**4. Amyloid precursor protein (APP) gene**

Tau alternative splicing occurs in exons 2, 3, and 10 and its form is of the (a) type that corre‐

The studies conducted on Tau alternative splicing have been comprehensive, and most of them

Exon 10 displays a splicing pattern of inclusion and it is not present on fetal neurons. It is influenced by exon 9, which promotes its inclusion [18]. Exon 10 codes the second region of the (R) (KXGS) repeats in Tau. Alternative splicing generates Tau isoforms with 3 or 4 repeats that bind to microtubules. In mature brains, the level of 3R and 4R is similar. Exon 10 disruption

Exon 10 is flanked by a long 13.6-kb intron and a short 3.8-kb intron, possessing a weak 5′ splicing site, which is similar to that in 3′. This would allow the inclusion or not of exon 10 in

Exon 2 alternative splicing has been less studied. However, the studies conducted in our laboratory show that when PC12 cells (rat pheochromocytoma) cultures are exposed to the β1 → 42 amyloid peptide; alternative splicing of exons 2 and 3 is affected, as immature forms of Tau mRNA are transcribed in mature PC12 cells (phenotype differentiated into neuron). We observed that processes in these cells begin to retract. In spite the mechanism is still unknown, the effect produced in these cells indicate that immature Tau forms cease to stabilize micro‐ tubules in these cells processes [26] (**Figure 4**). The inclusion of exons 2 and 3 promotes the shift from immature Tau forms to their mature counterparts, stabilizing microtubules.

**Figure 4.** Tau exons 1–9 (modified from [50]), the primers Ex1 and Ex5A, amplify from exons 1 to 5. The exons 2 and 3 are amplified by (Ex1/Ex5A), the exon 6 is amplified by (Ex5/Ex9, Ex6S/Ex6AS) and exon 8 by (Ex7/Ex9). (**A**) Untreated undifferentiated cells and (**B**) NGF-induced differentiated PC12 cells; and from PC12 cells exposed to Aβ(1–42) peptide in (**C**) undifferentiated cells and (**D**) NGF-induced differentiated cells. Differentiation inhibits fetal tau expression. Aβ exposure promotes exclusion of exons 2/3 in undifferentiated and differentiated cells, and exclusion of exon 6 in undif‐

is able and enough to cause neuron degeneration or taupathies [23].

order to generate proteins with or without it [24, 25].

sponds to exon exclusion or inclusion.

have been focused on exon 10.

8 Update on Dementia

ferentiated cells. Scheme taken from [26].

APP is one of the three members of a small gene family coding type I membrane proteins possessing an extracellular domain and a small cytoplasmic region. Only APP contains the sequence coding the Aβ domain. APP human gene is located at chromosome 21, which is involved in autosomal dominant inheritance in some families affected by early Alzheimer's disease. This gene contains 18 exons, and it is more than 170 kb long [33]. More than 25 mutations have been identified to cause the familial type of AD. All of these mutations substitute amino acids near or within the Aβ domain [34]. Aβ is derived from APP by proteolytic cleavage due to an alternative splicing process (generating three isoforms com‐ posed by 695, 751, and 770 residues, respectively) [35], of exons 7, 8, and 15 (**Figure 5**). The APP form without the residues coded by exon 15 is called L-APP, and this isoform is found in most tissues [36]. The APP695 isoform is predominantly expressed in neurons, whereas

**Figure 5.** APP isoforms, showing the different APP proteins from alternative splicing of exons 7 and 8.

APP751 is expressed in all tissues and it includes exon 7, codifying a domain similar to that of the Kunitz protease inhibitor [37].

In neurons, APP is found on terminal vesicles in axons and it can be transported in an anterograde or retrograde manner [38]. Other brain cells also express APP and release variable amounts of Aβ, including astrocytes and microglia.

APP may be subjected to protelytic cleavage during and after its transit through the secretory pathway. The first of them is carried out by the α-secretase enzyme resulting in the release of a large and soluble ectodomain fragment (α-APP) [39] in the extracellular space, while retaining a 83-residue C-terminal fragment (CTF) in the membrane. Alternatively, some APP molecules that were not cleaved by α-secretase may be processed by the activity of an enzyme named β-secretase, generating a β-APP ectodomain that retains one residue from the 99 CTF [6].

The main β-secretase in neurons is a transmembrane aspartyl protease named BACE1, predominantly located at the transgolgi network (TGN) and also in endosomes [40]. The cleavage mediated by BACE1 generates the N-terminal fragment of Aβ. The high level of neuronal BACE1 expression preferably targets APP to the amyloidogenic processing pathway in the brain [34]. Aβ is constitutively released from cells expressing APP in normal conditions. The cleavage generated by β-secretase is followed by a constitutive trim at the C-terminal of the Aβ region, and it is carried out by the activity of γ-secretase. Simultaneously, a peptide fragment designated as p3 is produced from the sequential activity of both α- and γ-secretases [6]. A substantial amount of α-APP is generated by γ-secretase that acts on the inserted APP in plasma membrane.

The Aβ40 and Aβ42 fragments are generated to a large extent during APP internalization and endosomal processing. Most of the Aβ generated within the cell is destined for secretion.

APP has autocrine and paracrine functions during growth regulation. It has been best characterized as trophic as it has been demonstrated that it stimulates neurite growth. This phenotype is compatible with its increased expression during neuron maturation [41, 42].


**Acknowledgements**

The authors would like to thank the financial contribution of Tshwane University of Technology towards the publication of this chapter

## **Conflict of interest**

The authors declare no conflict of interest

*Therapy Approaches in Neurological Disorders*


**4. Cultivation systems of microalgae**

There are currently four cultivation technologies in use for the production of commercial microalgae including open ponds and raceway ponds (open systems), photobioreactors and fermenters [49]. In open systems, microalgae are grown in open areas, including tanks, lakes, and ponds, deep channels, among others. In closed systems, crops are grown in transparent bioreactors, exposed to sunlight or artificial radiation for photosynthesis and fermenters.

#### **4.1 Opens systems**

*Biotechnological Applications of Biomass*

neutraceutics [31].

and anticancer drugs [33].

expected to be worth of about \$ 1.1 billion by 2024.

the most favorable choice for large-scale production [36].

antiviral and anti-cancer properties, being highly active [39].

use of fertile land and fresh drinking water [43].

The growing expansion of these products is part of a wide range of utilities inserted in the most different commercial niches, expanding the possibilities of use and adding value to the market. According to Hu et al. [30], the global algae market is

Microalgae are inserted in a wide variety of species, distinguishing one from the other due to their biological structure. In this sense, these microorganisms offer potential possibilities for CO2 biofixation, remediation, effluent treatment, production of biofuels, high-value products including pharmaceuticals, food and

According to Rizwan et al. [32], microalgae can be a source of antioxidant compounds, carotenoids, enzyme polymer, lipid, natural dye, polyunsaturated fatty acid, peptide, toxin and sterols, which are widely used in industry. In addition, they are used for the synthesis of antiviral, antimicrobial, antiviral, antibacterial

Commercial microalgae cultivation systems are operated to produce mainly pigments and metabolites for nutritional supplements [34]. The algae that have technical and economic viability of production are Spirulina (Arthrospira) for supplements with a high protein content, Haematococcus as a source of astaxanthin and *Dunaliella salina* for the production of pro-vitamin A [31]. Spirulina represents 60% of all biomass produced on a large scale [35]. This species can be easily grown in tropical regions and is well adapted to extreme environments, being relatively less susceptible to contamination than other microalgae, making it

Spirulina consists mainly of proteins (50–70%), being widely used in human nutrition to combat malnutrition [37]. This species is rich in essential amino acids, beta-carotene, minerals, essential fatty acids, vitamins, polysaccharides, among others. Chlorella accumulate high concentrations of carotenoids (astaxanthin, lutein, β-carotene, violaxanthin and zeaxanthin), antioxidants, vitamins, polysaccharides, proteins, peptides and fatty acids [5, 38]. In addition to all the benefits mentioned, the bioactive compounds of microalgae can have a biological, immune,

Global warming has been worrying environmentalists across the planet. Although there are different ways of capturing CO2, the biological method stands out as a potentially attractive alternative. The requirements for producing and obtaining biomass from microalgae are basically CO2 and a source of light, be it natural or artificial [2]. Carbon dioxide can be converted into organic matter by performing photosynthesis using sunlight as an energy source [40–42]. Microalgae are more efficient for fixing CO2 and have a higher productivity rate (ton/ha/year) when compared to terrestrial plants. In addition, CO2 biofixation can be combined with other processes, such as the treatment of organic waste, being advantageous in terms of economic viability and environmental sustainability. Microalgae can also be grown in nutrient-rich organic effluents, salt and brackish water, reducing the

Studies involving the mixotrophic cultivation of microalgae using industrial residues from agro-industry as a source of organic carbon have been carried out to minimize the cost of biomass production, treat the effluent and promote CO2 biofixation [7]. In this sense, expanding the ways in which these residues are used, avoiding their incorrect disposal, minimizes the effects of environmental pollution and adds value to industrial processes, encouraging a cleaner and more sustainable

Currently, fossil fuels represent the main source of energy in the world, but unsustainable and directly related to the pollution of air, land, water and climate change. The burning of fossil fuels consolidated to increase the atmospheric

**404**

bioeconomy.

Natural and artificial lakes and ponds, where most of the systems commonly used are large, shallow ponds and tanks, represent open pond systems. The main advantages of these systems are the ease of construction and operation when compared to photobioreactors and the possibility of operating hybrid processes involving the cultivation of algae associated with the treatment of wastewater. However, the disadvantages are inefficient light distribution, losses through evaporation, diffusion of CO2 into the atmosphere, contamination and the requirement for large areas of land [50]. Open ponds are currently in use for wastewater treatment and production of *Dunaliella salina*, characterized as a hybrid process. These systems are used by Ognis Australia Pty Ltd. to produce β-carotene from *Dunaliella salina* in Hutt Lagoon and Whyalla. In terms of surface area used, these are among the largest algae production systems in the world.

Closed or artificial ponds (circular ponds and raceway ponds) are more efficient than open systems for producing microalgae, since control over the production environment is much better than open ponds or extensive ponds. The cost of raceway ponds is higher than that of open lagoon systems, but lower than that of photobioreactors. These systems are the most used due to their potential to produce large quantities of biomass for commercial application. Raceway pond ponds are commonly used to grow *Chlorella sp*., *Spirulina platensis*, *Haematococcus sp*. and *Dunaliella salina*, with a biomass production rate of 60–100 mg of dry biomass/L/day [50]. Raceway ponds are used to produce Spirulina at Earthrise Nutraceuticals in the USA and Cyanotech Corp. in Hawaii [49].

## **4.2 Closed systems**

Photobioreactors were designed to overcome the problems associated with open growth systems. It has been shown that cultivation of microalgae in these systems are capable of producing large amounts of biomass as they allow an effective control of process parameters, such as pH, temperature, CO2 concentration, level of contamination, among others. However, photobioreactors are much more expensive than open ponds and raceway ponds. Commercial photobioreactor productions include the production of *H. pluvialis* in Israel and Hawaii and *C. vulgaris* in Germany. Production costs are very high, reaching \$ 100/kg [49]. As a result, biofuel production based entirely on photobioreactors is generally considered unlikely to be commercially viable [6, 49, 50].

Closed fermenters are used for the production of heterotrophic algae, where sugars or other simple carbon sources are used for growth instead of CO2 and light. Open fermenters similar to the fermenters used in the production of ethanol in industries are not suitable for the growth of algae, since these microorganisms have slow growth when compared to yeasts and bacteria. In the USA, India and China ω-3 fatty acids are produced from *Thraustochytrids* by heterotrophic fermentation through sugars and O2. As it is a high value-added product, it is sold for 100 \$ /kg, which justifies the high cost of the process [49].

### **5. Growth metabolism**

Microalgae have different growth metabolisms, which characterizes their versatility. Cultivation conditions define the metabolic route for the production of biocompounds, including proteins, carbohydrates, pigments and fatty acids. Although the production of microalgae has traditionally been photoautotrophic, these microorganisms have different forms of energy metabolism including heterotrophic and mixotrophic, which use an organic carbon source for the growth and production of biomass. The understanding of these metabolisms allows the diversification of current growth systems aiming at increasing the production of biomass and certain specific metabolites.

#### **5.1 Photoautotrophic**

Photoautotrophic growth is the most common way to cultivate microalgae through photosynthesis. In these systems, high concentrations of CO2 are sequestered, but productivity is low when compared to heterotrophic and mixotrophic systems that provide high yields of biomass and secondary metabolites [51, 52]. Through photobioreactors is possible to obtain the maximum cell density of 40 g/L, while in outdoor open-pond or raceway-pond cultures, the cell concentration is

**407**

*Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review*

usually lower than 10 g/L. This significantly increases the energy consumption of cell harvesting and the cost of biomass production [53]. Usually scale-up of microalgae cultivation in wastewater is fulfilled phototrophically, which may be hindered by inefficient illumination and the low biomass density, leading to poor removal of

For photosynthesis, light is used as an energy source and CO2 is used as a carbon source [55]. The carbon source is essential for growth and the higher its concentration, the higher the productivity. Nitrogen sources in the culture medium are also essential for the synthesis of proteins, nucleic acids and other biocomposites necessary for cell growth and survival [56] and the concentration must be compatible with the amount of carbon in the medium [6]. The type of cultivation, the nutrients, the carbon source, the salinity of the medium, the irradiance and the temperature vary according to the chosen species and also considerably influence the success of the microalgae

Microalgae can grow in the absence of light in culture media assimilating organic carbon. Studies show that some species, including *Chlorella*, *C. protothecoides*, *C. vulgaris*, *C. zofingensis*, *C. minutissima*, *Tetraselmis* and *Neochloris* [57] are able to grow in both autotrophic and heterotrophic conditions [56]. According to Hosoglu et al. [58], microalgae of the *Chlorella Beyerinck* genus are those that have the greatest potential for large-scale heterotrophic production, with emphasis on

In heterotrophic crops, microalgae acquire carbon and energy from organic sources via oxidative phosphorylation, consuming O2 and releasing CO2. According to Behrens [59], the cost of producing the kg of dry biomass produced in photoautotrophic conditions can be 5.5 times higher than in heterotrophic conditions. In heterotrophic systems there are no light limitation problems, since microalgae grow in the absence of light using organic carbon as a carbon and energy source reaching high concentrations of biomass reaching up to 100 g/L, which considerably facilitates the harvesting process [60]. In heterotrophic processes, 18% of the energy obtained can be converted into adenosine triphosphate (ATP) whereas in photoautotrophic cultures this percentage is only 10% [61]. However, despite the advantages, the cultivation of microalgae under heterotrophic conditions, due to the use of organic carbon, requires reactors and techniques of greater complexity and high cost

Under mixotrophic conditions, microalgae grow both photoautotrophically and heterotrophically, being able to assimilate organic compounds as a carbon source and use inorganic carbon as an electron donor [62]. In this metabolism, the energy is captured through the catalysis of external organic compounds through respiration and the light energy is converted into chemical compounds via photosynthesis, being a promising solution in the processes of environmental remediation, being able to treat the effluents of the agribusiness and produce biomass rich in metabo-

In photoautotrophic crops, self-shade caused by the high density of cells makes light penetration difficult, causing photoinhibition and may be the limiting factor to its propagation leading to low biomass yields. Under heterotrophic conditions, not all microalgae species are able to grow and the strict use of only organic substrates as a source of carbon and energy makes the process more prone to contamination.

the species *C. protothecoides* that has been widely studied.

to avoid contamination caused by other microorganisms.

*DOI: http://dx.doi.org/10.5772/intechopen.93964*

nutrients [54].

production.

**5.2 Heterotrophic**

**5.3 Mixotrophic**

lites of industrial interest.

#### *Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review DOI: http://dx.doi.org/10.5772/intechopen.93964*

usually lower than 10 g/L. This significantly increases the energy consumption of cell harvesting and the cost of biomass production [53]. Usually scale-up of microalgae cultivation in wastewater is fulfilled phototrophically, which may be hindered by inefficient illumination and the low biomass density, leading to poor removal of nutrients [54].

For photosynthesis, light is used as an energy source and CO2 is used as a carbon source [55]. The carbon source is essential for growth and the higher its concentration, the higher the productivity. Nitrogen sources in the culture medium are also essential for the synthesis of proteins, nucleic acids and other biocomposites necessary for cell growth and survival [56] and the concentration must be compatible with the amount of carbon in the medium [6]. The type of cultivation, the nutrients, the carbon source, the salinity of the medium, the irradiance and the temperature vary according to the chosen species and also considerably influence the success of the microalgae production.

#### **5.2 Heterotrophic**

*Biotechnological Applications of Biomass*

USA and Cyanotech Corp. in Hawaii [49].

unlikely to be commercially viable [6, 49, 50].

which justifies the high cost of the process [49].

**5. Growth metabolism**

and certain specific metabolites.

**5.1 Photoautotrophic**

**4.2 Closed systems**

Closed or artificial ponds (circular ponds and raceway ponds) are more efficient than open systems for producing microalgae, since control over the production environment is much better than open ponds or extensive ponds. The cost of raceway ponds is higher than that of open lagoon systems, but lower than that of photobioreactors. These systems are the most used due to their potential to produce large quantities of biomass for commercial application. Raceway pond ponds are commonly used to grow *Chlorella sp*., *Spirulina platensis*, *Haematococcus sp*. and *Dunaliella salina*, with a biomass production rate of 60–100 mg of dry biomass/L/day [50]. Raceway ponds are used to produce Spirulina at Earthrise Nutraceuticals in the

Photobioreactors were designed to overcome the problems associated with open growth systems. It has been shown that cultivation of microalgae in these systems are capable of producing large amounts of biomass as they allow an effective control of process parameters, such as pH, temperature, CO2 concentration, level of contamination, among others. However, photobioreactors are much more expensive than open ponds and raceway ponds. Commercial photobioreactor productions include the production of *H. pluvialis* in Israel and Hawaii and *C. vulgaris* in Germany. Production costs are very high, reaching \$ 100/kg [49]. As a result, biofuel production based entirely on photobioreactors is generally considered

Closed fermenters are used for the production of heterotrophic algae, where sugars or other simple carbon sources are used for growth instead of CO2 and light. Open fermenters similar to the fermenters used in the production of ethanol in industries are not suitable for the growth of algae, since these microorganisms have slow growth when compared to yeasts and bacteria. In the USA, India and China ω-3 fatty acids are produced from *Thraustochytrids* by heterotrophic fermentation through sugars and O2. As it is a high value-added product, it is sold for 100 \$ /kg,

Microalgae have different growth metabolisms, which characterizes their versatility. Cultivation conditions define the metabolic route for the production of biocompounds, including proteins, carbohydrates, pigments and fatty acids. Although the production of microalgae has traditionally been photoautotrophic, these microorganisms have different forms of energy metabolism including heterotrophic and mixotrophic, which use an organic carbon source for the growth and production of biomass. The understanding of these metabolisms allows the diversification of current growth systems aiming at increasing the production of biomass

Photoautotrophic growth is the most common way to cultivate microalgae through photosynthesis. In these systems, high concentrations of CO2 are sequestered, but productivity is low when compared to heterotrophic and mixotrophic systems that provide high yields of biomass and secondary metabolites [51, 52]. Through photobioreactors is possible to obtain the maximum cell density of 40 g/L, while in outdoor open-pond or raceway-pond cultures, the cell concentration is

**406**

Microalgae can grow in the absence of light in culture media assimilating organic carbon. Studies show that some species, including *Chlorella*, *C. protothecoides*, *C. vulgaris*, *C. zofingensis*, *C. minutissima*, *Tetraselmis* and *Neochloris* [57] are able to grow in both autotrophic and heterotrophic conditions [56]. According to Hosoglu et al. [58], microalgae of the *Chlorella Beyerinck* genus are those that have the greatest potential for large-scale heterotrophic production, with emphasis on the species *C. protothecoides* that has been widely studied.

In heterotrophic crops, microalgae acquire carbon and energy from organic sources via oxidative phosphorylation, consuming O2 and releasing CO2. According to Behrens [59], the cost of producing the kg of dry biomass produced in photoautotrophic conditions can be 5.5 times higher than in heterotrophic conditions. In heterotrophic systems there are no light limitation problems, since microalgae grow in the absence of light using organic carbon as a carbon and energy source reaching high concentrations of biomass reaching up to 100 g/L, which considerably facilitates the harvesting process [60]. In heterotrophic processes, 18% of the energy obtained can be converted into adenosine triphosphate (ATP) whereas in photoautotrophic cultures this percentage is only 10% [61]. However, despite the advantages, the cultivation of microalgae under heterotrophic conditions, due to the use of organic carbon, requires reactors and techniques of greater complexity and high cost to avoid contamination caused by other microorganisms.

#### **5.3 Mixotrophic**

Under mixotrophic conditions, microalgae grow both photoautotrophically and heterotrophically, being able to assimilate organic compounds as a carbon source and use inorganic carbon as an electron donor [62]. In this metabolism, the energy is captured through the catalysis of external organic compounds through respiration and the light energy is converted into chemical compounds via photosynthesis, being a promising solution in the processes of environmental remediation, being able to treat the effluents of the agribusiness and produce biomass rich in metabolites of industrial interest.

In photoautotrophic crops, self-shade caused by the high density of cells makes light penetration difficult, causing photoinhibition and may be the limiting factor to its propagation leading to low biomass yields. Under heterotrophic conditions, not all microalgae species are able to grow and the strict use of only organic substrates as a source of carbon and energy makes the process more prone to contamination.

Thus, an alternative to maximize production can be through mixotrophic cultivation. Therefore, the cells would multiply with autotrophic metabolism until reaching the maximum cell density, at which point a source of organic carbon is added to stimulate, also, heterotrophic growth. Thus, due to the high cell density in the medium, problems with contamination of microorganisms would be less likely to happen. For the success of this technique, the cultivated species must be able to grow in heterotrophic conditions without microbial contamination and the organic compound to be added, as well as its ideal concentration must be known.

Reducing use of light in mixotrophic processes decreases the demand for energy, which minimizes the operational cost in processes with artificial light. According to Perez-Garcia and Bashan [63] in these systems there is a better control capable of regulating the growth rate of the species, which minimizes the risk of contamination by photosynthetic microorganisms. Due to the significant heterotrophic contribution of the organic fraction, the reactor design does not require a maximized area to expose the microalgae to light to perform photosynthesis, decreasing the process cost.

According to Patel et al. [7], mixotrophic cultivation has advantages over photoautotrophic and heterotrophic metabolism. In this process, in addition to the higher growth rates reducing the microalgae growth cycle, there is also growth in the dark phase mediated by respiration, potentiating the production of biomass. There is also a comparatively prolonged exponential growth phase, great flexibility to change the metabolism from heterotrophic to photo-autotrophic and vice versa, prevention of photo-oxidative damage caused by O2 accumulated in closed photobioreactors and reduction in substrate uptake photoinhibition. It has been shown that microalgae cultivated under mixotrophic conditions can present, under controlled conditions, higher biomass productivity when compared to photoautotrophic and heterotrophic cultures [64].

#### **6. Growth microalgae using waste from agro-industry**

The advancement of agro-industry has generated a large amount and variety of waste causing serious environmental problems and is one of the sectors that generate more waste rich in organic matter. According to Dahiya et al. [65], approximately 1.3 billion tons of foods are wasted each year during its production, handling, storage, processing, distribution or consumption. The composition of food processing residues is extremely varied and depends on both the nature of the raw material and the production technique employed.

Due to the scarcity of available areas close to large urban centers for the disposal of industrial and urban waste, the vast majority of companies do not carry out treatment and/or correct disposal of this material, which contributes to the increase in environmental pollution. Pollution is due to high concentrations of organic matter and heavy metals causing contamination, eutrophication of water bodies, death of aquatic organisms and local vegetation, ecological imbalance and health problems for the population. In this sense, the use of agro-industrial waste as a source of organic carbon for the cultivation of microalgae is presented as an option for the bioremediation of these effluents added to the production of biomass rich in biocompounds with different applications.

A lot of studies have evaluated the mixotrophic growth of microalgae using glucose, glycerol and acetate as a source of organic carbon and observed that the biomass yields were higher, which could decrease the cost of the process. Liang et al. [66] investigated *C. vulgaris* strain under autotrophic, mixotrophic and heterotrophic growth conditions. Mixotrophic growing on glucose with light produced the highest

**409**

*Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review*

lipid productivity compared with other growth modes. Garcia et al. [67] studied the mixotrophic growth of tricornutum UTEX-640 using acetate, lactic acid, glycine, glucose and glycerol. The best results were obtained using with urea, which resulted in maximum biomass and eicosapentaenoic acid productivities significantly higher than those obtained for the photoautotrophic control, which suggest the possibility of using mixotrophy for the mass production of microalgae. Cheirsilp & Torpee [68] cultivated *Chlorella sp*., *Chlorella sp*., *Nannochloropsis sp*. and *Cheatoceros sp*. under mixotrophic condition using glucose as source of organic carbon. They observed that the biomass and lipid production of all tested strains in mixotrophic culture were notably enhanced in comparison with photoautotrophic and heterotrophic cultures. Due to the success of the microalgae mixotrophic cultivation method, recent studies have been evaluating the possibility of using residues from the agro-industry as source of organic matter as a strategy to treat the effluent, produce biomass and

Hu et al. [69] evaluated the mixotrophic cultivation of *Chlorella sp*. UMN271 utilizing swine manure as nutrient supplement for evaluate the nutrient removal efficiencies by alga. The results showed that addition of 0.1% (v/v) acetic, propionic and butyric acids, respectively, could promote algal growth, enhance nutrient removal efficiencies and improve total lipids productivities. They concluded that Chlorella sp. grown on acidogenically-digested manure could be used as a feedstock

Li et al. [70] investigated the effects of autotrophic and mixotrophic growth on cell growth and lipid productivity of green microalgae *Chodatella sp* and Piggery wastewater served as nutrient sources for mixotrophic growth. The specific growth rate, biomass production, and lipid productivity obtained with mixotrophic growth were until 5.6 times higher than those obtained with autotrophic growth. The mixotrophic cultivation simultaneously assimilated 99.7% ammonia nitrogen and 75.9% total phosphorus from piggery wastewater, which reduced the required nutrient for the culture of microalgae, thereby reducing the cost of biomass for

León-Vaz et al. [71] cultivated *C. sorokiniana* microalgae under mixotrophic conditions utilized Oxidized wine waste lees among other agro-industrial wastes as carbon source. The fed-batch strategy and the medium optimization, with nutrient supplementation, have been found to be very effective in enhancing biomass and neutral lipid productivity, suggesting that this is a promising strategy for production of microalgal biomass. The algal biomass concentration was 11gL-1 with a lipid

Bhatnagar et al. [72] evaluated mixotrophic growth of *Chlamydomonas globosa*, *Chlorella minutissima* and *Scenedesmus bijuga* under medium supplemented with different organic carbon substrates and wastewaters. The mixotrophic growth of these microalgae resulted in 3–10 times more biomass production relative to phototrophy. Poultry litter extract as growth medium recorded up to 180% more biomass growth compared to standard growth medium, while treated and untreated carpet industry wastewaters also supported higher biomass, with no

Andrade & Coosta [73] determined the effects of molasses concentration and light levels on mixotrophic biomass production by *Spirulina platensis*. Molasses concentration was the main factor influencing maximum biomass concentration (Xmax) reached 2.94 g L− 1 and μmax 0.147 d − 1. Molasses, suggesting that this industrial by-product could be used as a low-cost supplement for the growth of

Melo et al. [74] evaluated the growth, nutrients and toxicity removal of *Chlorella vulgaris* cultivated under autotrophic and mixotrophic conditions using corn steep

significant effect of additional nitrogen supplementation.

Spirulina platensis, stimulated the production of biomass.

*DOI: http://dx.doi.org/10.5772/intechopen.93964*

reduce the cost of the process.

for high-quality biodiesel production.

diverse application.

content of 38% (w/w).

#### *Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review DOI: http://dx.doi.org/10.5772/intechopen.93964*

lipid productivity compared with other growth modes. Garcia et al. [67] studied the mixotrophic growth of tricornutum UTEX-640 using acetate, lactic acid, glycine, glucose and glycerol. The best results were obtained using with urea, which resulted in maximum biomass and eicosapentaenoic acid productivities significantly higher than those obtained for the photoautotrophic control, which suggest the possibility of using mixotrophy for the mass production of microalgae. Cheirsilp & Torpee [68] cultivated *Chlorella sp*., *Chlorella sp*., *Nannochloropsis sp*. and *Cheatoceros sp*. under mixotrophic condition using glucose as source of organic carbon. They observed that the biomass and lipid production of all tested strains in mixotrophic culture were notably enhanced in comparison with photoautotrophic and heterotrophic cultures.

Due to the success of the microalgae mixotrophic cultivation method, recent studies have been evaluating the possibility of using residues from the agro-industry as source of organic matter as a strategy to treat the effluent, produce biomass and reduce the cost of the process.

Hu et al. [69] evaluated the mixotrophic cultivation of *Chlorella sp*. UMN271 utilizing swine manure as nutrient supplement for evaluate the nutrient removal efficiencies by alga. The results showed that addition of 0.1% (v/v) acetic, propionic and butyric acids, respectively, could promote algal growth, enhance nutrient removal efficiencies and improve total lipids productivities. They concluded that Chlorella sp. grown on acidogenically-digested manure could be used as a feedstock for high-quality biodiesel production.

Li et al. [70] investigated the effects of autotrophic and mixotrophic growth on cell growth and lipid productivity of green microalgae *Chodatella sp* and Piggery wastewater served as nutrient sources for mixotrophic growth. The specific growth rate, biomass production, and lipid productivity obtained with mixotrophic growth were until 5.6 times higher than those obtained with autotrophic growth. The mixotrophic cultivation simultaneously assimilated 99.7% ammonia nitrogen and 75.9% total phosphorus from piggery wastewater, which reduced the required nutrient for the culture of microalgae, thereby reducing the cost of biomass for diverse application.

León-Vaz et al. [71] cultivated *C. sorokiniana* microalgae under mixotrophic conditions utilized Oxidized wine waste lees among other agro-industrial wastes as carbon source. The fed-batch strategy and the medium optimization, with nutrient supplementation, have been found to be very effective in enhancing biomass and neutral lipid productivity, suggesting that this is a promising strategy for production of microalgal biomass. The algal biomass concentration was 11gL-1 with a lipid content of 38% (w/w).

Bhatnagar et al. [72] evaluated mixotrophic growth of *Chlamydomonas globosa*, *Chlorella minutissima* and *Scenedesmus bijuga* under medium supplemented with different organic carbon substrates and wastewaters. The mixotrophic growth of these microalgae resulted in 3–10 times more biomass production relative to phototrophy. Poultry litter extract as growth medium recorded up to 180% more biomass growth compared to standard growth medium, while treated and untreated carpet industry wastewaters also supported higher biomass, with no significant effect of additional nitrogen supplementation.

Andrade & Coosta [73] determined the effects of molasses concentration and light levels on mixotrophic biomass production by *Spirulina platensis*. Molasses concentration was the main factor influencing maximum biomass concentration (Xmax) reached 2.94 g L− 1 and μmax 0.147 d − 1. Molasses, suggesting that this industrial by-product could be used as a low-cost supplement for the growth of Spirulina platensis, stimulated the production of biomass.

Melo et al. [74] evaluated the growth, nutrients and toxicity removal of *Chlorella vulgaris* cultivated under autotrophic and mixotrophic conditions using corn steep

*Biotechnological Applications of Biomass*

thesis, decreasing the process cost.

trophic and heterotrophic cultures [64].

**6. Growth microalgae using waste from agro-industry**

raw material and the production technique employed.

biocompounds with different applications.

Thus, an alternative to maximize production can be through mixotrophic cultivation. Therefore, the cells would multiply with autotrophic metabolism until reaching the maximum cell density, at which point a source of organic carbon is added to stimulate, also, heterotrophic growth. Thus, due to the high cell density in the medium, problems with contamination of microorganisms would be less likely to happen. For the success of this technique, the cultivated species must be able to grow in heterotrophic conditions without microbial contamination and the organic compound to

Reducing use of light in mixotrophic processes decreases the demand for energy, which minimizes the operational cost in processes with artificial light. According to Perez-Garcia and Bashan [63] in these systems there is a better control capable of regulating the growth rate of the species, which minimizes the risk of contamination by photosynthetic microorganisms. Due to the significant heterotrophic contribution of the organic fraction, the reactor design does not require a maximized area to expose the microalgae to light to perform photosyn-

According to Patel et al. [7], mixotrophic cultivation has advantages over photoautotrophic and heterotrophic metabolism. In this process, in addition to the higher growth rates reducing the microalgae growth cycle, there is also growth in the dark phase mediated by respiration, potentiating the production of biomass. There is also a comparatively prolonged exponential growth phase, great flexibility to change the metabolism from heterotrophic to photo-autotrophic and vice versa, prevention of photo-oxidative damage caused by O2 accumulated in closed photobioreactors and reduction in substrate uptake photoinhibition. It has been shown that microalgae cultivated under mixotrophic conditions can present, under controlled conditions, higher biomass productivity when compared to photoauto-

The advancement of agro-industry has generated a large amount and variety of waste causing serious environmental problems and is one of the sectors that generate more waste rich in organic matter. According to Dahiya et al. [65], approximately 1.3 billion tons of foods are wasted each year during its production, handling, storage, processing, distribution or consumption. The composition of food processing residues is extremely varied and depends on both the nature of the

Due to the scarcity of available areas close to large urban centers for the disposal

of industrial and urban waste, the vast majority of companies do not carry out treatment and/or correct disposal of this material, which contributes to the increase in environmental pollution. Pollution is due to high concentrations of organic matter and heavy metals causing contamination, eutrophication of water bodies, death of aquatic organisms and local vegetation, ecological imbalance and health problems for the population. In this sense, the use of agro-industrial waste as a source of organic carbon for the cultivation of microalgae is presented as an option for the bioremediation of these effluents added to the production of biomass rich in

A lot of studies have evaluated the mixotrophic growth of microalgae using glucose, glycerol and acetate as a source of organic carbon and observed that the biomass yields were higher, which could decrease the cost of the process. Liang et al. [66] investigated *C. vulgaris* strain under autotrophic, mixotrophic and heterotrophic growth conditions. Mixotrophic growing on glucose with light produced the highest

be added, as well as its ideal concentration must be known.

**408**

liquor, cheese whey and vinasse as source of organic matter. The results demonstrated that corn steep liquor toxicity was totally eliminated and cheese whey and vinasse toxicity were minimized by *C. vulgaris*. They demonstrated that the mixotrophic cultivation of *C. vulgaris* is able to increase cellular productivity and could be an alternative to remove the toxicity from agroindustrial by-products.

Hugo et al. [75] studied the growth of forty microalgae strains under mixotrophic conditions using sugarcane vinasse as source of organic matter. *Micractinium sp*. Embrapa|LBA32 and *C. biconvexa* Embrapa|LBA40 presented expressive growth in a light-dependent manner even in undiluted vinasse under non-axenic conditions. Microalgae strains presented higher biomass productivity in vinasse-based medium compared to autotrophic medium. This research showed the potential of using residues derived from ethanol plants to cultivate microalgae for the production of energy and bioproducts.

Mitra et al. [76] cultivated *Chlorella vulgaris* under mixotrophic/heterotrophic conditions using dry-grind ethanol thin stillage and soy whey as nutrient feedstock. The results showed the biomass yields from thin stillage, soy whey and modified basal medium after 4 days of incubation at mixotrophic conditions in the bioreactor were 9.8, 6.3 and 8.0 g.L− 1 with oil content at 43, 11, and 27% (w/w) respectively. This research highlights the potential of these agro-industrial co-products as microalgal growth media with consequent production of high-value microalgal oil and biomass.

Salati et al. [77] cultivated *Chlorella vulgaris* using cheese whey, white wine lees and glycerol as carbon sources under mixotrophic conditions. The mixotrophic biomass production was1.5–2 times higher than autotrophic growth. Furthermore, it gave much higher energy recovery efficiency, i.e. organic carbon energy efficiency of 32% and total energy efficiency of 8%, suggesting the potential for the culture of algae as a sustainable practice to recover efficiently waste-C and produce biomass.

Piasecka et al. [78] studied the growth of *Tetradesmus obliquus* by supplementation with beet molasses in photoheterotrophic and mixotrophic culture conditions. The highest protein content was obtained in the mixotrophic growth suggesting this metabolism promising for protein production.

Tsolcha et al. [79] evaluated a mixed cyanobacterial-mixotrophic algal population, dominated by the filamentous cyanobacterium *Leptolyngbya sp.* and the microalga *Ochromonas* under non-aseptic conditions for its efficiency to remove organic and inorganic compounds from second cheese whey, poplar sawdust, and grass hydrolysates. Nutrient removal rates, biomass productivity, and the maximum oil production rates were determined. The highest lipid production was achieved using the biologically treated dairy effluent (up to 14.8% oil in dry biomass corresponding to 124 mg L − 1), which also led to high nutrient removal rates (up to 94%). Lipids synthesized by the microbial consortium contained high percentages of saturated and mono-unsaturated fatty acids (up to 75% in total lipids) for all the substrates tested, which implies that the produced biomass may be harnessed as a source of biodiesel.

Grupta et al. [80] cultivated the *Chlorella* microalgae under mixotrophic conditions using a raw food-processing industrial wastewater. About 90% reduction in TOC and COD were obtained for all dilutions of wastewater. Over 60% of nitrate and 40% of phosphate were consumed by microalgae from concentrated raw wastewater. The degradation kinetics also suggested that the microalgae cultivation on a high COD wastewater is feasible and scalable.

Yeesang et al. [81] evaluated *B. braunii*, a microalgae rich in oil under mixotrophic cultivation using molasses, a cheap by-product from the sugar cane plant as a carbon source and under photoautotrophic cultivation using nitrate-rich wastewater supplemented with CO2. The mixotrophic cultivation produced a high amount of

**411**

*Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review*

biomass of 3.05 g L − 1 with a high lipid content of 36.9%. The photoautotrophic cultivation in nitrate-rich wastewater supplemented with 2.0% CO2 produced a biomass of 2.26 g L − 1 and a lipid content of 30.3%. They showed that these strategies could be promising ways for producing cheap lipid-rich microalgal biomass as biofuel

Gélinas et al. [82] studied the mixotrophic growth and lipid production of *Chlorella consortium* using residual corn hydrolysate and corn silage juice as source of organic and compared to heterotrophic conditions. Maximum microalgal biomass of 0.8 g/L was obtained with 1 g/L of residual corn hydrolysate whatever the trophic strategy. Under mixotrophic conditions, the use of residual corn hydrolysate led to an increase of 21% and 22% in comparison with the biomass produced with glucose or silage juice, respectively. This increase varied between 11% and 28% under heterotrophic condition. They observed that at the end of the experiment, algae exposed to silage juice decreased significantly. Residual corn hydrolysate represented an interesting and efficient alternative as an organic carbon source. However, silage juice needs additional treatments to be implemented as a

Nur et al. [83] studied palm oil mill effluent (POME), one of the wastewaters generated from palm oil mills, as source of organic carbon for mixotrophic microalgae growth. The aim of this research was to identify the growth of *Chlorella vulgaris* cultured in POME medium under mixotrophic conditions in relation to a variety of organic carbon sources added to the POME mixture. The research was conducted with 3 different carbon sources (D-glucose, crude glycerol and NaHCO3) in 40% POME. They showed that *C. vulgaris* using D-glucose as carbon source gained a

Manzoor et al. 2020 presented the growth of *Scenedesmus dimorphus* NT8c cultivated mixotrophically on sugarcane bagasse hydrolysate, a low-value agricultural by-product. Under mixotrophic conditions the *S. dimorphus* NT8c showed higher growth rates compared to photoautotrophic cultivation and the biomass productivity was 119.5mg Ld − 1, protein contents was 34.82% and fatty acid contents was 15.41%. They concluded that mixotrophically-cultivated microalgae are able to increase the biomass and lipid productivity. However, the concentrations of supplementation need to be studied because higher level of organic carbon can result in unfavorable levels of turbidity and bacterial growth, reducing microalgal

Microalgal biomass represents a sustainable alternative to fossil consumption and bioproducts for food and pharmaceutical industry. Microalgae can growth under photoautotrophic, heterotrophic or mixotrophic modes where the latter two trophic modes require organic carbon to grow efficiently. Actually, researchers have highlighted the role of low cost-efficient agro-industrial by-products used as supplements in algal culture media. However, supplementation of organic carbon contributes significantly to a higher cost of microalgae production and this can compete with human and animal alimentation. Agro-industrial by-products and wastes are of great interest as cultivation medium for microorganisms because of

Faced with this scenario, biotechnological technologies are necessary to develop the production of microalgae on a large scale and expand the range of utilities that, in the short or long term, contribute to the improvement of industrial processes. In addition, mixotrophic microalgae growth is a great strategy to reduce environmental

*DOI: http://dx.doi.org/10.5772/intechopen.93964*

feedstock and animal feeds.

culture medium.

lipid productivity of 195 mg/l/d.

biomass productivity.

their low cost, renewable nature, and abundance.

**7. Conclusions**

#### *Microalgae Growth under Mixotrophic Condition Using Agro-Industrial Waste: A Review DOI: http://dx.doi.org/10.5772/intechopen.93964*

biomass of 3.05 g L − 1 with a high lipid content of 36.9%. The photoautotrophic cultivation in nitrate-rich wastewater supplemented with 2.0% CO2 produced a biomass of 2.26 g L − 1 and a lipid content of 30.3%. They showed that these strategies could be promising ways for producing cheap lipid-rich microalgal biomass as biofuel feedstock and animal feeds.

Gélinas et al. [82] studied the mixotrophic growth and lipid production of *Chlorella consortium* using residual corn hydrolysate and corn silage juice as source of organic and compared to heterotrophic conditions. Maximum microalgal biomass of 0.8 g/L was obtained with 1 g/L of residual corn hydrolysate whatever the trophic strategy. Under mixotrophic conditions, the use of residual corn hydrolysate led to an increase of 21% and 22% in comparison with the biomass produced with glucose or silage juice, respectively. This increase varied between 11% and 28% under heterotrophic condition. They observed that at the end of the experiment, algae exposed to silage juice decreased significantly. Residual corn hydrolysate represented an interesting and efficient alternative as an organic carbon source. However, silage juice needs additional treatments to be implemented as a culture medium.

Nur et al. [83] studied palm oil mill effluent (POME), one of the wastewaters generated from palm oil mills, as source of organic carbon for mixotrophic microalgae growth. The aim of this research was to identify the growth of *Chlorella vulgaris* cultured in POME medium under mixotrophic conditions in relation to a variety of organic carbon sources added to the POME mixture. The research was conducted with 3 different carbon sources (D-glucose, crude glycerol and NaHCO3) in 40% POME. They showed that *C. vulgaris* using D-glucose as carbon source gained a lipid productivity of 195 mg/l/d.

Manzoor et al. 2020 presented the growth of *Scenedesmus dimorphus* NT8c cultivated mixotrophically on sugarcane bagasse hydrolysate, a low-value agricultural by-product. Under mixotrophic conditions the *S. dimorphus* NT8c showed higher growth rates compared to photoautotrophic cultivation and the biomass productivity was 119.5mg Ld − 1, protein contents was 34.82% and fatty acid contents was 15.41%. They concluded that mixotrophically-cultivated microalgae are able to increase the biomass and lipid productivity. However, the concentrations of supplementation need to be studied because higher level of organic carbon can result in unfavorable levels of turbidity and bacterial growth, reducing microalgal biomass productivity.

## **7. Conclusions**

*Biotechnological Applications of Biomass*

energy and bioproducts.

metabolism promising for protein production.

be harnessed as a source of biodiesel.

on a high COD wastewater is feasible and scalable.

and biomass.

liquor, cheese whey and vinasse as source of organic matter. The results demonstrated that corn steep liquor toxicity was totally eliminated and cheese whey and vinasse toxicity were minimized by *C. vulgaris*. They demonstrated that the mixotrophic cultivation of *C. vulgaris* is able to increase cellular productivity and could

conditions using sugarcane vinasse as source of organic matter. *Micractinium sp*. Embrapa|LBA32 and *C. biconvexa* Embrapa|LBA40 presented expressive growth in a light-dependent manner even in undiluted vinasse under non-axenic conditions. Microalgae strains presented higher biomass productivity in vinasse-based medium compared to autotrophic medium. This research showed the potential of using residues derived from ethanol plants to cultivate microalgae for the production of

Hugo et al. [75] studied the growth of forty microalgae strains under mixotrophic

Mitra et al. [76] cultivated *Chlorella vulgaris* under mixotrophic/heterotrophic conditions using dry-grind ethanol thin stillage and soy whey as nutrient feedstock. The results showed the biomass yields from thin stillage, soy whey and modified basal medium after 4 days of incubation at mixotrophic conditions in the bioreactor were 9.8, 6.3 and 8.0 g.L− 1 with oil content at 43, 11, and 27% (w/w) respectively. This research highlights the potential of these agro-industrial co-products as microalgal growth media with consequent production of high-value microalgal oil

Salati et al. [77] cultivated *Chlorella vulgaris* using cheese whey, white wine lees and glycerol as carbon sources under mixotrophic conditions. The mixotrophic biomass production was1.5–2 times higher than autotrophic growth. Furthermore, it gave much higher energy recovery efficiency, i.e. organic carbon energy efficiency of 32% and total energy efficiency of 8%, suggesting the potential for the culture of algae as a sustainable practice to recover efficiently waste-C and produce biomass. Piasecka et al. [78] studied the growth of *Tetradesmus obliquus* by supplementation with beet molasses in photoheterotrophic and mixotrophic culture conditions. The highest protein content was obtained in the mixotrophic growth suggesting this

Tsolcha et al. [79] evaluated a mixed cyanobacterial-mixotrophic algal popula-

Grupta et al. [80] cultivated the *Chlorella* microalgae under mixotrophic conditions using a raw food-processing industrial wastewater. About 90% reduction in TOC and COD were obtained for all dilutions of wastewater. Over 60% of nitrate and 40% of phosphate were consumed by microalgae from concentrated raw wastewater. The degradation kinetics also suggested that the microalgae cultivation

Yeesang et al. [81] evaluated *B. braunii*, a microalgae rich in oil under mixotrophic cultivation using molasses, a cheap by-product from the sugar cane plant as a carbon source and under photoautotrophic cultivation using nitrate-rich wastewater supplemented with CO2. The mixotrophic cultivation produced a high amount of

tion, dominated by the filamentous cyanobacterium *Leptolyngbya sp.* and the microalga *Ochromonas* under non-aseptic conditions for its efficiency to remove organic and inorganic compounds from second cheese whey, poplar sawdust, and grass hydrolysates. Nutrient removal rates, biomass productivity, and the maximum oil production rates were determined. The highest lipid production was achieved using the biologically treated dairy effluent (up to 14.8% oil in dry biomass corresponding to 124 mg L − 1), which also led to high nutrient removal rates (up to 94%). Lipids synthesized by the microbial consortium contained high percentages of saturated and mono-unsaturated fatty acids (up to 75% in total lipids) for all the substrates tested, which implies that the produced biomass may

be an alternative to remove the toxicity from agroindustrial by-products.

**410**

Microalgal biomass represents a sustainable alternative to fossil consumption and bioproducts for food and pharmaceutical industry. Microalgae can growth under photoautotrophic, heterotrophic or mixotrophic modes where the latter two trophic modes require organic carbon to grow efficiently. Actually, researchers have highlighted the role of low cost-efficient agro-industrial by-products used as supplements in algal culture media. However, supplementation of organic carbon contributes significantly to a higher cost of microalgae production and this can compete with human and animal alimentation. Agro-industrial by-products and wastes are of great interest as cultivation medium for microorganisms because of their low cost, renewable nature, and abundance.

Faced with this scenario, biotechnological technologies are necessary to develop the production of microalgae on a large scale and expand the range of utilities that, in the short or long term, contribute to the improvement of industrial processes. In addition, mixotrophic microalgae growth is a great strategy to reduce environmental pollution generated by residues rich in organic matter and can reduce the cost of the industrial process. However, more investments, development and greater knowledge of the metabolism of these microalgae and their effectiveness in the generation of new bioproducts are increasingly necessary.


**1. Active Navigation in Virtual Environments Benefits Older Adults' Spatial Memory**

Spatial navigation deficits are frequently experienced in the older adult population [1–3]. For example, age-related deficits are commonly observed in the memory for locations, landmarks, routes, and maps [2,4]. Such declines in navigation and spatial memory are critical, because they pose a potential threat to independent living for older adults [5–9], prompting the need to find ways to lessen these age-related deficits. The assessment of age-related impairments in spatial cognition has been facilitated by recent advancements in virtual reality (VR) technology [10], allowing for the efficient examination of navigation performance in virtual environments. In the current study, we took advantage of VR methodology to investigate ways for improving spatial memory performance in a navigation task, in older and younger adults. Specifically, the goal of the current study was to determine whether age-related differences in memory for virtual routes are reduced following active exploration, compared to passive guidance. Additionally, we examined whether individual differences

in visuo-spatial ability, and cognitive indices during route exploration, meaningfully contributed to spatial memory performance.

#### **2. Active vs. Passive Encoding of Spatial Information**

The term active navigation refers to a condition wherein the participant has motor and/or volitional control of their movement through an environment, whereas passive navigation generally consists of a guided tour [11]. Active navigation has been described to contain both cognitive (mental manipulation of spatial information, allocation of attention, and decision-making) and physical (motor control for locomotion and proprioceptive and vestibular sensory information) components [11], which combine to create rich multi-modal associations that benefit subsequent memory performance [12]. A variety of past studies in young adults suggest significant spatial memorial benefits when encoding is active, rather than passive [13–16].

The memory benefit from active, relative to passive, navigation can be conceptualized as a subject-performed task effect (SPT effect) [17,18]. The SPT effect describes a pattern of superior memory performance when encoding involves the direct engagement of the participant, such as when performing rather than watching an action that is associated with a to-be-remembered word [19]. Importantly, the SPT effect remains unchanged with aging, enhancing memory performance in older adults [20–22]. The encoding tasks that involve an SPT have been described to have a high degree of environmental support, accounting for a boost in memory in seniors [23,24]. With regard to encoding manipulations, the environmental support refers to a situation in which older adults do not need to self-initiate encoding operations. By this account, active navigation should provide a good form of environmental support, as it involves the SPT of physically and cognitively engaging with the to-be-remembered spatial information. Past studies, however, have reported inconsistent results regarding the benefits of active relative to passive navigation in older adults. Specifically, benefits from active navigation are sometimes observed in older adults [15,25,26], but there is also evidence showing that active encoding hinders memory performance in seniors [2,27].

The degree of involvement of both decision-making processes and motor processing in active exploration conditions could be critical for explaining the inconsistencies regarding age differences in active/passive manipulation. It is well-known that older adults have to allocate additional cognitive resources to control their locomotion [28,29], suggesting that the physical components of active navigation are unlikely to be the reason for any memory benefit in seniors. Indeed, active navigation with low levels of motor control benefits spatial memory, whereas a high degree of motoric requirement creates an additional cognitive load that impairs memory in older adults [27].

Some have suggested that the cognitive components of active navigation benefit the memory (for directions and the locations of landmarks), more so than physical motor processing [29–31]. Decision-making in particular has been shown to be beneficial in older adults for episodic memory of landmarks and objects [15] and it may elicit a self-reference effect that is known to benefit older adults' spatial memory [32–34]. In other words, the engagement of cognitive and decision-making processes during active navigation conditions could be particularly beneficial for older adults. Likewise, factors related to decision-making, such as intrinsic motivation or curiosity [35,36] may also contribute to the memory benefit from active exploration. As recently claimed by [37], momentary feelings of curiosity can help older adults' memories and executive functions, because the phasic activation of the noradrenergic and dopaminergic systems modulate hippocampal activity [38], facilitating memory and learning. As such, it may be that the cognitive components of active navigation drive the observed active encoding memory benefits in older adults.

Related to this, it is possible that the cognitive processes engaged by the active exploration of an environment allow for more unique associations to be formed with landmarks, and thus enhance the memory for the links between certain environments and paths travelled. Others have suggested that such relational knowledge is critical, as retrieval becomes more likely when the target is part of a rich network of associations, as opposed to being encoding in isolation [12]. This relational encoding can take the form of distinctive sensorimotor associations, and increased elaboration during encoding, due to goal-directed exploration. By actively encoding an experience, and thereby increasing the amount of associations that are credited to an episodic memory, later memory recall can be expected to be improved [12].

#### **3. Using Virtual Reality to Examine Spatial Memory**

In recent years, VR technology has become widely used for studying spatial cognition and navigation performance in both younger [39–42], and older adults [10,12,43]. One of the major advantages of VR is the high degree of experimental control that is afforded, to investigate the cognitive and behavioral components that are involved in spatial navigation. For example, VR provides the ability to control for variables that differ between age groups, such as physical fatigue and mobility issues during navigation. Furthermore, the use of VR in the current study allowed for the creation of a series of environments with controlled parameters, including environment size, number of intersections, and visual content, as well as the novel approach of examining spatial memory following trials of either active or passive encoding. Additionally, we developed a means to precisely measure the overlaps in the routes travelled at encoding and retrieval, to provide a measure of memory accuracy in re-tracing a previously travelled route.

Research into the effects of aging on route navigation have largely focused on differences in learning performance and the assessment of knowledge for a highly familiar environment. However, it is not the case that we will only ever need to navigate through familiar environments; rather we frequently encounter new spaces by we need to successfully navigate after brief encounters. As such, in the present study, we took the novel approach of investigating the age difference in memory for routes that were previously travelled on only one occasion. We created a set of virtual environments in which participants traveled routes, and then later re-traced those routes to assess spatial memory performance. During the encoding, participants either traveled routes by actively exploring through volitional control of their own movements, by passively viewing a guided tour. This paradigm was analogous to drivers and passengers in a car, with active trials affording the control that a driver has in deciding on the route and manipulating movement, whereas the passive trials are akin to a passenger simply viewing the route travelled within the environment. We expected that our active encoding manipulation would provide a higher degree of environmental support, as it constitutes a SPT; given that environmental support is particularly helpful to seniors [23,24], active versus passive encoding should benefit older adults' memories more so than younger adults.

## **4. Method**

## *4.1. Participants*

Twenty-two younger (Mage = 19.71, SD = 2.19, 11 females) and 22 older (Mage = 74.55, SD = 7.82, 10 females) adults participated in our study. Younger adults were undergraduate students at the University of Waterloo who signed up for the study and received partial course credit for their participation. Older adults were recruited from the Waterloo Research in Aging Participant (WRAP) database, and received \$10.00 remuneration for their participation. WRAP is a database of healthy seniors residing in the Kitchener–Waterloo area recruited by means of newspaper ads, flyers, and local television segments to participate in research studies taking place on campus at the University of Waterloo. All older adults completed the Montreal Cognitive Assessment (MoCA) to assess cognitive decline [44]. The average MoCA score was 26.95 out of 30 (SD = 2.29), with a range of 20 to 30. Two participants fell below the cut-off a score of 26 (obtaining scores of 24 and 20); which was generally used to indicate normal healthy aging; however, we opted to include all older adults who were tested, as we are not aiming to make a distinction between normal healthy aging and MCI in the current study.

#### *4.2. Materials*

VR equipment. The VR program was run on an Asus© Zenfone 3 Laser ™ (see specificities on www.asus.com, Asus, Taipei, Taiwan) and a lightweight hand-held cardboard VR headset for viewing. The VR application run on the Android phone was designed using the 3D engine Unity© (ver. 2017.2.0.f3, Unity Technologies ApS, San Francisco, CA, USA). Twelve virtual environments (VE) were built according to three styles of: city streets, mall hallways, and park trails (see Figure 1 for samples). The virtual environments were topographically similar, with an average area of 234 square meters. Additionally, each environment had six intersections, which is a point where 3+ roads converge.

**Figure 1.** First-person view and bird's-eye view of the three virtual environment styles. For each style, we created 4 exemplars, though only one from each is shown here. The Stars are visible in the environments during encoding, but not during retrieval. Participants only experienced the environments from a first-person perspective.

Neuropsychological assessments. Spatial visualization ability was evaluated with the Hooper Visual Orientation test [45], which had good internal reliability, as indicated by a coefficient α of 0.88 [46]. This test is scored out of 30; it contains 30 line drawings of common objects that are portrayed as having been cut up and misaligned; participants had to mentally rotate and piece together the visual information, to identify the depicted object. Performance on the Hooper test was included to determine whether spatial visualization abilities were related to, and predictive of, spatial memory performance following active and passive encoding.

The spatial orientation ability was measured with the Santa Barbara Sense-of-Direction scale, which has good internal reliability, as indicated by a coefficient α of 0.88 [47]. For this, the test participants rated their endorsement of 15 statements about spatial orientation abilities, such as "I am good at giving directions', indicating whether they strongly agreed or disagreed, using a 7-point scale. After reverse scoring the necessary items, the responses were summed and divided by 15 to provide an average score of between 1–7 [47]. This measure was included to determine whether the perceived spatial orientation abilities were related to spatial memory performance following active and passive encoding.

#### **5. Procedure**

The study received clearance from the ethics review board at the University of Waterloo; all participants gave their written informed consent prior to beginning the experiment. Throughout the entire experiment, participants were seated in comfortable chairs that could turn a full 360◦ circle, which was placed in the center of a testing room. Following a verbal explanation of how to use the VR headset, participants were given unlimited time to complete a training phase with the VR equipment designed to help them learn how to move in the virtual space. Specifically, they were told that to move forward in VR, they should press a button on the headset, and to change their direction within VR, they were to physically rotate in the spinning chair with their legs. Participants were advised to take as much time as needed in the training phase, to become comfortable with the environment and the movement mechanism. Immediately following the training phase, the experimental session began.

In the experimental session, participants completed 12 cycles of encoding-filler-retrieval phases. The encoding phase was 30 seconds in duration. Once 30 seconds elapsed, the screen turned black and the participant immediately performed a filler task which involved counting backwards by 7's from 100, for 20 seconds, in order to reduce recency effects [48]. In the retrieval phase, participants were placed within the VR environment at the same starting point as the environment they had just experienced. Participants were told that they had 30 seconds to re-trace the exact path just previously travelled in the environment. Participants were given a 1- to 5-min break halfway through the experimental session (i.e., after completing six encoding-filler-retrieval cycles), resuming when they indicated they were ready.

Active vs. passive manipulation. The within-subjects active/passive navigation manipulation was implemented during the encoding phase. Of the 12 virtual environments, six were actively encoded, and six were passively encoded. The order of active and passive trials was mixed, with an instruction appearing at the beginning of each new encoding-filler-retrieval cycle, indicating whether the participant was to explore the environment (active) or watch a guided tour (passive). Participants knew that they would subsequently be asked to retrace the travelled route. In the active trials, the participant had full volitional control of their movement along the paths in the environment, and they were free to choose the route they traveled. To motivate participants to continue to move and explore the environment in the active condition (and not to simply walk back and forth so that their route was later easier to remember) they were told to 'collect' as many star icons along the path as possible, by walking over the stars. Each environment had 10 stars dispersed throughout the environment, along the paths (as shown in Figure 1). On the passive trials, the participant did not control their movement, or route, in the environment, and instead experienced a recording from the active exploration of a previous participant. Using a previous participant's active exploration for the passive trials, in this yoked control manner, was purposely done to equate route length, pauses along the path, and the number of turns, across the active and passive trials.

#### **6. Results**

Participant Characteristics. While age differences in spatial abilities were of primary interest in the current study, we also wanted to determine whether any sex differences that we presented in our data, given the previous work, suggested that males perform better than females on a variety of spatial processing measures [49]. To determine whether there were differences between the age groups and sexes, on the neuropsychological assessments included in the study, we ran two 2 (Age: younger, older) × 2 (Sex: male, female) ANOVAS separately, for the performance on the Hooper Visual Orientation test [45] and on the Santa Barbara Sense-of-Direction scale [47]. The analysis on the Hooper Visual Organization performance revealed a main effect of Age, *F*(1, 40) = 26.12, *MSE* = 0.26, *p* < 0.001, and *η*2 = 0.40, such that the scores were higher for younger than for older adults, but there was no effect of Sex, *F*(1, 40) = 2.09, *MSE* = 0.02, *p* = 0.16, η2 = 0.05. There were no main effects of Age, *F*(1, 40) = 0.06, *MSE* = 0.14, *p* = 0.80, *η*2 = 0.002, or Sex, *F*(1, 40) = 0.48, *MSE* = 1.07, *p* = 0.50, *η*2 = 0.01 on the Santa Barbara Sense-of-Direction scale, and no interactions on either measure.

Calculation of Route Overlap. The dependent variable of interest was the accuracy in re-tracing the same route at retrieval that had been traveled during encoding. To obtain this measure of spatial memory performance, the overlap between the routes travelled by each participant at encoding and

retrieval, for each trial type, was calculated, to produce a 'percent overlap' value for each environment. The percent overlap values were computed by using the Python programming language (https: //www.python.org/), Python Software Foundation, Wilmington, DE, USA), to first normalize the routes travelled (during both encoding and retrieval) against the established paths in the environments, and then to compare those normalized routes. The process of normalizing the participant's paths involved removing positional data points that corresponded to the subject travelling between specified points on the pathways in the environments. The normalized path that this process produced is a distilled representation of the position data, which allowed us to measure the overlap values that were not obscured by other effects (such as a participant travelling in a straight line during encoding, versus a sinusoidal path in retrieval). See Figure 2 for a visual representation of the normalized paths used in the calculation of the Route Overlap values. This process resulted in 12 values for percent overlap scores per person, 6 of which corresponded to active trials, and six to passive trials. For each participant, the averages were calculated for the active and passive trials separately, resulting in one measure representing performance following active encoding, and another following passive encoding. See the example in Figure 2.

**Figure 2.** Example of the paths travelled in one of the virtual environments at encoding and retrieval before and after normalization.

Route overlap analyses. A mixed measures ANOVA (Analysis of variance) (2 (Age: younger, older) × 2 (Trial type: active, passive) × 2 (Sex: male, female)) was conducted on the Route Overlap values, with the Trial type as a within-subjects factor, and Age and Sex as between-subjects factors. The analysis revealed a significant main effect of Trial type, *F*(1, 40) = 23.07, *MSE* = 0.27, *p* < 0.001, *η*2 = 0.37, such that the Route Overlap was more accurate for active than passive encoding. A main effect was also found for Age, *F*(1, 40) = 83.44, *MSE* = 1.99, *p* < 0.001, *η*2 = 0.68, such that the Route Overlap was higher for younger than for older adults. No main effect was found for Sex, *F*(1, 40) = 0.03, *MSE* = 0.001, *p* = 0.86, *η*2 = 0.06. There was a significant two-way interaction between Trial type and

Age *F*(1, 40) = 5.65, *MSE* = 0.07, *p* = 0.03, *η*2 = 0.12. The Age × Sex interactions was non-significant, *F*(1, 40) = 2.42, *MSE* = 0.06, *p* = 0.13, *η*2 = 0.06, as was the Trial type × Sex, *F*(1, 40) = 1.38, *MSE* = 0.02, *p* = 0.25, *η*2 = 0.03. The 3-way interaction was also non-significant, *F*(1, 40) = 0.38, *MSE* = 0.005, *p* = 0.54, *η*2 = 0.01.

To better understand the interaction between Age and Trial type, two paired-sample *t*-tests were conducted on with Route Overlap accuracy, separately for younger and older adults. For younger adults, there was a marginally significant difference, *t*(21) = 1.98, *p* = 0.06, *d* = 0.35, such that the Route Overlap was higher following active encoding, compared to passive encoding. For older adults, the Route Overlap was significantly higher following active encoding, compared to passive encoding, *t*(21) = 4.55, *p* < 0.001, *d* = 0.61. See Figure 3 for the mean values.

**Figure 3.** Average percent Route Overlap values for active and passive encoding in younger and older adults. Error bars represent the standard error. The star represents significant differences between active and passive conditions for older adults.

Correlations with neuropsychological assessments. To determine whether spatial memory performance was related to performance in the Hooper Visual Orientation test [45] and the Santa Barbara Sense-of-Direction scale [47], we ran a series of partial correlations separately, for active and passive encoding, while controlling for age. A significant and positive relationship was found between Route Overlap accuracy and the Hooper Visual Orientation test, for both active (*r* = 0.51, *p* = 0.001) and passive (*r* = 0.42, *p* = 0.005) Trial types, suggesting, as expected, that better spatial visualization abilities are related to superior spatial memory performance, regardless of whether one actively or passively encodes a route. Correlations with the Santa Barbara Sense-of-Direction scale were non-significant.

Correlations with cognitive indices. Given our goal of determining the factors underlying the benefits of Active encoding, we conducted exploratory analyses on a number of measures obtained from our virtual reality program. Specifically, a number of partial correlations were analyzed. Table 1 shows the factors considered, along with the correlation and the statistical significance values. Given the large number of correlations, we applied both the Bonferroni [50] and Benjamini–Hochberg corrections [51], the latter of which was included as an alternative to the highly conservative Bonferroni correction. Three correlations remained significant after the Benjamini–Hochberg correction. As the number of stars collected during the active trials increased, so did memory accuracy (*r* = 0.39, *p* = 0.009); this relationship held only for active but not passive encoding trials. During encoding, there were occasions where the participant stopped along the route (during both active and passive trial types); we measured the distance of a stop from the nearest intersection, and found that as this distance got

smaller, memory accuracy increased (*r* = −0.39, *p* = 0.01). Similarly, we measured this distance during the retrieval phase (when a participant was re-tracing their route), and found the same relationship (*r* = −0.38, *p* = 0.01). The relationships the distances of the stops from the intersections and Route Overlap held only for the active, and not the passive encoding trials.

**Table 1.** Correlations between memory accuracy (percent overlap values) following active and passive encoding, and various neuropsychological factors, as well as task performance measures.

Both the Bonferroni and Benjamini–Hochberg corrections for multiple comparisons were conducted; SBSD = Santa Barbara Sense of Direction scale; a Correlations were significant following Bonferroni correction; b Correlations were significant following Benjamini–Hochberg correction.

Regression analyses. Step-wise regression analyses were used to examine whether any of the factors considered in the correlational analyses (see Table 1) significantly predicted participants' Route Overlap accuracies following active or passive encoding.

In Step 1 of the analysis of performance following active encoding, Age entered into the regression equation, and was significantly related to Route Overlap, *F*(1,42) = 75.86, *p* < 0.001. At Step 2, the Hooper Visual Orientation test performance was entered into the equation, *t* = 3.05, *p* = 0.004, and at Step 3, the distance of participants' stops from the nearest intersection during encoding was entered, *t* = −2.18, *p* = 0.035. At Step 4, the number of stars collected failed to be entered into the equation, *t* = 1.94, *p* = 0.06. The final model significantly predicted Route Overlap, *F*(3, 40) = 38.00, *p* < 0.001, with an *R*2 of 0.740, decreasing with Age (β = −0.56) and distance from the nearest decision point (β = −0.19), and increasing with the Hooper performance (β = 0.27).

In Step 1 of the passive encoding analysis, Age entered into the regression equation and was significantly related to Route Overlap, *F*(1,42) = 101.82, *p* < 0.001. At Step 2, the Hooper Visual Orientation test performance was entered into the equation, *t* = 2.24, *p* = 0.031, and at Step 3, the length of the path travelled at retrieval failed to be entered into the equation, *t* = −1.60, *p* = 0.12. The final model significantly predicted Route Overlap, *F*(2, 41) = 58.26, *p* < 0.001, with an *R*2 of 0.74, decreasing with Age (β = −0.68) and increasing with Hooper performance (β = 0.24).

#### **7. Discussion**

In this study we created a series of virtual environments in which younger and older adult participants travelled routes and then later re-traced those routes, allowing us to assess spatial memory performance. Critically, participants either traveled routes during encoding by active exploration through the volitional control of their movements, or by passively viewing a guided tour. As expected, we found that actively navigating through virtual environments resulted in more accurate performances in re-tracing the travelled routes, than passively viewing a recorded navigation during encoding. As expected, those with higher scores on the Hooper Visual Organizational test had more accurate spatial memories, regardless of how the routes were encoded. Importantly, we showed that the memory enhancement from active encoding was larger for older than younger adults, indicating that age-related deficits in spatial memory can be reduced by active encoding. Our analysis also revealed that the cognitive approach of stopping close to an intersection benefitted later spatial memory following active encoding. We suggest that active encoding provides a high degree of environmental support for older adults, by prompting cognitive engagement with the route information, which can serve to improve encoding and subsequent retrieval of that information.

The positive relationship found between memory accuracy and scores on the Hooper Visual Orientation test suggests that individuals who can create better mental visualizations are better at creating a mental image of the travelled routes, which benefits performance when retracing those routes. The Hooper Visual Orientation test measures one's ability to mentally rotate and piece together misarranged visual information to identify the depicted object [45,46]. As such, it may be the case that individuals with higher scores on this test are better at mentally translating and combining the visual components of travelled sections of the route, to create a survey representation of the environment. Indeed, this relationship emerged for both active and passive encoding, which indicates that regardless of how one interacts with an environment, the ability to visualize a mental map supports the memory retrieval of route information.

The finding that older adults' spatial memory performance was enhanced following active relative to passive encoding, is consistent with other findings in the literature [15,25,26]. For example, Sauzéon and colleagues [26] demonstrated that older adults had better memories for objects in a virtual apartment following active exploration rather than a passively guided tour. Additionally, active encoding has been found to benefit older adults' memory for spatial information [15,25] showing that when participants are active 'drivers', their memory is better than when they act as passive 'passengers' in virtual environments. Our findings build on this previous work, by demonstrating that when attempting to retrace a route that is encountered on only one previous occasion, having actively been in control of the initial navigation benefitted memory more than passive guidance. These findings contribute to the literature, suggesting that age-related deficits in spatial memory abilities can be ameliorated through the use of active encoding strategies for navigation.

The finding that memory accuracy was related to the number of stars collected following active, but not passive encoding suggests that during active navigation, attention is indeed oriented towards information that is relevant to the traveled route. Notably, no relationship was found between memory accuracy and the length of the path traveled, meaning that the collection of stars must have provided a unique benefit towards encoding. One possibility is that a memory benefit was obtained through intrinsic motivation or curiosity [35,36] associated with collecting the stars. As described in the introduction, it has been suggested that momentary feelings of curiosity boost memory and executive functions [37], because phasic activation of the noradrenergic and dopaminergic systems modulate hippocampal activity [38]. Regardless of the potential mechanism, it is clear from our results that having a larger number of interesting or notable targets in the environment during encoding enhanced memory recall.

We also found a relationships between mental accuracy and the distance of stops from the nearest intersection during encoding and retrieval, such that stopping near an intersection was related to better performance in retracing the route. Furthermore, in the regression analysis, stopping near an intersection during active encoding was found to be predictive of the accuracy in route overlap. When participants stopped moving near an intersection within the environment during encoding, it facilitated their later memory in recalling the spatial route travelled. It may also be the case that the intersections, or decision points, are critical components of the overall spatial representation of the route. Enhanced encoding of these points likely led to a better spatial representation of the details of the route. When the participants stopped moving in the environment during retrieval, they were possibly attempting to remember the decisions that were previously made at the intersection during encoding, and correcting their planned route if necessary. However, it is important to note that only stopping near an intersection during active encoding, and not retrieval, was a significant predictor of route overlap accuracy. This finding suggests that stopping to focus attention on decision points during exploration in the environment aids spatial memory performance.

In older adults, links have been found between poor spatial representation abilities and declines in both executive functioning [52,53] and episodic memory [27]. The effortfulness hypothesis [54], which states that, because of age-related sensory limitations, there are more cognitive resources that are allocated to the identification of perceptual information, rather than to the encoding of meaningful content, could explain why spatial memory deficits occur in older adults. For example, if limited cognitive resources are directed towards the identification or perceptual processing of a landmark, encoding of the spatial location of the landmark or of the route travelled would be hindered. Indeed, navigational strategies are effortful and cognitively demanding, requiring executive functioning, spatial abilities, and memory [43], which are well-documented as being in decline in older adults. Critically, however, it should be possible to reduce age-related differences in spatial memory performance by implementing encoding strategies that direct cognitive resources specifically to the encoding of spatial information. While our task involved physical components (motor control for locomotion, proprioceptive, and vestibular sensory information) from pressing the movement button and spinning the chair to change directions, it was likely not difficult or demanding enough to create a cognitive burden on the older adults. It would be interesting for future work, to include a divided attention condition, in which participants need to respond to a secondary task while simultaneously navigating during encoding, to determine if heavily taxing their available cognitive resources results in a diminished effect of active encoding.

Notably, we found that older adults had significantly poorer spatial visualization abilities than younger adults, which may be linked to a common deficit that is associated with the poorer spatial memory performance observed in this age group. Older adults have been found to rely more on egocentric rather than allocentric representations during navigation [55,56], the latter of which involves a more highly developed survey representation of space [57], and this is possibly supported by processes that are similar to those supporting general spatial visualization functions. Evidence suggests that degradation in the hippocampal region is related to declines in survey abilities in older adults [58,59] and also to visual perceptual processing [60], which may be involved in spatial scene construction abilities that are supported by the hippocampus [61,62]. Indeed, older adults have been shown to have worse performances than younger adults in both spatial and scene construction tasks that involve imagining an event [63]. An interesting avenue for future work would be to investigate the links between visual imagery abilities, scene construction, and spatial representations, and how these change in the aging brain.

#### **8. Conclusions**

The results from this study demonstrate that actively navigating, rather than passively encoding environments results in a more accurate performance when re-tracing a travelled route. Importantly, the magnitude of the memory enhancement from active relative to passive encoding was larger for older than younger adults, reducing the commonly observed age-related deficit in spatial memory performance. Additionally, our findings demonstrate that higher scores on the Hooper Visual Organizational test, and pausing near intersections during active encoding are related to, and predictive of, route overlap accuracy, illuminating the cognitive components that contribute to the benefits of active navigation memory. Overall, our findings suggest that older adults can improve their spatial memory performance for travelled routes by actively controlling their exploration of new environments.

**Author Contributions:** Conceptualization, M.E.M., M.A.F., and H.S.; methodology, M.E.M., J.G.M., and M.A.F.; software, J.G.M.; validation, M.E.M., J.G.M., and M.A.F.; formal analysis, M.E.M.; investigation, M.E.M.; resources, M.A.F., and H.S.; data curation, M.E.M., and J.G.M.; writing—original draft preparation, M.E.M.; writing—review and editing, M.E.M., J.G.M., M.A.F., and H.S.; visualization, M.E.M. and J.G.M.; supervision, M.A.F.; project administration, M.E.M. and M.A.F.; funding acquisition, M.A.F. and H.S.

**Funding:** This research was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 2015–2020 awarded to author M.F., and graduate scholarship from NSERC awarded to author M.E.M., as well as a Waterloo-Bordeaux International Initiative grant awarded to M.A.F. and H.S.

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