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With love and affection to my mother and father, whose loving spirit sustains me still
Ayman S. El-Baz
To my late loving parents, immediate family, and children
Jasjit S. Suri
Contributors
Francisco Alcantud-Marín Universitat de València, València, Spain
Marah AlHalabi Abu Dhabi University, Abu Dhabi, United Arab Emirates
Mohamed T. Ali Bioengineering Department, University of Louisville, Louisville, KY, United States
Yurena Alonso-Esteban Universitat de València, València, Spain
Muhammad Awais Bin Altaf Lahore University of Management Sciences, Lahore, Pakistan
Sandra Amador Department of Computer Science and Technology, University of Alicante, Alicante, Spain
Rushil Anirudh Lawrence Livermore National Laboratory (LLNL), Livermore, CA, United States
Abdul Rehman Aslam Lahore University of Management Sciences, Lahore, Pakistan
Oresti Baños Department of Computer Architecture and Technology, Granada, Spain
Pura Ballester Department of Clinical Pharmacology, Pediatrics and Organic Chemistry, Miguel Hernandez University, Elche, Spain
Gregory Barnes Neurology Department, University of Louisville, Louisville, KY, United States
Sevgi Bayari Department of Physics Engineering, Hacettepe University, Ankara, Turkey
Charles M. Borduin Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
Cynthia E. Brown Department of Psychology, Stony Brook University, Stony Brook, NY, United States
Lianhua Chi La Trobe University, Melbourne, VIC, Australia
Lauryn Dooley Cellular Neurobiology and NeuroNanotechnology Laboratory, Department of Biological Sciences, University of Limerick, Limerick, Ireland
Ayman S. El-Baz Bioengineering Department, University of Louisville, Louisville, KY, United States
Yaser ElNakieb Bioengineering Department, University of Louisville, Louisville, KY, United States
Taban Eslami School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, United States
Rui Fausto Department of Chemistry, University of Coimbra, CQC, Coimbra, Portugal
Luay Fraiwan Abu Dhabi University, Abu Dhabi, United Arab Emirates
Mohammed Ghazal Abu Dhabi University, Abu Dhabi, United Arab Emirates
David Gil Department of Computer Science and Technology, University of Alicante, Alicante, Spain
Andreas M. Grabrucker Cellular Neurobiology and Neuro-Nanotechnology Laboratory, Department of Biological Sciences, University of Limerick, Limerick; Bernal Institute, University of Limerick, Limerick; Health Research Institute (HRI), University of Limerick, Limerick, Ireland
Reem Haweel Bioengineering Department, University of Louisville, Louisville, KY, United States
Biwei Huang Carnegie Mellon University, Pittsburgh, PA, United States
Gulce Ogruc Ildiz Department of Physics, Faculty of Sciences and Letters, Istanbul Kultur University, Istanbul, Turkey; Department of Chemistry, University of Coimbra, CQC, Coimbra, Portugal
Muhammad Nazrul Islam Department of Computer Science and Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh
Ashraf Khalil Abu Dhabi University, Abu Dhabi, United Arab Emirates
Nabila Shahnaz Khan Department of Computer Science, University of Central Florida, Orlando, FL, United States
Adel Khelifi Abu Dhabi University, Abu Dhabi, United Arab Emirates
Li Li Harvard University, Cambridge, MA, United States
Ali Mahmoud Bioengineering Department, University of Louisville, Louisville, KY, United States
Javier Medina Department of Computer Science, University of Jaen, Jaen, Spain
Sakib Mostafa Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
Kazi Shahrukh Omar Department of Computer Science and Engineering, Uttara University, Dhaka, Bangladesh
Ana M. Peiró Clinical Pharmacology Unit, Alicante General University Hospital, Alicante, Spain
Jesús Peral Department of Software and Computing Systems, University of Alicante, Alicante, Spain
Aurora Polo Department of Computer Science, University of Jaen, Jaen, Spain
Lauren B. Quetsch Department of Psychological Sciences, University of Arkansas, Fayetteville, AR, United States
Joseph S. Raiker School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, United States
Sayna Rotbei Department of Information Technology, Mehralborz University, Tehran, Iran
Fahad Saeed School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, United States
Ann Katrin Sauer Cellular Neurobiology and NeuroNanotechnology Laboratory, Department of Biological Sciences, University of Limerick, Limerick, Ireland
Ahmed Shalaby Bioengineering Department, University of Louisville, Louisville, KY, United States
Ahmed Soliman Bioengineering Department, University of Louisville, Louisville, KY, United States
Jasjit S. Suri AtheroPoint, Roseville, CA, United States
Andrew Switala Bioengineering Department, University of Louisville, Louisville, KY, United States
Jayaraman J. Thiagarajan Lawrence Livermore National Laboratory (LLNL), Livermore, CA, United States
Mirac Baris Usta Department of Child and Adolescent Psychiatry, Ondokuz Mayis University, Samsun, Turkey
Aoife Vaughan Cellular Neurobiology and NeuroNanotechnology Laboratory, Department of Biological Sciences, University of Limerick, Limerick, Ireland
Haishuai Wang Fairfield University, Fairfield, CT, United States
Fang-Xiang Wu Division of Biomedical Engineering; Department of Computer Science; Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
Nese Yorguner Department of Psychiatry, Marmara University, Pendik Training and Research Hospital, Istanbul, Turkey
Hong Yang The University of Sydney, Sydney, NSW, Australia
Jawad Yousaf Abu Dhabi University, Abu Dhabi, United Arab Emirates
Ziping Zhao Tianjin Normal University, Tianjin, China
Biography of the editors
Ayman S. El-Baz is a Distinguished Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, Kentucky. Dr. El-Baz earned his BSc and MSc degrees in electrical engineering in 1997 and 2001, respectively. He earned his PhD in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 17 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 500 technical articles (145 journals, 27 books, 78 book chapters, 225 refereed-conference papers, 172 abstracts, and 30 US patents and Disclosures).
Jasjit S. Suri is an innovator, scientist, visionary, industrialist, and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his PhD from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was crowned with President’s Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.
Acknowledgments
The completion of this book could not have been possible without the participation and assistance of so many people whose names may not all be enumerated. Their contributions are sincerely appreciated and gratefully acknowledged. However, the editors would like to
express their deep appreciation and indebtedness particularly to Dr. Ali H. Mahmoud and Ahmed Sharafeldeen for their endless support.
Ayman S. El-Baz
Jasjit S. Suri
1 Prediction of outcome in children with autism spectrum disorders
Mirac Baris Usta*
Department of Child and Adolescent Psychiatry, Ondokuz Mayis University, Samsun, Turkey
*Corresponding author
1 Introduction
Autism spectrum disorder (ASD) is an umbrella term for neurodevelopmental disabilities that affect social and communication skills, and in recent years, the trend toward escalating prevalence is seen [1]. In children with ASD, outcome prediction is a vital component of planning behavioral interventions, treatment, and also prognosis in clinical settings. In particular, adult outcome studies of ASD were done in the early 1960s in literature [2,3], but the proportion of research focused on the outcome is very small compared to etiology, genetics, and treatment studies. Children with ASD are a very complex and heterogenic group, so researchers need to reach large-scale patient group data to explain the nature of ASD across life span. Also, researchers need this data to support children with autism to accomplish the best possible outcome.
At present, the trajectory of development and prognosis for a baby or toddler diagnosed with ASD typically cannot be anticipated at the time of diagnosis [4]. But most of the children
who had a diagnosis of ASD after expert clinician examination and valid methods at less than 3-year old have maintained their diagnosis [5]. It is challenging to recognize and diagnose mild symptoms of ASD in babies and toddlers, particularly if they have average or excellent intelligence and cognitive skills [6]. Also, across developmental stages, ASD symptoms tend to decrease, possibly due to maturation and/ or medical/educational intervention [7]. Many studies showed that children with ASD show significant decreases in social and behavioral symptoms severities [8–11].
Heterogeneity adds another difficulty to understanding outcomes in ASD. Comorbid conditions are widespread in children with ASD and have a significant impact on prognosis. Medical conditions such as sleep disorders, epilepsy, attention deficit and hyperactivity disorder, and also irritability problems like self-injury or food refusal have a high impact on functioning of children and families [12,13]. Also, diagnostic definitions of ASD are shifted. Lastly, in 2013 DSM 5 [14], and recently, children with ASD with average Intelligence quotient (IQ) and adaptive skills
1. Prediction of outcome in children with autism spectrum disorders
in addition to less-severe symptom presentations are growing. Most of these studies have focused on direct observation strategies and intelligence testing, and better IQ scores and the least-severe symptoms presentations are the essential aspects for prognosis. In recent years, the rising of the big data for health records and big clinical databases has provided researchers to use state-of-the-art techniques like machine learning to understand and evaluate this complex disorder outcome more precisely [15]. Also results of the laboratory research, including electroencephalography (EEG), eye tracking, facial tracking, and functional magnetic resonance imaging (MRI) studies, have been giving hope for future research and practice [16–18]. These techniques possibly will be superior to observational methods in the future, and they will make it possible to evaluate toddlers and even babies, which is vital for starting only known treatment strategies, very early intervention, and behavioral therapies [19].
Recently machine learning methods, such as support vector machines, decision trees, and neural networks, are used to derive accurate diagnosis predictive models from ASD datasets. These methods are mostly automated, which normally require minimal researcher involvement during preprocessing and also in the data process. Researchers use software packages for these methods, for example, R [20], Weka [21], RapidMiner [22], Knime [23], and MATLAB toolbox [24]. In this research, the machine learning process is doing the predictive task, and researchers are trying to build a classifier to predict cases of ASD or neurotypical for toddlers. These classifiers are usually composed of a labeled dataset and evaluated on independent test instances to measure its sensitivity and specificity in predicting the diagnosis.
This chapter concerns the outcome of children and babies with ASD in later life, focusing on new biomedical research and also state-ofthe-art techniques that are multidisciplinary between engineering and clinical research. Major
trends and challenges of this research and methods for children with ASD in laboratory, clinical, and real-life settings will be discussed.
2 Screening data studies
It has recommended screening all children for autism symptoms with a combination of autismspecific screening and developmental tests and questionnaires at the age of 18 and 24 months [25]. Toddlers with ASD may be identified at this age; early interventions possibly make the outcome better [26]. Clinicians are using electronic health records and valid screening tools to complete the administration and scoring. Also, results of screening tests are not diagnostic; they help one to identify a child at risk and to choose those who require additional evaluation. Most of the screening tests are parent-completed questionnaires, and the worldwide most commonly used tool is the modified checklist for autism in toddlers (M-CHAT) [27]. It is used for screening children between 16 and 30 months of age in primary care settings. Toddlers who have three to seven items positive for ASD after the clinician clarifying questions have 47% risk of ASD diagnosis, and a 95% chance of being diagnosed with other developmental delays or disorders would need intervention [28]. Very early screening may not truly describe many children with milder autism symptoms; therefore surveillance remains a necessity [29]. In the literature, researchers examine the utility of automated strategies, which may ease accessibility or shorten screening procedures. Ben-Sasson et al. [30] used a machine learning algorithm to predict ASD risk with a combination of the parent’s text and a single screening question. Achenie et al. [31] used the data of M-CHAT validation study, including socioeconomic data and M-CHAT items of 16.168 toddlers and families, prediction ASD diagnosis with feedforward neural networks result that yielded correct classification (ASD or not ASD) at a rate of 99.72%. Al Farsi et al. [32] presented
fuzzy cognitive maps (FCM), in which algorithms modify the answers of each question to three options, reduced the complexity of and increased the expressivity of the M-CHAT. Also Abbas et al. [33] used clinical and video data of 162 children at ASD risk, presented a chained tree of machine learning algorithms, one of which operates on the basis of a parental questionnaire and the other of which operates on home cell phone videos that can outperform the M-CHAT’s classification rate. Machine learning classification of ASD risk has advantages over classical methods on calculating individual risk rates; moreover, with a real-time support system they may help clinicians, teachers, and other professionals who do screening for ASD.
3 Diagnostic test data studies
The most used clinical diagnostic methods are Autism Diagnostic Interview [34] and Autism Diagnostic Observation Schedule—Revised (ADOS–R) [35], which have shown acceptable accuracy and validity in numerous trials. It is also self-reported or parent-based methods such as autism-spectrum quotient [36] and social communication questionnaire [37]. These instruments are mostly used for diagnosis by experienced clinicians and require substantial time to apply patients. The majority of these instruments have mathematical summation formulas made by hand by the clinician. Researchers have recently started to investigate this data to improve the diagnostic process of ASD. The primary aim of these studies is to improve diagnostic accuracy, provide computer-assisted decision-making and also quick access to health-care services for patients. Machine learning methods seem to be a good solution to these diagnostic challenges; however, some biases and overfitting issues may initially appear.
Wall et al. [38] tried to shorten the direct observation-based coding of the ADOS to provide a shorter and computer-assisted version of this
structured method while maintaining validity. Authors have shown that the algorithm they have used performs with outstanding sensitivity, specificity, and accuracy in differentiating patients with autism from neurotypicals. The classifier used by authors consisted of eight questions performed with >99% accuracy. Duda et al. [39] showed an observation-based classifier, which uses a decision tree algorithm, that represented a 72% reduction in length from the ADOS while retaining >97% statistical accuracy. Pratap and Kanimozhiselvi used childhood autism rating scale [40] data, which is another direct observation tool, to measure Naive Bayes and artificial neural network algorithms on the classification problem of ASD, and results have shown that when models are integrated with unsupervised learning methods, the accuracy of classification of ASD cases improves [41]. These classification results appear very promising, but they come with some shortcomings. First, the reliability of direct observation studies was only established in the clinical environment, while clinicians were administering all tasks. Second, autism and normally developed children were used in classification, ignoring the autism spectrum that is subtlest, more heterogeneous category. There is need for more studies to show clinical validation and feasibility of this method to be used as a tool for ASD diagnosis.
These studies suggest that internet platforms for fast measurement of autism risk, for example, mobilized approaches, will help one to shorten screening and diagnostic processes for clinicians. The application of machine learning diagnostic processes requires an understanding of both clinical and computational content domains. The researcher must consider the properties and sources of the data that must be unbiased and not overstated. Ignoring heterogenic autism context and focusing on only data processing may produce misleading results. Future research must consider algorithms for children with subtle ASD symptoms and may add other neurodevelopmental classifiers (IQ, language
1. Prediction of outcome in children with autism spectrum disorders
levels, and gender). It will be necessary to get large databases that include enough numbers of children who vary on these classifiers. Moreover, engineers and data scientists need to collaborate with clinicians for a validation of these algorithms. Still, machine learning algorithms have a significant potential to improve certain aspects of ASD diagnostic instrument design by dimension reducing vast behavioral information and multiple aggregating instruments.
4 EEG and ERP data studies
The electroencephalogram (EEG) is a tool that assesses the electrical activity of the brain from electrodes placed on the scalp surface. The signals are obtained from various brain regions and used for the evaluation of the brain’s electrical activity. The event-related potential (ERP), a specific type of EEG, indicates transformations of the electrical activity of the brain due to the occurrence of a specific event. They are using in autism developmental studies extensively.
EEG and ERP are noninvasive neurophysiological tools for investigating infants’ brain activities and have also recently been used to understand mechanisms underlying atypical development. In long term, they may be used as biomarkers of autism and may point “red flags,” in which infant needs very early interventions. In addition, EEG/ERP tools are more noninvasive and straightforward to be used in clinical settings, which make them easy to practice for the study of brain development and behavior in infants.
EEG and ERP have different frequencies of wavebands, and its computational analysis is performed on these varying bandwidths. EEG waves are classified as delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–35 Hz), and gamma (>35 Hz) waves. Grossi et al. [42] used EEG patterns to classify ASD patients and they did 15 ASD and 10 neurotypical test subjects resting EEG with 19 electrodes, closed and open
eyes. EEG data was analyzed in three phases: in the first phase “Squashing,” signals translated into vectors. Multiscale ranked organizing maps and multiscale entropy were used to classify different algorithms. Dataset randomly divided training and test set and noise elimination performed only on the training set. In the final phase, authors used multiple validation protocols and did leave-one-out cross-validation for algorithms. Naive Bayes, random forest, logistic regression, sequential minimal optimization, kNN algorithms were used for classification, and at the end of the process, logistic regression and naive Bayes had 100% accuracy with all the classification models.
Bosl et al. [43] used multiclass support machines to differentiate high-risk and neurotypical infants’ EEG data. A total of 49 infants were there with one of their relatives to confirm ASD diagnoses performed by a psychiatrist, whereas the other 39 infants had no risk of ASD. Data was collected from each subject in five different sessions between 6 and 24 months. Raw EEG signals were processed using modified MSE. High, mean, and low modified multiscale entropy for each curve was calculated to create a feature set of 192 values. This algorithm classified healthy and high-risk infants after the age of 9 months with 90% accuracy.
Jayarathna et al. [44] used EEG data to predict human-pointed ADOS-2 diagnostic test results. They analyzed 24 adolescents with ASD and 22 neurotypical subjects while watching joint-attention movies, during the ADOS-2 test with human and baseline rest EEG. They used 32 electrodes for each subject. The authors used statistical and entropy values to extract features from EEG data. Researchers used entropy and statistical values, including standard deviation, mean, and combined mean on the data for the feature analysis. For joint attention, they used principle component analysis and random forest for classifying autism traits. Both algorithms yielded as high as 90% accuracy to separate ASD and non-ASD groups.
ERP is commonly used to investigate auditory perception in infants because they do not require direct attention or clear behavioral response. Ravan et al. [45] used three age groups (6 months, 12 months, and adult) ERP data for classification. The authors used the SVM and FCM methods after preprocessing and found these classification performances of algorithms above 94% at differentiating subjects’ ages. Stahl et al. [46] used discrimination methods and machine learning for infant ERP data. A total of 36 infants’ data were used for the classification of atypical neurodevelopment, and in each session, a static fixation stimulus was presented followed by a color image of a female face, with either direct gaze or averted gaze from the infant. Support vector machines and regularized discriminant function analysis were able to classify infants with ASD risk and control infants with using sixfold cross-validation with 64% and 61% accuracies.
ERP and EEG data with machine learning methods show potential in clinical research as an effective instrument for the classification high risk of ASD and the prediction of outcome. Another advantage of EEG is its ability to be applied to natural and ecological tests, also clinical settings in valid contexts, that will allow one to obtain data from multiple participants (e.g., mother–infant dyads) simultaneously. In the future, we need to assess the relationship between caregivers with an infant to understand the complex nature of social behavior and also the risk of ASD in the very first months of life.
5 Eye-tracking data studies
ASD is rarely diagnosed with current diagnostic systems before the age of 2 years and visual attention to the face, eye contact, which starts as early as 5 months [47]. Eye tracking has become a valuable tool for investigating very young infants’ behavior over the last decade and may use for early diagnosis in ASD. The previ-
ous work on this field has failed to find evidence of eye tracking, gaze differences in infants at risk of ASD or in infants who received an ASD diagnosis in toddlerhood. Improving machine learning methods may help one to increase prediction accuracy rates.
Liu et al. [48] used eye-tracking dataset of 48 neurotypical, 39 ASD, and 22 children with intellectual disability. The authors used a bag of words algorithm for the area of interest, eye movements, eye coordinates data, after that, the SVM model with RBF kernel trained with preprocessed data. As a result, they reached around 87% accuracy. Jiang et al. [49] used data from 19 neurotypical controls and 20 adults with ASD in a free-viewing task. The authors used a database that contains many natural scenes. Eye-tracking data was processed by the authors for further analysis. Cluster Fix was used for fixations and saccades deducted from raw data. After this, the authors computed fixation maps to classify two groups. In the last part of the study, the authors trained with the SVM model used leave-one-out cross-validation for performance assessment. This model showed real promise for eye tracking in ASD with 92% accuracy to classify ASD and a neurotypical group.
Eye-tracking studies may benefit from stateof-the-art machine learning methods to offer a more accurate diagnosis. Free image viewing, face recognition, and gaze studies contain the data-driven approach of automatically learned traits of ASD. The gaze is the essential part of psychiatry examination for children with ASD, and experimental studies show promising performance and so in future eye tracking may be a core tool for machine learning in ASD.
6 MRI data studies
MRI studies are the most used noninvasive methods for investigating brain morphology. In ASD, increased cerebellar hemisphere and total brain volume are the most replicated
1. Prediction of outcome in children with autism spectrum disorders
abnormalities in conventional MRI. Also fronto–temporo-parietal cortex, limbic system, and cerebellum network abnormalities may underlie the pathophysiology of ASD, which can be investigated with a special type of MRI, diffusion tensor imaging. Subsequent reports suggested that brain overgrowth and network abnormalities appear during very early childhood [50], and machine learning methods may help one to investigate this extensive data of high-risk infants.
Hazlett et al. [51] used data of 106 high-risk ASD infants and 42 low-risk infants to investigate the predictive value of brain volume. The authors showed that the overgrowth of the cortical areas between 6 and 12 months predicts ASD at the age of 24 months in infants. Social deficit and repetitive behaviors are also linked with the emergence of the brain’s overgrowth. They used a deep learning algorithm with a value of 81% specificity and sensitivity of 88% to classify ASD patients. The machine learning analysis was based on a three-stage deep learning network using unweighted and unbiased data. Ecker et al. [52] investigated MRI data for classifying 20 ASD and 20 neurotypical adults and used a set of five morphometric parameters to reveal spatially distributed patterns of discriminating regions. The authors used the SVM method for the spatially distributed pattern of regions with maximal weights and algorithm classified ASD group with 90% sensitivity and 80% specificity.
7 Conclusions
The prediction studies presented in this chapter are mostly preliminary and require more data and replication across different children with ASD. We need more observational data about ASD to predict the outcome and arrange treatment options until data-gathering technology arrives. In the near future, all screening methods, biological investigations, and treatment strategies will be using machine learning technology for toddlers and children.
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2 Autism spectrum disorder and sleep: pharmacology management
Pura Ballester a
and Ana M. Peiró b,*
aDepartment of Clinical Pharmacology, Pediatrics and Organic Chemistry, Miguel Hernandez University, Elche, Spain; bClinical Pharmacology Unit, Alicante General University Hospital, Alicante, Spain
*Corresponding author
1 Autism spectrum disorder
Autism spectrum disorder (ASD) is a mental disorder of the neurodevelopmental system. They are characterized by restricted interest and stereotyped repetitive behaviors, patterns of behavior, and difficulties with social communication and interaction. Unfortunately, ASD diagnosis is progressively increased from 1/2500 to 1/68 in last three decades and, usually, it is accompanied by other comorbidities, such as sleep problems [e.g., insomnia or circadian rhythm sleep–wake disorders (CRSWDs)] and intellectual disability (ID) [1]. Its approach is to use to combine education, supportive care, and behavioral therapy, together with drug prescription to target most frequent related conditions as depression, anxiety, hyperactivity, and obsessive–compulsive behaviors. Thus ASD subjects, especially with certain pathologies or multimorbidity, are usually exposed to polypharmacy or potentially inappropriate
medications, although scarce data about longdrug use effectiveness and even worst, about safety profile exist.
1.1 Autism and intellectual disability prevalence
ID is a lifelong manifestation that impacts significantly on individuals’ health and family quality of life. Furthermore, when ID coexists with ASD, it uses to be underestimated and consequently not taking account for early interventions and the support that individuals need. Factors affecting prevalence and its underestimation in neurocognitive diseases include its own difficulty to determinate with usual questionnaires. Also, in research, criteria used to identify ID can vary and are not matched with age group or sociodemographic status of the population. Even more, prevalence of ID varies according to the definition and the cutoff scores of diagnostic tools [2].
1.2 Potential drug targets in autism
Currently any medication has been proven effective in treating core characteristics of ID or autism even less, there is any cure. Psychotropic medications are frequently prescribed to treat comorbid symptoms, in across all ages, despite the little evidence related to their efficacy.
These comorbid conditions surrounding the ASD may result from several interactions between genetic (variations in the number of copies or in chromosomal sequences) and environmental (related to food, metabolites, and drug exposure) factors. There are also other autism risk factors, such as hypovitaminosis D, previously identified in other studies [3]. Data indicate that vitamin D is a novel and promising treatment that can ameliorate the core ASD manifestations in children [4,5]. Also, γ-aminobutyric acid (GABA) is a neurotransmitter that promotes the excitatory/inhibitory shift [6]. There is a lack of GABA regulation in patients with ASD. In ASD children, bumetanide improves clinical outcomes through changing GABAergic responses from excitation to inhibition [7]. Recently, this result has been confirmed in adult population, and bumetanide has proved to be effective for the clinical symptoms of ASD, through the GABAergic signaling pathway [8].
This chapter aimed to summarize current evidence on effectiveness and safety profile of pharmacological interventions for the most frequent symptoms and disorders associated to autism, with a special focus on sleep problems and to ID. To this day, clinicians should use pharmacotherapy with caution, carefully weighing risks and benefits, and as a part of a comprehensive personalized approach.
2 Sleep comorbid conditions in ASD
ASD has prevalent cooccurring medical conditions, including a wide range that goes from sleep problems, gastrointestinal disorders
(constipation or feeding difficulties), attention deficit/hyperactivity disorders, epilepsy, anxiety, or disruptive behaviors. All those conditions add complexity to the ASD diagnose and disturb the finding of an appropriate treatment and intervention [9]. For example, recent studies suggest that up to 8 from 10 ASD children or adolescents have any difficulty falling and/or staying asleep at night [10]. These issues make it harder for them to concentrate, decreasing their capacity to function, and lead to poor behavior. Families that have an autistic member who struggles with sleep often report higher stress levels [11]
2.1 Classification
Sleep problems are any condition that affects, disrupts, or involves sleep and have nocturnal/ diurnal manifestations. Thus this is a condition that frequently affects one’s ability to get enough quantity or appropriate quality of sleep [12]. They can be classified following International Classification of Sleep Disorders-Third Edition (ICSD-3) criteria (Table 2.1).
2.1.1 Insomnia
This disorder is a “persistent difficulty with sleep initiation, duration, consolidation or quality that occurs despite adequate opportunity and circumstances for sleep and results in some form of daytime impairment” [13].
2.1.2
Circadian rhythm sleep–wake disorders (CRSWDs)
Circadian system regulates endogenous rhythms (as the sleep–wake cycle) in most organ systems, which are synchronized, with a recurring periodicity of approximately 24 hours, by the central pacemaker in the suprachiasmatic nucleus and light/dark cycle [14]
In mammals the circadian clock network controls circadian rhythms and coordinates the expression of a variety of genes, helping the organism to respond in advance and face to environmental changes. Circadian clock genes
TABLE 2.1 Sleep disorders summarized according to the International Classification of Sleep Disorders Third Edition (ICSD-3) criteria.
REM (rapid eye movement) and NREM (non-REM) sleep phase disorders
• Delayed sleep–wake phase disorder (DSWPD)
• Advanced sleep–wake phase disorder (ASWPD)
• Non-24-hour sleep–wake rhythm disorder (N24SWD)
• Irregular sleep–wake rhythm disorder (ISWRD)
• Sleep walking
• Teeth grinding
• Absence of the muscle paralysis
could participate in sleep problems, psychiatric symptoms related to brain development timing, interfere in neuroendocrine or body temperature rhythms. That may impair how the individual integrates his/her internal and external rhythms [15]
CRSWDs arise when the circadian system and sleep–wake cycle become desynchronized. This category of disorders includes conditions, in which sleep timing is out of alignment with social, work, and sleep normal patterns across the 24-hour day. These circadian disorders are characterized by the inability to sleep at wished times, difficulties initiating and/or keeping sleep, and early awakening and impairments in daytime functioning. They require symptoms to be present for almost 3 months and confirmed with 14 days of actigraphy [13]. ICSD-3 categorizes seven different disorders; however, the most important for autism are those with the circadian phase shifted to an earlier time (phase advance), or later time (phase delay) [16,17].
2.2 Prevalence
Most of the literature related to sleep problems in ASD is developed in children, as sleep is
Characteristics
Difficulty with sleep initiation, duration, consolidation, or quality
• Difficulty with falling asleep and with waking at the times imposed, but sleep quality is typically reported as normal
• Difficulty staying awake during evening hours and wake time is undesirably early
• Failure in entraining to the 24-hour light–dark cycle and clock times
• Lacks a clear circadian pattern of sleep/ wake behavior
Episodes of arousal due to the absence of the normal physiologic muscle paralysis
important in the early stages of life. Sleep problems prevalence in this population (50%–86%) is significant higher compared to age-matched typically developing children (9%–50%) and, even more, with non-ASD developmental disabilities children [10,18–20]. This would further support the possible specific and currently unknown relationship of ASD and, at the same time, a sleep problem.
Moving toward the literature describing the adulthood, there is scarce research data from adults on the spectrum, even less, when ID is present. Nevertheless, ASD is a lifelong condition, and recent research proved a continuance of sleeping difficulties.
First study was conducted in 1993 which was a three–case study published by Simblett and Wilson, and they described for a participant: “sleep disturbance with early morning waking” [21]. Recent studies have displayed a 41% of sleep problems in a longitudinal follow-up study with a sample of 92 cognitively able autistic participants [22], and 45% of autistic adults with severe ID had sleep problems according to the results of the DASH II questionnaire [23].
[24–32] and advanced or delayed CRSWD [31,33,34] have been described regardless the cognitive ability.
2.3 Diagnosis
All children with ASD should be screened for insomnia with sleep questionnaire annually, together with any medical condition (pain, allergies, reflux, epilepsy, and dental issues between others) that may be contributing to any sleep problem and refer to appropriate subspecialist. This is relevant because ASD population need to be assessed for sleep disturbance before any appropriate treatment is initiated. In fact, a 2-week sleep log can be used to compute sleep parameters, or diagnose sleep problems and prove the efficacy of any pharmacological intervention considered, that should be starred with melatonin. Other studies have spotted significant differences in sleep architecture using subjective measures (e.g., questionnaires) [35], but as our adults with ASD and ID are nonverbal, most questionnaires are not validated for them. Recently, actigraphy has been very useful. This portable device stores information related to a week of rest/activity transitions, which is useful to assist in the diagnosis in various sleep disorders, including insomnia or circadian rhythm disorders. Ambulatory circadian monitoring (ACM) sleep parameters, for 5–7 consecutive nights with the same actigraph, can give a various and objectively information about sleep parameters and circadian sleep–wake rhythm indexes [36]
2.3.1
Sleep parameters
ACM can give information about several sleep parameters analyzed. Here we point out (1) total sleep time (TST), as the number of minutes defined as sleep between night sleep onset and sleep offset; (2) time in bed (TIB), as the number of minutes in bed at night until sleep offset; (3) sleep onset latency (SoL) as the time until night sleep onset; (4) number of awakenings
(number awake) as the number of wake up during TIB; (5) waking after sleep onset (WASO) as the minutes of wake up during the TST interval; and (6) SE percentage calculated as [(TST/ TIB) × 100]. Sleep parameters were calculated from ACM recordings, and sleep/wake diaries filled by caregivers were used as a backup for these results.
2.3.2 Circadian sleep–wake rhythm indexes
CRSWDs were calculated from ACM recordings as previously described [37]. These indexes serve to characterize the status of the circadian clock and also the rest-activity cycle [38,39]. Relative amplitude (where higher values indicate better circadian rhythmicity that is a greater difference between wake and sleep status values), interdaily stability (higher values indicate stronger circadian rhythm because values are similar across days), and intraday variability (higher values indicate weaker circadian rhythm due to high variability within each day) are measured. Also, other parameters are calculated: M (5, 10) and L (5, 10) indicate consecutive 5- and 10-hour periods of maximum (M-onset) and least activity (L-onset); these phase markers are presented as a mean value in an hour basis and accompanied by a value (V). Finally, circadian function index can assess the status for circadian rhythmicity.
3 Pharmacological treatment of sleep comorbid conditions in ASD
Given the involvement of neurotransmitters and hormones in the regulation of the sleep–wake cycle, showed at Table 2.2, and the evidences of their dysregulation in at least some individuals with autism, pharmacological treatments may be useful in treating their insomnia symptoms and CRSWDs. However, there are no efficacious treatments or practice guidelines for sleep problems in
TABLE 2.2 Brain areas and molecules in charge of sleep/wake transition and maintenance in ASD and its use in sleep problems treatment.
Sleep
Neurotransmitter Mechanism of action Brain areas ASD
GABA Neuronal inhibition
Suprachiasmatic nucleus, cortical areas, hypothalamus, and pineal gland
Melatonin Soporific and chronobiotic effects Pineal gland
Adenosine Mostly inhibitory neuromodulator of excitatory neurons (ADORA1 and 2 receptors), sometimes inhibitory neurons
Reduced in cortical areas, decreased expression of receptors and aberrant function of glutamic acid decarboxylase
Not normal increase during the night and elevated levels during the day
Cortical cholinergic areas Negatively correlated with process S and polymorphisms in the receptor ADORA2
Melanin concentrating hormone REM sleep and NREM sleep The brainstem, the sublaterodorsal, dorsal raphe nucleus, and locus coeruleus nuclei
Wake
Serotonin High serotoninergic activity promotes wakefulness and inactivity REM sleep
Raphe nuclei of the brainstem
In mice models of ASD the gene that produces this hormone is upregulated in the medial preoptic nucleus
Dopamine Initiate and maintain wakefulness, and production and modulation of REM sleep is also modulated by dopaminergic pathways
Acetylcholine Generates the brain-activated states of wakefulness and REM sleep
Glutamate Main excitatory neurotransmitter
Orexin system Two excitatory peptides that play a role in wakefulness
Ventral tegmental area and pineal gland
Alteration its activity in the dorsal raphe nucleus and interaction with the amygdala. Increased whole-blood serotonin levels in children with ASD and unaffected relatives. Also, higher levels of the intermediate metabolism of the transformation of serotonin into melatonin, altered serotonin synthesis, variation in its transport, and degradation
Disruptive gene encoding the DAD2 receptors that may confer risk for ASD and dysfunction of the brainstem dopaminergic pathways
Pontine reticular formation, cerebral cortex to limbic structures, and hypothalamus
Cortex and thalamus
Amygdala
Decreased concentrations of a precursor of acetylcholine and were correlated with the severity of autism
Increased in plasma and aberrant functioning that affects REM
Reduced inputs in the amygdala
2.
adolescents and adults on the autism spectrum [40]. Therefore behavioral therapy is usually the first option for poor sleep, with pharmacological therapies when needed to help patients function in their activities [41,42]. But, there are only two drugs: aripiprazole [43,44] and risperidone [45,46] that have US Food and Drug Administration approval for their use in ASD, to treat irritability and disruptive behavioral symptoms, respectively.
Thus there is a diverse range of off-label pharmacotherapeutics options and combined prescriptions that may induce polypharmacy (understood as four or more active prescriptions during at least 6 months) in this population without clear evidence of effectiveness. The off-label use of some drugs to treat sleep problems could increase the risk of developing side effects. Those that are concerning include metabolic disturbances, weight gain, extrapyramidal symptoms, and hyperprolactinemia, which should be kept in mind when prescribing antipsychotics [47].
Even more, most failures of pharmacological treatments may be due to the lack of matching treatment to the underlying cause of the sleep problem.
3.1 Melatonin receptor agonists
Nowadays, most promising molecules are melatonin receptor agonist drugs. As mentioned earlier, melatonin can be one of the inceptions of ASD sleep problems in relation to its levels and/or peak times. Pineal gland releases melatonin in a cyclical, daily pattern with low signal during the day and increased at night [48]. And, melatonin circadian rhythm is a phase marker of the sleep–wake transition, and the evening rise in melatonin marks circadian rhythmicity (dim light melatonin onset) [49]. In fact, lower urinary 6-sulphatoxymelatonin [50] and plasma melatonin [51] levels have been found in ASD individuals in some studies, indicating some abnormalities in this pathway.
3.1.1 Melatonin
Melatonin is the first pharmacological step for sleep problems in autism with minimal adverse events [52]. It has been demonstrated effective for (1) reducing insomnia symptoms as SoL [53–55], increasing SE [55], reducing number of awakenings [56], increasing TST [54,57], and also improving CRSWD [57,58]. A reduction of wakes after sleep onset (WASO) may be achieved using prolonged release forms that have already proved safety in a trial up to 2 years [59]; (2) improving CRSWD through sleep phase improvement [10,60].
However, data are based in scarce studies either young adults [61], adults [62] or even less, in children [63]. Thus there is a lack of clinical studies to test the effectiveness of pharmacological treatments for sleep problems, generally in adults with ASD, particularly in autistic adults with ID.
3.1.2 Melatonin combined with behavioral interventions
Melatonin, together with behavioral interventions (e.g., functional analysis of the problem or stimulus control) [64] and sleep hygiene (reducing naps, increasing morning physical activity, and modulating the light intensity received), has been used to treat sleep problems in ASD [65]. However, outcomes from clinical trials with melatonin vary considerably depending on the age range studied [66], the diagnostic measures employed [67–69], the length of the study [70,71], the different doses used [58,63,70], and improved sleep outcomes after treatment [72]
3.1.3 Agomelatine
Agomelatine (Valdoxan) was first reported in 1992, acting as an agonist for melatonin MT1 and MT2 receptors and as an antagonist on serotonin 5HT2c receptors. The most exciting behavior of agomelatine is its procircadian effect, accelerating the restoration of circadian rhythms. The binding affinity of agomelatine for MT1 and
MT2 is as strong as melatonin, but its half-life is longer. And also that antidepressant efficacy could be stronger due to melatonin secretion through monoaminergic mechanisms [73,74].
AGOTEA study is the largest sample of autistic adults with ID enrolled in a randomized clinical trial, where an objective tool as ACM assesses the effectiveness of a pharmacological treatment for sleep problems [30]. There is only a clinical trial that has a bigger sample size than this with 27 participants. However, the sample contained different mental pathologies, so there were no conclusions about sleep improvement only in autistic participants [61]
3.1.4
Agomelatine long-term use
In autism, there is no evidence about longterm side effects of agomelatine [75]. A large clinical trial (n = 711, 8-week treatment) reported that seven individuals suffered an elevation of transaminase levels (six recovered normal values without leaving the trial). In spite of AGOTEA sample already consuming a median of five ongoing medications, the safety was acceptable, with only 4% of prevalence of adverse event. In contrast, agomelatine was effective increasing TST. This was coherent with earlier publications for melatonin in the younger ones [57,58].
3.1.5
Agomelatine use for CRSWD
The phase correction of peripheral temperature, a marker of circadian activity following agomelatine [76], specifically when a phase advancement is present, indicates an improvement of CRSWD [77]. Furthermore, the phase correction seen in AGOTEA study temperature rhythm was described earlier in an open-label study in an elderly sample and using a larger dose of agomelatine [78]. The previous was also described in a study with a range of doses of agomelatine [79].
It is important to schedule its administration and also to measure pharmacokinetic parameters when using a chronobiotic therapy. To
advance a rhythm the intake must occur multiple hours prior bedtime. On the other hand, at the time of a phase advancement some experts have advised for a low dose of melatonin [80], but several authors pointed the need for further studies in the field of the circadian regulation [80–82]. The chronobiotic results obtained for agomelatine are consistent with what has been described using a sleep screening questionnaire [83] to assess CRSWD.
It is unknown if our results can be extended to adults with ASD and no ID, or to children with ASD with sleep problems, whether agomelatine may also improve other situations such as the daytime sleepiness, shorter SoL, or increased night waking also found in individuals with ASD.
3.1.6 Agomelatine combinations with other therapy comorbidities
Apart from melatonin, donepezil, an acetylcholinesterase inhibitor, has been studied in an open-label study, tested by polysomnography, in children [84]. An increase in TST and rapid eye movement (REM) sleep values was obtained. Additionally, risperidone, used for autistic symptoms, has subsequently improved participants’ sleep rhythm [85,86]. Sleep improvements shown here are also consistent with results for agomelatine studies in patients with depression [87,88]. The increase in TST after active treatment, seen in ACM recordings, has been described in adults with major depressive disorder using PSG [87–89]. In an open-label study [90], similar increase in TST, and also increased SE and non-REM stage 3 were found with the same dose as used in our study.
The following TST increment has been reported in studies in participants with mood disorders or anxiety that used subjective sleep measures [91,92], and a reduction in insomnia symptoms has been observed after agomelatine treatment [93]. Nevertheless, some authors have described no effects on sleep after agomelatine treatment [94,95].
3.2 Other psychotropic drugs
Apart from the ones mentioned earlier, some drugs that prompt sleep (neuroleptics, antidepressants, or stimulants) are recommended to children, adolescents, and adults on the spectrum [96–100]. Although none comes with sleep problems, drug side effects on sleep function are very extensive. Sedative effects that psychotropic medications used for ASD mental health and behavioral issues [101] or anticonvulsants for epilepsy could promote sleep but to date, no one has tested this hypothesis. Longitudinal studies proved that there is an increase in prescriptions of psychotropic medication across the time from 31% in children to 45% in adults with autism [96]. Also, recent studies reported an increment of psychotropic prescriptions up to the 73%, in young autistic adults with ages from 18 to 21 years [98]. The abovementioned experience occurs even though there is limited evidence of pharmacological effectiveness derived from clinical trials in the ASD population.
3.2.1 Antipsychotics (risperidone and quetiapine)
Risperidone and quetiapine, both neuroleptics, and selective serotonin reuptake inhibitors, are used to control some behavioral symptoms in autism, but their effects on sleep have been also described [102,103]. An open-label study in a small number of children on the spectrum showed that donepezil, an inhibitor of ACh degradation by acetyl cholinesterase enzyme, was effective in increasing REM sleep percentage during nighttime [104]. Clonidine, an ACh agonist, for attentional deficits [105], mirtazapine for depressive disorders [106,107], or gabapentin for epilepsy [107] in autism are under research. Some authors suggest that clonazepam, a GABA agonist, could be a good approach to increasing REM sleep [108], without a clear conclusion.
Risperidone remains unlicensed for use in ASD children, in the United Kingdom, but it is
approved for the treatment of persistent aggression in conduct disorder in children of subaverage intellectual functioning. Thus although when sleep problems are present as autistic comorbid conditions we could choose those treatments that also aid sleep [109], this cannot be a general recommendation based on evidence. Further attention should be paid to the metabolic phenotypes and possible drug–drug interactions, because this could predispose ASD individuals to side effects that derive from the treatment of different comorbidities with offlabel pharmacological use [110], as hasten clearance of melatonin when it is combined with anticonvulsants [111]
3.2.2 Drug–drug interaction potential side effects
Clinicians should be careful not to potentiate adverse events taking into account that subjects may be genetically predisposed P450 slow metabolizers [112], and as melatonin is metabolized by the mentioned via, and together with drugs used for mood disorders, there is a concern that these antiepileptic drugs may hasten the clearance of melatonin [111].
4 Conclusions
Due to the different causes and features of problematic sleep in ASD, future research into the efficacy of sleep interventions may point to phenotypic subclassifications rather than a wide analysis, including the whole autistic spectrum together [113]. The definition of sleep status by specific subtype will help one to gather better longitudinal data about responses to specific treatments providing a better individualized therapy. Even more, the use of ambulatory and objective methods to study sleep disorders, as actigraphy, could help for future research because it is critical to disentangle the relationship between measures of sleep captured by reports and sleep objective tools. Additional
research should be done related to the high frequency of polypharmacy in autism population that could impact in their morbidity.
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criterion, I may mention his separation of the sulphates of baryta and strontia, which had 323 previously been confounded. Among crystals which in the collections were ranked together as “heavy spar,” and which were so perfect as to admit of accurate measurement, he found that those which were brought from Sicily, and those of Derbyshire, differed in their cleavage angle by three degrees and a half “I could not suppose,” he says, 22 “that this difference was the effect of any law of decrement; for it would have been necessary to suppose so rapid and complex a law, that such an hypothesis might have been justly regarded as an abuse of the theory.” He was, therefore, in great perplexity. But a little while previous to this, Klaproth had discovered that there is an earth which, though in many respects it resembles baryta, is different from it in other respects; and this earth, from the place where it was found (in Scotland), had been named Strontia. The French chemists had ascertained that the two earths had, in some cases, been mixed or confounded; and Vauquelin, on examining the Sicilian crystals, found that their base was strontia, and not, as in the Derbyshire ones, baryta. The riddle was now read; all the crystals with the larger angle belong to the one, all those with the smaller, to the other, of these two sulphates; and crystallometry was clearly recognized as an authorized test of the difference of substances which nearly resemble each other.
22 Traité, ii. 320.
Enough has been said, probably, to enable the reader to judge how much each of the two persons, now under review, contributed to crystallography. It would be unwise to compare such contributions to science with the great discoveries of astronomy and chemistry; and we have seen how nearly the predecessors of Romé and Haüy had reached the point of knowledge on which these two
crystallographers took their stand. But yet it is impossible not to allow, that in these discoveries, which thus gave form and substance to the science of crystallography, we have a manifestation of no common sagacity and skill. Here, as in other discoveries, were required ideas and facts; clearness of geometrical conception which could deal with most complex relations of form; a minute and extensive acquaintance with actual crystals; and the talent and habit of referring these facts to the general ideas. Haüy, in particular, was happily endowed for his task Without being a great mathematician, he was sufficiently a geometer to solve all the problems which his undertaking demanded; and though the mathematical reasoning might have been made more compendious 324 by one who was more at home in mathematical generalization, probably this could hardly have been done without making the subject less accessible and less attractive to persons moderately disciplined in mathematics. In all his reasonings upon particular cases, Haüy is acute and clear; while his general views appear to be suggested rather by a lively fancy than by a sage inductive spirit: and though he thus misses the character of a great philosopher, the vivacity of style, and felicity and happiness of illustration, which grace his book, and which agree well with the character of an Abbé of the old French monarchy, had a great and useful influence on the progress of the subject.
Unfortunately Romé de Lisle and Haüy were not only rivals, but in some measure enemies. The former might naturally feel some vexation at finding himself, in his later years (he died in 1790), thrown into shade by his more brilliant successor. In reference to Haüy’s use of cleavage, he speaks 23 of “innovators in crystallography, who may properly be called crystalloclasts. ” Yet he adopted, in great measure, the same views of the formation of crystals by laminæ, 24 which Haüy illustrated by the destructive
process at which he thus sneers. His sensitiveness was kept alive by the conduct of the Academy of Sciences, which took no notice of him and his labors; 25 probably because it was led by Buffon, who disliked Linnæus, and might dislike Romé as his follower; and who, as we have seen, despised crystallography. Haüy revenged himself by rarely mentioning Romé in his works, though it was manifest that his obligations to him were immense; and by recording his errors while he corrected them. More fortunate than his rival, Haüy was, from the first, received with favor and applause His lectures at Paris were eagerly listened to by persons from all quarters of the world. His views were, in this manner, speedily diffused; and the subject was soon pursued, in various ways, by mathematicians and mineralogists in every country of Europe.
23 Pref p xxvii
24 T ii p 21
25 Marx. Gesch. d. Cryst. 130.
CHAPTER III.
R C H C
I
HAVE not hitherto noticed the imperfections of the crystallographic views and methods of Haüy, because my business in the last section 325 was to mark the permanent additions he made to the science. His system did, however, require completion and rectification in various points; and in speaking of the crystallographers of the subsequent time, who may all be considered as the cultivators of the Hauïan doctrines, we must also consider what they did in correcting them
The three main points in which this improvement was needed were; a better determination of the crystalline forms of the special substances;—a more general and less arbitrary method of considering crystalline forms according to their symmetry; and a detection of more general conditions by which the crystalline angle is regulated The first of these processes may be considered as the natural sequel of the Hauïan epoch: the other two must be treated as separate steps of discovery
When it appeared that the angle of natural or of cleavage faces could be used to determine the differences of minerals, it became important to measure this angle with accuracy. Haüy’s measurements were found very inaccurate by many succeeding crystallographers: Mohs says 26 that they are so generally inaccurate, that no confidence can be placed in them. This was said, of course, according to the more rigorous notions of accuracy to which the establishment of Haüy’s system led. Among the persons who principally labored in ascertaining, with precision, the crystalline
angles of minerals, were several Englishmen, especially Wollaston, Phillips, and Brooke. Wollaston, by the invention of his Reflecting Goniometer, placed an entirely new degree of accuracy within the reach of the crystallographer; the angle of two faces being, in this instrument, measured by means of the reflected images of bright objects seen in them, so that the measure is the more accurate the more minute the faces are In the use of this instrument, no one was more laborious and successful than William Phillips, whose power of apprehending the most complex forms with steadiness and clearness, led Wollaston to say that he had “a geometrical sense.”
Phillips published a Treatise on Mineralogy, containing a great collection of such determinations; and Mr. Brooke, a crystallographer of the same exact and careful school, has also published several works of the same kind. The precise measurement of crystalline angles must be the familiar employment of all who study crystallography; and, therefore, any further enumeration of those 326 who have added in this way to the stock of knowledge, would be superfluous.
26 Marx. p. 153.
Nor need I dwell long on those who added to the knowledge which Haüy left, of derived forms. The most remarkable work of this kind was that of Count Bournon, who published a work on a single mineral (calcspar) in three quarto volumes. 27 He has here given representations of seven hundred forms of crystals, of which, however, only fifty-six are essentially different. From this example the reader may judge what a length of time, and what a number of observers and calculators, were requisite to exhaust the subject.
27 Traité complet de la Chaux Carbonatée et d’Aragonite, par M. le Comte de Bournon. London, 1808.
If the calculations, thus occasioned, had been conducted upon the basis of Haüy’s system, without any further generalization, they would have belonged to that process, the natural sequel of inductive discoveries, which we call deduction; and would have needed only a very brief notice here. But some additional steps were made in the upward road to scientific truth, and of these we must now give an account
CHAPTER IV.
E D S C W
M .
N Haüy’s views, as generally happens in new systems, however true, there was involved something that was arbitrary, something that was false or doubtful, something that was unnecessarily limited. The principal points of this kind were; his having made the laws of crystalline derivation depend so much upon cleavage; his having assumed an atomic constitution of bodies as an essential part of his system; and his having taken a set of primary forms, which, being selected by no general view, were partly superfluous, and partly defective.
How far evidence, such as has been referred to by various philosophers, has proved, or can prove, that bodies are constituted of indivisible atoms, will be more fully examined in the work which treats of the Philosophy of this subject. There can be little doubt that the 327 portion of Haüy’s doctrine which most riveted popular attention and applause, was his dissection of crystals, in a manner which was supposed to lead actually to their ultimate material elements. Yet it is clear, that since the solids given by cleavage are, in many cases, such as cannot make up a solid space, the primary conception of a necessary geometrical identity between the results of division and the elements of composition, which is the sole foundation of the supposition that crystallography points out the actual elements, disappears on being scrutinized: and when Haüy, pressed by this difficulty, as in the case of fluor-spar, put his integrant octohedral molecules together, touching by the edges only, his
method became an empty geometrical diagram, with no physical meaning.
The real fact, divested of the hypothesis which was contained in the fiction of decrements, was, that when the relation of the derivative to the primary faces is expressed by means of numerical indices, these numbers are integers, and generally very small ones; and this was the form which the law gradually assumed, as the method of derivation was made more general and simple by Weiss and others.
“When, in 1809, I published my Dissertation,” says Weiss, 28 “I shared the common opinion as to the necessity of the assumption and the reality of the existence of a primitive form, at least in a sense not very different from the usual sense of the expression. While I sought,” he adds, referring to certain doctrines of general philosophy which he and others entertained, “a dynamical ground for this, instead of the untenable atomistic view, I found that, out of my primitive forms, there was gradually unfolded to my hands, that which really governs them, and is not affected by their casual fluctuations, the fundamental relations of those Dimensions according to which a multiplicity of internal oppositions, necessarily and mutually interdependent, are developed in the mass, each having its own polarity; so that the crystalline character is coextensive with these polarities.”
28 Mem Acad Berl 1816, p 307
The “Dimensions” of which Weiss here speaks, are the Axes of Symmetry of the crystal; that is, those lines in reference to which, every face is accompanied by other faces, having like positions and properties Thus a rhomb, or more properly a rhombohedron, 29 of
328 calcspar may be placed with one of its obtuse corners uppermost, so that all the three faces which meet there are equally inclined to the vertical line. In this position, every derivative face, which is obtained by any modification of the faces or edges of the rhombohedron, implies either three or six such derivative faces; for no one of the three upper faces of the rhombohedron has any character or property different from the other two; and, therefore, there is no reason for the existence of a derivative from one of these primitive faces, which does not equally hold for the other primitive faces. Hence the derivative forms will, in all cases, contain none but faces connected by this kind of correspondence. The axis thus made vertical will be an Axis of Symmetry, and the crystal will consist of three divisions, ranged round this axis, and exactly resembling each other. According to Weiss’s nomenclature, such a crystal is “threeand-three-membered.”
29 I use this name for the solid figure, since rhomb has always been used for a plane figure.
But this is only one of the kinds of symmetry which crystalline forms may exhibit. They may have three axes of complete and equal symmetry at right angles to each other, as the cube and the regular octohedron; or, two axes of equal symmetry, perpendicular to each other and to a third axis, which is not affected with the same symmetry with which they are; such a figure is a square pyramid; or they may have three rectangular axes, all of unequal symmetry, the modifications referring to each axis separately from the other two.
These are essential and necessary distinctions of crystalline form; and the introduction of a classification of forms founded on such relations, or, as they were called, Systems of Crystallization, was a
great improvement upon the divisions of the earlier crystallographers, for those divisions were separated according to certain arbitrarily-assumed primary forms. Thus Romé de Lisle’s fundamental forms were, the tetrahedron, the cube, the octohedron, the rhombic prism, the rhombic octohedron, the dodecahedron with triangular faces: Haüy’s primary forms are the cube, the rhombohedron, the oblique rhombic prism, the right rhombic prism, the rhombic dodecahedron, the regular octohedron, tetrahedron, and six-sided prism, and the bipyramidal dodecahedron This division, as I have already said, errs both by excess and defect, for some of these primary forms might be made derivatives from others; and no solid reason could be assigned why they were not. Thus the cube may be derived from the tetrahedron, by truncating the edges; and the rhombic dodecahedron again from the cube, by truncating its edges; while the square pyramid could not be legitimately identified with the derivative of any of these forms; for if we were to 329 derive it from the rhombic prism, why should the acute angles always suffer decrements corresponding in a certain way to those of the obtuse angles, as they must do in order to give rise to a square pyramid?
The introduction of the method of reference to Systems of Crystallization has been a subject of controversy, some ascribing this valuable step to Weiss, and some to Mohs. 30 It appears, I think, on the whole, that Weiss first published works in which the method is employed; but that Mohs, by applying it to all the known species of minerals, has had the merit of making it the basis of real crystallography. Weiss, in 1809, published a Dissertation On the mode of investigating the principal geometrical character of crystalline forms, in which he says, 31 “No part, line, or quantity, is so important as the axis; no consideration is more essential or of a higher order than the relation of a crystalline plane to the axis;” and
again, “An axis is any line governing the figure, about which all parts are similarly disposed, and with reference to which they correspond mutually.” This he soon followed out by examination of some difficult cases, as Felspar and Epidote. In the Memoirs of the Berlin Academy, 32 for 1814–15, he published An Exhibition of the natural Divisions of Systems of Crystallization. In this Memoir, his divisions are as follows: The regular system, the four-membered, the twoand-two-membered, the three-and-three-membered, and some others of inferior degrees of symmetry These divisions are by Mohs (Outlines of Mineralogy, 1822), termed the tessular, pyramidal, prismatic, and rhombohedral systems respectively. Hausmann, in his Investigations concerning the Forms of Inanimate Nature, 33 makes a nearly corresponding arrangement; the isometric, monodimetric, trimetric, and monotrimetic; and one or other of these sets of terms have been adopted by most succeeding writers.
30 Edin Phil Trans 1823, vols xv and xvi
31 pp 16, 42
32 Ibid.
33 Göttingen, 1821.
In order to make the distinctions more apparent, I have purposely omitted to speak of the systems which arise when the prismatic system loses some part of its symmetry; when it has only half or a quarter its complete number of faces;—or, according to Mohs’s phraseology, when it is hemihedral or tetartohedral Such systems are represented by the singly-oblique or doubly-oblique prism; they are termed by Weiss two-and-one-membered, and one-and-onemembered; by other writers, Monoklinometric, and Triklinometric Systems There are also other 330 peculiarities of Symmetry, such,
for instance, as that of the plagihedral faces of quartz, and other minerals.
The introduction of an arrangement of crystalline forms into systems, according to their degree of symmetry, was a step which was rather founded on a distinct and comprehensive perception of mathematical relations, than on an acquaintance with experimental facts, beyond what earlier mineralogists had possessed. This arrangement was, however, remarkably confirmed by some of the properties of minerals which attracted notice about the time now spoken of, as we shall see in the next chapter.
~Additional material in the 3rd edition.~
CHAPTER V.
R C D S
C .
DIFFUSION D S . The distinction of systems of crystallization was so far founded on obviously true views, that it was speedily adopted by most mineralogists. I need not dwell on the steps by which this took place. Mr. Haidinger’s translation of Mohs was a principal occasion of its introduction in England. As an indication of dates, bearing on this subject, perhaps I may be allowed to notice, that there appeared in the Philosophical Transactions for 1825, A General Method of Calculating the Angles of Crystals, which I had written, and in which I referred only to Haüy’s views; but that in 1826, 34 I published a Memoir On the Classification of Crystalline Combinations, founded on the methods of Weiss and Mohs, especially the latter; with which I had in the mean time become acquainted, and which appeared to me to contain their own evidence and recommendation. General methods, such as was attempted in the Memoir just quoted, are part of that process in the history of sciences, by which, when the principles are once established, the mathematical operation of deducing their consequences is made more and more general and symmetrical: which we have seen already exemplified in the history of celestial mechanics after the time of Newton. It does not enter into our plan, to dwell upon the various steps in this way 331 made by Levy, Naumann, Grassmann, Kupffer, Hessel, and by Professor Miller among ourselves. I may notice that one great improvement was, the method introduced by Monteiro and Levy, of determining the laws of derivation of forces by means of the parallelisms of edges; which
was afterwards extended so that faces were considered as belonging to zones. Nor need I attempt to enumerate (what indeed it would be difficult to describe in words) the various methods of notation by which it has been proposed to represent the faces of crystals, and to facilitate the calculations which have reference to them.
34 Camb. Trans. vol. ii. p. 391.
[2nd Ed.] [My Memoir of 1825 depended on the views of Haüy in so far as that I started from his “primitive forms;” but being a general method of expressing all forms by co-ordinates, it was very little governed by these views. The mode of representing crystalline forms which I proposed seemed to contain its own evidence of being more true to nature than Haüy’s theory of decrements, inasmuch as my method expressed the faces at much lower numbers. I determine a face by means of the dimensions of the primary form divided by certain numbers; Haüy had expressed the face virtually by the same dimensions multiplied by numbers. In cases where my notation gives such numbers as (3, 4, 1), (1, 3, 7), (5, 1, 19), his method involves the higher numbers (4, 3, 12), (21, 7, 3), (19, 95, 5). My method however has, I believe, little value as a method of “calculating the angles of crystals.”
M. Neumann, of Königsberg, introduced a very convenient and elegant mode of representing the position of faces of crystals by corresponding points on the surface of a circumscribing sphere He gave (in 1823) the laws of the derivation of crystalline faces, expressed geometrically by the intersection of zones, (Beiträge zur Krystallonomie.) The same method of indicating the position of faces of crystals was afterwards, together with the notation, re-invented by
M. Grassmann, (Zur Krystallonomie und Geometrischen Combinationslehre, 1829.) Aiding himself by the suggestions of these writers, and partly adopting my method, Prof. Miller has produced a work on Crystallography remarkable for mathematical elegance and symmetry; and has given expressions really useful for calculating the angles of crystalline faces, (A Treatise on Crystallography Cambridge, 1839 )]
Confirmation of the Distinction of Systems by the Optical Properties of Minerals. Brewster. I must not omit to notice the striking confirmation which the distinction of systems of crystallization received from optical discoveries, especially those of Sir D. Brewster. Of the 332 history of this very rich and beautiful department of science, we have already given some account, in speaking of Optics. The first facts which were noticed, those relating to double refraction, belonged exclusively to crystals of the rhombohedral system. The splendid phenomena of the rings and lemniscates produced by dipolarizing crystals, were afterwards discovered; and these were, in 1817, classified by Sir David Brewster, according to the crystalline forms to which they belong. This classification, on comparison with the distinction of Systems of Crystallization, resolved itself into a necessary relation of mathematical symmetry: all crystals of the pyramidal and rhombohedral systems, which from their geometrical character have a single axis of symmetry, are also optically uniaxal, and produce by dipolarization circular rings; while the prismatic system, which has no such single axis, but three unequal axes of symmetry, is optically biaxal, gives lemniscates by dipolarized light, and according to Fresnel’s theory, has three rectangular axes of unequal elasticity.
[2nd Ed.] [I have placed Sir David Brewster’s arrangement of crystalline forms in this chapter, as an event belonging to the confirmation of the distinctions of forms introduced by Weiss and Mohs; because that arrangement was established, not on crystallographical, but on optical grounds. But Sir David Brewster’s optical discovery was a much greater step in science than the systems of the two German crystallographers; and even in respect to the crystallographical principle, Sir D. Brewster had an independent share in the discovery He divided crystalline forms into three classes, enumerating the Hauïan “primitive forms” which belonged to each; and as he found some exceptions to this classification, (such as idocrase, &c.,) he ventured to pronounce that in those substances the received primitive forms were probably erroneous; a judgment which was soon confirmed by a closer crystallographical scrutiny. He also showed his perception of the mineralogical importance of his discovery by publishing it, not only in the Phil. Trans. (1818), but also in the Transactions of the Wernerian Society of Natural History. In a second paper inserted in this later series, read in 1820, he further notices Mohs’s System of Crystallography, which had then recently appeared, and points out its agreement with his own.
Another reason why I do not make his great optical discovery a cardinal point in the history of crystallography is, that as a crystallographical system it is incomplete. Although we are thus led to distinguish the tessular and the prismatic systems (using Mohs’s terms) 333 from the rhombohedral and the square prismatic, we are not led to distinguish the latter two from each other; inasmuch as they have no optical difference of character. But this distinction is quite essential in crystallography; for these two systems have faces formed by laws as different as those of the other two systems.
Moreover, Weiss and Mohs not only divided crystalline forms into certain classes, but showed that by doing this, the derivation of all the existing forms from the fundamental ones assumed a new aspect of simplicity and generality; and this was the essential part of what they did.
On the other hand, I do not think it is too much to say as I have elsewhere said 35 that “Sir D. Brewster’s optical experiments must have led to a classification of crystals into the above systems, or something nearly equivalent, even if crystals had not been so arranged by attention to their forms.”]
35 Philosophy of the Inductive Sciences, B. viii. C. iii. Art. 3.
Many other most curious trains of research have confirmed the general truth, that the degree and kind of geometrical symmetry corresponds exactly with the symmetry of the optical properties. As an instance of this, eminently striking for its singularity, we may notice the discovery of Sir John Herschel, that the plagihedral crystallization of quartz, by which it exhibits faces twisted to the right or the left, is accompanied by right-handed or left-handed circular polarization respectively. No one acquainted with the subject can now doubt, that the correspondence of geometrical and optical symmetry is of the most complete and fundamental kind.
[2nd Ed.] [Our knowledge with respect to the positions of the optical axes of the oblique prismatic crystals is still imperfect. It appears to be ascertained that, in singly oblique crystals, one of the axes of optical elasticity coincides with the rectangular crystallographic axis. In doubly oblique crystals, one of the axes of optical elasticity is, in many cases, coincident with the axis of a
principal zone. I believe no more determinate laws have been discovered.]
Thus the highest generalization at which mathematical crystallographers have yet arrived, may be considered as fully established; and the science of Crystallography, in the condition in which these place it, is fit to be employed as one of the members of Mineralogy, and thus to fill its appropriate place and office.
~Additional material in the 3rd edition.~ 334
CHAPTER VI.
C L A S
DISCOVERY I . M .—The discovery of which we now have to speak may appear at first sight too large to be included in the history of crystallography, and may seem to belong rather to chemistry But it is to be recollected that crystallography, from the time of its first assuming importance in the hands of Haüy, founded its claim to notice entirely upon its connexion with chemistry; crystalline forms were properties of something; but what that something was, and how it might be modified without becoming something else, no crystallographer could venture to decide, without the aid of chemical analysis. Haüy had assumed, as the general result of his researches, that the same chemical elements, combined in the same proportions, would always exhibit the same crystalline form; and reciprocally, that the same form and angles (except in the obvious case of the tessular system, in which the angles are determined by its being the tessular system,) implied the same chemical constitution. But this dogma could only be considered as an approximate conjecture; for there were many glaring and unexplained exceptions to it. The explanation of several of these was beautifully described by the discovery that there are various elements which are isomorphous to each other; that is, such that one may take the place of another without altering the crystalline form; and thus the chemical composition may be much changed, while the crystallographic character is undisturbed.
This truth had been caught sight of, probably as a guess only, by Fuchs as early as 1815. In speaking of a mineral which had been
called Gehlenite, he says, “I hold the oxide of iron, not for an essential component part of this genus, but only as a vicarious element, replacing so much lime. We shall find it necessary to consider the results of several analyses of mineral bodies in this point of view, if we wish, on the one hand, to bring them into agreement with the doctrine of chemical proportions, and on the other, to avoid unnecessarily splitting up genera ” In a lecture On the Mutual Influence of 335 Chemistry and Mineralogy, 36 he again draws attention to his term vicarious (vicarirende), which undoubtedly expresses the nature of the general law afterwards established by Mitscherlich in 1822.
36 Munich, 1820
But Fuchs’s conjectural expression was only a prelude to Mitscherlich’s experimental discovery of isomorphism. Till many careful analyses had given substance and signification to this conception of vicarious elements, it was of small value. Perhaps no one was more capable than Berzelius of turning to the best advantage any ideas which were current in the chemical world; yet we find him, 37 in 1820, dwelling upon a certain vague view of these cases, that “oxides which contain equal doses of oxygen must have their general properties common;” without tracing it to any definite conclusions But his scholar, Mitscherlich, gave this proposition a real crystallographical import. Thus he found that the carbonates of lime (calcspar,) of magnesia, of protoxide of iron, and of protoxide of manganese, agree in many respects of form, while the homologous angles vary through one or two degrees only; so again the carbonates of baryta, strontia, lead, and lime (arragonite), agree nearly; the different kinds of felspar vary only by the substitution of one alkali for another; the phosphates are almost
