**9. Recommendation**

334 Aquaculture

fry and resulting low profit margins of genetic improvement for breeders and hatcheries. The latter has made genetic improvement an insecure investment objective, perhaps also due to the ease of illegally reproducing highly fertile fish and marketing 'pirate copied' material. The last two decades, R&D funds have tended to be prioritized for research in molecular genetics and genomics with less funding for further development and establishment of selective breeding that has proved to give long term genetic gains. However, also for applying genomic information efficiently a selective breeding program

With interesting similarities to the ABS debate in the CBD, there seems to be little immediate value in breeding and breeding programmes. Previous studies have shown that the incentives for capitalizing on salmon breeding materials have been virtually non-existent, due to low roe prices and low profit for improved breeding material; a trend that does not seem likely to change in the near future (Olesen et al., 2007; Rosendal et al., 2006). Similarly, there seems to be little profit to be reaped from increased knowledge about and improvements of genetic resources and their traits, as has also been claimed in the ABS debate (Grajal, 1999). At the same time, it is hard to refute the great profits from biotechnology – from traditional breeding to genetic engineering – and there is a growing business interest in access to valuable genetic material (Laird & Wynberg, 2005). This seems paradoxical also with a view to the valuable good of faster growing and hence cheaper salmon, which is resulting from the breeding programs. Here also we see that the willingness to pay is small but the interest in access is paradoxically high. The problem is that bringing forth fast growing, disease free fish is relatively expensive, whereas the result can be copied at very low costs. This has led to a pressure towards profitability and privatisation in the aquaculture sector, including the public breeding programmes. However, the cost of maintaining a good, disease free product is relatively high and the question is whether the market can be expected to deliver this service, when there is such a high degree of uncertainty regarding profits. Due to the high fertility of aquatic species, aquaculture breeding programs have shown to give high benefit/cost ratios and tremendous value creation for the society. This is also the case for family based programs aiming for a broad and sustainable breeding goal with many traits. The paradox illustrates the challenge of securing policy goals of affordable access to genetic improvements in breeding and to stimulate sustainability and innovation in aquaculture. The alternative to the continued funding of public breeding programmes may portend forfeiting the

normative ideal of providing improved breeding material on an affordable basis.

In general, aquaculture is experiencing pressure towards higher production efficiency and short term profits. Hence, actors face emerging difficulties pertaining to adequate funding for sustainable breeding programmes and affordable access to improved genetic material. Historically, aquaculture in India and Norway has mainly been based on public investments to increase production, develop and widely disseminate material to as many users as possible, rather than creating proprietary products. The same was true for the original objectives of the GIFT tilapia project. This illustrates the nature of breeding material as a public good. Greater involvement of private sector leads however to stronger need for legal protection of genetic material. As this keeps knowledge out of the public domain, it is perceived to have negative implications for aquaculture. In a study by Rosendal et al. (forthcoming) it was found similarities between Norway, India and the GIFT donors regarding their normative objective

is a prerequisite.

Aquaculture is a source for food all around the world (FAO, 2009). Can it with its biotechnological development be sustainable on both short and long-term? As we have presented in this chapter there are several challenges that actors within the aquaculture sector is facing and decisions has to be made.

In the EU communication (2002) 'A strategy for the sustainable development of European aquaculture' it is emphasised that 'The fundamental issue is therefore the maintenance of competiveness, productivity, durability of the aquaculture sector. Further developments of the industry must take an approach where farming technologies, socio-economics, natural resources use and governance are all integrated so that sustainability can be achieved.' This is a very ambitious vision for the future and implies an integrated assessment of environmental, social, economic and legal issues. To do so we recommend that:


**2. Materials and Methods**

This study used cast commercial A356 (Si—7 wt.%, Mg—0.149 wt.%, Fe—0.126 wt.%, Cu—0.01 wt.%, Mn—0.002 wt.%, Zn—0.006 wt.%, Cr—0.001 wt.%, Ti—0.178 wt.%) alloy casted in ingot, with initial dimensions of 80 mm × 40 mm × 140 mm (width × thickness × length). Using a graphite crucible, the as-cast was melted at a temperature of 750 ◦C. For the cooling slope casting process, the cooling slope of stainless steel, with a slope length of 250 mm as well as a tilt angle of 60◦, was used. The reason for selecting these conditions was based on our work [31]. The apparatus used for this process is shown in Figure 1a. This study specifically selected 620 ◦C as the pouring temperature to limit the superheating of the melt [31]. The molten metal was poured downward, on a stainless steel slope, into a mold with a vertical surface, before quenching in water. In line with the T6 procedure, this study performed a heat treatment process to the as-cast and cooling slope-cast samples, which involved the following processes in this order: (1) an eight-hour sequence of solution treatment at 540 ◦C, (2) water quenching, and (3) three-hour aging process at a temperature of 180 ◦C [32]. Following that, as illustrated in Figure 1b, as-cast and cooling slope-cast samples were machined into a rod shape, with a diameter of 9.8 mm. The samples were ECAPed in a die with a channel angle of 120◦, following route A (where the sample is not rotated between each pass). It should be noted that molybdenum disulfide (MoS2) grease was used as a lubricant in this study. The pressing of as-cast and cooling slope-cast A356 alloy samples was carried out using a 50-ton hydraulic press. As-cast samples were pressed up to four passes, without any cracks on the surface while the cooling slope casting samples were successfully pressed to six passes.

**Figure 1.** Schematic of (**a**) cooling slope casting and (**b**) ECAP mold.

Both ECAPed samples were subsequently examined under a field emission scanning electron microscope (FESEM, Zeiss, Oberkochen, Germany) and optical microscope (OM, Olympus corporation, Tokyo, Japan). Additionally, an energy dispersive x-ray (EDX) (equipped to FESEM) was used for elemental analysis. A Vickers hardness tester (micro Vickers hardness tester, Zwick, Germany; ZHVμ) was used to measure the hardness of the average of three samples per case. These samples were also prepared for microstructure analysis using silicon carbide (SiC) papers of grit between 180 and 2000, followed by a polishing process using 3 μm and 1 μm of diamond paste (Al2O3). Meanwhile, an etching process was performed using Keller's reagent (1% HF, 1.5% HCl, 2.5% HNO3, H2O solution) as an etchant. This study performed quantitative metallography analysis to measure the grain size, according to the ASTM E112 standard. The size of Si particles (the width and length of particles) was measured using the Smart Tiffv2 software, considering at least 200 particles in each case. Following that, an electrochemical experiment was performed in naturally aerated 3.5% NaCl solution at room temperature, with pH 6.5. A potentiostat GAMRY 3.2 was used to measure the rate of corrosion (characterized by *i*corr) of these samples. For this, a three-electrode cell was used, which comprised of (1) test material (as a working electrode), (2) graphite (as a counter electrode), and (3) a silver or silver chloride (Ag or AgCl) electrode (as a reference electrode). The potential dynamic polarization tests were performed at a scanning rate of 1 mV s−1 with a range from −250 mV versus open circuit potential (OCP) to the final potential of 250 mV versus OCP. The potentiodynamic tests were started after about 15 min of immersion in 3.5% NaCl. Immersion tests were performed during 14 days in 3.5 wt.% NaCl naturally aerated solution to study the surface appearance. Basically, each sample was mounted in epoxy that was aired for 24 hours. Finally, these samples were smoothened using up to 1200-grit SiC before each corrosion test.


**2.11 Feed back**

**2.5 Synthesis to create a detailed solution**

**2.6 Evaluation of ideas and solutions**

involving all the factors with interact.

**2.7 Optimization**

*Slope Engineering*

are specified.

**2.8 Recommendation**

**2.9 Communication**

Where,

knowledge effectively.

**2.10 Implementation**

**48**

and C means the communication.

procedures and the experimentation are involved.

On the basis of analysis of the individual solution component, all design is focused to furnish comprehensive alternative solutions. In this phase of design, calculations, specifications, performance predictions, cost estimates, scheduling

In this phase the solution is interpreted and compare with the original hypothesis, specification, facts assumptions, requirements or constraints. This demand for a clear understanding of the all relevant interacting factors that's needed for the engineering judgments. The solution for engineering problems should be balanced

Optimization is the assortment of a best solution (with regard to some criteria)

Recommendation is the principle of the whole Engineering design process. It provides a refined endorsement of the solution to problem, point out limitations

The conclusive aim of the all design stages is the creation or instigation of a progression accomplishment. In order to achieve the objective requires the engineer must communicate the finding effectively. Effective communication means that all relevant aspects should be appropriately presented. If a mathematician were to sum

E means effectiveness of the subject, M mean the mastery of the subject matter

So for effective communication one should have sound knowledge of the subject matter and good communication skills. The design engineer must have the capability to communicate views and ideas concisely and clearly and to convey technical

This is the final stage of design procedures. The finding or results communicated are applied under the given circumstance and proper monitoring is carried out for

*E* ¼ *MC*2 (1)

and shows the trend to be followed in applying the solution.

up these thoughts, he might well do so by the Eq. (1).

from some set of available alternative solutions [4]. There are always multiple solutions available to any engineering problem. Refinement and modification of a solution may then be required to reach a practicable agreement between the generally contradictory constraint and assets. The effectiveness of an optimization process mostly depends upon simplicity and clarity with which problem and solution

After implementation of the design, its performance is monitored and recorded. Remedial measurements are suggested for more improvement of the performance the solution design.


**2. Theoretical basis of wettability**

Contact angle (CA) assigned as θ, which can give the quantitative measurement of wetting of a solid by a liquid. Therefore, it can be defined as the angle measured by a liquid where the liquid-gas interface meets at solid surface as shown in **Figure 1**.

*A Triphasic Superwetting Catalyst for Photocatalytic Wastewater Treatment DOI: http://dx.doi.org/10.5772/intechopen.109509*

**Figure 1.** *Schematic illustration of the triple-phase contact line.*

### **2.1 Theoretical models**

Wettability is an essential property of solid materials. When liquid droplet contacts a solid surface in air, a three-phase contact line is formed at the three-phase junction. The contact line expands outward up to the droplet reaches a static state. In a steady-state system, a three-phase contact line is contact because of an equilibrium tangential forces created by the interfacial and surface tensions. The wetting properties of the liquid on solid surface is measured by the CA such as when θ < 90°, the surface considered to be hydrophilic and when θ > 90°, the surface is hydrophobic. However, when the θ > 150° , the surfaces are highly hydrophobic and called superhydrophobic. Similarly, superhydrophilic surfaces have a θ < 10° .

The Wetting properties of the solid surfaces is governed by the Young, Wenzel, and Cassie-Baxter equation. The young are derived by balancing the interfacial forces at a three-phase contact line Eq. (1).

$$\cos \theta = \frac{\mathcal{Y}\_{sv} - \mathcal{Y}\_{sl}}{\mathcal{Y}\_{lv}} \tag{1}$$

Here θ is the CA of the liquid droplet, γsv (solid-gas) γlv (liquid-gas), and γsl (solidliquid) interfacial force per unit length of the contact line which is surface tension. Moreover, the balance of angle formed by the liquid at the three-phase boundary is defined as young's contact angle (**Figure 2a**). According to the value of contact angle, the surface should be hydrophilic (θ < 90°, γS > γSL) and hydrophobic (θ > 90°, γS < γSL) (**Figure 2b** and **c**).

For the solid surface which has rough morphology, then Wenzel introduced the factor of surface roughness, r, into Young's equation after considering the influence of rough surface structure on wettability. Therefore, the definition is the actual surface area to the projected surface area is "r". In this case, the liquid fills the microstructure of the solid surface (**Figure 3a**). For a liquid droplet in the Wenzel state, the measured/apparent CA ( Θ*w* ) can be expressed by the Wenzel equation Eq. (2) which can be expressed the surface roughness.

$$r\cos\Theta w = r.\cos\theta\tag{2}$$

**Figure 2.**

*(a) The wetting regime for Young's model, measurement of contact angle for (b) hydrophilic and (c) hydrophobic surfaces.*

#### **Figure 3.**

*Various states of droplets on a solid surface (a) Wenzel model, and (b) Cassie–Baxter model.*

For hydrophobic surfaces, the roughness is very high and large r value. Sometimes, the liquid does not penetrate in to the roughness, so that the layer of trapped air between the solid surface and the liquid. In this case the contact interface between liquid and the solid surface consists of liquid-solid contact and liquid-air contact (**Figure 3b**) and expressed by Cassie equation Eq. (3)

$$
\cos\Theta\_c = f\_1 \cdot \cos\Theta\_1 + f\_2 \cdot \cos\Theta\_2 \tag{3}
$$

Where θ*c* is the CA, θ1, θ2 are the CA of the liquid droplet on the solid and air phases and f1, f2 are the area ratio of liquid-solid contact and the liquid-air contact, respectively. Again, for solid microstructure surface, if the liquid-solid contact area fraction is f, then liquid-air contact will be (1 − f). So that, the Cassie equation will be Eq. (4):

$$
\cos \theta \mathbf{c} = f \cos \theta\_1 + (1 - f) \cos \mathbf{1} \mathbf{8} \mathbf{0}^\circ \tag{4}
$$

According to wettability, superwetting materials are generally categorized into four types: superhydrophobic, superhydrophilic, superoleophilic, and superoleophobic states. Specially, the first two kinds of superwetting materials which can be used for oil/water separation. These superwetting materials are believed to be promising

materials for removing pollutants from water due to their superwetting property towards oils and water [7, 8].


**2.1 Crouzon syndromes**

*Spina Bifida and Craniosynostosis - New Perspectives and Clinical Applications*

Therefore, corrective surgery in syndromic craniosynostosis were developed in relation to its deformities and functional issues. Conventional craniofacial surgical techniques, such as strip craniectomy, fronto-orbital advancement, and Le Fort III procedures proved to be reliable to treat symptomatic syndromic craniosynostosis. However, limitations were observed in severe conditions where large segmental advancement were required, difficulty to close the gap primarily as well as inadequate stability secondary to soft tissue restriction and unstable bone segment fixation. These limitations thus causing relapse and creating less than an ideal long-term outcome [4]. Hence distraction osteogenesis (DO) were introduced to provide a reliable surgical alternative in achieving superior segmental advancement compared with conventional techniques in treating functional issues in syndromic

Syndromic craniosynostosis patients usually presented with multiple major functional disturbances which requires multi-disciplinary management including maxillofacial surgery, neurosurgery, plastic surgery and ENT among others. As such, the indication for each major surgery in paediatric patients with this condition should be discussed by the craniofacial team members as the procedure carries

Craniosynostosis was first known as craniostenosis that was introduced by German pathologist, Virchow in 1851. It was then changed to craniosynostosis and widely accepted ever since [6]. Craniosynostosis is a condition whereby the process of early or premature fusion of the skull sutures happens that leads to the unwanted growth pattern of the skull. The skull will not be able to grow perpendicular to the fused suture but instead will grow in parallel direction to the fused suture.  In cases whereby the skull does not have any spaces due to fused sutures, the brain will continue to grow thus causing increased in intracranial pressure hence patient will develop visual disturbances, sleeping impairment due to airway disruption, eating difficulties because of unusual jaw growth and reduction

Most known craniosynostosis cases are nonsyndromic and can occur as an isolated event or associated with other skeletal and developmental anomalies in specific clinical features for recognized syndromes. Patients who have been diagnosed with syndromic craniosynostosis are much more complex and require a multidisciplinary approach to effectively manage all the problems faced. Most of the syndromic craniosynostosis cases are due to genetic defect that may present as autosomal dominant, autosomal recessive and X-linked patterns of inheritance. The molecular genetic protocol for the diagnosis of syndromic craniosynostosis such Crouzon syndrome includes first-line tests of FGFR2 exons IgIIIa and IgIIIc followed by second-line tests of FGFR2 exons 3, 5, 11, and 14 to 17 and FGFR3 Pro250Arg and Ala391Glu as proposed by Wilkie et al. [7]. There are multiple types of syndromic craniosynostosis cases but almost all of the them shared the same craniomaxillofacial features such as exophthalmos, midface hypoplasia, cranial base anomalies as well as abnormal face with the additional limb anomalies [8–9]. Syndromic craniosynostosis occurs in 1:8750 newborns [10–13]. The most common syndromic craniosynostosis cases identified and managed are Crouzon, Apert, and Pfeiffer syndromes. These syndromes may be presented with identical craniomaxillofacial features. Therefore, it is prudent to differentiate to achieve an accurate

diagnosis by relating to other features such digital or limb anomalies.

**2. Syndromic craniosynostosis and the genetic perspectives**

**130**

craniosynostosis.

in mental development.

substantial mortality and morbidity risks [5].

Clinically, patients may present with brachycephaly, small or shallow orbits with exophthalmos, midface hypoplasia and occlusal anterior open bite. However, there are no recorded cases that anomalies involving limbs are present. It is an autosomal dominant inheritance pattern that showed mutations in the fibroblast growth factor receptor 2 (FGFR-2) and occurred in 1 in 25,000 live births thus the most common syndromic craniosynostosis identified. Patients who have been diagnosed often have normal intelligence. Several cases identified as higher risk of increased intracranial pressure compared to other syndromic craniosynostosis cases [8, 14, 15]. The most common synostosis pattern observed is bicoronal synostosis which leads to brachycephalic shape, others such as scaphocephaly, trigonocephaly and cloverleaf skull have been diagnosed. Early fusion of cranial sutures resulted in shallow orbits and eye proptosis, small & high arched palate and anterior open bite. Eye proptosis or exorbitism can cause exposure conjunctivitis, keratitis, visual acuity problems and herniation of the globe. The synostosis will lead to midface hypoplasia as well and with normal development of mandible, class III skeletal profile & malocclusion formed. There are also other conditions reported such conductive hearing deficit, strabismus and hydrocephalus.


**1. Introduction**

The giant magneto-resistance (GMR), independently discovered by the two groups of Fert and Grünberg [1,2] at the end of the 1980s, was first observed for stacks of very thin multilayers of alternating ferromagnetic/antiferromagnetic Fe and Cr. These layers couple magnetically, resulting in a giant decrease of resistivity with an increasingly applied magnetic field. Some years after the discovery of GMR, it was found that this phenomenon is not only restricted to layered systems but can also be found for materials containing dispersed ferromagnetic particles (granules) [3,4]; thus it was labeled as granular GMR.

If grains are not subjected to an external magnetic field, a random orientation of magnetic moments or domains prevails. With an increasing magnetic field, the magnetic domains gradually align by rotating the magnetization, and become aligned parallel to the magnetic field. This results in an overall decrease of resistance. It was shown that the increase in resistance for randomly oriented magnetic particles originates from spin-dependent scattering of conduction electrons at the magnetic-nonmagnetic interfaces [5,6]. Rabedeau et al. [5] found this fact by using small angle X-ray scattering measurements on thin films, making the particle sizes accessible. If the GMR originates

from scattering within the ferromagnetic particles, the GMR would weakly depend on the particle size (providing that all the ferromagnetic atoms can be found in the particles). However, as GMR scaled with the inverse of the cluster size (interfaces per volume, r2/r3) instead, an interfacial spin-dependent scattering was proposed.

Upon applying a magnetic field, the gradual change in the magnetization direction of single domain particles leads to a gradual change of the resistivity and this property can be directly linked to the hysteresis loop of the material. One method to characterize the relationship between the specimens' resistance and the magnetic field is the squared global relative magnetization μ (*H*) = (*M* (*H*)/MS) 2 [4], which is the ratio of the magnetization *M* (*H*) at a certain applied field *H* and the saturation magnetization MS. The GMR-effect is described in the following way:

$$\text{GMR} = \frac{\Delta \mathcal{R}}{\mathcal{R}} = \frac{\mathcal{R}(H) - \mathcal{R}(H = 0)}{\mathcal{R}(H = 0)} = \text{ A } \mu(H)^2 \text{ } \tag{1}$$

where A determines the effect amplitude and is different for each experimental setup. In some cases, the GMR of Equation (1) is expressed by *R* (*H*) in the denominator instead of *R* (*H* = 0), or *R* (*H* = 0) is replaced by *R* (*H* = *H*C). It is stated [4] that the change in resistance, which is a measure for the GMR-effect, is proportional to A\* (M (*H*)/Ms) 2; thus, the strength of the GMR-effect can be quantified by the proportionality factor A. For a magnetron sputtered thin film specimen consisting of 84 at% Cu and 16 at% of Co (Cu84Co16) and a temperature of 5 K, this proportionality factor A was found to be −0.065 [4]. To allow comparison, all compositions in this work will be given in at%, except stated otherwise.

Research on granular GMR first started with thin films. They were produced using a variety of different techniques, such as magnetron sputtering [3,4,7–11], molecular beam epitaxy [5], ion beam co-sputtering [12], cluster beam deposition [13], thermal evaporation [14], or by electrochemical deposition [15–17]. Later, research was extended to bulk materials and mixed granular materials were produced using different techniques such as mechanical alloying/ball milling [18–22] or melt spinning [9,23–25]. Research focused on a small number of binary, sometimes ternary systems such as CuCo [3–5,9,14,16,17,19,21,23,25,26], AgCo [4,6,7,9,11,12,18], CuFe [13,19], CoFe-Cu [10,20,22], CrFe [8], and AuCo [9,24]. A common feature of these systems is the small mutual solubility of ferromagnetic and non-magnetic elements, as well as the nonmagnetic phase representing the major phase. Reduced solubility promotes the production of small, finely dispersed ferromagnetic particles within a nonmagnetic metal matrix; either directly during production or after adequate annealing treatments. With increasing ferromagnetic content, a transition towards anisotropic magneto-resistance (AMR) is found [10]. Thomson discovered this anisotropy of ferromagnetic materials in magnetic fields [27], where the resistance for currents parallel and perpendicular to the magnetic field is different. For parallel alignment, the magneto-resistance increases with an increasing field and for the perpendicular alignment, the magneto-resistance decreases. The effect of AMR is typically in the size of the GMR or about one magnitude smaller and with a change of the sign of A (A > 0), depending on the investigated material.

The restricted mutual solubility is known for Cu and Co. and the granular GMR was discovered on magnetron sputtered CuCo [3,4]. They found a strong dependence of the resistance with the applied magnetic field, and the resistivity being highest in the initial non-magnetized state decreases with increasing applied field. The resistivity increases again upon decreasing the field and reaches a local maximum at the coercive field. However, resistivity is slightly lower than in the initial, non-magnetized state. To give a first idea on the amount of GMR present, some results of original works [3,4] are presented: Berkowitz et al. [3] investigated Cu containing 12, 19, and 28 at% Co and found for Cu81Co19 (as an example) a GMR of 10% at 10 K at the highest applied magnetic fields of 20 kOe. Negligible GMR was found at room temperature. Xiao et al. [4] found a GMR of 16.5% at 5 K for magnetron sputtered Cu80Co20, annealed for 10 min at 500 ◦C. A review on GMR, including granular GMR, is provided in reference [28].

The goal of this work was to produce bulk materials of different amounts of ferromagnetic and diamagnetic components. This enables the investigation of the influence of composition and processing parameters on the evolving microstructure, on the development of small, ferromagnetic particles and thus on the GMR. The chosen method is high pressure torsion (HPT) [29], a special method of severe plastic deformation (SPD), as it provides the opportunity to easily produce bulk samples from elemental powder mixtures [30]. The principle idea of HPT is based on the work of Bridgman [31], where material is confined under high hydrostatic pressure between two anvils. One anvil is rotated against the other and the material is severely deformed by shear deformation and the microstructure gets refined. This refinement saturates at a certain grain size—mostly depending on the amount of alloying elements, impurities, and deformation temperature [32].

Regarding the investigation of different magnetic properties of materials deformed by HPT, numerous studies focusing on the magnetic properties of HPT-processed materials are available [33–42]. Other techniques to apply severe deformation onto materials include ball milling, mechanical alloying, and equal channel angular pressing (ECAP), with some studies focusing on the magnetic properties of these alternative processing routes [18–21,26,43,44]. However, to the best knowledge of the authors, there are only two studies on HPT-deformed materials, which also contain information on magneto-resistive properties [34,37]. In references [34,37], the authors used arc-melted Cu-10 wt % Co for HPT deformation. The magneto-resistive drop was ~0.25% at room temperature and ~ 2.5% at 77 K, with both measured in fields of 6 kOe.

In summary, a detailed GMR—Study of the influences of HPT process parameters including deformation temperature and composition on GMR is lacking, which is the motivation for and aim of the presented work. Within this study, the Cu-Co-system was thoroughly investigated to understand the dependency of composition on the GMR, and to demonstrate the applicability of HPT throughout the whole compositional range.


*2.3. Sector System*

The Constitution of South Africa and specifically the Bill of Rights enshrines the basic human right to have access to adequate drinking water in section 27(1)(b), an environment that is not harmful to human health or well-being in section 24(a), and a healthy and safe environment in section 152(1)(d) [68]. These constitutional rights mandate the state in section 27(2) of the Constitution [68], through the Department of Water and Wastewater (DWS), to ensure that the water resources of the country are sustainably consumed and managed as well as protected [77].

#### **Table 3.** National cybersecurity challenges.


In this study, we examined several different pipelines for ASD classification. These included 6 different network extraction techniques over 5 age ranges. Furthermore, we used 10-fold cross validation to examine the accuracy of the algorithm for each pipeline which is shown to be better than the leave-one-out cross validation used in previous studies (Kohavi, 1995). In addition, 10-fold cross validation may be used as a substitute for having a separate testing set because the model is evaluated on datapoints it has not seen before. Because of not having the exact models trained in previous studies, we compare our findings with them only by using the reported accuracy, specificity, and sensitivity. All models trained in this study were statistically compared with each other using a 10 by 10 cross validation t-test.

Previous studies were not able to report high prediction accuracies for the ABIDE dataset relative to similar studies on other neurological diseases such as AD. This can be related to the fact that this dataset consists of recordings conducted over multiple sites, some with slightly different image acquisition parameters. Moreover, the whole dataset covers a wide age range (5–64 years). To minimize the effects of age, we separated the dataset into 5 age ranges and trained separate models on each range. To allow for easier reproducibility and thus more meaningful comparisons, we chose to use a publicly available preprocessed version of the data through the Preprocessed Connectomes Project (http://preprocessedconnectomes-project.org/).

**Table 3** shows a detailed comparison with previously reported ASD classification models. It is necessary to state all of the mentioned papers other than Chen et al. (2015) used the complete dataset to train their model while in this study separate models where trained for different age ranges. The crossvalidation results in this study provide an estimate of how the models would perform if data from their respective age ranges were fed to them. Therefore, it can be hypothesized that the performance over the entire dataset would not be worse than the worst preforming age-range if, based on the subject's age, the correct model is used for a previously unseen dataset. Additional

FIGURE 3 | Visualization of the top 10 selected features for each Age range. Two age-ranges show only 9 features. This is because in the 5–10 range PreCG.L was selected two times. In the >30 group the last selected feature was the global Characteristic path length. The full region names along with the abbreviations can be found in Supplementary Table B.

data is needed to confirm this hypothesis. Our worst preforming model, the model for the 10–15 age range, outperformed almost all the previous models in specificity while having an accuracy comparable to that of the other SVM models. All other age ranges showed higher accuracy than all previous models except the Chen et al. random forest. This could be attributed to the fact that the performance metrics for the random forest model were assessed using a different scheme called out of bag prediction errors as opposed to the cross validation used in our models and all other previously reported studies mentioned here.

### Comparison Between Pipelines

While in all age ranges except the 10–15 range, the top model showed a statistical significance in performance than most of the other models, our results do not reach a consensus about what network creation pipeline preforms best in all cases. However, the bend correlation pipeline's model was the second best model over all age range but the >30 range. Furthermore, it did not show any statistically significant difference in model performance from the top preforming model for the 10–15, 15–20, and 20–30 age ranges. Based on this, we would suggest bend correlation to be the first network construction pipeline for graph theoretical analysis of the ABIDE dataset if computational time is limited.

A possible explanation for the relatively lower performance of the 10–15 range compared to other age ranges is that the larger number of subjects in this group translated into higher between site variability in the data. Therefore, even though our model achieved higher specificity than most previous studies, further steps are needed to address the inherent heterogeneity of the ABIDE dataset.

### Analysis of the Selected Features

Centrality measures were shown to be most operative in providing features for the classification tasks in the top 10 selected features. This also held true when selecting the top 5 features. Centrality measures have been shown to undergo changes in ASD. A previous study on the structural network of the brain found that autism is accompanied by centrality alterations in regions relevant for social and sensorimotor processing (Balardin et al., 2015). Another study found changes in hubness of ASD brain networks using resting-state fMRI (Itahashi et al., 2014). Our results suggest that the changes in centrality measures play a key role in being able to differentiate between ASD and HC. The only exception was observed for the 5–10 years age range where clustering coefficient, a measure of segregation, was chosen more times than the rest. This also held true when only looking at the top 5 features. This suggests that at a young age, there may not be many changes to the hubs of the brain network but the organization of the network into sub-networks is altered.

### LIMITATIONS

There are several limitations in the current study. First, ABIDE I data was used in different age ranges to investigate the prediction accuracy of our pipelines while minimizing the effects of aging on the resting-state networks. Furthermore, although to the best of our knowledge ABIDE is the most comprehensive database for ASD functional imaging, further analyses are needed to confirm its representability of the whole ASD population. Second, we relied on a single preprocessing pipeline for the sake of easier comparison between our work and previous studies. It is entirely possible that another preprocessing pipeline is better suited to this graph theoretical approach. Future studies will need to investigate this limitation. Additionally, the comparison between our models and previous studies only used three metrics (accuracy, sensitivity, and specificity). A statistical test may be needed to further analyze the significance of our findings. However, this is not possible without access to the exact cross validation folds or out of bag sample errors of those studies. Nevertheless, due to the observed improvement, we suspect that our algorithm has reached a statistically significant improvement over previous results.

Another shortcoming that is not limited to this study is related to how the classification task is formulated. To the best of our knowledge, all research in this field including the present study have focused on distinguishing HCs from ASDs. However, as the name suggests, ASD is a spectrum and individual cases can vary greatly in how the disorder affects them. To address this issue, databases such as ABIDE will play a vital role. Extensive detailed clinical analysis data will be needed to correctly approximate the position of an individual on the spectrum.

Finally, variability present in the ABIDE dataset, such as different imaging parameters and devices, due to it being a multisite initiative may lead to uncontrolled variations in the data or model being biased toward better represented sites. While the normalization steps in the preprocessing help reduce the variations, further investigations will be needed to confirm if they have been eliminated to a sufficient degree. Our results show better overall performance over previous investigations which suggests these limitations may have been addressed in a satisfactory manner.

### CONCLUSION

In this study we utilized graph theory and ML to propose a novel pipeline for automatic diagnosis of ASD which significantly

improved performance over previously proposed models. The relative strength of our method suggests graph theoretical analysis paired with the right preprocessing pipeline can nullify the effects of multi-site and multi-device image acquisition to a good degree and is more robust than previous methods. Our pipeline automatically selected 10 biomarkers for each age range being investigated. Measures of centrality were shown to be most operative in distinguishing between ASD and HC.

### DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

### ETHICS STATEMENT

We used the data collected as part of the ABIDE database and complied with everything that they have asked to be included in any manuscript using that data. The original ethics statement form the (Di Martino et al., 2014) paper is as follows: All contributions were based on studies approved by local IRBs, and data were fully anonymized (removing all 18 HIPAA protected health information identifiers, and face information from structural images). All data distributed were visually inspected prior to release.

### REFERENCES

### AUTHOR CONTRIBUTIONS

The work presented here was carried out in collaboration between all authors. The research was designed by both authors. AK acquired and analyzed the data and carried out the experiment with RS providing supervision and guidance. The manuscript was written by AK and revised by RS. All authors have read and approved the submission of the manuscript.

### ACKNOWLEDGMENTS

This work was partially supported by the Biomedical engineering research scholarship of the University of Calgary. The database used in this study was part of the openly available ABIDE I. The funding source for that project is as follows. Primary support for the work by Adriana Di Martino was provided by the NIMH (K23MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by a NIMH award to MPM (R03MH096321).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins. 2018.01018/full#supplementary-material

short of biomarker standards. YNICL 7, 359–366. doi: 10.1016/j.nicl.2014. 12.013

**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 © 2019 Kazeminejad and Sotero. 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.

Edited by: Guoyan Zheng, Universität Bern, Switzerland

#### Reviewed by:

Qi Dou, The Chinese University of Hong Kong, China Chunliang Wang, KTH Royal Institute of Technology, Sweden

#### \*Correspondence:

Min Du<EMAIL_ADDRESS>Xiaobo Qu<EMAIL_ADDRESS>
†Data used in the preparation of this paper were obtained from the ADNI database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at the website (http://adni.loni.usc.edu/wpcontent/uploads/how\_to\_apply/ ADNI\_Acknowledgement\_List.pdf).

#### Specialty section:

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

Received: 04 July 2018 Accepted: 05 October 2018 Published: 05 November 2018

#### Citation:

Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X and The Alzheimer's Disease Neuroimaging Initiative (2018) Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment. Front. Neurosci. 12:777. doi: 10.3389/fnins.2018.00777

## Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment

Weiming Lin1,2,3, Tong Tong3,4, Qinquan Gao1,3,4, Di Guo5 , Xiaofeng Du5 , Yonggui Yang6 , Gang Guo6 , Min Xiao2 , Min Du1,7 \*, Xiaobo Qu8 \* and The Alzheimer's Disease Neuroimaging Initiative†

1 College of Physics and Information Engineering, Fuzhou University, Fuzhou, China, 2 School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China, 3 Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China, 4 Imperial Vision Technology, Fuzhou, China, 5 School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China, 6 Department of Radiology, Xiamen 2nd Hospital, Xiamen, China, 7 Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Nanping, China, 8 Department of Electronic Science, Xiamen University, Xiamen, China

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

Keywords: Alzheimer's disease, deep learning, convolutional neural networks, mild cognitive impairment, magnetic resonance imaging

## INTRODUCTION

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Alzheimer's disease (AD) is the cause of over 60% of dementia cases (Burns and Iliffe, 2009), in which patients usually have a progressive loss of memory, language disorders and disorientation. The disease would ultimate lead to the death of patients. Until now, the cause of AD is still unknown, and no effective drugs or treatments have been reported to stop or reverse AD progression. Early diagnosis of AD is essential for making treatment plans to slow down the progress to AD. Mild cognitive impairment (MCI) is known as the transitional stage between normal cognition and dementia (Markesbery, 2010), about 10–15% individuals with MCI progress to AD per year (Grundman et al., 2004). It was reported that MCI and AD were accompanied by losing gray matter in brain (Karas et al., 2004), thus neuropathology changes could be found several years before AD was diagnosed. Many previous studies used neuroimaging biomarkers to classify AD patients at different disease stages or to predict the MCI-to-AD conversion (Cuingnet et al., 2011; Zhang et al., 2011; Tong et al., 2013, 2017; Guerrero et al., 2014; Suk et al., 2014; Cheng et al., 2015; Eskildsen et al., 2015; Li et al., 2015; Liu et al., 2015; Moradi et al., 2015). In these studies, structural magnetic resonance imaging (MRI) is one of the most extensively utilized imaging modality due to non-invasion, high resolution and moderate cost.

To predict MCI-to-AD conversion, we separate MCI patients into two groups by the criteria that whether they convert to AD within 3 years or not (Moradi et al., 2015; Tong et al., 2017). These two groups are referred to as MCI converters and MCI non-converters. The converters generally have more severe deterioration of neuropathology than that of non-converters. The pathological changes between converters and non-converters are similar to those between AD and NC, but much milder. Therefore, it much more difficult to classify converters/nonconverters than AD/NC. This prediction with MRI is challenging because the pathological changes related to AD progression between MCI non-converter and MCI converter are subtle and inter-subject variable. For example, ten MRI-based methods for predicting MCI-to-AD conversion and six of them perform no better than random classifier (Cuingnet et al., 2011). To reduce the interference of inter-subject variability, MRI images are usually spatially registered to a common space (Coupe et al., 2012; Young et al., 2013; Moradi et al., 2015; Tong et al., 2017). However, the registration might change the AD related pathology and loss some useful information. The accuracy of prediction is also influenced by the normal aging brain atrophy, with the removal of age-related effect, the performance of classification was improved (Dukart et al., 2011; Moradi et al., 2015; Tong et al., 2017).

 In recent years, deep learning, as a promising machine learning methodology, has made a big leap in identifying and classifying patterns of images (Li et al., 2015; Zeng et al., 2016, 2018). As the most widely used architecture of deep learning, convolutional neural networks (CNN) has attracted a lot of attention due to its great success in image classification and analysis (Gulshan et al., 2016; Nie et al., 2016; Shin et al., 2016; Rajkomar et al., 2017; Du et al., 2018). The strong ability of CNN motivates us to develop a CNN-based prediction method of AD conversion.

In this work, we propose a CNN-based prediction approach of AD conversion using MRI images. A CNN-based architecture is built to extract high level features of registered and agecorrected hippocampus images for classification. To further improve the prediction, more morphological information is added by including FreeSurfer-based features (FreeSurfer, RRID:SCR\_001847) (Fischl and Dale, 2000; Fischl et al., 2004; Desikan et al., 2006; Han et al., 2006). Both CNN and FreeSurfer features are fed into an extreme learning machine as classifier, which finally makes the decision of MCI-to-AD. Our main contributions to boost the prediction performance include: (1) Multiple 2.5D patches are extracted for data augmentation in CNN; (2) both AD and NC are used to train the CNN, digging out important MCI features; (3) CNN-based features and FreeSurfer-based features are combined to provide complementary information to improve prediction. The performance of the proposed approach was validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI – Alzheimer's Disease Neuroimaging Initiative, RRID:SCR\_003007) (Wyman et al., 2013) and compared with other state-of-the-art methods (Moradi et al., 2015; Tong et al., 2017) on the same datasets.

### MATERIALS AND METHODS

The proposed framework is illustrated in **Figure 1**. The MRI data were processed through two paths, which extract the CNNbased and FreeSurfer-based image features, respectively. In the left path, CNN is trained on the AD/NC image patches and then is employed to extract CNN-based features on MCI images. In the right path, FreeSurfer-based features which were calculated with FreeSurfer software. These features, which were further mined with dimension reduction and sparse feature selection via PCA and Lasso, respectively, were concatenated as a features vector and fed to extreme learning machine as classifier. Finally, to evaluate the performance of the proposed approach, the leaveone-out cross validation is then used.

### ADNI Data

Data used in this work were downloaded from the ADNI database. The ADNI is an ongoing, longitudinal study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of AD. The ADNI study began in 2004 and its first 6-year study is called ADNI1. Standard analysis sets of MRI data from ADNI1 were used in this work, including 188 AD, 229 NC, and 401 MCI subjects (Wyman et al., 2013). These MCI subjects were grouped as: (1) MCI converters who were diagnosed as MCI at first visit, but converted to AD during the longitudinal visits within 3 years (n = 169);

(2) MCI non-converters who did not convert to AD within 3 years (n = 139). The subjects who were diagnosed as MCI at least twice, but reverse to NC at last, are also considered as MCI non-converters; (3) Unknown MCI subjects who missed some diagnosis which made the last state of these subjects was unknown (n = 93). The demographic information of the dataset are presented in **Table 1**. The age ranges of different groups are similar. The proportions of male and female are close in AD/NC groups while proportions of male are higher than female in MCI groups.

### Image Preprocessing

MRI images were preprocessed following steps in Tong et al. (2017). All images were first skull-stripped according to Leung et al. (2011), and then aligned to the MNI151 template using a B-spline free-form deformation registration (Rueckert et al., 1999). In the implementation, we follow the Tong's way to register images (Tong et al., 2017), showing that the effect of deformable registration with a control point spacing between 10 and 5 mm have the best performance in classifying AD/NC and converters/non-converters. After that, image intensities of

TABLE 1 | The demographic information of the dataset used in this work.

MCIc means MCI converters. MCInc means MCI non-converters, MCIun means MCI unknown.

the subjects were normalized by deform the histogram of each subject's image to match the histogram of the MNI151 template (Nyul and Udupa, 1999). Finally, all MRI images were in the same template space and had the same intensity range.

### Age Correction

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Normal aging has atrophy effects similar with AD (Giorgio et al., 2010). To reduce the confounding effect of age-related atrophy, age correction is necessary to remove age-related effects, which is estimated by fitting a pixel regression model (Dukart et al., 2011) to the subjects' ages. We assume there are N healthy subjects and M voxels in each preprocessed MRI image, and denote **y**m∈**R** 1 × N as the vector of the intensity values of N healthy subjects at mth voxel, and α∈**R** 1 × N as the vector of the ages of N healthy subjects. The age-related effect is estimated by fitting linear regression model **y**m = ωmα + bm at mth voxel. For nth subject, the new intensity of mth voxel can be calculated as y 0 mn = ωm(C−αn) + ymn, where ymn is original intensity, αn is age of nth subject. In this study, C is 75, which is the mean age of all subjects.

### CNN-Based Features

A CNN was adopted to extract features from MRI Images of NC and AD subjects. Then, the trained CNN was used to extract image features of MCI subjects. To explore the multiple plane images in MRI, a 2.5D patch was formed by extracting three 32 × 32 patches from transverse, coronal, and sagittal plane centered at a same point (Shin et al., 2016). Then, three patches were combined into a 2D RBG patch. **Figure 2** shows an example of constructing 2.5D patch. For a given voxel point, three patches of MRI are extracted from three planes and then concatenated into a three channel cube, following the same way of composing a colorful patch with red/green/blue channels that are commonly used in computer vision. This process allows us to mine fruitful information form 3D views of MRI by feeding the 2.5D patch into the typical color image processing CNN network. Data augmentation (Shin et al., 2016) was used to increase training samples, by extracting multiple patches at different locations from MRI images. The choice of locations has three constraints, (1) The patches must be originated in either left or right hippocampus region which have high correlation with AD (van de Pol et al., 2006); (2) There must be at least two voxels distance between each location; (3) All locations were random chosen. With these constraints, 151 patches were extracted from each image and the sampling positions were fixed during experiments. The number of samples was expanded by a factor of 151, which could reduce over-fitting.

Typically extracted patches are presented in **Figure 3**. **Figure 3A** shows four 2.5D patches obtained from one subject. These patches are extracted from different positions and show different portions of hippocampus, which means these patches contain different information of morphology of hippocampus. When trained with these patches that spread in whole hippocampus, CNN learns the morphology of whole hippocampus. **Figure 3B** shows patches extracted in same position from four subjects of different groups, demonstrating that the AD subject has the most severe atrophy of hippocampus and expansion of ventricle. This implies that obvious differences are existed between AD and NC. However, the MCI subjects have the medium atrophy of hippocampus, and non-converter is more like NC rather than AD, and converter is more similar to AD. The difference between converter and non-converter is smaller than the difference between AD and NC.

The architecture of the CNN is summarized in **Figure 4**. The network has an input of 32 × 32 RGB patch. There are three convolutional layers and three pooling layers. The kernel size of convolutional layer is 5 × 5 with 2 pixels padding, and the kernel size and stride of pooling layers is 3 × 3 and 2. The input patch has a size of 32 × 32 and 3 RBG channels. The first convolutional layer generates 32 feature maps with a size of 32 × 32. After max pooling, these 32 feature maps were down-sampled into 16 × 16. The next two convolutional layers and average pooling layers finally generate 64 features maps with a size of 4 × 4. These features are concatenated as a feature vector, and then fed to full connection layer and softmax layer for classification. There are also rectified linear units layers and local response normalization layers in CNN, but are not shown for simplicity.

The CNN was trained with patches from NC and AD subjects, and there are 62967 (subject number 417 times 151) patches which are randomly split into 417 mini-batches. Mini-batch stochastic gradient descent was used to update the coefficients of CNN. In each step, a mini-batch was fed into CNN, and then error back propagation algorithm was carried out to computer gradient gj of jth coefficient θj , and update the coefficient as θ 0 j = θj + Oθn j, in which Oθn j = mOθn−1 j− η(gj + λθj) is the increment of θj at nth step. The momentum m, learning rate η and weight decay λ are set as 0.9, 0.001, and 0.0001, respectively, in this work. It is called one epoch with all mini-batches used to train CNN once. The CNN was trained with 30 epochs. Once the network was trained, CNN will be used to extract high level features of MCI subjects' images. The 1024 features output by the last pooling layer were taken as CNN-based features. Thus, CNN generates 154624 (1024 × 151) features for each image.

### FreeSurfer-Based Features

The FreeSurfer (version 4.3) (Fischl and Dale, 2000; Fischl et al., 2004; Desikan et al., 2006; Han et al., 2006) was used to mine more morphological information of MRI images, such as cortical volume, surface area, cortical thickness average, and standard deviation of thickness in each region of interest. These features can be downloaded directly from ADNI website, and 325 features are used to predict MCI-to-AD conversion after age correction. The age correction for FreeSurfer-based features is similar as described above, but on these 325 features instead of on intensity values of MRI images.

### Features Selection

Redundant features maybe exist among CNN-based features, thus we introduced the principle component analysis (PCA) (Avci and Turkoglu, 2009; Babaoglu et al., 2010 ˘ ; Wu et al., 2013) and least absolute shrinkage and selection operator (LASSO) (Kukreja et al., 2006; Usai et al., 2009; Yamada et al., 2014) to reduce the final number of features.

PCA is an unsupervised learning method that uses an orthogonal transformation to convert a set of samples consisting of possibly correlated features into samples consisting of linearly uncorrelated new features. It has been extensively used in data analysis (Avci and Turkoglu, 2009; Babaoglu et al., 2010 ˘ ; Wu et al., 2013). In this work, PCA is adopted to reduce the dimensions of features. Parameters of PCA are: (1) For CNN-based features, there are 1024 features for each patch. After PCA, PC features were left for each patch, since there are 151 patches for one subject, there are still PC × 151 features for each subject; (2) For FreeSurfer-based features, PF features were left for each MCI subject.

LASSO is a supervised learning method that uses L1 norm in sparse regression (Kukreja et al., 2006; Usai et al., 2009; Yamada et al., 2014) as follows:

$$\min\_{\alpha} 0.5||\mathcal{y} - \mathsf{D}\alpha||\_2^2 + \lambda||\alpha||\_1 \tag{1}$$

Where **y**∈**R** 1 × N is the vector consisting of N labels of training samples, **D**∈**R** N × M is the feature matrix of N training samples consisting of M features, λ is the penalty coefficient that was set to 0.1, and α∈**R** 1 × M is the target sparse coefficients and can be used for selecting features with large coefficients. The LASSO was solved with least angle regression (Efron et al., 2004), and L features are selected after L iterations. Parameters of LASSO are: (1) For CNN-based features, LC features were selected from PC × 151 features for each MCI subject; (2) For FreeSurfer-based features, LF features were selected from PF features. After PCA and LASSO, there were LC + LF features.

**Figure 5** shows more details of CNN-based features. 151 patches are extracted from all MRI images, including AD, NC, and MCI. First, the CNN is trained with patches of all AD and NC subjects. After that, the trained CNN is used to output 1024 features from each MCI patch. The 1024 features of each patch are reduced to PC features by PCA, and then features of all 151 patches from one subject are concatenated, and Lasso is used to select LC most informative features from them.

### Extreme Learning Machine

The extreme learning machine, a feed-forward neural network with a single layer of hidden nodes, learns much faster than common networks trained with back propagation algorithm (Huang et al., 2012; Zeng et al., 2017). A special extreme learning machine, that adopts kernel (Huang et al., 2012) to calculates the outputs as formula (2) and avoids the random generation of input weight matrix, is chosen to classify converters/non-converters with both CNN-based features and FreeSurfer-based features. In formula (2), the is a matrix with elements Ωi,j = **K**(**x**i , **x**j), where **K**(**a, b**) is a radial basis function kernel in this study, [**x1**,. . ., **xN**] are N training samples, **y** is the label vector of training samples, and **x** is testing sample. C is a regularization coefficient and was set to 1 in this study.

$$f\left(\mathbf{x}\right) = \begin{bmatrix} K\left(\mathbf{x}, \mathbf{x\_1}\right) \\ \vdots \\ K\left(\mathbf{x}, \mathbf{x\_N}\right) \end{bmatrix}^T \left(\Omega + 1/C\right)^T \mathbf{y} \tag{2}$$

### Implementation

In our implementation, CNN was accomplished with Caffe1 , LASSO was carried out with SPAMS2 , and extreme learning machine was performed with shared online code3 . The hippocampus segmentation was implemented with MALPEM

1http://caffe.berkeleyvision.org/

2http://spams-devel.gforge.inria.fr/

3http://www.ntu.edu.sg/home/egbhuang/

4 (Ledig et al., 2015) for all MRI images. Then all hippocampus masks were registered as corresponding MRI images, and then overlapped to create a mask containing hippocampus regions. All image features were normalized to have zero mean and unit variance before training or selection. To evaluate the performance, Leave-one-out cross validation was used as (Coupé et al., 2012; Ye et al., 2012; Zhang et al., 2012).

### RESULTS

### Validation of the Robustness of 2.5D CNN

To validate the robustness of the CNN, several experiments have been performed with the CNN. In experiments, the binary decisions of CNN for 151 patches were united to make final diagnosis of the testing subject. We compared the performance in four different conditions: (1) The CNN was trained with AD/NC patches and used to classify AD/NC subjects; (2) The CNN was trained with converters/non-converters patches and used to classify converters/non-converters; (3) The CNN was

The results are shown in **Table 2**. The CNN has a poor accuracy of 68.49% in classifying converters/non-converters when trained with converters/non-converters patches, but CNN has obtained a much higher accuracy of 73.04% when trained with AD/NC patches. This means that the CNN learned more useful information from AD/NC data than that from converters/non-converters data. And the prediction performance of CNN is close when different sampling patches are used.

### Effect of Combining Two Types of Features

In this section, we present the performance of CNN-based features, FreeSurfer-based features, and their combinations. The PC, PF, LC, and LF parameters were set to 29, 150, 35, and 40, respectively, which were optimized in experiments. Finally, 75 features were selected and fed to the extreme learning machine.

Performance was evaluated by calculating accuracy (the number of correctly classified subjects divided by the total number of subjects), sensitivity (the number of correctly classified MCI converters divided by the total number of

trained with AD/NC patches and used to classify converters/nonconverters; (4) The condition is similar with (3), but with different sampling patches in each validation run.

4http://www.christianledig.com/

#### TABLE 2 | The performance of the 2.5D CNN.

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MCIc means MCI converters. MCInc means MCI non-converters. The results were obtained with 10-fold cross validations, and averaged over 50 runs.

TABLE 3 | The performance of different features used, and the performance without age correction.

Bold values indicate the best performance in each column.

MCI converters), specificity (the number of correctly classified MCI non-converters divided by the total number of MCI non-converters), and AUC (area under the receiver operating characteristic curve). The performances of the proposed method and the approach with only one type of features are summarized in **Table 3**. These results indicates that the approaches with only CNN-based features or FreeSurfer-based features have similar performances, and the proposed method combining both features achieved best accuracy, sensitivity, specificity and AUC. Thus, it is meaningful to combine two features in the prediction of MCI-to-AD conversion. The AUC of the proposed method reached 86.1%, indicating the promising performance of this method. The receiver operating characteristic (ROC) curves of these approaches are shown in **Figure 6**.

### Impact of Age Correction

We investigated the impact of age correction on the prediction of conversion here. The prediction accuracy in **Table 3** and the ROC curves in **Figure 6** implied that age correction can significantly improve the accuracy and AUC, Thus, age correction is an important step in the proposed method.

### Comparisons to Other Methods

In this section, we first compared the extreme learning machine with support vector machine and random forest. The performances of three classifiers are shown in **Table 4**, indicating that extreme learning machine achieves the best accuracy and AUC among three classifiers.

Then we compared the proposed method with other stateof-the-art methods that use the same data (Moradi et al., 2015; Tong et al., 2017), which consists of 100 MCI non-converters and 164 MCI converters. In both methods, MRI images were first preprocessed and registered, but in different ways. After that, features selection was performed to select the most informative voxels among all MRI voxels. Moradi used regularized logistic regression algorithm to select a subset of MRI voxels, and Tong used elastic net algorithm instead. Both methods trained feature selection algorithms with AD/NC data to learn the most discriminative voxels and then used to selected voxels from MCI data. Finally, Moradi used low density separation to calculate MRI biomarkers and to predict MCI converters/non-converters. Tong used elastic net regression to calculate grading biomarkers from MCI features, and SVM was utilized to classify MCI converters/non-converters with grading biomarker.

For fair comparisons, both 10-fold cross validation and leave-one-out cross validation were performed on the proposed method and method of Tong et al. (2017) with only MRI data was used. Parameters of the compared approaches were optimized to achieve best performance. **Table 5** shows the performances of three methods in 10-fold cross validation and **Table 6** summarizes the performances in leave-one-out cross validations. These two tables demonstrate that the proposed method achieves the best accuracy and AUC among three methods, which means that the proposed method is more accurate in predicting MCI-to-AD conversion than other methods. The sensitivity of the proposed method is a little lower than the method of Moradi et al. (2015) but much higher than the method of Tong et al. (2017), and the specificity of the proposed method is between other two methods. Higher sensitivity means lower rate of missed diagnosis of converters, and higher specificity means lower rate of misdiagnosing non-converters as converters. Overall, the proposed method has a good balance between the sensitivity and specificity.

### DISCUSSION

The CNN has a better performance when trained with AD/NC patches rather than MCI patches, we think the reason is that the pathological changes between MCI converters and nonconverters are slighter than those between AD and CN. Thus, it is more difficult for CNN to learn useful information directly from MCI data about AD-related pathological changes than from AD/NC data. The pathological changes are also hampered by inter-subject variations for MCI data. Inspired by the work in Moradi et al. (2015) and Tong et al. (2017) which use information

#### TABLE 4 | Comparison of extreme learning machine with other two classifiers.

Implementation of SVM was performed using third party library LIBSVM (https://www.csie.ntu.edu.tw/~cjlin/libsvm/), and the random forest was utilized with the third party library (http://code.google.com/p/randomforest-matlab). Both classifiers used the default settings.

TABLE 5 | Comparison with others methods on the same dataset in 10-fold cross validation.

The performances of MRI biomarker and global grading biomarker are described in Moradi et al. (2015) and (Tong et al., 2017). The results are averages over 100 runs, and the standard deviation/confidence intervals of accuracy and AUC of the proposed method are 1.19%/[0.7922, 0.7968] and 0.83%/[0.8358, 0.8391]. Bold values indicate the best performance in each column.

TABLE 6 | Comparison with others methods on the same dataset in leave-one-out cross validation.

The global grading biomarkers was download from the web described in Tong et al. (2017) and the experiment was performed with same method as in Tong et al. (2017). Bold values indicate the best performance in each column.

TABLE 7 | The 15 most informative FreeSurfer-based features for predicting MCI-to-AD conversion.

of AD and NC to help classifying MCI, we trained the CNN with the patches from AD and NC subjects and improved the performance.

After non-rigid registration, the differences between all subject's MRI brain image are mainly in hippocampus (Tong et al., 2017). So we extracted 2.5D patches only from hippocampus regions, that makes the information of other regions lost. For this reason, we included the whole brain features calculated by FreeSurfer as complementary information. The accuracy and AUC of classification are increased to 79.9 and 86.1% from 76.9 to 82.9% with the help of FreeSurferbased features. To explore which FreeSurfer-based features contribute mostly when they are used to predict MCI-to-AD conversion, we used Lasso to select the most informative features, and the top 15 features are listed in **Table 7**, in which the features are almost volume and thickness average of regions related to AD. The thickness average of frontal pole is the most discriminative feature. The quantitative features of hippopotamus are not listed, indicating they contribute less than these listed features when predicting conversion. The CNN extract the deep features of hippopotamus morphology, rather than the quantitative features of hippopotamus, which are discriminative for AD diagnosis. Therefore, The CNN-based features and FreeSurfer-based features contain different useful information for classification of converters/non-converters, and they are complementary to improve the performance of classifier.

Different from the two methods used in Moradi et al. (2015) and Tong et al. (2017), which directly used voxels as features, the proposed method employs CNN to learn the deep features from the morphology of hippopotamus, and combined CNNbased features with the globe morphology features that were computed by FreeSurfer. We believe that the learnt CNN features might be more meaningful and more discriminative than voxels. When comparing with these two methods, only MRI data was used, but the performances of these two methods were improved when combined MRI data with age and cognitive measures, so investigating the combination of the propose approach with other modality data for performance improvement is also one of our future works.

We have also listed several deep learning-based studies in recent years for comparison in **Table 8**. Most of them have an accuracy of predicting conversion above 70%, especially the last three approaches (including the proposed one) have the accuracy above 80%. The best accuracy was achieved by Lu et al. (2018a), which uses both MRI and PET data. However, when only MRI data is used, Lu's method declined the accuracy to 75.44%. Although an accuracy of 82.51% was also obtained with PET data (Lu et al., 2018b), PET scanning usually suffers from contrast agents and more expensive cost than the routine MRI. In summary, our approach achieved the best performance when only MRI images were used and is expected to be improved by incorporating other modality data, e.g., PET, in the future.

In this work, the period of predicting conversion was set to 3 years, that separates MCI subjects into MCI non-converters and MCI converters groups by the criterion who covert to AD within 3 years. But not matter what the period for prediction is, there is a disadvantage that even the classifier precisely predict a MCI non-converters who would not convert to AD within a specific period, but the conversion might still happen half year or even 1 month later. Modeling the progression of AD and predicting the time of conversion with longitudinal data are more meaningful

TABLE 8 | Results of previous deep learning based approaches for predicting MCI-to-AD conversion.

MCIc means MCI converters. MCInc means MCI non-converters. Different subjects and modalities of data are used in these approaches. All the criteria are copied from the original literatures. Bold values indicate the best performance in each column.

(Guerrero et al., 2016; Xie et al., 2016). Our future work would investigate the usage of CNN in modeling the progression of AD.

### CONCLUSION

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In this study, we have developed a framework that only use MRI data to predict the MCI-to-AD conversion, by applying CNN and other machine learning algorithms. Results show that CNN can extract discriminative features of hippocampus for prediction by learning the morphology changes of hippocampus between AD and NC. And FreeSurfer provides extra structural brain image features to improve the prediction performance as complementary information. Compared with other state-of-theart methods, the proposed one outperforms others in higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity.

### AUTHOR CONTRIBUTIONS

WL and XQ conceived the study, designed the experiments, analyzed the data, and wrote the whole manuscript. TT and

### REFERENCES

QG provided the preprocessed data. WL, XQ, DG, XD, and MX carried out experiments. YY and GG helped to analyze the data and experiments result. MD and XQ revised the manuscript.

### FUNDING

This work was partially supported by National Key R&D Program of China under Grants (No. 2017YFC0108703), National Natural Science Foundation of China under Grants (Nos. 61871341, 61571380,<PHONE_NUMBER>1, 61672335, 61773124, 61802065, and 61601276), Project of Chinese Ministry of Science and Technology under Grants (No. 2016YFE0122700), Natural Science Foundation of Fujian Province of China under Grants (Nos. 2018J06018, 2018J01565, 2016J05205, and 2016J05157), Science and Technology Program of Xiamen under Grants (No. 3502Z20183053), Fundamental Research Funds for the Central Universities under Grants (No.<PHONE_NUMBER>6), and the Foundation of Educational and Scientific Research Projects for Young and Middle-aged Teachers of Fujian Province under Grants (Nos. JAT160074 and JAT170406).

of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410. doi: 10.1001/jama.2016.17216

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**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 Lin, Tong, Gao, Guo, Du, Yang, Guo, Xiao, Du, Qu and The Alzheimer's Disease Neuroimaging Initiative. 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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*2.7. MTT Assay*

MTT assay is based on cellular mitochondrial reductase activity. The absorption measured using this technique is directly proportional to the amount of active enzyme, which gives an idea on the number of viable cells and their metabolic activity.

The cells were seeded at a density of 20,000 cells/mL/sample in 24 well tissue culture plates on the hybrid material and glass surfaces and cultured for 24, 48, 72 and 96 h at 37 ◦C. The samples were then gently transferred to new tissue culture plates containing a solution of MTT (0.2 mg/mL in PBS) and incubated for 2 h at 37 ◦C. The solution was subsequently removed and the formazan crystals dissolved in ethanol (96%, Vilniaus degtine, Vilnius, Lithuania). The absorption was measured using ˙ a Varioskan Flash microplate reader (Thermo Fisher Scientific, Waltham, MA, USA) at 570 nm with ethanol as a reference. A total of 3 independent experiments were performed with 3 repetitions per material within each experiment.