we observed in real data, such as similar distributions of the correlation values.

By constructing and studying an appropriate generative model containing covariates that have linear and non-linear relationship with age, we demonstrated that the correlation between covariates and age should be considered when making inferences about the relationship between BrainAGE and these covariates.

# ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Belmont report and the U.S. Department of Health and Human Services PART 46: PROTECTION OF HUMAN SUBJECTS. The protocol was approved by the Western IRB. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

# AUTHOR CONTRIBUTIONS

TL, RK, BM, H-WY, WT, and MP contributed to the design of the study. TL and RK wrote the manuscript. TL performed simulation analyses and RK performed empirical analyses. The T-1000 investigators contributed to the conception of the study, selection of the assessments and discussion regarding the interpretation of the results. All authors revised the manuscript critically for important intellectual content and approved the final manuscript.

# FUNDING

This work has been supported in part by The William K. Warren Foundation, the National Institute of Mental Health Award Numbers K23MH112949 (SSK), K23MH108707 (RLA), K01MH096175-01 (WKS), and the National Institute of General Medical Sciences Center Grant Award Number 1P20GM121312. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

# REFERENCES

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2018.00317/full#supplementary-material

placebo-controlled, dose-response exploratory study. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 3, 836–843. doi: 10.1016/j.bpsc.2018.05.002

**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 © 2018 Le, Kuplicki, McKinney, Yeh, Thompson, Paulus and Tulsa 1000 Investigators. 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) and the copyright owner(s) 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.

# Pre-aging of the Olfactory Bulb in Major Depression With High Comorbidity of Mental Disorders

Fabian Rottstaedt 1 \*, Kerstin Weidner 1 , Thomas Hummel 2 and Ilona Croy 1

1Department of Psychosomatic Medicine and Psychotherapy, Technische Universität Dresden, Dresden, Germany, 2Smell & Taste Clinic, Department of Otorhinolaryngology, Technische Universität Dresden, Dresden, Germany

Recent studies suggest that accelerated aging of the brain is a neuroanatomical signature of the state of mental diseases. In major depression, this pre-aging effect is negatively associated with the duration since the first onset of the disease. The olfactory bulb (OB) shrinks with age in healthy subjects and patients with mental diseases show reduced OB volumes, especially those with major depression. It is unclear whether this OB reduction in mental diseases resembles a pre-aging process and whether it is associated to the duration since the onset of the mental disease. To this aim, we investigated OB volume in 73 patients (mean-age 40.4 years, SD = 12.1 years, 57 women) with major depression and mixed comorbid mental diseases (diagnoses ranged from 1 to 6, median: 3) and 51 healthy controls (mean-age 39.2 years, SD = 13.0 years, 26 women) matched for age and sex. Patient's first onset of disease ranged from 15 to 53 years (mean 24.2 years). All participants underwent structural MR imaging with a spin-echo T2-wheighted sequence covering the anterior and middle segments of the skull base. All results were corrected for total intracranial volume (TIV) and sex. Individual OB volume was calculated by planimetric manual contouring and the pronounced diameter change in transition from bulb to tract was used as the distal demarcation of the OB. Inter-rater correlation between two independent persons analyzing the data was high (IRC = 0.81, p < 0.005). An age-dependent decline of the OB volume was confirmed in healthy controls (r = −0.34, p < 0.05). However, this pattern was altered in patients where the OB volume was not related to age, but to the duration since the onset of the mental disease (r = −0.25, p < 0.05). This association remained stable when controlling for age. Additionally, analyses of age sub-groups revealed that the association between duration since the onset of the mental disease and OB volume was mainly driven by the group aged 50 years and above (r = −0.68; p < 0.01). We conclude that there are time windows where the OB volume is susceptible for the effects of a mental disease, e.g., depression. These effects result in cumulative pre-aging in the OB in older patients with mental diseases.

Keywords: depression, olfactory bulb, MRI, biological markers, aging, premature

### Edited by:

Christian Gaser, Friedrich-Schiller-Universität Jena, Germany

### Reviewed by:

Martin Witt, Universitätsmedizin Rostock, Germany Shengying Qin, Shanghai Jiao Tong University, China

### \*Correspondence:

Fabian Rottstaedt<EMAIL_ADDRESS>
Received: 12 March 2018 Accepted: 17 October 2018 Published: 08 November 2018

### Citation:

Rottstaedt F, Weidner K, Hummel T and Croy I (2018) Pre-aging of the Olfactory Bulb in Major Depression With High Comorbidity of Mental Disorders. Front. Aging Neurosci. 10:354. doi: 10.3389/fnagi.2018.00354

# INTRODUCTION

Throughout human life, the brain's neural architecture undergoes a steady transformation. Whereas the early years from birth to adolescence are determined by the interplay of neural growth and differentiation (Shaw et al., 2008), adulthood is mostly characterized by depletion (Fjell et al., 2013). Especially after midlife, brain weight (Skullerud, 1985) and whole brain volume decrease (Scahill et al., 2003; Hedman et al., 2012) and equally the volume of most brain structures shrinks with increasing age (Allen et al., 2005; Raz and Rodrigue, 2006). Accordingly, the olfactory bulb (OB) shows its peak volume around the age of 40 years from where it linearly decreases with age (Buschhüter et al., 2008). In line with this, olfactory functioning shows the same trajectory, peaking around the age of 40 years decreasing thereafter (Buschhüter et al., 2008). Most investigation of the aging brain are based on the frontal cortex and hippocampus (Fjell et al., 2014), showing that over the life span both regions are particularly vulnerable to age and undergo comparable decline in GM volume (Fjell et al., 2014).

Depressed patients show alterations that are similar to the described processes of aging. Those affect particularly brain networks involving limbic and prefrontal regions. Similar to aging, the typical GM reduction patterns in depression especially concern the hippocampus and prefrontal cortex (Bora et al., 2012; Sacher et al., 2012; Singh et al., 2013). Interestingly, depression is also related to premature reduction of OB volume (Negoias et al., 2010; Croy et al., 2013) which was suggested as biological marker for the disease (Kohli et al., 2016; Croy and Hummel, 2017; Rottstaedt et al., 2018). We hence aimed to investigate the association of OB volume decline with age in healthy controls and depressed patients. We assumed that the age-related decline of OB volume in healthy participants is shifted to younger age in patients with depression.

# MATERIALS AND METHODS

### Participants

This study was embedded in a larger design (for complete Methods and Materials information please compare; Rottstaedt et al., 2018). Of the patient cohort investigated there, only patients with diagnosed Major Depression (n = 73, all inpatients of the Department of Psychosomatic and Psychotherapy of the Dresden University Hospital) were included in this investigation. Structured anamnestic interviews (German version of the SCID-I; Wittchen, 1997) performed by trained psychotherapists had previously been completed. The patient group included 57 females and 16 males, aged between 19 and 62 (M ± SD = 40.4 ± 12.1) years (compare **Table 1** for further demographic and illness-related parameters). Diagnoses included unipolar depression or recurrent depressive disorder (N = 73), anxiety disorders (N = 47) somatoform disorders (N = 20), posttraumatic stress disorder (N = 37), substance abuse (N = 9) and eating disorders (N = 18; compare supplementary information for individual data) and hence the median of the number of overall diagnosed mental disorders was 3 (range from 1 to 6 diagnoses; compare **Supplementary Table S1** for an overview of all patients and their diagnoses). The majority of patients received medical treatment (compare **Supplementary Table S1**). Fifty-one age and sex matched healthy controls (26 females and 25 males; 20–69 years; M ± SD = 39.16 ± 13.0 years) were recruited who were required not to meet criteria of a mental disorder (confirmed by the Patient Health Questionnaire; Spitzer et al., 1999).

The groups did not differ in terms of age (t(124) = 0.68, p = 0.50). Sex distribution appeared not to be equal between groups (X 2 (1,124) = 10.8, p < 0.01). Hence all statistical analyses were controlled for sex differences.

# Ethics Statement

The study followed the Declaration of Helsinki on Biomedical Research Involving Human Subjects and was approved by the local Ethics Committee (EK 51022015). All participants provided written informed consent.

### Magnetic Resonance Imaging Procedures

MRI scans were performed using an 8-channel phased-array head coil (3T Siemens Magnetom Verio scanner; Siemens Healthcare, Erlangen, Germany).

In order to obtain OB volume measures, a fast spin-echo T2-wheighted sequence covering the anterior and middle segments of the skull base was acquired (TR = 8,090 ms; TE = 97 ms; voxel size 2 × 2 × 2 mm3 ; flip angle 123◦ , in total 36 contiguous slices of 2 mm thickness, coronal orientation with no gap).

# Statistical Analysis

Data was analyzed using SPSS 21 for Windows (SPSS Inc., Chicago, IL, USA). AMIRA 3D visualization and modeling system (Visage Imaging, Carlsbad, CA, USA) was used to calculate OB volumes.

### Calculation of OB Volumes

Based on manual segmentation of the acquired T2-weighted coronal data, all OB volumes were calculated by the same experimenter (FR) blinded to the diagnosis of the participant. The pronounced diameter change in transition from bulb to tract was used as the proximal demarcation of the OB. On each coronal slice, right and left OB's shape was outlined manually and OB volumes were calculated by planimetric manual contouring (surface in mm2 ). All surfaces were then added and multiplied by 2 (2-mm slice thickness) to obtain an estimated overall volume (compare **Figure 1**). The volume of the left and right OB was calculated for each participant. The larger of the two volumes was then used for all further analyses. Hence the term OB volume refers to ''best OB volume.'' This approach of calculating and analyzing OB volumes has previously shown to be highly reliable

### TABLE 1 | Descriptive characteristics of the patient and control group.

Age is shown in years, volumes of OB and Total Intracranial Volume are shown in mm3 , n, number; %, percentage; OB, olfactory bulb; OBleft, volume of left OB, OBright, volume of right OB, OBbest, greater volume of left or right OB, BDI, score in Beck's Depression Inventory; ∗∗p < 0.01, ∗∗∗p < 0.001.

and accurate (Yousem et al., 1997; Hummel et al., 2015). Interrater correlation between two independent persons analyzing the OB volume was high (IRC = 0.81, p < 0.005).

### Correlation Analysis

Pearson-correlation coefficients were computed in healthy controls and depressed patients separately to assess associations between age and OB volume. In depressed patients Pearsoncorrelation coefficients for the association between OB volume and the duration since the first onset of mental disease were computed and additionally controlled for age. The first onset of mental disease was taken from the documentations of the structured anamnestic interviews (German version of the SCID-I; Wittchen, 1997) where a detailed inquiry of episodes of mental disease is essential.

Furthermore, we divided the group of depressed patients into three different age groups of young (18–34 years), middle-aged (35–49 years) and old (above 50 years) individuals. For every group we computed Pearson-correlation coefficients for the association between OB volume and the duration since the first onset of the mental disease separately.

All analyses were controlled for six medical conditions (medication in general, Antidepressants, Neuroleptics, Antiepileptics, Soporifics/Tranquilizer, other drugs) by

introducing them sequentially as confounding variables in a partial correlation design.

### Between Group Analysis

OB volumes were compared between patients and controls using ANCOVA with sex and total intracranial volume (TIV) as covariate. Sex was chosen as covariate because women exhibit a smaller OB volume than men (Buschhüter et al., 2008) and sex distribution was not equal between groups; TIV was chosen as covariate as it was positively associated with OB volume (r = 0.24; p < 0.01).

All analyses were controlled for six medical conditions (medication in general, Antidepressants, Neuroleptics, Antiepileptics, Soporifics/Tranquilizer, other drugs) by introducing them sequentially as confounding variables in a partial correlation design.

### RESULTS

In healthy controls, OB volume was negatively associated with age (r = −0.34, p < 0.05; compare **Figure 2A**) which was not the case in depressed patients (r = −0.10; p = 0.39). However, when controlling for age OB volume was related to the duration since the first onset of mental disease (r = −0.25, p < 0.05; **Figure 2B**). In the group of patients, the oldest individuals (aged 50 years and above) showed the strongest association between the duration since the first onset of the mental disease and OB volume (r = −0.68; p < 0.01) when controlling for age, whereas for the youngest (aged between 18 years and 34 years; r = 0.26; p = 0.18) and middle-aged (aged between 34 years and 50 years; r = −0.25; p = 0.33) patients the associations did not reach significance.  Inclusion of medication did not change the results.

Depressed patients exhibited smaller OB volumes than healthy controls (F(2,124) = 6.0; p < 0.05; for further comparisons of the two groups please compare **Table 1**). Inclusion of medication did not change the results.

### DISCUSSION

An age-dependent decline of the OB volume had been shown in healthy people (Yousem et al., 1998; Buschhüter et al., 2008). However, this pattern was altered in patients where OB volume was not related to age, but to the duration since the first onset of the mental disease.

In detail, the OB volume showed accelerated decrease with the duration of the mental disease in patients of the oldest age-group in the sample. Notably, the data suggests that this acceleration was evident after about 5 years of duration of the mental disease. No such association was found for the middle-aged subgroup. The youngest patient sub-group showed highly reduced OB volumes compared to the respective healthy control group. However, this group still showed a trend towards an increase of the OB volume over time which can also be seen in healthy controls at this age in other studies (Yousem et al., 1998; Buschhüter et al., 2008). We conclude that there are time windows where the OB volume is susceptible for the effects of depression, namely the young and the old age. This is in line with the observation of developmental time windows of the human brain (Lupien et al., 2009) which implies that certain brain areas show increased vulnerability during specific development stages.

Two mechanisms are possible here: (a) reduced OB volume could be a pre-existing factor of the mental disease and hence indicate increased vulnerability for a mental disease (as already formulated by Croy and Hummel, 2017); and (b) The OB volume reduction could be the consequence of the mental disease. In line with the vulnerability hypothesis, the data shows that patients of the youngest age group exhibit reduced OB volumes compared to healthy controls. On the other hand, the very same result can also be interpreted as a consequence of reduced OB volume growth which happens during this time window in healthy individuals and seems diminished in patients with depression. Furthermore, the results of the group of older patients are in favor of the consequence hypothesis: in healthy aging, the OB volume starts to decrease at the age of about 40 years (Buschhüter et al., 2008). The manifestation of a mental disease increases those normal aging effects. Also known as the neurotoxicity hypotheses, this theory suggests that a long-term increase of individual stress levels leads to prolonged exposure to glucocorticoids which reduces the ability of neurons to resist insults, increasing the rate at which they are damaged by other toxic challenges or ordinary attrition (Lupien et al., 2009). In patients aged 35–49 years however, the absence of a relation between depression and OB volume could be interpreted as a higher resilience to damage of the OB during middle-age.

Gray matter volume reductions throughout the brain are well-known in depression and affect particularly brain networks involving limbic and prefrontal regions (Bora et al., 2012; Sacher et al., 2012; Singh et al., 2013). Recently, the OB volume reduction was suggested as an additional biomarker for the disease (Croy and Hummel, 2017; Rottstaedt et al., 2018). As depression is connected with increased stress levels (Liu and Alloy, 2010), risk for hypertension (Grippo and Johnson, 2009), diabetes (Talbot and Nouwen, 2000; Ali et al., 2006) and reduced physical activity (Camacho et al., 1991; Teychenne et al., 2008)—conditions known to drive brain aging processes (Raz and Rodrigue, 2006; Lupien et al., 2009; Fjell et al., 2013)—the neural alterations usually reported in depression could also be seen as manifestations of accelerated brain aging. Supporting this assumption, it could be shown that especially early-onset depression is associated with accelerated brain aging (Koutsouleris et al., 2013) and that depression duration rather than age predicts hippocampal volume loss (Sheline et al., 1999). Our findings point in the same direction and we assume that the manifestation of depression provokes cumulative pre-aging in the OB, especially in older subjects.

To sufficiently explore which of the provided theories best explains the association between OB volume reduction and depression, long-term studies are necessary. It is possible that both mechanisms—preterm aging as a consequence of

depression and increased vulnerability—could work together in a vicious circle and hence are not mutually exclusive (Lupien et al., 2009; Croy and Hummel, 2017): exposure to stress during developmental periods of certain brain regions might alter their development and lead to increased vulnerability to mental disorders (de Kloet et al., 2005; Cohen et al., 2006), e.g., depression. On the other hand long-term exposure to stress during periods of mental disease could also affect brain organization (Frodl et al., 2008). For the OB that would mean cumulative volume reduction that accelerates the decline seen in older individuals.

### LIMITATIONS

There are two major limitations when interpreting the results: (1) the presented data is cross-sectional. To sufficiently explore aging trajectories, longitudinal designs are necessary; and (2) The group sizes of the investigated age-sub-groups are rather small.

# REFERENCES

Hence, the interpretations of the results concerning those sub-groups should be treated with care. Investigations in larger cohorts are necessary to finally confirm or disprove the divided interpretations.

### AUTHOR CONTRIBUTIONS

FR: data acquisition, data analysis and interpretation, drafting the article. KW and TH: substantial contribution to design of the study, critical revision of article. IC: design of the study, data interpretation, revision of article for important intellectual content.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2018.00354/full#supplementary-material

effects of stress? Arch. Gen. Psychiatry 65, 1156–1165. doi: 10.1001/archpsyc.65. 10.1156

**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 © 2018 Rottstaedt, Weidner, Hummel and Croy. 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) and the copyright owner(s) 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.

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**2.1.1 Gnotobiotic piglets - 1st experiment**

The experiment was carried out in 4 gnotobiotic units, each consisting of reserve, waste and rearing isolator (Velaz s.r.o., Prague, Czech Republic). All experimental materials, including milk substitute, distilled water, saline solution and glass and metal materials were sterilised by autoclaving at 121oC and pressure 1.3 MPa for 30 minutes and cellulose wadding and other sanitary material was gamma-irradiated (Bioster, Veverská Bitýška, Czech Republic). The isolators were sterilized with a 2% solution of peracetic acid (36%, Merci s.r.o., Brno, Czech Republic), sealed for 24 hrs, and vented for a minimum of 72 hrs prior to placing pigs inside. Isolators were maintained under positive pressure, the filtering unit consisting of a fan with preliminary EU 3 filter and two-stage filtering chamber (Velaz s.r.o., Prague, Czech Republic). The first stage of filtration consisted of a frame filter type KS-W, filtration class F 7, the second stage used a KS MIKRO S filter, filtration class H 13, for removing of microparticles. The vented air passed through a frame filter KS W/48, filtration class F 5. The filtration unit assured a minimum of 10 exchanges of air per hour at overpressure of 50- 70 kPa and air flow 8-30 m3.

The experiment was carried out on 18 gnotobiotic piglets of Slovak white × Landrace breed. Gnotobiotic sucklings were obtained using the method of open hysterotomy on day 112 of pregnancy. After opening the abdominal cavity and uterus the piglets were immediately transferred through a disinfectant bath containing 2% Incidur® (Ecolab GmbH & Co. OHG, Düsseldorf, Germany) into a hysterectomy box were they were subjected to preliminary treatment and then were placed into 1 of 4 gnotobotic rearing isolators. The floor of isolators was heated by electric underfloor heating system to ensure floor temperature of 34oC for new born piglets and 30oC for 7-14 days old piglets.  The milk substitute was fed to piglets individually from a glass bottle six times daily (2, 6, 10, 14, 18, 22 h), *ad libitum.* A total of 18 gnotobiotic animals derived from 2 litters were divided into 4 isolators. From the first day of life, a probiotic strain of *Enterococcus faecium* isolated from non autoclaved milk substitute (Sanolac Ferkel, Germany) was administered continuously at a dose of 2 ml of inoculum;

Differences in the Development of the Small Intestine

**2.2.1 Microbiological analysis**

forming units (cfu)g-1 of sample.

**2.2.2 Biochemical analysis**

Czech Republic).

Between Gnotobiotic and Conventionally Bred Piglets 379

of conventional experiments, three piglets from each group were sacrificed at 3 hours post partum and at 2, 7, 14, 21, and 28 days of age. In the second experiment the piglets were slaughtered also on days 35 and 42 of age. The gastrointestinal tract was immediately removed and divided into six segments as follows: stomach, three equal segments of the small intestine, caecum and colon. The total content of each segment was weighed, pH was immediately measured. Intestinal tissue (1 cm2) were taken from the duodenum (5 cm distal to the orifice of the pancreatic duct) and the medial part of both the jejunum and ileum. The samples were fixed in 4% formalin solution for microscopic assessment of mucosal morphology. Sections of jejunum, ileum and caecum (1 g) were collected and processed for microbial counting and short-chain fatty acids (SCFAs) determinations were carried out in the contents from jejunum, ileum and colon. The intestinal segments (duodenum, jejunum and ileum) were rinsed thoroughly with ice-cold saline solution, opened lengthwise and blotted dry. The mucosa was scraped using a glass slide and immediately frozen in liquid nitrogen. Samples of mucosa

For microbial analysis, about 1 g of samples (jejunum, ileum, caecum) was placed in a sterile polyethylene stomacher Lab Blender bag (Seward Medical Limited, London, UK) with 9 ml of sterile anaerobic diluent (0.4 g Na HCO3, 0.05g L-cysteine HCl, 1 ml resazurine 0.1%), 7.5 ml mineral solution I (0.6% K2HPO4), 7.5 ml mineral solution II (1.2% NaCl, 1.2% (NH4)2SO4, 0.6% KH2PO4, 0.12% CaCl2, 0.25% MgSO4 and 84 ml distilled water, pH 6.8) and stomached (Stomacher Lab Blender 80, Seward Medical Limited, London, UK) for 5 min under a CO2 atmosphere. A series of 10-fold dilutions (10-2 to 10-8) were made in the same diluents. From appropriate dilutions, 0.1 ml aliquots were spread onto one non-selective agar plate: trypticase soy blood agar with 10% sheep blood (BBL, Microbiology systems, Cockeysville, USA) for aerobes. Aliquots (0.1 ml) were also spread on 5 selective agar media as follows: Beerens medium (Beerens, 1990) for *Bifidobacterium,* Rogosa agar (Imuna, Šarišské Michaľany, Slovak Republic) for *Lactobacillus,* Enterococcosel agar (BBL) for *Enterococcus*, MacConkey agar (Imuna) for *Coliforms* and Endo agar (Imuna) for *Enterobacteriaceae.* Plates for the enumeration of *aerobic* bacteria were incubated for 2 days at 37oC. Colonies were counted and bacteria were Gram stained and visualized under a microscope for morphological characterization. The viable counts are expressed as the log 10 of colony

After the collection, 1 g of digesta (jejunum, ileum, colon) was diluted in 50 ml of deionized H2O and applied at a volume of 30 µl for analysis of SCFAs. The concentration of formic, acetoacetic, lactic, succinic, acetic, propionic, butyric and valeric acids in the intestinal content was determined by capillary isotachophoresis (ITP). The measurements were done on an "Isotachophoretic analyser ZKI 01" (SR). In the pre-separation capillary, a leading electrolyte of the following composition was used: 10-2 M HCl + 2.2. 10-2 M ε-aminocaproic acid + 0.1% methylhydroxyethylcellulosic acid, pH 4.3. As finishing electrolyte, a solution of 5.10-3 M caproic acid + histidine was used. This electrolytic system worked at 250µA in preseparation and 50 µA in the analytic capillary. pH was measured by a pH meter (LP Prague,

were then stored at -70oC until the analysis of digestive enzyme activities.

1 ml contained 1 × 104 cfu (data analysed in the Laboratory of Gnotobiology). From the 5th day of life, autoclaved water was available to piglets *ad libitum* and they were fed irradiation-sterilized rations intended for early weaning of piglets. At the age of 28 days, the suckling piglets were weaned and fed irradiation-sterilized starter feedstuff *ad libitum* (OŠ-02®, Tajba Čaňa, Slovak Republic, in 1 kg dry matter: crude protein 180 g, fibre 45 g, lysine 11.5 g, methionine and cysteine 6.3 g, threonine 7.5 g, Ca 7 g, P 5.8 g, Na 1.5 g, Cu 10 mg, Zn 100 mg, Mn 30 mg, ME 13 MJ, vitamin A 8 000 IU, vitamin D3 1 000 IU, vitamin E 20 mg, Fe 125 mg, vitamin B2 3 mg, vitamin B12 20 μg, choline 600 mg). A routine microbiological control of gnotobiotic isolators was performed throughout the experiment. Microbiological swabs were taken from isolator walls, surface of animals and from their rectum. The samples were cultivated in PYG medium (Imuna, Slovak Republic). The microbiological control was verified every day on TSA agar with 5% ram's blood (BBL, Microbiology systems, Cockeysville, USA).

#### **2.1.2 Conventional suckled piglets - 2nd experiment**

In the experiment, 24 piglets of both sexes (Large white breed x Landrace) from two litters were included. The pigs were housed in two pens, 12 piglets in each, equipped with automatic heating, forced ventilation and completely slatted floors. The suckling piglets had access to sows 6 times daily (2, 6, 10, 14, 18, 22 h.) and from day 5 onwards the animals were provided commercial mixed feed OŠ-01® (Tajba Čaňa, Slovak Republic, in 1 kg dry matter: crude protein 200 g, fibre 40 g, lysine 14 g, methionine and cysteine 6.3 g, threonine 9.1 g, Ca 8 g, P 6.7 g, Na 2 g, Cu 10 mg, Zn 100 mg, Mn 30 mg, ME 13.3 MJ, vitamin A 8 000 IU, vitamin D3 1 000 IU, vitamin E 20 mg, Fe 125 mg, vitamin B2 3 mg, vitamin B12 20 μg, choline 300 mg) *ad libitum*. The piglets were weaned at 28 days of age, fed starter feedstuff *ad libitum* (OŠ-02®, Tajba Čaňa, Slovak Republic) and moved to 2 pens (375 x 165 cm) where three piglets were housed per pen. The temperature in the nursery was maintained at 32oC during the first week, and was gradually reduced to 25oC between weeks two and six. The animals had free access to water throughout the experiment (42 days).

#### **2.1.3 Conventional replacer-fed piglets - 3rd experiment**

The experiment included 26 piglets of both sexes (Large white breed x Landrace) from two litters. The experiment was carried out in two blocks, 13 piglets in each. The piglets were separated from the sow immediately after birth and had no contact with sow faeces. They were born naturally, were non-colostral, and were fed a commercial milk replacer diluted with distilled water 1:5 (Sanolac Ferkel, Germany), enriched by *Enterococcus faecium* 0.1 × 104 cfu/g of feed*.* Milk was given to piglets individually from a glass bottle 6 times daily (2, 6, 10, 14, 18, 22 h.), *ad libitum.* Starting from day 5, the suckling piglets were offered the same commercial mixed feed OŠ-01® (Tajba Čaňa, Slovak Republic) and were housed under the same hygiene conditions as those in the second experiment.

#### **2.2 Experimental procedure**

All pigs were sacrificed by intracardial euthanasia with 1 ml/kg BW T61® (Intervet International B.V. Boxmeer, The Netherlands). In the first experiment, three hours after birth and at the age of two and seven days, two piglets of each indicated age were sacrificed. Three piglets of each indicated age were sacrificed at the age of 14, 21, 28 and 35 days. In the course

1 ml contained 1 × 104 cfu (data analysed in the Laboratory of Gnotobiology). From the 5th day of life, autoclaved water was available to piglets *ad libitum* and they were fed irradiation-sterilized rations intended for early weaning of piglets. At the age of 28 days, the suckling piglets were weaned and fed irradiation-sterilized starter feedstuff *ad libitum* (OŠ-02®, Tajba Čaňa, Slovak Republic, in 1 kg dry matter: crude protein 180 g, fibre 45 g, lysine 11.5 g, methionine and cysteine 6.3 g, threonine 7.5 g, Ca 7 g, P 5.8 g, Na 1.5 g, Cu 10 mg, Zn 100 mg, Mn 30 mg, ME 13 MJ, vitamin A 8 000 IU, vitamin D3 1 000 IU, vitamin E 20 mg, Fe 125 mg, vitamin B2 3 mg, vitamin B12 20 μg, choline 600 mg). A routine microbiological control of gnotobiotic isolators was performed throughout the experiment. Microbiological swabs were taken from isolator walls, surface of animals and from their rectum. The samples were cultivated in PYG medium (Imuna, Slovak Republic). The microbiological control was verified every day on TSA agar with 5% ram's blood (BBL, Microbiology

In the experiment, 24 piglets of both sexes (Large white breed x Landrace) from two litters were included. The pigs were housed in two pens, 12 piglets in each, equipped with automatic heating, forced ventilation and completely slatted floors. The suckling piglets had access to sows 6 times daily (2, 6, 10, 14, 18, 22 h.) and from day 5 onwards the animals were provided commercial mixed feed OŠ-01® (Tajba Čaňa, Slovak Republic, in 1 kg dry matter: crude protein 200 g, fibre 40 g, lysine 14 g, methionine and cysteine 6.3 g, threonine 9.1 g, Ca 8 g, P 6.7 g, Na 2 g, Cu 10 mg, Zn 100 mg, Mn 30 mg, ME 13.3 MJ, vitamin A 8 000 IU, vitamin D3 1 000 IU, vitamin E 20 mg, Fe 125 mg, vitamin B2 3 mg, vitamin B12 20 μg, choline 300 mg) *ad libitum*. The piglets were weaned at 28 days of age, fed starter feedstuff *ad libitum* (OŠ-02®, Tajba Čaňa, Slovak Republic) and moved to 2 pens (375 x 165 cm) where three piglets were housed per pen. The temperature in the nursery was maintained at 32oC during the first week, and was gradually reduced to 25oC between weeks two and six. The animals

The experiment included 26 piglets of both sexes (Large white breed x Landrace) from two litters. The experiment was carried out in two blocks, 13 piglets in each. The piglets were separated from the sow immediately after birth and had no contact with sow faeces. They were born naturally, were non-colostral, and were fed a commercial milk replacer diluted with distilled water 1:5 (Sanolac Ferkel, Germany), enriched by *Enterococcus faecium* 0.1 × 104 cfu/g of feed*.* Milk was given to piglets individually from a glass bottle 6 times daily (2, 6, 10, 14, 18, 22 h.), *ad libitum.* Starting from day 5, the suckling piglets were offered the same commercial mixed feed OŠ-01® (Tajba Čaňa, Slovak Republic) and were housed under the

All pigs were sacrificed by intracardial euthanasia with 1 ml/kg BW T61® (Intervet International B.V. Boxmeer, The Netherlands). In the first experiment, three hours after birth and at the age of two and seven days, two piglets of each indicated age were sacrificed. Three piglets of each indicated age were sacrificed at the age of 14, 21, 28 and 35 days. In the course

systems, Cockeysville, USA).

**2.1.2 Conventional suckled piglets - 2nd experiment**

had free access to water throughout the experiment (42 days).

**2.1.3 Conventional replacer-fed piglets - 3rd experiment**

same hygiene conditions as those in the second experiment.

**2.2 Experimental procedure**

of conventional experiments, three piglets from each group were sacrificed at 3 hours post partum and at 2, 7, 14, 21, and 28 days of age. In the second experiment the piglets were slaughtered also on days 35 and 42 of age. The gastrointestinal tract was immediately removed and divided into six segments as follows: stomach, three equal segments of the small intestine, caecum and colon. The total content of each segment was weighed, pH was immediately measured. Intestinal tissue (1 cm2) were taken from the duodenum (5 cm distal to the orifice of the pancreatic duct) and the medial part of both the jejunum and ileum. The samples were fixed in 4% formalin solution for microscopic assessment of mucosal morphology. Sections of jejunum, ileum and caecum (1 g) were collected and processed for microbial counting and short-chain fatty acids (SCFAs) determinations were carried out in the contents from jejunum, ileum and colon. The intestinal segments (duodenum, jejunum and ileum) were rinsed thoroughly with ice-cold saline solution, opened lengthwise and blotted dry. The mucosa was scraped using a glass slide and immediately frozen in liquid nitrogen. Samples of mucosa were then stored at -70oC until the analysis of digestive enzyme activities.


*2.1.3 Influence of failure on system function (SF)*

The SF index mainly considers the impact of object failure on the entire production system or the function of mechanical equipment. In order to ensure continuous production operations, the important mechanical equipment in petrochemical companies generally has certain spare. During the production operation, once such mechanical equipment failure, you can immediately switch the standby equipment and quickly resume operations, which can effectively control downtime and avoid large economic losses. Therefore, the degree of impact of object failures with spare parts on system functions will be reduced accordingly. Therefore, in evaluating the object failures on the system, it needs to be divided into two cases: the object has standby and no standby. Based on the actual production process, the level of the SF index is determined, and its scoring standards are established, as shown in **Table 4**.

*2.1.5 Influence of failure on production loss (PL)*

**Table 5.**

**Table 6.**

**Table 7.**

**335**

*Scoring criteria of MC.*

*Scoring criteria of PL.*

*Scoring criteria of FF.*

*Maintenance Decision Method Based on Risk Level DOI: http://dx.doi.org/10.5772/intechopen.91913*

*2.1.6 Influence of maintenance cost (MC)*

standards are shown in **Table 7**.

The PL indicator is due to the failure of components, subsystems, or systems, which will cause changes in product operation, cause standby products to be put into operation, and cause production problems such as drilling stoppages, which will cause economic losses in the enterprise. In combination with the production of offshore oil and gas, in the case of production loss caused by failure, the PL index is divided into six levels, and the specific scoring standards are shown in **Table 6**.

**Serial number MTBF (hours) Grading** >10,000 0 5000–10,000 1–2 3000–5000 3–4 1500–3000 5–6 500–1500 7–8 <500 9–10

**Serial number Outage loss Score** No loss 0 Little loss 1–2 3 Loss 3–4 Big loss 5–7 Great loss 8–10

**Serial number Maintenance cost (Yuan) Score** <10,000 0–2 10,000–50,000 3–5 50,000–150,000 6–8 >150,000 9–10

The MC index needs to consider the complexity of the maintenance object, the labor cost of maintenance, the cost of spare parts, and the inventory cost. Therefore, considering the above aspects of the mechanical equipment of petroleum and petrochemical enterprises, MC is divided into four levels, and the specific scoring