The interest in 'biomarkers' seen across a spectrum of biomedical disciplines reflects the rise of molecular biology and genetics. A host of 'omics' disciplines in addition to genomics, marked by multidimensional data and complex analyses, and enabled by bioinformatics, have pushed the trajectory of biomarker development even further. They have also made more tractable the complex mappings of genotypes to phenotypes - genome-to-phenome mapping - to which the concept of a biomarker is central. Genomic investigations of the brain are beginning to reveal spectacular associations between genes and neural systems. Neural and cognitive phenomics are considered a necessary complement to genomics of the brain. Other major omics developments such as connectomics, the comprehensive mapping of neurons and neural networks, are heralding brain maps of unprecedented detail. Such developments are defining a new era of brain science. And in this new research environment, neural systems and cognitive operations are pressed for new kinds of definitions - that facilitate brain-behavioral alignment in an omics operating environment. This volume explores the topic of markers framed around the constructs of cognitive and neural systems. 'Neurophenotype' is a term adopted to describe a neural or cognitive marker that can be scientifically described within an associative framework - and while the genome-to-phenome framework is the most recognized of these, epigenetics and non-gene-regulated neural dynamics also suggest other frameworks. In either case, the term neurophenotype defines operational constructs of brain-behavioral domains that serve the integration of these domains with neuroscientific and omics models of the brain. The topic is critically important to psychiatry and neuropsychology: Neurophenotypes offer a 'format' and a 'language' by which psychiatry and neuropsychology can be in step with the brain sciences. They also bring a new challenge to the clinical neurosciences in terms of construct validation and refinement. Topics covered in the volume include: Brain and cognition in the omics era Phenomics, connectomics, and Research Domain Criteria Circuit-based neurophenotypes, and complications posed by non-gene regulated factors The legacy of the endophenotype concept - its utility and limitations Various potential neurophenotypes of relevance to clinical neuroscience, including Response Inhibition, Fear Conditioning and Extinction, Error Processing, Reward Dependence and Reward Deficiency, Face Perception, and Language Phenotypes Dynamic (electrophysiological) and computational neurophenotypes The challenge of a cultural shift for psychiatry and neuropsychology The volume may be especially relevant to researchers and clinical practitioners in psychiatry and neuropsychology and to cognitive neuroscientists interested in the intersection of neuroscience with genomics, phenomics and other omics disciplines.

Bioinformatics involves specialized application of computer technology to investigative and conceptual problems in biology and medicine; neuroinformatics (NI) is the practice of bioinformatics in the neurosciences. Over the past two decades the biomedical sciences have been revolutionized by databases, data mining and data modeling techniques.

The Human Genome Project, which depended on informatics methods, has been the most well recognized bioinformatics undertaking. Bioinformatics has since been applied all across biology and medicine, and has also transformed almost every avenue in neuroscience. Yet in neuropsychology, NI perspectives remain largely unrealized. Ironically, NI offers enormous potential to the essential praxis of neuropsychology - assessing cognitive behavior and relating cognition to neural systems. Neuroinformatics can be applied to neuropsychology as richly as it has been applied across the neurosciences.

Neuroinformatics for Neuropsychology is the first book to explain the relevance and value of NI to neuropsychology. It systematically describes NI tools, applications and models that can enhance the efforts of neuropsychologists. It also describes the implications of NI for neuropsychology in the 21st century fundamental shifts away from the conventional modes of research, practice and communication that have thus far characterized the field. One of the foremost experts on the subject:

• Illustrates the vital role NI is playing throughout the neurosciences.
• Provides a sampling of NI tools and applications in neuroscience research, and lays out current organization structures that support NI.
• Describes the lack of NI in neuropsychology, differentiates between NI systems for neuropsychology and conventional computerized assessment methods, and proposes criteria for neuropsychology-specific NI systems.
• Describes NI applications and models currently in use in neuropsychology, and NI models for neuropsychology that are being pioneered in phenomics research.
• Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology.
• Projects the future of neuropsychological research and practice in light of the new generation of the internet, Web 2.0, geared to collective knowledge building.

A vital introduction to a profound technological practice, Neuroinformatics for Neuropsychology is important reading for clinical neuropsychologists, cognitive neuroscientists, behavioral neurologists, and speech-language pathologists. Researchers, clinicians, and graduate students interested in informatics for the brain-behavioral sciences will especially welcome this unique volume."

The interest in 'biomarkers' seen across a spectrum of biomedical disciplines reflects the rise of molecular biology and genetics. A host of 'omics' disciplines in addition to genomics, marked by multidimensional data and complex analyses, and enabled by bioinformatics, have pushed the trajectory of biomarker development even further. They have also made more tractable the complex mappings of genotypes to phenotypes - genome-to-phenome mapping - to which the concept of a biomarker is central. Genomic investigations of the brain are beginning to reveal spectacular associations between genes and neural systems. Neural and cognitive phenomics are considered a necessary complement to genomics of the brain. Other major omics developments such as connectomics, the comprehensive mapping of neurons and neural networks, are heralding brain maps of unprecedented detail. Such developments are defining a new era of brain science. And in this new research environment, neural systems and cognitive operations are pressed for new kinds of definitions - that facilitate brain-behavioral alignment in an omics operating environment. This volume explores the topic of markers framed around the constructs of cognitive and neural systems. 'Neurophenotype' is a term adopted to describe a neural or cognitive marker that can be scientifically described within an associative framework - and while the genome-to-phenome framework is the most recognized of these, epigenetics and non-gene-regulated neural dynamics also suggest other frameworks. In either case, the term neurophenotype defines operational constructs of brain-behavioral domains that serve the integration of these domains with neuroscientific and omics models of the brain. The topic is critically important to psychiatry and neuropsychology: Neurophenotypes offer a 'format' and a 'language' by which psychiatry and neuropsychology can be in step with the brain sciences. They also bring a new challenge to the clinical neurosciences in terms of construct validation and refinement. Topics covered in the volume include: Brain and cognition in the omics era Phenomics, connectomics, and Research Domain Criteria Circuit-based neurophenotypes, and complications posed by non-gene regulated factors The legacy of the endophenotype concept - its utility and limitations Various potential neurophenotypes of relevance to clinical neuroscience, including Response Inhibition, Fear Conditioning and Extinction, Error Processing, Reward Dependence and Reward Deficiency, Face Perception, and Language Phenotypes Dynamic (electrophysiological) and computational neurophenotypes The challenge of a cultural shift for psychiatry and neuropsychology The volume may be especially relevant to researchers and clinical practitioners in psychiatry and neuropsychology and to cognitive neuroscientists interested in the intersection of neuroscience with genomics, phenomics and other omics disciplines.

Bioinformatics involves specialized application of computer technology to investigative and conceptual problems in biology and medicine; neuroinformatics (NI) is the practice of bioinformatics in the neurosciences. Over the past two decades the biomedical sciences have been revolutionized by databases, data mining and data modeling techniques. The Human Genome Project, which depended on informatics methods, has been the most well recognized bioinformatics undertaking. Bioinformatics has since been applied all across biology and medicine, and has also transformed almost every avenue in neuroscience. Yet in neuropsychology, NI perspectives remain largely unrealized. Ironically, NI offers enormous potential to the essential praxis of neuropsychology - assessing cognitive behavior and relating cognition to neural systems. Neuroinformatics can be applied to neuropsychology as richly as it has been applied across the neurosciences. Neuroinformatics for Neuropsychology is the first book to explain the relevance and value of NI to neuropsychology. It systematically describes NI tools, applications and models that can enhance the efforts of neuropsychologists. It also describes the implications of NI for neuropsychology in the 21st century - fundamental shifts away from the conventional modes of research, practice and communication that have thus far characterized the field. One of the foremost experts on the subject: Illustrates the vital role NI is playing throughout the neurosciences. Provides a sampling of NI tools and applications in neuroscience research, and lays out current organization structures that support NI. Describes the lack of NI in neuropsychology, differentiates between NI systems for neuropsychology and conventional computerized assessment methods, and proposes criteria for neuropsychology-specific NI systems. Describes NI applications and models currently in use in neuropsychology, and NI models for neuropsychology that are being pioneered in phenomics research. Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology. Projects the future of neuropsychological research and practice in light of the new generation of the internet, Web 2.0, geared to collective knowledge building. A vital introduction to a profound technological practice, Neuroinformatics for Neuropsychology is important reading for clinical neuropsychologists, cognitive neuroscientists, behavioral neurologists, and speech-language pathologists. Researchers, clinicians, and graduate students interested in informatics for the brain-behavioral sciences will especially welcome this unique volume.

In the behavioral and clinical sciences, single-subject designs have increasingly become important tools for determining a treatment efficacy. Despite a large number of recommendations in recent years for more use of the designs, the majority of typical research methods textbooks still do not provide sufficient direction and information about single-subject designs. One of the main reasons is that data analysis of single-subject designs is still foreign to the vast majority of the investigators, practitioners, and students. The authors have developed a practical guide of the most commonly used approaches in analyzing and interpreting single-subject data. In doing so, they have arranged the methodologies used in a logical sequence using an array of research studies from the existing published literatures to illustrate specific applications. The handbook is also laid out for the readers in a highly lucid and straightforward manner, beginning with a brief discussion of each approach such as visual, inferential, and probabilistic model, the applications for which it is intended, and a step-by-step illustration of the test as used in an actual research study. Presented is a brief evaluation of the strengths and limitations of the test along with its suitability, or lack thereof, for particular scales of measurement. Also included are statistical applications of such computer programs as Minitab and SPSS for the analysis of statistical data. This new handbook provides the readers with a concise yet comprehensive approach to help them further understand the concepts as effectively and simply as possible.