Productivity of corn according to different row spacing and plant densities.
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",isbn:"978-1-83881-922-4",printIsbn:"978-1-83881-921-7",pdfIsbn:"978-1-83881-923-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"dcfc52d92f694b0848977a3c11c13d00",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",keywords:"Agricultural Engineering, Technologies, Application, Sustainable Agriculture, Information Technology in Agriculture, Food Security, Renewable Energies, Precision Farming, Smart Agriculture, Farm Mechanization, Robotics, Post Harvest Technologies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 25th 2020",dateEndSecondStepPublish:"December 23rd 2020",dateEndThirdStepPublish:"February 21st 2021",dateEndFourthStepPublish:"May 12th 2021",dateEndFifthStepPublish:"July 11th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Ahmad is a researcher in the field of agricultural mechanization and agricultural equipment engineering, in-charge of Farm Machinery Design Laboratory at Bahauddin Zakariya University, with expertise in modeling and simulation. He applied for two patents at the national level.",coeditorOneBiosketch:"Renowned researcher with a focus on developing energy-efficient heat- and/or water-driven temperature and humidity control systems for agricultural storage, greenhouse, agricultural livestock and poultry applications including HVAC, desiccant air-conditioning, adsorption, Maisotsenko cycle (M-cycle), and adsorption desalination.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.jpg",biography:"Fiaz Ahmad obtained his Ph.D. (2015) from Nanjing Agriculture University China in the field of Agricultural Bioenvironmental and Energy Engineering and Postdoc (2020) from Jiangsu University China in the field of Plant protection Engineering. He got the Higher Education Commission, Pakistan Scholarship for Ph.D. studies, and Post-Doctoral Fellowship from Jiangsu Government, China. During postdoctoral studies, he worked on the application of unmanned aerial vehicle sprayers for agrochemical applications to control pests and weeds. He passed the B.S. and M.S. degrees in agricultural engineering from the University of Agriculture Faisalabad, Pakistan in 2007. From 2007 to 2008, he was a Lecturer in the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan-Pakistan. Since 2009, he has been an Assistant Professor in the Department of Agricultural Engineering, BZ University Multan, Pakistan. He is the author of 33 journal articles. He also supervised 6 master students and is currently supervising 5 master and 2 Ph.D. students. In addition, Dr. Ahmad completed three university-funded projects. His research interests include the design of agricultural machinery, artificial intelligence, and plant protection environment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.jpeg",biography:"Muhammad Sultan completed his Ph.D. (2015) and Postdoc (2017) from Kyushu University (Japan) in the field of Energy and Environmental Engineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese Government) during Ph.D. and Postdoc studies, respectively. In 2019, he did Postdoc as a Canadian Queen Elizabeth Advanced Scholar at Simon Fraser University (Canada) in the field of Mechatronic Systems Engineering. He received his Master\\'s in Environmental Engineering (2010) and Bachelor in Agricultural Engineering (2008) with distinctions, from the University of Agriculture, Faisalabad. He worked for Kyushu University International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years. Currently, he is working as an Assistant Professor at the Department of Agricultural Engineering, Bahauddin Zakariya University (Pakistan). He has supervised 10+ M.Eng./Ph.D. students so far and 10+ M.Eng./Ph.D. students are currently working under his supervision. He has published more than 70+ journal articles, 70+ conference articles, and a few magazine articles, with the addition of 2 book chapters and 2 edited/co-edited books. 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The equatorial northern region has a rainy climate, and is covered by the world’s largest area of pristine tropical rainforest, while the Northeast is mostly semi-arid with some irrigated areas. The Midwest, Southeast and South are the principal grain-producing regions.
Corn (Zea mays) is the grain cultivated in the largest volume worldwide, with the United States, China and Brazil being the principal producers. In Brazil, 15,922.5 million hectares were planted with corn in the 2015–2016 season, with a mean productivity of 4178 kg ha−1, rising to 16,772 million hectares in 2016–2017, with an expected mean productivity of 5305 kg ha−1, with a total harvest of 88,969.40 million tons [2].
The technological advances in the production of corn in Brazil involved the correction of the soil (acidity, neutralisation of aluminium and increase in base saturation). Over the subsequent years, direct planting was adopted as a strategy for the protection of the soil, using corn stover as a way of increasing the amount of organic matter in the soil. Subsequent research tested the reduction of the spacing of the rows from 0.90 to 0.45–0.50 m to optimise the performance of seeding machines and improve the density of plantations, leading to an improvement in the absorption of soil nutrients by the roots of the plants.
The reduction in spacing also contributed to an improvement in the control of weeds, through the more rapid formation of ground cover and shading of the soil, in addition to an increase in the efficiency of fertilisers. Subsequently, the introduction of genetically modified organisms for the control of the fall armyworm (Spodoptera frugiperda), the principal insect pest of corn plantations, also resulted in gains in productivity.
Recent advances in biotechnology have included the incorporation of a number of proteins derived from the bacterium Bacillus thuringiensis to control of a range of insect pests (Elasmopalpus lignosellus, Agrotis ipsilon, S. frugiperda, Diatraeia saccharalis and Helicoverpa zea), reducing damage to the plants, and improving productivity. The subsequent introduction of hybrids resistant to insects and herbicides (glyphosate and ammonium glufosinate) has further reduced the costs of the control of insect pests and weeds. The combination of these technologies has brought significant gains in the productivity of corn, both in Brazil, and the rest of the world.
In Brazil, the corn seed industry involves a number of national and multinational corporations, as well as public entities that are all working to develop new cultivars and technologies [3]. In recent years, these enterprises have marketed cultivars that target specific productive sectors, which rely on high, medium and low levels of technology. The former two sectors use hybrids, while the low technology sector still relies on many freely pollinated varieties.
Araújo et al. [4] investigated the collaborative public networks of corn research in Brazil between 2006 and 2010, and found close ties between the institutions involved in the enhancement of cultivars and those working on the development of technology for the improvement of productivity (Figure 1). Research efforts are concentrated in Southeast Brazil, where the Brazilian Public Agricultural Research Corporation (EMBRAPA) and São Paulo State University (UNESP) have close links with a number of other research institutions, developing collaborative research projects for the divulgation of new technologies for corn production.
Nucleus of the collaborative public corn research network in Brazil (2006–2010). Source: Araújo et al. [4].
Galvão et al. [5] evaluated the advances in the production of corn in Brazil since the 1940s and found that technology has contributed to an increase in productivity of 379% over the past 70 years. Research institutions have contributed to this increase in productivity through the development of research, cultivars and technologies, the training of specialised personnel, and the communication of information to farmers. This technological development has resulted in Brazil reaching third place in the world ranking of corn producers and exporters, with total production increasing from 5.6 million tons in 1944 to more than 89 million tons in 2017.
The Brazilian National Technical Commission for Biosecurity (NTCBIO) was created by federal decree number 1520/95. This organ is responsible for the development of legal norms on the biosecurity of genetically modified organisms (GMOs) and the classification of their potential risks. The commission was initially responsible for the authorisation of experiments on transgenic plants in Brazil. The cultivation of genetically modified plants in Brazil began in the 1990s with the illegal introduction of the Roundup Read (RR) soybean, which is resistant to the herbicide glyphosate, in the state of Rio Grande do Sul.
In the specific case of transgenic corn, the importation of seed from Argentina was first authorised in 2005, in an attempt to overcome the poor harvest of this year. Eventually, in May 2007, the NTCBIO authorised the sale of transgenic corn in Brazil. Currently, most areas planted with corn in Brazil involve some transgenic variety, and the vast majority of hybrids are now resistant to insects (lepidopterans) and herbicides (glyphosate and ammonium glufosinate). In 2007, the NTCBIO authorised the planting of Bt corn, which contains the protein cry1fAb for the control of S. frugiperda and Diatraeia saccharalles, and in 2008, it permitted the sale of RR corn seed, which is resistant to glyphosate-based herbicides, as an alternative for the management of weeds, due to the ample spectrum of control of these plants.
In the most recent Brazilian harvest (2016–2017), transgenic corn, resistant to insects and/or herbicides, should account for 82% of the summer crop and 92% of the second planting, with transgenic hybrids thus being planted in more than 88% of the total area cultivated.
Tollenaar and Lee [6] concluded that the productivity of corn is dependent on the specific genetic characteristics of the hybrid planted, favourable environmental conditions and the adoption of adequate farming technology. The potential for the production of grain will be influenced by the interaction between the hybrid and the cultivation conditions, with the same hybrid responding differently to distinct conditions, depending on the ambient temperatures, the incidence of sunlight and the availability of water.
Each year, a number of new hybrids are marketed, following extensive testing in the principal corn-producing regions of the country to determine the conditions to which the hybrid is best adapted. In a study of 22 hybrids at 14 different sites, Cardoso et al. [7] observed varying responses, with some cultivars being well-adapted to a wide range of conditions, in which they maintain their productivity, whereas others are better adapted to certain specific conditions.
In a study of 10 hybrids during 3 different planting periods (18/11/2011, 31/01/2012 and 20/02/2012), Buso and Arnhold [8] recorded variation in the performance of the cultivars under different seasonal conditions. In this analysis (Figure 2), the hybrid AGN 30A77H performed better than all the other hybrids in the first two periods (18/11/2011 and 31/01/2012), whereas the third period (20/02/2012) was found to be unfavourable due to water stress.
Graph produced in GGEbiplot showing the perspective of different hybrids in three distinct seeding periods, E1 (18/11/2011), E2 (31/01/2012) and E3 (20/02/2012). Codes: g1 = Truck, g2 = Formula, g3 = P30F53, g4 = P3646H, g5 = P30F35H, g6 = AGN 30A77H, g7 = AGN 30A37H, g8 = AG 8088 PRO, g9 = DKB 390 and g10 = DKB Bi9440. Source: Buso and Arnhold [8].
Sousa et al. [9] evaluated 36 corn hybrids cultivated under water stress and found that the performance of these cultivars varied according to the humidity of the soil, with some hybrids performing much better than others under these extreme conditions. The testing of these hybrids contributed to the identification of the cultivars best adapted to the second planting in Brazil, principally under conditions of water stress, in the different Brazilian regions. The interim harvest is planted between January and March. Silva et al. [10] noted that, due to the precocity of the hybrids, the interim crop is favoured by the fact that the flowering period coincides with the rainy season, when more groundwater is available, contributing to productivity.
In addition to genetic enhancement and the use of biotechnology, other agricultural practices contributed to the increase in corn productivity, such as nutrient management, the reduction in row spacing, adjustments of plant density and the use of direct seeding. The adjustment of the spatial arrangement of the plants (in particular the density of the plantation) and the reduction of row spacing had positive effects on productivity, through the increase in the incidence of sunlight and the better exploitation of the environment by the genotype [11]. The increase in population density results in gains in productivity up to an optimum number of plants per unit area, which varies according to the hybrid and the environmental conditions, with productivity decreasing at densities above this optimum level [12]. Increasing the density of plants leads to an increase in the competition among plants for water, nutrients, sunlight and CO2 [13], and may also induce sterility and reduce the amount of grain per cob, resulting in a loss of productivity.
In their analysis of different row spacing parameters and population densities (Table 1), Farinelli et al. [14] observed that productivity was influenced by the reduction in spacing and the increase in the density of seeding, with the highest productivity (7842 kg ha−1) being obtained with the most reduced spacing, of 40 cm (Table 1). This result may be related to the increased efficiency of the plants in the interception of sunlight, and a decrease in the competition for sunlight, water and nutrients among the plants in the same row. These authors also recorded an increase in productivity with increasing population density, up to 80,000 plants ha−1 (Table 1). These gains in productivity accruing to increasing population density are related to the use of hybrids better adapted to high population densities. These hybrids are smaller, have more erect leaf architecture, rapid emission of the style-stigma, coordination of the anthesis with the emission of the stigmas, rapid development of the first cob, reduced size of the tassel and an even greater efficiency in the production of grain per unit area.
Spacing (m) | Productivity (kg ha−1) | Density (plants ha−1) | Productivity (kg ha−1) |
---|---|---|---|
0.4 | 7842 a | 40,000 | 6320 b |
0.6 | 7372 ab | 60,000 | 7777 a |
0.8 | 6974 b | 80,000 | 8091 a |
Productivity of corn according to different row spacing and plant densities.
The mean values in the same column followed by different letters are significantly different (p ≤ 5%) from each other, based on Tukey’s test. Adapted from Farinelli et al. [14].
Silva et al. [15] found that a row spacing of 0.45 m resulted in a 17% gain in productivity in comparison with a 0.90 m spacing (Table 2), and found many other studies with similar results, showing that considerable gains can be obtained by reducing the 0.90 m row spacing that had been used for many years. These authors also found that densities of 60,000 and 80,000 plants ha−1 resulted in gains in productivity of 12.5 and 13.6%, respectively, in comparison with the more traditional density of 40,000 plants ha−1 (Table 2). These results indicate that the hybrids tested tolerate an increase in planting density without affecting productivity. However, the density of 60,000 plants ha−1 appears to be the most viable option, considering that the gain in productivity is only negligibly lower from that at 80,000 plants ha−1, while the adoption of a greater plant density implies higher costs for the purchase of seed.
Spacing (m) | Productivity (kg ha−1) | Density (plants ha−1) | Productivity (kg ha−1) |
---|---|---|---|
0.45 | 8514 a | 40,000 | 7256 b |
0.90 | 7263 b | 60,000 | 8163 a |
– | – | 80,000 | 8246 a |
Productivity of corn under different standards of row spacing and plant density.
The mean values in the same column followed by different letters are significantly different (p ≤ 5%) from each other, based on Tukey’s test. Adapted from Silva et al. [15].
In an analysis of the harvests of 2 years, Buso et al. [16] recorded different patterns of productivity between years for different parameters of row spacing and planting density (Table 3). In the first year, productivity was greater at the higher densities (70,000 and 80,0000 plants ha−1), with 10,922–11,796 kg ha−1, while the lower density (60,000 plants ha−1) produced only 9118 kg ha−1. In the second year, the middle density (70,000 plants ha−1) was significantly more productive (6253 kg ha−1) than either of the other densities, with 60,000 plants ha−1 producing only 5045 kg ha−1 of corn and 80,000 plants ha−1 producing 5606 kg ha−1 (Table 3).
Harvest | Plant population (thousands ha−1) | Row spacing (m) | |||
---|---|---|---|---|---|
60 | 70 | 80 | 0.50 | 0.80 | |
2010/2011 | 9118 aB | 10,922 aA | 11,796 aA | 10,923 aA | 10,301 aA |
2011/2012 | 5045 bB | 6253 bA | 5606 bB | 6437 bA | 4831 bB |
Productivity of corn (kg ha−1) in the 2010/2011 and 2011/2012 harvests for different plant densities and row spacing.
The mean values in the same row followed by different letters are significantly different (p ≤ 5%) from each other, based on the Scott-Knott test. Adapted from Buso et al. [16].
The reduction in row spacing contributes to gains in productivity through the optimal distribution of the plants per unit area and provides the best management strategy for the control of weeds, due to the rapid growth of the plants, which closes over the gaps and increases the interception of sunlight, impeding the growth of weeds. It also increases the exploitation of the soil by the root system of the plants, and reduces planting costs, given that the same machinery used to seed other crops, such as soybean, bean and sorghum, can be used to plant the corn, due to the fact that these crops use the same row spacing.
The majority of the 16 million hectares used to produce corn in Brazil are cultivated by direct planting [2]. However, the adequate management of the soil is essential to guarantee the efficiency of this system [17]. This requires mechanical-, edaphic- and vegetation-based conservation practices, in particular, the use of cover crops to form a layer of stover, increase the organic material and contribute to the greater retention of nutrients during the organic phase.
The maintenance of the surface stover is determined by the Carbon:Nitrogen (C:N) ratio and the lignin concentrations found in the different plant species used as cover and for the formation of the stover. Climatic conditions influence the velocity of the decomposition of the stover by microbial organisms, by determining the micro-environmental conditions for their development.
Carvalho et al. [17] studied the effects of cover crops and the successive cultivation of corn, and found that productivity was influenced by the type of stover, varying from 11,666 kg ha−1 (following wheat) to 12,780 kg ha−1 (following ruzi grass) during the 2010/2011 harvest (Table 4). Productivity was significantly higher for ruzi grass, brown hemp, Brazilian jackbean and pearl millet, in comparison with velvet bean and wheat. Productivity was highest in the context of the more accelerated decomposition of the residues of some of these species, which is associated with the quantity of dry matter produced. The chemical composition of the cover crops with the lowest concentrations of lignin, such as ruzi grass and Brazilian jackbean, and the production of greater volumes of dry matter may have favoured not only the quantity of nutrients, but also the synchrony of the liberation of the plantation for the seeding of the corn.
Cover crop | Level of N in the leaf (g kg−1) | Productivity (kg ha−1) |
---|---|---|
Ruzi grass (Urochloa ruziziensis) | 26.0 | 12,780 a |
Brown hemp (Crotalaria juncea) | 27.1 | 12,710 a |
Brazilian jackbean (Canavalia brasiliensis) | 25.9 | 12,580 ab |
Pigeon pea BRS mandarin (Cajanus cajan) | 24.1 | 12,500 ab |
Pearl millet ‘BR05’ (Pennisetum glaucum) | 25.2 | 12,130 abc |
Velvet bean (Mucuna aterrima) | 26.4 | 11,750 c |
Forage radish (Raphanus sativus) | 28.8 | 12,280 abc |
Sorghum ‘BR 304’ (Sorghum bicolor) | 26.2 | 11,960 bc |
Wheat (Triticum aestivum) | 25.0 | 11,670 c |
Native vegetation | 24.4 | 11,940 c |
Level of N in the leaves of different cover crops and the productivity of the corn planted after these species.
The mean values in the same column followed by different letters are significantly different (p ≤ 5%) from each other, based on the Tukey-Kramer test. Adapted from Carvalho et al. [17].
In general, the ruzi grass contributes to nutrient cycling and the excellent quality of the stover produced, which results in an increase in the levels of organic matter, protecting the soil from the direct impacts of erosive agents, as well as facilitating the management of weed growth. This grass also has a very aggressive root system, capable of recuperating nutrients that the planted crops are unable to access due to their depth in the soil profile.
The use of cover crops is essential to guarantee the sustainability of many different types of crops in all regions of Brazil, in particular those of the Cerrado domain, where the soils tend to be intensely weathered. In this case, the mineralization of the organic matter formed by the cover crops provides nutrients for the corn plantations. The most important nutrient for this crop (corn) is nitrogen, and the need for supplementation with this nutrient will depend on a series of factors, such as the history of the area and the crop planted before the corn, the definition of which will help define the optimum dosage, sources and the forms of nitrogen to be applied.
One other management option, recommended by some authors, is the application of bacteria that contribute to the growth of the plants through a number of different mechanisms for the nitrogenous nutrition of the corn plantations. The most-studied crop-associated diazotophic bacteria are those of the genus Azospirillum. Portugal et al. [18] observed that inoculation of the corn seed with Azospirillum had different results, depending on the associated cover crop (Table 5). In this study, inoculation associated with Crotalaria juncea and Cajanus cajan did not result in any gains in productivity (Table 5), given that these two plants also fix nitrogen in the soil, benefiting the subsequent corn crop. In areas planted with grasses or left fallow, however, inoculation with Azospirillum brasilense tends to have an effect on productivity.
Cover crop | Inoculated | Not inoculated |
---|---|---|
Crotalaria juncea | 7795 b A | 9124 a AB |
Cajanus cajan | 8299 b A | 9338 a A |
Pennisetum americanum | 8487 a A | 8159 a B |
Pennisetum americanum + Crotalaria juncea | 8632 a A | 8569 a AB |
Pennisetum americanum + Cajanus cajan | 8164 a A | 8796 a AB |
Fallow | 8288 a A | 8153 a B |
Productivity (kg ha−1) of corn from seed inoculated with the bacterium Azospirillum brasilense and seed not inoculated, raised following the planting of different cover crops.
The mean values in the same row followed by different lower case letters, and in the same column by different upper case letters, are significantly different (p ≤ 5%) from each other, based on the Tukey-Kramer test. Adapted from Portugal et al. [18].
Consciousness is a complex term to tackle objectively due to its broad epistemological spectrum. From a clinical view, consciousness has been neurophysiologically and behaviorally parameterized for its assessment [1, 2]. It is a central nervous process (reduccionism) that multiple neural long-range connections control (conexionism) and that is teleonomically goal directed. This neurofunctional point of view converges with theories about the emergence of new features in complex systems [3]. Various authors propose that high brain connectivity between distinct and distant neural groups is an elemental characteristic for the emergence of consciousness [3, 4, 5]. In this respect, consciousness is a neurophysiological phenomenon regulated by different brain networks that create qualia, the subjective experience of consciousness [6, 7, 8, 9, 10, 11].
\nConsciousness should be interpreted as a physiological state of the central nervous system that changes over time and space. This functional mutability allows high-order cognitive functions to take place [6, 12, 13] to produce an overt and/or covert behavior that can be measured via direct observation or neuroimage [14, 15, 16]. All of these intermingled processes are supported via various brain networks that integrate endogenous and exogenous information with the intention of responding effectively to organic and psychological demands [6, 8, 11, 17, 18]. In this regard, acquired brain damage can impair the regular activity of brain networks, disorganizing cognition and behavior (mild, moderate, or severe brain damage), or even inhibiting the experience of consciousness (disorder of consciousness) [14, 19, 20, 21]. Therefore, from a clinical view, the structural and neurophysiological integrity of the neural substrate that underlies consciousness will determine the functional behavior of individuals [6, 22, 23]. Thus, consciousness can be described as a basal, dynamic, and transitive brain state that supports the high-order cognitive processing of information to produce suitable behaviors for environmental demands [24].
\nA huge number of theories seem to agree on many assumptions about consciousness, although they diverge regarding the descriptive approach. Some of them, such as the Global Neural Workspace Theory, focus on its neurophysiological components [11]. Meanwhile, others, such as the Global Workspace Theory, focus on its cognitive components [25]. In addition, the Integrated Information Theory focuses on its computational components [8, 26, 27]; the Temporo-Spatial Theory of Consciousness focuses on its inner space and time characteristics [6]; and the PFC-feedback System [28] focuses on its feedforward and feedback components. Crick and Koch introduced one of the first approaches to the study of consciousness [9]. Their approach posits that the experience of consciousness will be determined based on the long-range connectivity between the front and back parts of the brain. All of these authors and theories have shed light on the phenomena of consciousness and have probably contributed to the very first theoretical foundations for the study of consciousness objectively:
Consciousness depends on bioelectrical and biochemical brain activity.
Some neurophysiological processes are required to experience consciousness as awareness (i.e., the object or event has to trigger a P300 wave on the cortex).
These neurophysiological processes are regulated via various neural groups that process information in a rapid, automatic, and stereotypical manner (back brain), as well as via other neural groups that process information in a slow and voluntary manner (front brain).
Consciousness needs long-range connectivity between distinct and distant brain areas.
These long-range connections (probably in beta bands) assemble distinct and distant neural groups into extended neural networks that regulate various physiological and phenomenological dimensions that are necessary for the experience of consciousness.
One of the main neural models that are emerging currently about neural processing is the “predictive coding model” [29, 30]. This model posits that neural processing occurs within feedforward and feedback loops between upper and lower brain structures and slices. Lower structures/slices send predictions to upper structures and these structures send back error predictions to adjust neural processes to make the ongoing behavior efficient [29, 30, 31, 32, 33]. Llinás has already suggested that consciousness could be more related to a close-loop neural network than to the emergent consequence of a sensory input [34]. In this sense, a functional and preserved consciousness could depend on the predictive codification between inferior (brainstem and thalamus) and superior brain structures (cortex), where the prefrontal cortex (PFC) receives “end-of-the-line” bottom-up predictions and sends top-down error predictions to the thalamus to adjust new top-down projections [24, 35, 36, 37, 38, 39, 40].
\nDespite all of the theories and experimental evidence about the neural networks involved in consciousness, no global theoretical framework exists to describe how these neural networks operate to produce and maintain consciousness. The present chapter will introduce a neurofunctional model that organizes the interaction and functioning of the neural networks into three neurofunctional loops: (1) the Brainstem-Thalamic neural loop (B-T neural loop), (2) the Thalamo-Cortical neural loop (T-C neural loop), and (3) the Cortico-Cortical neural loop (C-C Neural Loop). Each of these loops are formed via differentiated and semi-independent neural structures that are involved in specific aspects of the phenomenological consciousness.
\nThe brainstem plays a key role in the regulation of consciousness due to the control that it exerts to the Ascending Reticular Activating System (ARAS) and therefore to wakefulness (wakefulness and awareness are the two clinical dimensions typically related to consciousness) [41, 42]. The ARAS is composed of myriad brainstem nuclei (dorsal raphe locus coeruleus, median raphe, pedunculopontine, and parabrachial nuclei), with connections to the thalamus, hypothalamus, and basal forebrain [42, 43, 44, 45, 46, 47, 48], and even with the prefrontal areas [49] and the precuneus (Pcu) [50]. The lower dorsal ARAS connects the pontine reticular formation to the intralaminar thalamic nuclei (ILN), the lower ventral ARAS connects the pontine reticular formation to the hypothalamus, and the upper ARAS connects the intralaminar thalamic nuclei to the cerebral cortex [51, 52, 53, 54]. Whereas hypothalamic-basal forebrain pathways regulate sleep-wakefulness cycles [48, 55, 56], the ILN, as part of the non-specific thalamic nuclei, can block thalamocortical rhythms and therefore the emergence of arousal and awareness [22, 57, 58, 59, 60]. Baars [18] called this circuit the Extended Reticular-Thalamic Activating System, which he considered to be the principal neural assembly in the experience of consciousness.
\nA significant amount of evidence points out that reciprocal interactions between the thalamus and cortex are a fundamental component of the proper functioning of the thalamo-cortical system [61], which is related to consciousness [62]. This thalamo-cortico-thalamic connectivity starts to develop in the late prenatal and early postnatal stages [61, 63, 64], and the efficient deployment of these developmental processes will determine the functional state of the thalamo-cortical system in the adult stage [65]. The thalamus has been proposed as the main neural structure of the thalamo-cortical system, as it operates as a regulator of cortical functional connectivity, whereby it is involved in the ongoing cognitive processes [66, 67, 68, 69, 70]. The thalamus can be divided into three nuclear groups: first-order thalamic relay nuclei, higher-order thalamic relay nuclei, or non-specific thalamic nuclei. First-order thalamic nuclei send afferent projections to the primary sensory cortical areas, whereas higher-order nuclei receive projections from the primary sensory cortical areas and send these projections back to the higher visual cortical areas forming the cortico-thalamo-cortico circuits. Finally, nonspecific thalamic nuclei are those that receive projections from the ARAS and send diffuse projections throughout the brain [71, 72, 73]. The nonspecific thalamic nuclei are composed of three main nuclear groups: the thalamic reticular nucleus (TRN), the ILN, and the midline thalamic nuclei (MTN). The TRN-ILN-MTN thalamic axis has been related to consciousness [22, 62, 74] with strong implications in the distribution of neural information throughout the brain [24].
\nThe functional extent of each nonspecific thalamic nuclei is related to the control and regulation of a specific cognitive domain [24] . The TRN is one of the main neural nodes that regulates the activity of the thalamus and therefore the activity of the entire thalamo-cortical system [75, 76, 77]. The TRN receives afferent glutamatergic projections from the entire brain, and in turn, it sends only efferent GABAergic projections to the thalamus, thus regulating thalamo-cortical and cortico-cortical activity [28, 78, 79]. On a morphological level, the TRN is divided into sensory and motor regions [80]. Whereas the sensory region modulates attentional processes via connections with the prefrontal cortex [38], the motor region is involved in limbic and motor processes due to high connectivity with the ILN-NMT, the ventrolateral, and the anterior thalamic nuclei [81, 82, 83, 84, 85]. Various authors have referred to the involvement of the TRN in the attentional processes as the “attention spotlight” and “attentional door” that regulate the flow of information between the thalamus and the cortex [35, 86, 87]. The capacity to control neural information throughout the brain is due to the inhibition that it exerts to the thalamic nuclei [37, 76, 86]. This inhibition mechanism underlying the “attention spotlight” selects the information needed to face psychological and physiological demands while suppressing those that are not relevant. Some authors suggest that the TRN is involved in the content of consciousness by controlling selective attentional processes and the thalamus activity [28, 86]. According to Crick [35], the short-term synaptic plasticity of the TRN could influence first-order thalamic relay nuclei in the formation of temporal connections between brain areas related to the content of consciousness [35]. Hence, this capacity to modulate the content of consciousness could be mediated by the control of attentional processes [88, 89, 90].
\nOn the other hand, the functions of the ILN and the MTN are functionally differentiated, but their activity are highly dependent [91, 92, 93, 94, 95]. Regarding consciousness, both nuclei (due to its multiple connections with the ARAS) activate the excitability of the cerebral cortex to maintain vigilance and arousal [42, 58, 59, 60, 76, 91]. For instance, the ILN send and receive projections from the prefrontal, motor, and parietal cortices. Meanwhile, the MTN is connected to the medial prefrontal cortex (mPFC) and the hippocampus (HPC). These diffuse connections spread to the cortex, thus allowing the synchronization of brain activity through the adjustment of the brain waves’ phases. Thus, distinct and distant neural groups assemble into cortico-cortical networks to facilitate the flow of neural information [91]. In addition, The ILN and MTN are also involved in the regulation of the striatal-thalamocortical circuits [96] due to the multiple efferent inhibitory connections that receive from the TRN, the basal ganglia, and the reticular formation of the ARAS [97, 98, 99]. These connections with the striatum, the brainstem, and the cortex highlight the relevance of the ILN and the MTN in the motor, somatic, and visceral functions, which are essential for controlling arousal, perception, and even emotion expression [100].
\nSpecifically, the ILN have been associated with the regulation of cortical activity and the restoration of consciousness [22, 68, 101, 102]. The anterior region of the ILN react to motor inputs [103, 104], whereas the posterior region organizes motor, limbic, and associative information [60, 97, 105, 106]. Projections to limbic structures and sensori-motor areas suggest the relevance of the integration of the affective and motor functions that underly propositional behaviors [107]. In addition, they are involved in tasks that require the focalization of attention and the selection of actions for unexpected events [108, 109]. Kinomura and colleagues pointed out that arousal and attention require the simultaneous activation of the reticular formation of the midbrain and the ILN [110]. This evidence places the ILN as the basic neural nodes for the integration of brain functions, such as arousal, attention, and motor control, to trigger high-level cognitive performance [86, 104, 110, 111, 112, 113]. This functional characteristic of the ILN in the regulation of the arousal has been employed for deep brain stimulation in cases of minimally conscious state. Schiff [22, 114] showed that stimulating the ILN in minimally conscious state patients could improve their motor behavior, but without showing any sign of “real” consciousness [22, 114, 115]. Therefore, although the ILN seems to be involved in consciousness, it cannot produce a constant and fluent stream of consciousness by itself.
\nFinally, the MTN have been reported as the main “gateway” of information to the HPC and the limbic system, with a high dependence on the individual’s arousal levels [116, 117, 118, 119]. Concretely, the nucleus reuniens and rhomboid of the MTN jointly with the mPFC and the HPC form a specialized neural circuit that contribute to learning and to the cognitive flexibility [120], probably due to its relationship with the working memory [116, 117]. This circuit constituted by the MTN-HPC-mPFC could be modified via the functional state of the TRN [121] and also affect the content of consciousness [122]. Other authors propose that the circuit formed via the orbital and mPFC, the amygdala, the hypothalamus, and the MTN could also be involved in the visceral and emotional control of human behavior [123, 124, 125, 126, 127, 128]. The MTN directly influences the arousal and attentional processes through its involvement in emotional regulation [129]. Thence, it is implicated in the emotional adjustment of behavior in a continuously changing environment [130]. According to these authors, the MTN could mediate the selection of the most suitable behavior depending on the emotional tone inputs received in a specific moment [118, 130]. This evidence places the MTN as a remarkable interface between the diverse structures of the limbic system to integrate memory, emotion, and cognition [100, 119, 129, 131].
\nAll of this evidence points out that the TRN-ILN-MTX thalamic axis and its connections throughout the brain are essential components for being conscious and aware of our surroundings due to the axis’s capacity to place the T-C neural loop in an optimal functional state [24, 35]. In this sense, it is important to distinguish between “be aware” and the “formation of consciousness.” Being aware of something means that our cognitive systems are prepared to receive and manipulate the content of consciousness, but the formation of the content of consciousness depends on other neural processes. The content of consciousness is formed mainly in the posterior cortex [132, 133] through cortico-thalamo-cortico circuits, which facilitate connections among various sensory cortical areas in the “content-specific Neural Correlates of Consciousness (NCC)” [70, 133, 134, 135, 136]. Regardless of the content-specific NCC, when it comes to accessing consciousness, some neurophysiological requirements, such as a late P300 wave, are needed to ignite a global brain activation that will trigger awareness [137]. The conscious perception of the content of consciousness is the end of the concatenation of neurophysiological events that propagate from the back to the front cortex [6, 138]. It would be like a competition among various neural coalitions to access consciousness, and once a winning coalition exists (the first to break neurophysiological requirements), a specific representation or the content of consciousness can be perceived as generating a genuine experience of consciousness [137]. Afterward, this content of consciousness is controlled by high-order cognitive functions and is incorporated into plans, desires, and/or thoughts [6, 139].
\nOnce the content of consciousness is created in the back brain [132, 133], various cortico-cortical networks consciously manipulate the information [140]. One of the main cortico-cortical networks, which is broadly documented, is the Default Mode Network (DMN) [141, 142, 143, 144]. This network is formed by the anterior and posterior cingulate cortex, the mPFC, the orbital PFC, the medial temporal lobe (parahippocampal cortex and HPC), the retrosplenial cortex, and the inferior parietal lobe [145] . The DMN is a rest neural network, whose activity is maximum when the subject is awake and the cognitive demand is low (low-level processing of exogenous information) [146]. Moreover, the DMN is characterized by a high metabolism during rest states [147, 148, 149, 150], a progressive deactivation when more cognitive resources are needed to process information [147], and a high connectivity with other cortico-cortical networks to exchange information [140, 143, 151]. Traditionally, the DMN has been related to internal processes, such as self-reference thoughts and mind-wandering [152, 153, 154], although some studies currently link its activity to extrinsic processes, such as certain attentional processes [155] and the recall of memories [156, 157, 158, 159]. Recently, it has been posed that the DMN could also be involved in the integration of spatial, self-reference, and temporal information, thus generating episodic memories [160]. These authors suggest that, henceforth, the DMN is mostly activated in all of the cognitive processes [160].
\nOne of the key points for understanding the role of the DMN in consciousness is to conceive it as a cognitive system that modulate cortico-cortical activity through its mediation in the transfer of information from resting states or task-negative networks to cognitively active states or task-positive networks [140, 147, 156, 161, 162, 163, 164]. When a subject is resting (with the low-level processing of exogenous information), the DMN controls cortical activity with the posterior cingulate cortex (PCC) and the precuneus (Pcu) as their main neural nodes. However, as long as elaborated processing is required and the load of the working memory increases, the physiological burden of the DMN decreases in favor of task-positive networks: the fronto-parietal central executive network (FPN), the dorsal attention network (DAN), and the salience network (SN). The FPN includes the dorsolateral PFC, the mPFC, the anterior insula (aINS), the Pcu, and the interior parietal lobe [140, 165, 166, 167]. On the other hand, the DAN is formed by the frontal eye field and the intraparietal sulcus [168], and the SN by the aINS, the dorsal anterior cingulate cortex, the amygdala, the ventral striatum, and the ventral tegmental area of the mesencephalon [169]. All of these networks share overlapping regions whereby they can exchange neural information depending on the ongoing cognitive activity [147, 149, 150, 170, 171, 172, 173] . The outcome of the continuous interactions among the cortico-cortical networks will define the functional conscious state of the individual [163].
\nThe FPN, DAN, and SN play a key role in conscious behavior due to its capacity to operate jointly and synchronically in a highly coordinated and temporally accurate manner [140, 165, 174]. For instance, the DAN has been related to focalized attention and working memory, whereas the SN has been related to social communication, social behavior, and self-consciousness [171, 175, 176, 177, 178] . When all of these task-positive networks are operating, the DMN needs to deactivate [179, 180, 181] to facilitate the transition from low-energy cognitive states to high-energy cognitive states [147]. In these high-energy cognitive states, the mPFC takes control of the global brain activity at the expense of the PCC and the Pcu [170, 182]. Therefore, the alteration of structural and functional connectivity “within and between cortico-cortical networks” could cause the individual to experience a broad spectrum of neuropsychiatric and neurocognitive disorders [162, 163, 180, 183, 184].
\nThe FPN and SN, especially in the prefrontal regions, regulate the cognitive processes involved in the achievement of conscious goals through the regulation of the physiological equilibrium between the DMN and the rest of the cortico-cortical networks (cognitive control) [140, 165, 166, 167, 185, 186]. Some studies point out that the mPFC and aINS regulate physiological equilibrium among brain networks [178, 187]. For instance, Crone and colleagues compared the activation/deactivation of the DMN in vegetative states (currently known as “unresponsive wakefulness state”), minimally conscious states, and individuals with preserved and functional consciousness (control subjects) [182]. They suggested that although the deactivation of the DMN was normal in control subjects, the same deactivation was significantly diminished in overlapped areas between the DMN and the FPN in a minimally conscious state, and it was absent in unresponsive wakefulness state patients. In other words, the cohesive and functional integrity between the DMN and the task-positive networks is a crucial factor in the transition between rest states (those with a low cognitive burden) to high-demand cognitive states (those with a high cognitive burden) [147]. Our team conducted an investigation whereby we compared cortical connectivity between minimally conscious states and severe neurocognitive disorders [4]. Our results revealed how the degree of connectivity between the anterior and the posterior cortex in the beta band was essential for maintaining a preserved consciousness. In this investigation, patients with minimally conscious states showed a low connectivity between the posterior and the anterior cortex, which could explain why their consciousness fluctuates over time [4]. In contrast, subjects with preserved consciousness showed a high connectivity between the anterior and the posterior cortex, whereby they can operate continuously without the absence of consciousness [4]. In this sense, in a case study, an unresponsive conscious patient emerged to a minimally conscious state when connectivity between the anterior and the posterior cortex increased [188]. Thus, the integration of the posterior and the anterior cortex into long-distance cortico-cortical networks is one of the principal prerequisites for maintaining functional consciousness [9, 182, 189, 190].
\n\n
The nFMC is a theoretical and referential framework from which the study of consciousness can be tackled in all of its operative dimensions: neurophysiological, clinical, neuropharmacological, and phenomenological.
Consciousness is a global neural process that keeps the individual in an optimal and continuous functional state, thus allowing qualia and high-order processes to take place to drive behavior.
The nFMC divides global neural activity into three large systems, or functional loops, that are morphologically differentiated (although they share overlapped areas) and have semi-independent neurophysiological processes: the B-T neural loop, T-C neural loop, and C-C neural loop (see Figure 1).
Cognitive, behavioral, and emotional expression due to brain damage will depend on the location and extension of the lesion within the neural loop, thus leading to clinical outcomes that they may vary from a mild cognitive impairment to a disorder of consciousness, such as a coma, minimally conscious state, or unresponsive wakefulness state.
Each neural loop is activated hierarchically and sequentially by its preceding level, thus extending a representation of the neural processes that took place in the lower level, as well as integrating and transforming this neural representation into new information.
The nFMC is in accordance with predictive coding models that present brain activity as a system in which lower brain structures project predictions/signals via bottom–up processing, and where higher cortical areas send prediction errors back via top-down processes.
Neural processes (both automatic and controlled) related to consciousness (such as P300, brain rhythms, and neurotransmitter discharges) can be localized within either of the neural loops or in their reciprocal interactions.
The nFMC is complementary and comprises several assumptions considered in previous theories and investigations of consciousness:
Consciousness can be deemed a Global Neural Workspace in which distinct neural networks compete to access consciousness [11, 25, 192].
Consciousness is the result of functional units or complexes that integrate information and that are activated or deactivated depending on the ongoing sensorial/visceral necessities [8, 26, 27].
Consciousness is a neurophysiological continuum commanded by inner spatio-temporal brain laws [6].
Regarding the neural mechanisms or processes involved in the formation of the content of consciousness, the nFMC aligns with models and evidence that posit that the contents of consciousness are formed in the back brain via cortico-thalamo-cortical connections [70, 132, 133, 134, 135, 136]. In addition, the nFMC recognizes that PFC top-down connections could modulate the selection and even the formation of the content of consciousness [28].
Consciousness is the phenomenological quality of human existence that arises from a hierarchical, parallel, and serial activation of long-distance brain networks [7], which operate as neural loops that “inform” upper and lower levels about their own operations [29, 30]. These loops receive input from lower levels (which contains new information/predictions) and input from upper levels (error predictions). The loop will integrate all of this new information, updating its own functional state and, consequently, also the functional state of the rest of the loops and the brain [29, 30, 31, 32, 191]. ARAS: Ascending reticular activating system; TNN: Task-negative networks; TPN: Task-positive networks.
Human behavior has to be understood as a global brain activity dominated by complex and hierarchical neural processes that cannot be divided and explained by isolated functional units. Consciousness is the “operating system” running underneath the “interface” of overt and covert human behavior, and it is dominated by the interactions of various neural levels composed of differentiated and semi-independent neural networks. Thence, the nFMC gathers reliable knowledge generated in the study on neural correlates of consciousness, providing a novel theoretical and referential framework that will help clinicians, researchers, and even students to localize the neural processes of interest within a global brain activity model. A further proposal should extend the structures and connectivity involved within and between each neural loop introduced in the nFMC.
\nThe authors have no conflict of interest to declare.
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