Thermophysical properties of the base fluid and Cu nanoparticles [26].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Shaheer Akhtar and Hyung-Shik Shin",coverURL:"https://cdn.intechopen.com/books/images_new/6517.jpg",editedByType:"Edited by",editors:[{id:"52613",title:"Dr.",name:"Sadia",surname:"Ameen",slug:"sadia-ameen",fullName:"Sadia Ameen"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6320",title:"Advances in Glass Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6d0a32a0cf9806bccd04101a8b6e1b95",slug:"advances-in-glass-science-and-technology",bookSignature:"Vincenzo M. 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Churchill, Maja Dutour Sikirić, Božana Čolović and Helga Füredi Milhofer",coverURL:"https://cdn.intechopen.com/books/images_new/8812.jpg",editedByType:"Edited by",editors:[{id:"219335",title:"Dr.",name:"David",surname:"Churchill",slug:"david-churchill",fullName:"David Churchill"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7960",title:"Assorted Dimensional Reconfigurable Materials",subtitle:null,isOpenForSubmission:!1,hash:"bc49969c3a4e2fc8f65d4722cc4d95a5",slug:"assorted-dimensional-reconfigurable-materials",bookSignature:"Rajendra Sukhjadeorao Dongre and Dilip Rankrishna Peshwe",coverURL:"https://cdn.intechopen.com/books/images_new/7960.jpg",editedByType:"Edited by",editors:[{id:"188286",title:"Associate Prof.",name:"Rajendra",surname:"Dongre",slug:"rajendra-dongre",fullName:"Rajendra Dongre"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7676",title:"Zeolites",subtitle:"New Challenges",isOpenForSubmission:!1,hash:"4dc664fa55f94b38c13af542041fc3cc",slug:"zeolites-new-challenges",bookSignature:"Karmen Margeta and Anamarija Farkaš",coverURL:"https://cdn.intechopen.com/books/images_new/7676.jpg",editedByType:"Edited by",editors:[{id:"216140",title:"Dr.",name:"Karmen",surname:"Margeta",slug:"karmen-margeta",fullName:"Karmen Margeta"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50801",title:"Performance Evaluation of Nanofluids in an Inclined Ribbed Microchannel for Electronic Cooling Applications",doi:"10.5772/62898",slug:"performance-evaluation-of-nanofluids-in-an-inclined-ribbed-microchannel-for-electronic-cooling-appli",body:'\nElectronics have turned smaller, quicker, and more powerful due to the current development in computing technology during the past few decades, resulting in a dramatic rise in the rate of heat generation from electronic appliances. One way to keep the heat generated by different parts of electronic devices within safety zone is to cool the chips via forced air flow. However, standard cooling procedures seem insufficient to deal with the parts which are comprised of billions of transistors functioning at high frequency, considering the fact that the temperature can rise up to a critical point. Thus, microscale cooling appliances like microchannel heat sinks have vital roles in heat removal applications in appliances including high-energy mirrors and laser diode arrays [1]. In 1981, Tuckerman and Pease brought up the concept of a microchannel heat exchanger first [2]. The major advantage of a microchannel heat sink is the fact that its heat transfer coefficient is much higher than the traditional heat exchangers [3]. This causes microchannels to become useful for being employed in semiconductor power devices, very large-scale integrated (VLSI) circuits, etc. [4]. The first idea was to utilize water as a coolant in microchannels as cooling systems [5, 6]. Nevertheless, water is subjected to weak thermophysical properties. The convective heat transfer rate of these types of working fluids can be enhanced by improving their thermophysical properties. Nanofluids prepared through dispersing nanosized particles into the base fluid for the sake of enhancing the thermophysical properties of the working fluid are considered to support higher heat transfer compared to conventional fluids, such as water [7], ethylene glycol [8], kerosene [9], etc.
\nRecently, heat transfer and nanofluid flow in microchannels have drawn enormous interests by researchers. However, most of the researches are concerned with the forced convection heat transfer in smooth microchannels. The laminar forced convection heat transfer of γ-Al2O3/deionized water nanofluid through a rectangular microchannel heat sink was studied by Kalteh et al. [10], using a finite volume method. Moreover, they carried out experimental study to make comparison between the outcomes with numerical results. Their findings demonstrated that average Nusselt number rises with a growth in Re and vol. % of nanofluid besides a reduction in the nanoparticle size.
\nThe theoretical study of laminar forced convection heat transfer of Al2O3/H2O nanofluid inside a circular microchannel accompanied by a uniform magnetic field was carried out by Malvandi and Ganji [11]. Due to the nonadherence of the fluid–solid interface accompanied by nanoparticle migration, considered as a slip condition, and also the microscopic roughness in circular microchannels, the Navier’s slip boundary condition was applied to the walls. The results of this research showed that the near-wall velocity gradients rise by applying the magnetic field, improving the slip velocity, and therefore, the pressure drop and heat transfer rate rise.
\nThe heat transfer and fluid flow of MWCNT/water-based nanofluids in a microchannel, with frequent change of heat flux and slip boundary condition, were studied by Nikkhah et al. [12]. Based on their results, local Nusselt number, along the length of the microchannel, changes periodically and enhances with the rising of Reynold’s number. Furthermore, it was pointed out that an increase in the weight percentage of nanoparticles and slip coefficient results in the rise of Nusselt number, which is higher in upper Reynolds numbers.
\nThe experimental investigation of forced convection of various nanofluids in a 500 μm width, 800 μm height, and 40 mm length microchannel was conducted by Nitiapiruk et al. [13]. Pure water and TiO2-water with 0.5–2 vol.% were studied in this research. According to the outcomes of this research, the use of nanofluid with a volume fraction of 2 vol.% and minimum rate of heat flux and Reynold’s number is more beneficial than other conditions.
\nAn analytical approach was taken to study the entropy generation of alumina–water nanofluid inside circular microchannels and minichannels by Hassan et al. [14]. In their research, the Reynolds number was maintained constant at 1500, while the nanoparticle volume fraction and the diameter of channels differed from 0 to 0.14, and 3 mm (minichannel) to 0.05 mm (microchannel), respectively. They realized that water/Al2O3 nanofluid is an excellent coolant in minichannels under laminar flow regime. Nonetheless, employing high-viscous H2O/Al2O3 nanofluid for laminar flow in microchannels is undesirable. Therefore, it is necessary to develop low-viscous Al2O3/water nanofluids in order to apply in microchannels under laminar flow condition.
\nRimbault et al. [15] investigated convection heat transfer of nanofluids in a rectangular microchannel heat sink. The nanofluids were comprised of CuO nanoparticles combined with water as the base fluid in 0.24–4.5 volume fractions. The findings reveal that employment of copper oxide/water nanofluid for microchannel under the examined conditions does not offer great heat transfer improvement, compared to water. Such results were inconsistent with the experimental results reported by Zhang et al. [16] for alumina–water nanofluid flow through a circular microchannel, who indicated a significant increase in heat transfer rate and Nusselt number while using 0.25–0.75 vol.% nanofluid. While employing non-Newtonian Al2O3 nanofluid of up to 4% in a rectangular microchannel, the findings of Esmaeilnejad et al. [17] were consistent with those of Zhang et al. [16].
\nThe literature survey reveals that combined use of nanofluids with microchannels gives higher heat transfer performance compared to the use of common, traditional fluids in conventional systems [18–20]. However, fulfilling the requirements from other applications of the microchannels needs additional advancement. A particular still uncomprehending case is the natural and mixed convection heat transfer of nanofluids in vertical and inclined ribbed microchannel heat sinks. In this work, dilute mixture of Cu nanoparticles and water has been analyzed in an inclined microchannel with four rectangular shaped ribs. Laminar mixed convection heat transfer was studied by the use of FLUENT software. Properties of nanofluids have been extracted from the available formulations in literature and introduced in the software. Model validation has been performed by the comparison of the simulation results and the existing literature. The focus was on the heat transfer of water-based nanofluids with variable volume fractions of solid nanoparticles in microchannels with different angles of inclination. Results of this study may be applied in the use of coolants in various electronic devices such as high-power light-emitting diodes (LED), VLSI circuits, and micro-electro mechanical system (MEMS) [21].
\nDimensionless governing equations comprised of continuity, momentum, and energy equations, which are solved for laminar, steady-state flow in Cartesian coordinate system, are as follows [22, 23]:
\nIn the above equations, the following dimensionless parameters are used [24, 22]:
\nTo calculate the local Nusselt number along the lower wall, the following relation is used [22]:
\nThe local Nusselt number across the ribs is given as
\nThe local Nusselt number along each horizontal and vertical part of the lower wall can be expressed as follows:
\nTotal Nusselt number on the surface of each rib is calculated by
\nTo calculate the local friction factor along the lower wall, the following relation is used:
\nSubstituting dimensionless parameters of Eq. (5) in Eq. (11), relation (12) and (13) are obtained as follows:
\nThe average friction factor along each horizontal part of the lower wall can be calculated
The average friction factor across each rib is defined as
\nTotal friction factor:
\nTable 1 shows the thermophysical properties of copper (as nanoparticles) and water (as base fluid). The thermophysical properties of the nanofluid can be acquired from the nanoparticles’ characteristics as well as that of the base fluid.
\n\n | Copper (Cu) | \nWater | \n
---|---|---|
8933 | \n997.1 | \n|
400 | \n0.613 | \n|
385 | \n4179 | \n|
0.0000167 | \n0.00021 | \n|
– | \n0.000891 | \n
Thermophysical properties of the base fluid and Cu nanoparticles [26].
Density and heat capacity of nanofluids can be computed through the recommended expressions by Goodarzi et al. [25], Togun et al. [26] and Safaei et al. [27]:
\nFor nanofluid thermal conductivity, Chon et al. [28] suggested a model for Al2 O3–water which includes the influences of Brownian motion, viscous sublayer thickness, and temperature [29]:
\nwhere
where O, J, and Q are constants. For water, they are equal to 247.8, 140, and
Dynamic nanofluid viscosity is evaluated based on the recommendations of Brinkman [31]:
\nThe thermal expansion coefficient can be obtained from the suggested formula by Khanafer et al. [32] and Abouali and Ahmadi [33]:
\nA 2-D microchannel with four same rectangular ribs was selected for the analysis. Investigation of heat transfer and fluid dynamics, including the study of velocity, thermal field, and friction effects, was performed in different angles of inclination. The schematic of the investigated microchannel is illustrated in Figure 1. The microchannel is 1350 μm long and 90 μm high. The length of the lower wall of the microchannel was divided into three parts. The temperature of 290.5 K was set at the inlet. The temperature of 305.5 K was considered in the middle part of the microchannel with the length of 450 μm, consisting of four ribs. The channel was insulated on the total length of the upper wall (L1) as well as on the length of 450 μm of both left and right sides of the lower wall. Rectangular ribs in all the studied cases were considered to have a pitch, width, and height of 90, 15, and 30 μm (one-third of the microchannel’s height), respectively. In all cases, inclination angle between the microchannel and the horizon line was changed from 0° (horizontal case) to 90° (vertical case). A Reynolds number equal to 50 was selected to investigate the laminar flow, and Richardson number was varied between 0.1 and 10. Water as the base fluid was mixed with 0, 2, and 4% volume fractions of Cu nanoparticles (φ = 0.00, 0.02, and 0.04).
\nIn this investigation, flow is considered to be incompressible, Newtonian, laminar, and single-phase. Thermophysical properties of nanofluid are assumed to remain unchanged with temperature.
\nSchematic of the analyzed configuration.
The FLUENT commercial code was used to solve the partial differential equations that govern to the flow. The software applies the finite volume method, which is a particular case of the residual weighting method. This procedure is based on dividing the computational domain into finite control volumes, each node of which surrounds with a control volume. The partial differential equation is afterward integrated over each finite volume [34].
\nThe QUICK scheme [35] was applied for the discretization of all convective terms, while the SIMPLEC algorithm [36] was employed for pressure/velocity coupling. At one point, when the residuals for all equations fell under 10-7, the calculation reached the convergence [37]. Heat transfer and fluid dynamics parameters can be assessed, after solving the governing equations.
\nResults of this study were compared with those of Salman et al. [38] for validation purposes. Validation has been performed with numerical and experimental data, considering fluid flow of water in a smooth microtube with Reynolds number equal to 90. Figure 2 shows an excellent agreement between the simulation results of this work with both experimental and numerical results.
\nLocal Nusselt number variation––comparison with the work of Salman et al. [38].
Aminossadati et al. [24] numerically investigated forced convection of water/Al2O3 in a horizontal microchannel. Middle part of the microchannel was exposed to a constant magnetic field and heated by a constant heat flux. The effect of parameters such as Reynolds number, volume fraction of solid nanoparticles, and Hartmann number on the flow field and thermal performance of the microchannel was studied.
\nFigure 3 demonstrates excellent agreement between the present model’s predictions and the numerical results of Aminossadati et al. [24] in different Reynolds numbers and volume fractions, in terms of average Nusselt number. This comparison shows that the present numerical method is reliable and is useful in predicting forced convection heat transfer inside a microchannel for nanofluids.
\nAveraged Nusselt number from present work versus that of Aminossadati et al. [24] for different values of Re and φ.
A structured, nonuniform grid has been chosen for the discretization of the computational domain. A more refined grid was applied near the walls, where temperature and velocity gradients are sensitive. Grid independency of the computational domain was tested by using various grid distributions. Average Nusselt number and dimensionless pressure drop for each number of grids are shown in Figure 4(A, B) , from which a grid of 60 × 900 was chosen for all the simulation cases.
\nGrid independence tests for the present study by comparison of average Nusselt number and dimensionless pressure drop in various mesh concentrations: (A) average Nusselt number and (B) pressure drop.
Inside an inclined microchannel with four rectangular ribs, mixed convection heat transfer of water–copper nanofluid is studied, utilizing finite volume method (Figure 1). The distances between the ribs and their lengths and widths are supposed to be constant. The simulation results are plotted in the form of contours and diagrams.
\nThe isotherm contours and streamlines for γ = 90°, Re = 50, different Richardson numbers, and volume fraction of 4% are shown in Figure 5 (A, B). After the microchannel’s entry, the flow attains a fully developed hydrodynamic regime. When the fluid reaches the ribs, its direction is diverted and will result in an increased vertical component of the velocity. Yet, increased Richardson number does not lead to any change in the streamlines’ variations. In case of isotherms’ illustrations along the microchannel, once fluid with temperature of Th arrives in the rib-roughened areas with Tc (surface temperature), temperature of fluid decreases, and heat is transferred between the rib-roughened surfaces and fluid. Along the microchannel, ribs function as a mixer and reduce the temperature gradient between the surface and the fluid, and afterwards, the rate of heat transfer increases. These variations in heat transfer improve as the inclination angle (γ) or Richardson number increases. The first influential factor is the resultant from the gravity and variations in its components––perpendicular to and in line with the flow fluid––along the microchannel. Once γ increases from 0 to 90°, the terms of diffusion and advection in natural convection heat transfer strengthen, which leads to isothermal line variations.
\n(A) Streamlines and (B) Isotherm contours for 4% volume fraction and γ = 90°.
The dimensionless velocity contours for Ri = 10, 2% volume fraction, and 30°, 45°, 60°, and 90° inclination angles, in line with (A) and perpendicular (B) to the flow are demonstrated in Figure 6 (A, B). The increasing γ exerts a significant effect on fluid flow behavior and heat transfer. The velocity components (perpendicular to and along with the flow) vary by flowing the fluid along the microchannel, because of the ribs. Increased inclination angle intensifies these variations. The outcomes showed that the perpendicular velocity component exhibited more variation, which causes vortexes and reverse flows in the flow field. Consequently, this velocity component is deemed more effective, because its intensification can improve flow mixing and rate of heat transfer.
\nDimensionless velocity contours for 2% volume fraction, different inclination angles, and Ri = 10: (A) along the flow and (B) perpendicular to the flow.
The average Nusselt number for a ribbed microchannel with various volume fractions, different Ri, and inclination angles is depicted in Figure 7. The results endorse that the average Nusselt number increased by increasing the Richardson number, inclination angle, and nanofluid volume fraction. Nevertheless, a significant increase in the average Nusselt number is seen compared to others for Ri = 10. Also, in all volume fractions, the average Nusselt number is higher when Ri = 1 compared with Ri = 0.1. This can be attributed to the fact that as the Richardson number increases, the effective terms in natural convection heat transfer are strengthened. Moreover, increased volume fraction of nanoparticles significantly affects fluids’ thermal conductivity, which enhances the rate of nanofluid heat transfer. Even though when Ri = 0.1, the increment of average Nusselt number is nearly independent of γ, the heat transfer is increased with an increase in γ in higher Richardson numbers. The reason can be the velocity component variations, which rise mixing of the fluid layers.
\nProfiles of average Nusselt number for different Richardson numbers and volume fractions of nanoparticles.
The local Nusselt number for the distillated water and nanofluids on the lower wall of the microchannel, γ = 90°, and different Richardson numbers is compared in Figure 8. As can be seen, nanofluid has a greater Nusselt number than the distillated water, because of the existence of nanoparticles with greater thermal conductivity and also the effect of Brownian motion on the nanofluid’s thermal conductivity. Other factors that increase the Nusselt number are the ribs, which lead to abrupt upsurge of the heat transfer rate in the rib-roughened parts. It is primarily due to improved mixing of the fluid layers between cold fluid and hot area. Thermal boundary layer is altered and reformed when the fluid hits the ribs, which eventually increases the rate of heat transfer.
\nLocal Nusselt number on lower rib-roughened wall for γ = 90°: (A) Ri = 0.1; (B) Ri = 10.
The pressure drop values for different Richardson numbers and volume fractions are shown in Figure 9. It was observed that in all studied cases, pressure drop augments as the volume fractions of nanoparticles, Richardson number, or inclination angle increase. In the cooling fluid, solid nanoparticles cause a significant drop in pressure owing to the flow of denser, high-viscous fluid, compared with the fluid with lower density and viscosity. More vortexes are created as a result of increased inclination angle of the microchannel and the flow is reversed, which necessitate more energy to increase the pressure drop. Likewise, the pressure drop increases by transition from forced convection domination to free convection one, as a result of high variations of gravity components in higher Richardson numbers.
\nPressure drop diagram for different Richardson numbers and volume fractions of nanoparticles.
The average friction factor for different Richardson numbers and volume fractions on the upper wall of the microchannel are shown in Figure 10. The average friction factor drops as the nanoparticle volume fraction decreases, and Richardson number and inclination angle increase. The density and dynamic viscosity of the fluid are intensified as the nanoparticle volume fraction increases, which leads to an increment in the average friction factor. Also, collision of particles with the microchannel’s surface increases in higher nanoparticle volume fraction, which raises the friction factor. The friction factor is more or less independent from γ for the case of forced convection domination. Nevertheless, with an increase in inclination angle or Richardson number, the friction factor on the upper wall drops because of gradient reduction in axial velocity in line with the upper wall of the microchannel.
\nAverage friction factor on microchannel’s top wall. (A) φ=0%; (B) φ=2%; (C) φ=4%.
The dimensionless temperature profiles in different inclination angles and microchannel cross sections for φ = 0.04 and Ri = 1 are demonstrated in Figure 11. It can be seen that approaching microchannel’s outlet cross section or increasing inclination angle of microchannel decreases the dimensionless temperature of hot fluid for all cases. This results in better mixing of fluid layers, and lastly, increase of heat transfer.
\nHigher inclination angles result in development of intensive vortexes and better fluid mixing, which significantly decrease the dimensionless temperature, particularly in near-inlet cross sections. Thus, the dimensionless temperature for the vertical microchannel has the least value in all cross sections, meaning that this microchannel angle has the maximum rate of heat transfer.
\nAt sections near to the entry, the dimensionless temperature profile drops, because the thermal boundary layer has not been developed yet. The thermal boundary layer becomes fully developed as the entry length is increased, which increases the dimensionless temperature.
\nProfiles of dimensionless temperature in different microchannel cross sections for Ri = 1 and φ = 0.04. (A) γ=30°; (B) γ=45°; (C) γ=60°; (D) γ=90°.
In this work, the fluid flow and heat transfer of laminar Cu–water nanofluid in a 2D rectangular ribbed microchannel with different inclination angles and Richardson numbers were investigated. Simulation of the problem was performed by the use of finite volume method. Reynolds number of 50 and Richardson numbers between 0.1 and 10 were applied to the simulation. Solid nanoparticles were chosen to have a volume fraction of 0.0–4.0%.
\nThe results of this research revealed that increasing the inclination angle of microchannel or volume fraction of solid particles enhances the heat transfer rate. Existence of ribs through the flow path results in velocity gradient and increases the fluid contact with the surfaces of the microchannel, which in turn enhances heat transfer, while increasing the average friction factor. Addition of nanoparticles to the base fluid does not majorly affect the hydrodynamic parameters of the flow such as fluid velocity. Of all the studied cases, maximum heat transfer can be seen in a vertical microchannel, dominated by natural convection, because of the significant effect of gravity on the fluid structure and enhanced mixing of the fluid layers; and the lowest Nusselt number belongs to the horizontal microchannel dominated by forced convection.
\nThe authors gratefully acknowledge High Impact Research Grant UM.C/HIR/MOHE/ENG/23 and Faculty of Engineering, University of Malaya, Malaysia for support in conducting this research work.
\nAlzheimer’s disease (AD) is a neurodegenerative disease that accounts for about 70% of the estimated 46 million dementia cases worldwide. Although there is no definitive treatment for AD, early diagnosis and correct follow-up to stop disease progression can improve the quality of life for the AD patients and caregivers [1].
Amnesia, aphasia, apraxia, and agnosia are the leading clinical signs of AD. The first symptom in AD is often the loss of the ability to learn new information (amnesia). Loss of episodic memory is the main symptom of AD. Episodic memory is particularly concerned with the hippocampus. In the beginning, the patient becomes forgetful, repeats the same things, and loses his things. Semantic memory is about social events and general knowledge. It is not destroyed as markedly as episodic memory in the early stages of AD. As the disease progresses, destruction starts in semantic memory [2, 3, 4].
According to the symptomatology, AD is divided into three stages: (1) preclinical, (2) mild cognitive impairment (MCI), and (3) AD-related dementia. (1) Preclinical AD: changes in brain, blood, and cerebrospinal fluid associated with AD begin to occur, but the patient does not show any symptoms. This stage may start years or decades before the first clinical symptoms of dementia [5]. (2) Mild cognitive impairment (MCI): MCI describes the clinical situation between normal aging and Alzheimer’s disease. In 1999, the information that memory impairment differs according to age, as well as educational level, was added to the definition made on MCI. MCI usually manifests itself with subjective complaints such as forgetting the names and not being able to remember where the items were placed. However, it has been observed that 30% of cases diagnosed with MCI do not progress to AD soon [6]. (3) Alzheimer’s dementia: Typically, the symptoms of the disease begin with mild memory difficulties and cognitive impairment develops into dysfunctions in complex daily activities and some other aspects of cognition. When AD is diagnosed clinically, neuron loss and neuropathological lesions occur in many brain regions [7]. However, there is no ideal biomarker to identify AD, and a definitive diagnosis can only be made by autopsy or biopsy. Therefore, the diagnosis of AD can be made by medical history, laboratory tests, neuroimaging, and neuropsychological methods. These clinical assessments are not specific and costly. As a result, an accurate, universal, specific, and cost-effective biomarker is needed for early diagnosis and to monitor disease progression and treatment response [8].
The National Institute on Aging-Alzheimer’s Association (NIA-AA) has developed new study criteria to use a panel of prognostic fluids and imaging biomarkers to determine the probability of AD pathology and preclinical AD staging and prodromal and later progression to clinical AD. These are cerebrospinal fluid (CSF) amyloid-β (Aβ) 42, amyloid positron emission tomography (PET), CSF total tau, threonine 181 (T181) phospho-tau, magnetic resonance imaging (MRI) mesial temporal lobe (MTL) atrophy, 18F-fluorodeoxyglucose (FDG)-PET temporoparietal/precuneus hypometabolism, or hypoperfusion [9]. Today, standard neuropsychological tests are used to diagnose AD and are widely supported by expensive neuroimaging methods and invasive laboratory tests. In recent years, electroencephalography (EEG) has emerged as an alternative noninvasive technique compared to more expensive neuroimaging methods such as MRI and PET [10, 11].
Data obtained with AD have shown the presence of amyloid plaques and neurofibrillary tangles in the pathology of the disease and that these pathological aggregates have a specific distribution pattern and density [12, 13].
Molecular studies have shown that the main component of amyloid plaques is amyloid beta (Aβ), and neurofibrillary tangles are tau protein. In AD patients, the pathway that forms the Aβ peptide is more active or is thought to be a defect in the mechanism of Aβ clearance. There are mature fibrils in the structure of amyloid plaques. In pathology studies in AD patients, amyloid plaques are indispensable pathological findings and Aβ formation in the pathogenesis of the disease is thought to initiate the pathogenesis of the disease. This hypothesis is defined as the “amyloid cascade hypothesis” [14]. In the pathogenesis of AD, tau hyperphosphorylation is known to impair microtubule stability and function, as well as to gain toxic function, for instance, tau aggregates induce apoptosis. However, like Aβ, it is thought that the tau oligomers they form are associated with neurodegeneration and memory impairment rather than the aggregates formed by the tau protein [15].
The source of routine EEG activity recorded from the scalp is the postsynaptic potentials of cortical pyramidal cells. According to the synaptic activity being excitatory and inhibitory, the postsynaptic membrane becomes depolarized or hyperpolarized. The total electrical current generated by these excitatory and inhibitory postsynaptic potentials from millions of neurons creates superficial EEG activity. Adeli and Ghosh-Dastidar developed the wavelet-chaos method for the analysis of delta, theta, alpha, and beta (Table 1) subbands of EEG to identify potential markers in Alzheimer’s disease.
Type of wave | Frequency |
---|---|
Beta | >13 Hz |
Alpha | 8–13 Hz |
Theta | 4–7 Hz |
Delta | 1–4 Hz |
EEG waves.
To evaluate the effect of visual warning and attention, evaluation is done with eyes open and eyes closed. EEGs from different loci in the brain are used to explore the responsible areas of the brain and directly measure the functioning of synapses in real time [16, 17].
In AD, a significant decrease in cortical alpha frequency (8–10.5 Hz) was observed, especially in the limbic, temporal, parietal, and central areas [6]. However, it has been reported that the age of onset of AD can change this criterion, and that focal and diffuse EEG abnormalities are more common in early onset AD patients than in late-age AD patients. In the first studies, an increase in theta activity in EEG was considered as one of the earliest changes in Alzheimer’s dementia, while alpha activity was found to decrease during or after the disease [18].
EEG markers showing the progression of the disease in MCI cases include an increase in delta and theta power and a decrease in beta or alpha power in the temporal and occipital regions [19]. Osipova et al. showed that alpha rhythm shifts from the parieto-occipital region to temporal regions in AD [20]. In other spontaneous EEG studies, it was found that frontal delta and occipital theta sources were higher in MCI patients than healthy ones, and frontal delta sources and the Mini-Mental State Examination (MMSE) showed negative correlations [21, 22].
The cortical cholinergic system has an important role in controlling many different functions such as cerebral blood flow, cortical activity, sleep/wake cycle, modulation of cortical plasticity, and cognitive performance and learning-memory processes. The presence of cholinergic neurons in the basal forebrain was first reported by Shute and Lewis in 1967. ACh deficiency is observed in the brains of individuals with AD in the entire cortex, especially the temporoparietal cortex [23, 24]. Also, one of the possible mechanisms underlying the observed relationship between Aβ42 and increased slow EEG activity is Aβ and cholinergic deficiencies in the brain in AD. Cholinergic therapy has different effects on delta and theta oscillation responses. Theta oscillations were similar in controls to those receiving cholinergic therapy in the AD patients, regardless of treatment. In other words, theta oscillation responses are affected by cholinergic therapy in AD patients, while the amplitudes of delta oscillation responses are not affected by cholinergic therapy [25].
Moreover, in studies evaluating the relationship between AD neuropathology, EEG frequencies, and CSF markers, amyloid β42 (Aβ42) showed a significant relationship with slow frequency (delta and theta) activity, while phospho-tau (p-tau) and total tau (t-tau) are associated with activity at only fast frequencies (alpha and beta) (26). Smailovic et al. demonstrated the correlation of qEEG and CSF abnormalities with the AD profile at different stages of cognitive impairment, which revealed that qEEG can demonstrate neurodegeneration-induced synaptic dysfunction [26].
Sensory-evoked oscillatory responses are obtained by digital filtering of the frequency bands such as delta, theta, alpha, beta, and gamma of the “evoked potential” that appears with the delivery of the sensory stimulus. Haupt et al. showed that gamma and beta2 bands showed a different distribution compared to both patient groups in the visual-evoked oscillatory responses that they examined in Alzheimer’s, MCI and healthy group controls, and the current density distribution followed a movement from the right hemisphere to the left hemisphere in these patient groups. In a visual sensory-evoked oscillatory study, the difference between AD patients and the healthy group was shown to disappear when the stimulus did not contain the cognitive load. Besides, when controlled, the parieto-occipital theta-stimulated oscillatory responses of the untreated Alzheimer’s patient group were found to be higher than those of the treated Alzheimer’s patient group and the healthy group [27].
Coherence or phase-locking statistics are the most common methods used to evaluate relationships between neural communities [28, 29]. Hogan et al. investigated memory-related EEG strength and coherence in the temporal and central areas in early stage AD patients and the normal control group and the behavioral performances of mild Alzheimer patients did not differ significantly from the healthy ones while they found a decrease in high alpha coherence between central and right temporal cortex of Alzheimer patients [30]. Rossini et al. measured the spontaneous EEG coherence of healthy control and MCI patients (progressive and constant) and found that the course of disease in patients with high coherence in the delta and gamma frequency bands progressed faster [31]. In another study examining the coherencies related to the event in patients with mild AD using visual sparse stimuli, the authors found higher OI coherence in the “delta,” “theta,” and “alpha” bands compared to the controls in the AD group that did not receive drug treatment. Alpha OI coherence values are higher in the medicated group compared to the drug-free AD group [32].
AD is a progressive neurocognitive disease in the elderly population. This disease is characterized by behavioral problems, cognitive impairment, delirium, and memory loss.
Most studies in AD have been done on frequency changes with EEG reactivity. When eyes are open, theta and alpha reactivity index and alpha/theta index were integrated into this study and were found as a useful approach to evaluate quantitative EEG (qEEG). EEG is advantageous compared to functional MRI or PET, which indirectly detects metabolic signals due to its noninvasive, wide availability, low cost, and direct access to neuronal signaling. Studies reveal that neuritic plaques, nodes, tangles, granulovacuolar degeneration, and the formation of amyloid angiopathy are some of the pathological variations that cause AD. Neuroprotective and symptomatic approaches such as antioxidants and neurotransmitters are effective in treating AD symptoms and delay their development. No cure can treat AD, but medications that can treat disease symptoms and delay its progression have been developed and will continue to be developed. Therefore, early diagnosis is the key in treating the disease. Advances in neuroimaging technology, cognitive neuroscience, psychopathology, neuropathology, and neurobiology lead to the discovery of AD biomarkers for early detection.
Researchers are also working on improving the accuracy of EEG-based AD diagnosis. New studies are needed to develop an algorithm in the early onset diagnosis of AD, and this will happen sometime in the future.
We would like to thank Grifolscompany for its support in publishing this section.
The authors proclaim that they have no competing interests.
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