Inter-percentile 25–75 intervals for bands and lobes in BPA and ES. Superposition is indicated with respect to the ES interval [26].
\r\n\t
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65947",title:"Autism in the Classroom: Educational Issues across the Lifespan",doi:"10.5772/intechopen.84790",slug:"autism-in-the-classroom-educational-issues-across-the-lifespan",body:'While much has been learned about how to educate children with autism spectrum disorder (ASD), and program successes are widely touted, children and youth with ASD are nonetheless at heightened risk for poor academic outcomes, including conflictual relationships with general education teachers [1] and more restrictive classroom placement [2]. Such outcomes are likely due to the presence of substantial social difficulties that are inherent to the ASD diagnosis [3], as well as behavioral difficulties that children with ASD often display [2, 4]. Indeed, it has been documented that individuals with ASD have significantly higher behavior problems than those with intellectual disability and those with typical development [5]. These heightened externalizing and internalizing problems evident in ASD, as well as poorer social skills, may also place these young students at risk for poor long-term school adjustment [1, 6].
As almost half (44%) of students with ASD fall in the typical range or above on cognitive ability [7], it is not uncommon for these students to be placed into general education classrooms [8]. Indeed, placement in general education has been shown to be beneficial for many students with ASD [9] and is often preferred by parents, yet, general education teachers receive little specialized training on how to effectively manage students with ASD in the classroom. Moreover, many school districts are unprepared to accommodate such students, as evidenced by the number of legal disputes with families of students with ASD [10]. The purpose of this chapter is to discuss selected recent advances in research on autism in the classroom and educational issues in the context of ASD in the twenty-first century.
Issued in 2004, the Individuals with Disabilities Education Improvement Act (IDEA) mandated that educational programs for children with disabilities be delivered in the least restrictive environment (LRE). Inherent to the definition of LRE is the notion that a child with special needs should be educated in an environment containing same-aged typically developing peers. Many school districts have implemented a full-inclusion policy for young children with disabilities, including those receiving services for ASD [2], in order to comply with this mandate. These policies are not without challenges. Indeed, students with ASD have been documented to have the highest number of contested individualized education plans (IEPs) and educational rights litigation [10, 11, 12, 13, 14]. Nonetheless, some investigators have reported that the only disability group making progress towards spending more time in general education was students with ASD [15].
Students with ASD who are educated in a general education setting often experience greater success than students who are placed in special education classrooms [9]. Parents of children with ASD are generally in favor of inclusion. Leyser and Kirk [16] surveyed 437 parents of children with disabilities, including autism, and found that the following were related to a more positive parental disposition towards inclusion: (1) children with more mild disabilities, (2) parents with younger elementary school children, (3) parents with a college education, and (4) parents who did not know their child’s educational placement. Moreover, White and colleagues [17] found that once the decision about placement had been determined, it rarely changed; students usually remained in that setting throughout their years of education. This further underscores the importance of helping schools and families make appropriate placement decisions in early school years; however, that decision can legally be revisited [18].
Research suggests that placement decisions for students with ASD may not necessarily be a function of student need. In order to examine the effects of school placement, Kurth and Mastergeorge [19] studied 15 students with ASD (12–15 years of age) who were enrolled in either an inclusive general education (n = 7) or special day class (n = 8). Although students enrolled in general education had a higher overall mean score in cognitive and adaptive functioning, this difference was not statistically significant, likely due to the small sample size.
Determinants of placement may also include beliefs and attitudes of school personnel [20, 21]. For example, in 2008, Horrocks and colleagues surveyed 571 principals in the Pennsylvania public schools. From a list of eleven principal characteristics (e.g., years in district, formal training, school level), there were two significant factors in predicting placement in regular education classrooms: the principals’ experience working with students with autism and their beliefs that children with ASD could be included in general education. Praisner [21] also found that school principals often based their placement decisions on their experiences and beliefs. Specifically, students with ASD and those with emotional disturbance were least likely to be recommended for placement in an inclusion classroom.
Other studies indicate that placement may be determined by certain child characteristics. For example, Lauderdale-Littin, Howell, and Blacher [22] investigated differences between the characteristics of 56 children with ASD placed in public and non-public school settings. Logistic regression analyses indicated that child age, family income, and social skills were predictive of educational placement, such that older children from higher income households and who have lower levels of social skills were more likely to be placed in non-public, more restrictive school settings. The more obvious predictor of school placement is likely IQ or cognitive functioning; Harris and Handleman [23] reported that higher IQ and younger age at intake to an early intervention program were predictive of regular class placement upon the transition to school.
An understanding of family impact is a crucial foundation for general and special education teachers of children with ASD to build a strong partnership. Such alliances between parents and teachers are beneficial to children with ASD [24]. The quality of parent-teacher relationships appears to be an important force in the early school experiences of children with ASD [25, 26] and in later academic skill acquisition [27]. For example, in a study of 121 parents of young children with ASD [26], parents who had positive relationships with teachers had formed more positive expectations for the school year. However, it is important to note that when child behavior problems were entered into analyses, they uniquely predicted parent expectations. Moreover, parents with strong relationships with their child’s teacher and who perceived the teacher to be warm and positive towards their child were less likely to end up in litigation [14, 28].
Disagreements regarding Individualized Education Plan (IEP) outcomes often contribute to the strain between parents and teachers. In a study of 142 parents of young children (ages 4–8 years) with ASD, a meaningful proportion of parents reported dissatisfaction with the IEP process [29]. Specifically, 41% of parents reported only some, or no, agreement between the IEP document and the services received. Parents’ relationship with the teacher and school involvement, as reported by both parents and teachers, were positively associated with IEP satisfaction. Notably, family income and financial resources were also significantly related to IEP satisfaction, such that more resourced families tended to be more satisfied. Nevertheless, these findings highlight the importance of working with parents to increase consistency across the home and school context [30, 31].
The transition to early schooling is a crucial milestone for all children, one that can be particularly challenging for young children with ASD. The quality of the student-teacher relationship (STR) is seen as crucial to successful academic outcomes [32, 33] and a strong predictor of long-term behaviors [34, 35, 36]. For example, close, supportive relationships with teachers are associated with stronger social skills [37] and higher peer acceptance [38] in typically developing young children. However, on average, children with ASD experience poor relationships with their teachers as evidence by low closeness and high conflict [35, 39].
In order to examine the relations among social impairments, behavior problems, and STRs, Blacher and colleagues [1] conducted a comparison study of children (M age of 8 years) with ASD (n = 36), intellectual disability (ID; n = 38), and typical development (TD; n = 91). The measure used to assess STRs (Student-Teacher Relationship Scale; [40]) included domains of closeness, conflict, and dependency. Results revealed that students with ASD scored significantly lower on closeness and significantly higher on conflict than the ID or TD groups. Scores of dependency were statistically similar for both the ASD and ID groups but higher than that for the TD group. Moreover, closeness was accounted for mainly by social skills, while conflict was accounted for by behavior problems.
Currently, there are approximately 1,308,100 paraprofessionals or teacher aides employed; this number is expected to grow at the average rate of 8% by 2026 [41, 42]. The projected increase in paraprofessionals can be attributed to the demand of inclusive education, which may perpetuate an overreliance on their role in the school system [43]. Therefore, there is an immense need to support paraprofessionals in the instruction and behavior management of students with disabilities in inclusive environments [41]. Moreover, a heavy reliance on paraprofessionals creates fewer opportunities for teachers to develop close and supportive relationships with these students.
Peer relationships seem to be the most salient issue for many middle and high school students with ASD. The lack of social skills that persists throughout adolescence has a negative impact on friendships and peer interactions [44]. In clinical populations, improvements in the social skills of children and adolescents with ASD are modest over time. Constantino and colleagues [45] examined the development of social skills at child ages 3 and 18 in a sample of 95 males without ASD and 85 males with pervasive developmental disorder. Results revealed that for the sample with clinical diagnoses, parent and teacher reports on the Social Responsiveness Scale (SRS; [46]) were correlated at both time points. While improvements were seen on the total SRS scores over time, these only reached significance in parent reports, leading the authors to conclude that social improvements over time were subtle or perhaps not obviously manifested in the school setting.
Enduring social difficulties have been found to lead to teasing and bullying by peers, more so for youth with ASD than their typically developing peers [47, 48]. Through interviews with early adolescents (age 13) with ASD, Zeedyk and colleagues [48] found that youth with ASD were victimized more frequently than youth with ID or TD. Above and beyond disability status, higher internalizing behavior problems and peer conflict were significant predictors of victimization. In 2018, Tipton-Fisler and colleagues did a follow-up study with the same youth at age 15 to explore how experiences of bullying and victimization changed for children with TD, ID, and ASD over time [47]. Consistent with Zeedyk et al., [48], they found that adolescents with ASD were bullied more frequently than those with ID or TD. Further, higher levels of internalizing behavior problems at age 13 related to higher levels of bullying at age 15.
These findings are consistent with previous research on internalizing behaviors in youth with ASD and victimization. Cappadocia and colleagues [49] found that internalizing behavior problems predicted victimization in a sample of youth with ASD. Whitehouse and colleagues [50] found that friendship conflicts were a significant predictor of depression in adolescents with ASD. Taken together, this body of research suggests a relationship between internalizing problems (e.g. depression, anxiety) and victimization and/or peer conflicts in youth with ASD. This work also underscores the importance of empirically supported interventions for youth with ASD who display internalizing behaviors. Such interventions could help youth make and keep friends, and provide information about how to deal with bullying. One example of such an intervention is the UCLA PEERS Program, which has a manualized curriculum designed to help youth with ASD make and keep friends, handle conflicts with friends, and bullying [51].
It is interesting that by middle or high school, students with ASD spend most of their time in a classroom with typically developing students, but interacting with other students with disabilities or with a paraprofessional, leaving few opportunities for socialization. Feldman and colleagues [52] demonstrated this in a study of high school youth. When they calculated the percent of time that students with ASD were present in the classroom, they found that, on average, that students with ASD were present for about 81% of a class period. When in class, students were in proximity to their peers in general education classrooms only for about a third (38%) of a class period.
Unfortunately, difficulties for youth with ASD continue into young adulthood. According to a 2012 study, nearly 80% of young adults with ASD lived at home, 40% did not have contact with friends, and more than half (52%) did not have employment or postsecondary training 1–2 years after high school [53]. One possible reason for these dismal outcomes is the significant “service cliff” which occurs once young adults transition to independence. This is particularly notable after public school services end, for many as late as age 22 [18]. Thus, families of youth with ASD should prepare for the transition to postsecondary environments as early as possible. Geller and Greenberg [54] suggest that by age 14, planning should begin, and focus on areas of independence, including self-determination, functional skills, and social-communication skills needed for postsecondary life, especially for those young adults who have concomitant intellectual disability.
Approximately half of students with ASD are cognitively high functioning, with IQs in the typically developing range [7]; for them, attending college is a realistic goal. Predictors of college participation for young adults with ASD include student, family, and transition planning factors, such as attendance in a regular high school; strong academic performance in high school higher household income; parental expectations for attending college; post-secondary goals identified through transition planning; and student participation in transition planning [55].
Youth with ASD have at least two pathways to post-secondary education: [1] attending a 2-year school (community or junior college, depending on the state), or [2] attending a 4-year college or institution. In their review, Zeedyk, Tipton, and Blacher [56] highlighted some of the main benefits of 2- and 4-year institutions specifically for students with ASD. Two-year schools may provide more individualized supports, offer vocational programs, be more likely to be populated by familiar peers from high school, and provide weekly homework assignments similar to high school. On the other hand, 4-year schools usually offer more generic support services, a variety of programs, a more diverse curriculum, more majors to choose from, and sometimes a larger campus community.
Using the National Longitudinal Transition Study-2, a nationally representative dataset on young adults across 10 years, Sanford et al. [57] found that more young adults with ASD attended 2-year colleges than 4-year colleges (32 versus 17%). Moreover, the percentages of college attendance were statistically significantly lower for students with ASD than students with other types of disabilities. Lastly, only 39% of students with ASD graduated college, compared to 41% of students with disabilities in general and 52% of students from the typical population.
Given the dismal college attendance and completion rates, researchers have investigated some of the challenges students with ASD face in postsecondary environments that may impede their success in college. These factors include social difficulties which are related to the characteristics of ASD (e.g., [58]) and inhibit academic success. For example, students may have difficulty adapting to changing school schedules, to the complex social environment associated with college life, and to independent living responsibilities. Unfortunately, supports available in the college setting that focus on academic needs do not address the unique needs of young adults with ASD. Rather, they are generic and largely designed for students with learning disabilities. For example, typical academic accommodations in postsecondary settings may include extended test time, distraction free testing, flexible due dates for assignments, breaks during class, the use of technology in the classroom, note takers, and possibly optional group activities [56, 59].
However, the range of needs of young adults with ASD extend beyond academics critical to postsecondary success [60, 61]. Aside from social concerns, mental health issues are commonly found in ASD populations. Among college students, about 70–90% have reported symptoms of anxiety and/or depression. In a recent study by Jackson and colleagues [62], approximately 18% of students expressed wanting to attempt suicide in the future. Other associated difficulties in ASD include a lack of daily living skills, organization and planning, and flexibility [63]. These areas may be more stressful to cope with in the college environment, as students have less structure in their routines and less support from parents after high school. Some students with ASD also struggle with inappropriate classroom behavior in college (e.g., frequently raising one’s hand, refusing to work in groups with classmates, calling out inappropriately).
Many of the challenges noted above are similar to other “invisible disorders,” primarily attention-deficit/hyperactivity disorder (ADHD; [60, 64, 65]). For this reason, Bolourian and colleagues [60] conducted a qualitative study of 13 young adults with ASD and 18 students with ADHD in 4-year universities located in Southern California, to determine if young adults with ASD had challenges specific to their disorder. From coded in-depth interviews, nine themes emerged, highlighting the similar deficits between the two disorders. The authors also calculated passage frequencies to determine and compare how often these themes were discussed by members of each groups. While most themes did not statistically significantly differ, the theme of Negative Peer Interactions was unique to students with ASD, indicating that university staff need to do more to expand services for college students with ASD. In particular, supports and services to foster more positive social interactions on campus would be helpful, whether in the form of social skills groups conducted on campus, peer volunteers, or even group counseling sessions offered by disabilities services on campus.
Student-faculty relationships may also be impaired for college students with ASD. While student-teacher relationships are more frequently examined in early school years, poor relationship quality has been reported through secondary school for students without disabilities, particularly in relation to academic motivation [66, 67]. Thus, it is plausible that the quality of these relationships, even for youth with ASD, can endure into postsecondary environments.
Not surprisingly, many young adults on the spectrum enter postsecondary education with a passion for a particular subject area, which may inspire the selection of their major. Faculty awareness of these special areas of interest may help them to work this interest into course projects or requirements, thereby increasing student interest or focus. Of course, more interest can lead to better student performance, leading to higher grades, the likelihood of college graduation, and future employment. Thus, there is a rationale for faculty to be aware of their students with ASD and to foster the same type of academic relationship with them that they have with their other students.
There are many things faculty need to know in order to appropriately and comfortably teach and interact with students on the autism spectrum. In a survey of 132 faculty [68], a little over half of professors did not include a statement about disability services in their syllabus (55%), nor did they make a statement about services at the start of the quarter (60%). In terms of their beliefs about students with autism, 47% expected students to interact with peers in the classroom and 78% agreed that they would consider allowing students to work in their lab or on their research team. With regard to student disability services, 12% did not understand the role of disability service offices on campus, and 46% agreed that students with ASD should disclose their disability to disability services. These findings emphasize the importance of educating university faculty on ASD and providing resources on how to support students with ASD in their classrooms.
There are a number of resources available to teachers of children with ASD, particularly those at the early childhood or elementary levels. Many of these focus on intervention methods, and although classroom teachers may not be responsible for their implementation, understanding the evidence-base is useful as the greatest gains are made during the early school year [69]. Behavioral therapy continues to be supported in the literature as an effective intervention for individuals with ASD (e.g., [70, 71]).
In 2014, the National Professional Development Center on Autism Spectrum Disorders (NPDC) and the Institute of Education Sciences funded a project to identify evidence-based practices (EBP) for individuals with ASD, from birth to 22 years [72]. This rigorous review yielded 27 practices, many of which are based on behavioral techniques, including reinforcement, naturalistic approaches, and self-management. While an update of this review is in progress, the NPDC created a free online curriculum that describes the key components of each identified EBP and provides a step-by-step approach to implementing these practices (i.e., Autism Focused Intervention Resources and Modules [AFIRM]; retrievable from
From the Ohio Center for Autism and Low-Incidence Disabilities, the Autism Internet Modules (AIM) is another free online training program (retrievable from
As research on the transition to postsecondary settings receives increasing attention, interventions aimed at supporting youth during these periods are being examined for their efficacy. For example, Lei and colleagues [73] implemented a 3-day, overnight summer school program in the UK for youth with ASD, ages 16 or older, who are preparing for university life. The curriculum included lessons on ‘work’ (i.e., experiencing a typical lecture, appropriate socialization between students and faculty/staff, and disclosure of diagnosis), ‘rest’ (i.e., stress reduction, management of anxiety, and the importance of physical wellbeing), and ‘play’ (i.e., on-campus clubs and societies, social outings, and informal social experiences). The program was facilitated by staff from the university’s psychology department, disability office, and career services office; one session was facilitated by a current or recently graduated student with ASD. The program also offered the support of a ‘student ambassador’ or current typically-developing university student trained to provide broader support in inclusion activities. Findings from a satisfaction survey of 125 participating students revealed promising results. For example, pre- and post-program data showed significant changes on endorsed concerns associated with the transition to university, including in the areas of socialization, independence, and academic functioning. Moreover, on average, participants rated the program to be very enjoyable and helpful, and viewed going to university somewhat positively. With regard to the program components, students found psychoeducation (14.6%) and information on clubs and societies (11.5%) to be most helpful. With regard to their future at the university, students were most enthusiastic in their response about looking forward to new social opportunities (55.7%).
Mentoring has also been examined in the context of social-emotional supports for students with ASD in postsecondary environments. Lucas and James [74] tested the effect of a mentoring program for three undergraduate students with ASD and two with mental health conditions. After the first term, mentees with ASD expressed that mentors had been helpful for exam support, maintenance and development of social relationships, and promotion of positive well-being, while mentees with mental health issues did not find mentors as helpful in these areas. Other identified suggestions for social-emotional support include disability support groups, access to counseling, and private living spaces. However, isolation in private living spaces may also contribute to mental health issues. Thus, individualizing these supports is highly recommended and may consist of coupling accommodations (e.g., private living space and participation in mentorship program; [75]).
Existing preparation programs for school administrators provide little knowledge considered necessary to understand the behaviors of students with ASD and implement inclusion programs [20, 76]. Teachers at the K-12 levels show wide variability in their ASD-specific knowledge and self-efficacy, with large numbers reporting a lack of training or readiness to teach students with ASD [72, 77]. However, the high incidence of ASD indicates that educators across the nation will inevitably encounter a student with ASD [78], underscoring the need to prepare special educators in evidence-based approaches that enhance the academic and social learning opportunities for these children [79]. With regard to the professional development of university faculty on ASD, research is still limited as to how to best help support faculty in accommodating students with ASD in their classrooms. It is clear that the development of a faculty training may be beneficial, as the volume of information available for how to best support these students may be overwhelming for professors to sift through themselves.
This paper was based on the activities of the Smooth Sailing Study, supported by the Institute of Education Sciences, Grant number R324A110086. We also appreciate the support from the SEARCH Family Autism Resource Center at UC Riverside and the UCR Vice Chancellor for Research.
Yasamin Bolourian, Katherine Stavropoulos, and Jan Blacher declare that they have no conflict of interest.
Electroencephalography (EEG) is one of the oldest diagnostic methods currently used in medicine. It was described one century ago by the German psychiatrist Hans Berger [1]. Since then, its use has rapidly spread, and practically every hospital in the world has an EEG device. However, although EEG is probably the fastest, cheapest, and most straightforward method to obtain neurophysiological information from the human brain in a non-invasive way [2], its use is sometimes excessively restricted to the diagnosis of epilepsy, even in patients in whom the level of consciousness should be carefully evaluated [3]. Nevertheless, it should be always remembered that the primary function of the cerebral cortex is to exchange information by generating bioelectrical signals, not only in epilepsy.
In addition to this excessive restriction to epilepsy, EEG is sometimes reported as a subjective method, depending strongly on the interpreter [4, 5, 6, 7, 8]. There has been an attempt to reduce inter-rater variability among interpreters by the introduction of a consensus for EEG interpretation [9, 10, 11, 12, 13]. Although these consensus and classifications introduce some objectivity in EEG analysis, the variables are mostly qualitative or binary (present/absent), and specificity is therefore not very high.
In the past few decades, clinicians, neuroscientists, mathematicians, physicists, and engineers (among other experts) have sought a way to overcome these flaws to increase the true field of EEG application and reduce subjectivity in diagnosis. Some research has been devoted to combining EEGs and imaging techniques, mainly magnetic resonance imaging [14, 15, 16]. Another direction has been to develop mathematical tools to increase the reliability and deepen the information extracted from neurophysiological recordings. Collectively, this approach is called quantitative EEG (qEEG [17]). A canonical formulation toolbox is not needed to define qEEG. Instead, we can say that every EEG recording that uses any kind of post hoc numerical method to obtain a result about frequency spectrum, synchronization, network dynamics, or anything else of bioelectrical magnitude can be considered qEEG. This approach has been increasing in popularity since the introduction of digitalization in electroencephalography. As an example, in the past few decades, the number of papers referenced in PubMed with the word “qEEG” in the title/abstract rose from two in 1979, to 31 in 1999, and 76 in 2019 (a factor of x38!). However, the use of qEEG in clinical practice is far from being generalized, with the exception of ICUs where long-term monitoring (continuous EEG, cEEG) and qEEG are slowly increasing [18, 19, 20, 21, 22, 23, 24, 25].
The time required for a cEEG review is one of the most commonly given reasons for the use of qEEG in the ICU and other diagnostic fields. However, we have taken a different approach to qEEG during cEEG or standard EEG for ambulatory/hospitalized patients: instead of just simplifying seizure detection (another manifestation of the excessive focus on epilepsy), our aim is to obtain a comprehensive and efficient view of the bioelectrical brain physiology/physiopathology in the most objective way. To do this, we have developed qEEG using classical mathematical methods, but in a neurophysiologically and clinically oriented fashion [26].
In this chapter, we want to describe in detail the physiological basis of qEEGs, the method implemented for its quantification, and provide some examples of its use.
We have adopted the assumption that EEG is founded in a homeostatic system to obtain the main variables of our method [26, 27, 28]. This aspect is of extraordinary relevance because its specific application in different pathologies will lead to specific changes in the different numerical variables obtained. EEG consists of the multivariate spatio-temporal determination of the electrical potentials generated by the brain and recorded on the surface of the scalp. The oscillatory activity of the EEG, in clinical practice, is divided into four bands, depending on its oscillation frequency: delta (δ; 0–4 Hz), theta (θ; 4–8 Hz), alpha (α; 8–13 Hz), and beta (β; 13–30 Hz). Frequencies above 30 Hz, although very important in cognitive research, are not customarily used in clinical practice.
Briefly, the regulation of different bands is given by the following systems [29]:
Delta. The hyperpolarization of the thalamic-cortical (TC) interstitial pacemaker cells by the nucleus reticularis, together with a lower excitatory effect of the ascending activating reticular system (AARS), releases the spontaneous activity of cortical cells, oscillating at <4 Hz. This oscillation throughout the putamen/globus pallidus inhibits the brainstem nuclei responsible for AARS.
Theta. There are two theta-generating systems: i) activation of the nucleus reticularis (activated by the AARS) and inhibiting TC neurons and ii) through the mesolimbic system, which includes multiple afferents from the entorhinal cortex, hippocampus, amygdala, septum and anterior cingulate cortex; activating the intralaminar thalamic nuclei, and projecting to layer I of the cerebral cortex.
Alpha. TC neurons spontaneously oscillate at 6–12 Hz and regulate the excitation of large cortical areas through thalamic-cortico-thalamic re-entry loops. The membrane potential of these cells determines the frequency of oscillation and is regulated by synaptic inputs from the AARS (which includes the intralaminar nuclei of the thalamus), mainly from the brainstem and cortical inputs. Within the cortex, this activity is propagated from some nodes of special importance through interneuronal connections.
Beta. Originates primarily from cortico-cortical interactions and is facilitated by diffuse activation of the AARS and depolarization of TC cells, allowing the free transfer of information from the sensory systems through the thalamus to the cerebral cortex.
This complex neuroanatomical homeostatic system is probably genetically determined and regulates basal levels of local synchronization, global interactions between different regions, spectral composition, and periodic signal space sampling [30, 31, 32, 33].
One of the main limits in our approach is to maintain a close relationship between numerical magnitude variation and the underlying anatomo-functional system. For example, an increase in cortical activity (e.g., a seizure) must always be associated with an increase in β and probably α bands. Obviously, it does not preclude an increase in slower bands, but rising activity in faster bands is mandatory [26].
There are two branches of analysis: power spectra and synchronization. For both of them, dynamic (i.e., varying along the time) and mean measurements (i.e., mean spectra or mean graph of synchronization) are obtained. The process is summarized in Figure 1.
Method of electroencephalogram (EEG) quantification in two branches: Power spectra (b–d) and synchronization (e,f). (a) Raw EEG tracing. The discontinuous rectangle shows the moving window used for analysis; (b) power spectra for each channel; (c) areas for delta, theta, alpha, and beta bands under the spectrum are highlighted in different colors; (d) dynamics of the four bands (and entropy in the lower row) for every lobe. Mean and SEM values for each tracing are displayed inside each graph. Red and blue lines indicate right and left hemispheres, respectively; (e) correlation matrix for the window; (f) mean correlation computed for all recordings [34].
The process used for qEEG followed these steps:
Different length raw records are exported from the EEG device (EEG32, NeuroWorks, XLTEK®, Oakville, ON, Canada) to an ASCII file. Usually, artifacts are excluded by the export of several artifact-free chunks, which are later combined for analysis. We have shown that this process does not changes the main properties analyzed (see below). This process (exportation to an ASCII file) would probably be different for other EEG suppliers, but we have not assessed this possibility. We have computed the export time (texport) as a function of the ASCII file size (S) and obtained a linear expression by means of least-square fitting (r = 0.9947):
Although the raw recordings were digitized at 512 or 1024 Hz, we down-sampled to 128 or 256 Hz.
Exported files are digitally are filtered by a sixth-order Butterworth digital filter between 0.5 and 30 Hz.
A differential EEG montage is then reconstructed. Topographic placement of channels is defined on the scalp as the midpoint between the electrode pairs defining the channel; e.g., the Fp1–F3 channel would be placed at the midpoint of the geodesic between the Fp1 and F3 electrodes.
All recording can be divided into different lengths of moving windows (1–5 s each) with different overlaps (between 0 to 50%, but usually 10%). The total length used during the fast Fourier transform (FFT) is directly related to frequency precision in the power spectrum (PS). Overlap is used to minimize the border effect produced by windowing [35].
For each window (n) and frequency (k), we computed the fast Fourier transform (FFT) of the voltage (
We also computed Shannon’s spectral entropy (SSE) according to:
where F is the maximum frequency computed and pk is the probability density of S, obtained from the expression:
We computed the area under the
The expression sup refers to the upper limit of each EEG band.
The absolute value of Pearson’s correlation coefficient (ρ) is computed for each pair of channels (i,j) according to the expression:
where Nwindow is the number of points included in a window (usually 128) and
The mean value of all windows is then computed, obtaining the mean correlation matrix.
Areas of the same band are grouped by cerebral lobes. In the case of the left hemisphere (shown as an example), we grouped the frontal
The total time of analysis (tanalysis) is obtained from this linear expression, which was obtained from least-square fitting:
From expressions 1 and 7, for a typical 88 MB file (10 min record), we can estimate the time spent in export + analysis as less than 2 min.
We can optionally introduce two time-markers to define different states (e.g., pre-ictal, ictal, and post-ictal periods) in order to statistically compare the changes.
We can optionally export the numerical results to an Excel® file (e.g., mean ± SEM of power, synchronization; and SSe for channels, lobes, and hemispheres). This last step is the most time-consuming (up to 3 min for a custom-length file).
Numerical analysis of EEG recordings was performed with custom-made MATLAB® software (MathWorks, Natick, MA, USA).
For power spectra as well as for synchronization, we can represent measurements either as dynamic time-dependent variables (Figure 1d), or as the mean values, averaged over the file (Figure 1b,f). Therefore, although complementary, information obtained from both kinds of computations must be interpreted differently. In other words, average measurements are only useful if the stationarity of the record is evident (e.g., basal recordings, well-defined phases of sleep, etc.).
A very important aspect of any numerical method is its robustness, e.g., the evaluation of the method wherein the results obtained are found to be reliable, even when performed under slightly varied conditions. It is the ability of a method to remain unaffected when slight variations are applied. It is extremely important to check trials of the numerical method within the same group of EEG records under the different conditions of i) down sampling, ii) windowing, or iii) overlapping. Moreover, it is important to check whether synchronization measures are affected by the global analysis of different, non-consecutive chunks.
For this purpose, we selected EEG recordings of five minutes in length, from six control individuals (without any neurological or psychiatric pathology, between 20 and 30 years old, and with no pharmacological treatment). We analyzed each EEG under different conditions, namely:
Down-sampling at 128 and 512 Hz, (f is for frequency).
Windows of 1, 3 or 5 s (w).
Overlapping at 10, 20, or 40% (o).
Overall, we had 18 combinations for frequency/windows/overlapping (f,w,o).
The structure of EEG for each patient (pi, i = 1,2,…6) can be described by a 10-element vector as:
Obviously, every structure can be described as
Structures of EEG for the 18 combinations of variables for (a) patient #1, (b) patient #2, (c) patient #3, (d) patient #4, (e) patient #5 and (f) patient #6. L = left, R = right, d = delta, t = theta, a = alpha, b = beta, and rho = correlation.
From Figure 2, we can observe that different combinations cannot affect the structure of EEG.
The effect of multiple compositions of the analyzed file on synchronicity was assessed as follows: an EEG record of 3 min was analyzed. Then, the same record was exported in three different chunks, and a new analysis was performed on a recombined file with the parts randomly ordered (1,3,2/2,1,3/2,3,1/3,1,2 or 3,2,1). We did not observed any difference in synchronization between the whole record and a recombined one (not shown).
In summary, these results demonstrate that the method is highly robust, at least for the limits addressed.
It is out of the scope of this chapter to illustrate the objective and quantified difference between the classical, de visu, analysis of EEG and qEEG. Instead, we will provide some examples of qEEG application to real clinical problems and the results. In these examples it will be shown that information obtained by qEEG clearly exceeds the possibility of de visu analysis.
Patients in the ICU usually undergo a limited clinical neurological examination because of either structurally or functionally altered conditions of the central nervous system (CNS) or due to the effects of drugs used for sedoanalgesia [36]. Therefore, we can evaluate brain function in these conditions by EEG. However, a dynamic evolution of injury is frequently observed in critically ill patients due to the occurrence of epileptic seizures (ES), status epilepticus (SE), or other brain insults [37, 38]. In this sense, cEEG allows the functional assessment of the cerebral cortex in real time for prolonged periods. It has been proven to be an extraordinarily useful tool for detecting electrographic seizures and non-convulsive epileptic status (NCES), modifying treatment and assessing the functional prognosis [10, 11, 39, 40, 41, 42].
The particularity of cEEG records of patients in the ICU comprises a high frequency of artifacts and frequently observed rhythmic and periodic patterns, which are easily confused with seizures. Both can be difficult to interpret not only in a raw EEG but also with the qEEG tools currently used [43, 44]. As indicated above, the use of multiple drugs acting on the CNS and primary and secondary injuries profoundly affect the bioelectrical brain dynamics. Therefore, it can be quite difficult to differentiate between bursts of periodic activity (BPA) and true ES/SE. The main problem is that EEG patterns analyzed de visu do not always exhibit the sharp morphology of ES/SE of not-ICU patients.
According to the ILAE, an ES is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the cortex of the brain [45]. Signs/symptoms are usually excluded in critically ill patients, but the excessive activity of the cortex is mandatory for a positive identification. We have used this pathophysiological feature for the numerical definition of ES. Therefore, we can use this method to exclude epilepsy in those cases where α/β activity does not change (or even decreases) during the event (see below). The limits of change for the different bands (Table 1) can be used to distinguish PBA from ES.
Variable | State | Frontal | Sup (%) | Parieto-occipital | Sup (%) | Temporal | Sup (%) |
---|---|---|---|---|---|---|---|
Delta | ES | 111.8–234.0 | 55.4 | 123.8–276.8 | 91.3 | 118.4–247.6 | 86.1 |
BPA | 166.3–370.4 | 137.1–278.7 | 136.3–253.9 | ||||
Theta | ES | 158.2–264.0 | 14.2 | 166.7–247.7 | 1.6 | 139.2–230.5 | 19.9 |
BPA | 92.8–173.2 | 84.3–168.0 | 93.1–157.4 | ||||
Alpha | ES | 158.8–244.0 | 0 | 146.0–248.6 | 0 | 144.3–243.5 | 0 |
BPA | 75.0–137.8 | 79.7–134.1 | 82.7–135.6 | ||||
Beta | ES | 141.9–373.6 | 0 | 146.7–261.8 | 0 | 136.5–274.4 | 10.4 |
BPA | 77.1–137.8 | 82.2–137-2 | 95.1–150.8 | ||||
Entropy | ES | 95.5–109.5 | 10.7 | 96.2–106.7 | 12.4 | 96.1–104.8 | 19.5 |
BPA | 78.9–97.0 | 84.3–97.5 | 82.3–97.8 |
Inter-percentile 25–75 intervals for bands and lobes in BPA and ES. Superposition is indicated with respect to the ES interval [26].
From this table we can observe that the superposition is very high for δ bands (i.e., this band is not discriminative), low for θ and Se, and practically null for α and β bands. Therefore, the intervals for increments of normalized activity defining an ES in these types of patients are as follows (excluding superposition and rounding):
We have defined the numerical features of ES and BPA in critically ill patients using the pathophysiological definition of epilepsy. This will facilitate its identification in clinical practice, allowing a precocious and more adequate treatment.
Neurological complications in COVID-19-infected patients have been reported. The CNS effects reported include encephalitis, toxic encephalopathy, ageusia and anosmia, headaches, or acute cerebrovascular disease [46, 47, 48, 49, 50, 51, 52]. The mechanisms of CNS infection in the pathophysiology of COVID-19 are still debated, and it has been proposed to result from direct invasion through the blood–brain barrier, a neuronal pathway, hypoxia damage, immune-response mediated injury, or angiotensin-converter enzyme 2 activity, among other possibilities [47, 53, 54]. Encephalopathy refers clinically to state of impaired cognition, generally acute or subacute [55]. Descriptions de visu of EEG are based on classical analysis by visual inspection and not on specific features [56, 57, 58].
We applied our method of qEEG to patients discharged from the ICU after COVID-19 infection [34]. We used two control groups from patients previously studied in our hospital: (i) patients with infectious toxic encephalopathy (ENC) and (ii) patients after cardiorespiratory arrest (CRA), as an example of severe hypoxic insult to the CNS.
Visually, in COVID-19, EEG records the apparent absence of delta/theta activity conferred a near-physiological aspect to the recordings. Despite the different visual aspects appearing between ENC and COVID-19, the mean spectra by channel were quite similar. Examples of typical recordings are shown in Figure 3.
Examples of raw recordings: (a) encephalopathy (ENC), (b) COVID-19, and (c) cardiorespiratory arrest (CRA). Right column shows mean spectra for channels. Red and blue lines indicate right and left hemispheres, respectively. Y-axis units in μV2/Hz [34].
We assessed the specific differences for each band in ENC, COVID-19, and CRA patients. Subsequently, we performed one-way ANOVA (ANOVA on ranks when normality failed) for each lobe and band, and for SSe and synchronization (Figure 4).
Box plots showing the comparison of EEG structures for different bands: (a) delta, (b) theta, (c) alpha, and (d) beta, (e) Se; (f) ρ. striped black box: ECN; striped red box: COVID-19; striped blue box: CRA; black asterisk: Difference between ENC and COVID-19; red asterisk: Difference between ENC and CRA; blue asterisk: Difference between COVID-19 and CRA.
The pattern of ENC and CRA was clearly different for all bands. However, the COVID-19 group was not completely different from the ENC and CRA groups, although it was evidently observed that the distribution is between the two extreme groups. Nonetheless, the behavior of temporal lobes clearly differs for the ENC and COVID-19 groups for the δ, α, and β bands. Contrary to EEG bands, SSe was higher for the COVID-19 group and lower for the ENC group. In fact, SSe was different for all groups at all lobes. This result may be surprising considering the kind of spectra shown in Figure 3c, where the distribution was apparently more complex. However, the presence of α and β bands (scarcely present in the CRA group) increased the SSe for COVID-19 patients.
Finally, although ρ was not as different between groups as SSe, a clear difference was seen in the hemispheric synchronization and frontal lobes of ENC and COVID-19 patients, with lower synchronization for the latter group.
In summary, we have demonstrated that qEEG can differentiate between encephalopathy types, and we have described the numerical features of each. In this context, we show that COVID-19 patients display EEG structures that are truly distinguishable from those of both infectious toxic encephalopathy and encephalopathies of patients who experience severe hypoxic conditions. Significantly, the EEG pattern of COVID-19 patients was between those of the ENC and CRA groups. Therefore, it can be speculated that hypoxia may show some participation in this electroclinical entity. However, the EEG structures of the CRA and COVID-19 groups were different enough to consider that other factors besides hypoxia must be responsible for the bioelectrical pattern.
It is extremely relevant to bear in mind that COVID-19 patients showed mild to severe cognitive symptoms despite de visu quasi-normal recordings. However, the severe numerical alterations of temporal lobes spectra, structure of SSe and synchronization were highly different and are probably explained by symptomatology with quasi-normal EEG traces.
Long-term EEG monitoring in the ICU (cEEG) has been one of the main developing fields of electroencephalography in recent years. There are several methods used for EEG monitoring, and most of them share a similar philosophy: to identify the presence of seizure/status epilepticus to make the information of monitoring easy and fast. As stated above, we think that this reductionist approach impoverishes the scope of electroencephalography.
We systematically used qEEG during cEEG for the below indications:
Monitoring the presence of ES/SE. More useful than identifying the presence of ES, qEEG is essential to discriminate between epileptic and non-epileptic patters, as we stated above.
Titration of sedation/anti-epileptic drugs (AEDs). Identifying changes in background from the variations in spectra or mean values of specific EEG bands is mandatory to increase/decrease the dose of sedation. Additionally, AEDs are adjusted with the help of qEEG, although de visu inspection of irritative activity is mandatory.
Long-term recordings in patients with alterations of consciousness without sedo-analgesia to evaluate brain physiology. Severe encephalopathy can induce a low-consciousness level. These recordings typically lack irritative activity, and changes in the background are slow. However, these changes correlate with the level of consciousness and predict the outcome. Therefore, it is important identify changes to adjust treatment as soon as possible, especially to avoid unnecessary therapeutic actions.
In this way, we have performed more than 250 cEEG + qEEG in the ICU in the last five years. This is a time-consuming task (especially considering that every cEEG takes 1.4–4.3 days (inter-percentile 25–75 range)), but the clinical utiliy is clear because the demand increased from 1.0 ± 0.2 cEEG/month in 2015 to 5.5 ± 0.8 cEEG/month in 2019.
There are numerous articles in the literature showing that the initial phases of dementia can be detected by qEEG [59, 60, 61, 62, 63]. We have used our numerical method in patients with either minimum cognitive impairment (MCI) or aphasia. Obviously, the de visu analysis of raw recordings shows only a nearly normal aspect or low-voltage. However, numerical analysis can show very relevant facts that are not observable by eye (Figure 5).
Initial steps of dementia. (a) Example of a male with primary aphasia and (b) a female with MCI. Left column = raw recordings of both patients; middle column = connectogram for patients; right column = connectogram of a control volunteer of the same sex and age (± 5 years).
At this time, we are conducting a study to identify specific properties of different pathologies (sub-types of primary aphasia, Alzheimer disease, vascular dementia, etc). Although we have not yet defined different groups of features specific to each pathology, what is clear from the above figure is that connectivity is a magnitude that is affected early and consistently in most cognitive alterations.
Finally, we provide two more representative examples of diagnosis highly aided by the use of qEEG.
Case 1. A 17-year-old male patient with severe cognitive and behavioral impairment, secondary to severe epileptic encephalopathy due to refractory epilepsy in childhood after a central nervous system infection. Daily seizure frequency, with countless seizures per day. In treatment with zonisamide (400 mg), valproic acid (1200 mg), oxcarbazepine (1400 mg), and clobazam (10 mg) daily. Rectal diazepam 10 mg if required. A video of EEG is performed in which it is observed that during sleep, the patient exhibits several episodes of lateral head movement and growls. De visu recordings (Figure 6a) can be described as global desynchronization. However, the dynamics of the EEG bands show a decrease (practically total) for all the bands (Figure 6b). As stated above, epilepsy is expected to be accompanied by an increase in cortical activity (α and β bands). Therefore, this event cannot be identified as epileptic.
Box plots showing a comparison of EEG structure for different bands: (a) raw recording during a complete event during sleep. The images under the recordings correspond to the periods indicated by arrows. (b) Lobar dynamics of EEG bands during the entire event (vertical red lines). Blue = left hemisphere; red = right hemisphere.
Although the patient also presented true epileptic events, the number of these was substantially lowered and AED was adjusted according to the real number of ES. The final diagnosis for this event was a nonepileptic behavioral disorder, secondary to severe epileptic encephalopathy.
Case 2. A 22-year-old female diagnosed with epilepsy at the age of 14 years and with anxious-depressive illness from the age of 20 years. The applied treatment was lamotrigine (100 mg/day) and clonazepam (0.5 mg/8 h). Seizures occurred every 2–3 days, described as the perception of black dots in the visual field, weakness, loss of consciousness and muscle tone, loss of balance and falling to the ground. During telemetry, we recorded one episode of black dot perception followed by a loss of consciousness while in bed. De visu EEG recordings showed no significant changes (Figure 7a). However, the dynamic variation of EEG bands indicated a generalized decrease, except for the occipital α band, which increased after the eyes closed (Figure 7b).
Psychogenic non epileptic seizure (a) raw recordings (in transverse and double banana differential montages) during the event. (b) Lobar dynamics of EEG bands during the entire event (vertical red lines). Blue = left hemisphere; red = right hemisphere.
The final diagnosis was psychogenic non-epileptic seizure (PNES), and the AEDs were slowly removed.
Numerous methods have been used for qEEG, although they are rarely used in daily clinical practice. There is, therefore, a huge gap between the promising (even spectacular) results obtained with qEEG and its practical usefulness. To the best of the knowledge of the authors, this issue has not been systematically addressed, although it has been said that electroencephalographers have poor trust in mathematical models [2, 17].
The degree of mathematical complexity and abstraction is quite different among the methods proposed. Not all of the mathematical models can be included in the same category, and it is of extreme importance that mathematical solutions be robust with respect to physiological assumptions [2]. Then, some procedures require relatively simple and involve straightforward methods as FFT [26, 34, 64, 65, 66] and synchronicity measurements [67, 68], approaches that clinicians are familiar with. In contrast, other numerical methods are more complex, or the mathematical approach deviates from physiological assumptions [69, 70, 71, 72, 73, 74], and this probably takes neurophysiologists out of their comfort zone.
We have developed a robust method that is physiologically founded and easy to use in daily clinical practice. The tools selected are neither unique nor are they necessarily the best. Other tools (e.g., coherence) can be implemented. A careful comparison between these methods will decide the fitted procedure for each pathology. From the beginning of 2016, we have performed more than 4100 analyses and used this toolbox in most patients, even those with EEG apparently evident. We did not use qEEG only when the record included so many artifacts (e.g., in agitated patients) that the results would not be reliable.
The method described can be implemented to automatically differentiate between paroxysmal events and ES during the long-term monitoring of ICU patients. This feature is very relevant for clinicians because it can shorten the review time, particularly during long cEEG, and can help apply adequate therapeutic measures, avoiding pharmacological blind trials that only delay correct treatment, increasing the inefficacy of treatment and diminishing the probability of recuperation. Therefore, considering that “time is brain”, a fast and accurate treatment is mandatory to increase the probability of a good outcome.
Finally, it is extremely important to keep in mind that qEEG is only a tool to help better understand and diagnose brain pathophysiology; therefore, it should not be thought that numerical analysis (at least as we use it today) is enough, without evaluation by an expert, to make an automatic diagnosis. Not all brain pathologies are likely to benefit to the same degree from analysis. For example, the method described in this chapter is not well fitted to detect low-frequency transitory waves as medium/small focal epileptiform discharges, although visual inspection can identify them very well. However, patterns that are not readily visible in de visu analysis (e.g., asymmetries in power spectra compositions) are easily detected.
We can summarize the conclusions of this work as follows:
qEEG is a robust and non-time-consuming method that is able to produce numerical and objective values of several bioelectrical magnitudes with clinical significance.
qEEG increases and facilitates diagnoses that are otherwise exceedingly difficult to obtain by de visu inspection. Therefore, the scope of the true applicability of EEG is expanded far beyond epilepsy.
We propose that qEEG use should be expanded globally for daily clinical practice. Thus, clinical neurophysiologists should be informed of the methods of numerical analysis procedures.
This work was financed by a grant from the Ministerio de Sanidad FIS PI17/02193, ISCIII (Instituto de Salud Carlos III) and partially supported by FEDER (Fonds Europeen de Developpement Economique et Regional).
The authors declare no conflict of interest.
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\\n\\nWhen distributing or re-publishing the Work, the Author agrees to credit the Monograph/Compacts as the source of first publication, as well as IntechOpen. The Author guarantees that Co-Authors will also credit the Monograph/Compacts as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Work.
\\n\\nThe Author agrees to:
\\n\\nThe Author will be held responsible for the payment of the agreed Open Access Publishing Fee before the completion of the project (Monograph/Compacts publication).
\\n\\nAll payments shall be due 30 days from the date of issue of the invoice. The Author or whoever is paying on behalf of the Author and Co-Authors will bear all banking and similar charges incurred.
\\n\\nThe Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Work worldwide for the full term of the above licenses, and shall provide to IntechOpen, at its request, the original copies of such consents for inspection or the photocopies of such consents.
\\n\\nThe Author shall obtain written informed consent for publication from those who might recognize themselves or be identified by others, for example from case reports or photographs.
\\n\\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\\n\\nAUTHOR'S WARRANTY
\\n\\nThe Author and Co-Authors confirm and warrant that the Work does not and will not breach any applicable law or the rights of any third party and, specifically, that the Work contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy.
\\n\\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\\n\\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\\n\\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\\n\\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\\n\\nTERMINATION
\\n\\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\\n\\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\\n\\nIntechOpen’s DUTIES AND RIGHTS
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\\n\\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\\n\\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\\n\\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\\n\\nMISCELLANEOUS
\\n\\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\\n\\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\\n\\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\\n\\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\\n\\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\\n\\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\\n\\nPolicy last updated: 2018-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'When submitting a manuscript, the Author is required to accept the Terms and Conditions set out in our Publication Agreement – Monographs/Compacts as follows:
\n\nCORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\nSubject to the following Article, the Author grants to IntechOpen, during the full term of copyright, and any extensions or renewals of that term, the following:
\n\nThe foregoing licenses shall survive the expiry or termination of this Publication Agreement for any reason.
\n\nThe Author, on his or her own behalf and on behalf of any of the Co-Authors, reserves the following rights in the Work but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Work as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Author, and any Co-Author, confirms that they are, and will remain, a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Work and all versions of it created during IntechOpen's editing process, including all published versions, is retained by the Author and any Co-Authors.
\n\nSubject to the license granted above, the Author and Co-Authors retain patent, trademark and other intellectual property rights to the Work.
\n\nAll rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the specific approval of the Author or Co-Authors.
\n\nThe Author, on his/her own behalf and on behalf of the Co-Authors, will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Work as a consequence of IntechOpen's changes to the Work arising from the translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits as determined by IntechOpen.
\n\nAUTHOR'S DUTIES
\n\nWhen distributing or re-publishing the Work, the Author agrees to credit the Monograph/Compacts as the source of first publication, as well as IntechOpen. The Author guarantees that Co-Authors will also credit the Monograph/Compacts as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Work.
\n\nThe Author agrees to:
\n\nThe Author will be held responsible for the payment of the agreed Open Access Publishing Fee before the completion of the project (Monograph/Compacts publication).
\n\nAll payments shall be due 30 days from the date of issue of the invoice. The Author or whoever is paying on behalf of the Author and Co-Authors will bear all banking and similar charges incurred.
\n\nThe Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Work worldwide for the full term of the above licenses, and shall provide to IntechOpen, at its request, the original copies of such consents for inspection or the photocopies of such consents.
\n\nThe Author shall obtain written informed consent for publication from those who might recognize themselves or be identified by others, for example from case reports or photographs.
\n\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\nAUTHOR'S WARRANTY
\n\nThe Author and Co-Authors confirm and warrant that the Work does not and will not breach any applicable law or the rights of any third party and, specifically, that the Work contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy.
\n\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\n\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\n\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\nTERMINATION
\n\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\n\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\n\nIntechOpen’s DUTIES AND RIGHTS
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\n\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\n\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\n\nMISCELLANEOUS
\n\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\n\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\n\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\n\nPolicy last updated: 2018-09-11
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