Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

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An integrative theory of prefrontal cortex function

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

genome are dramatically reshaping the research and development pathways for drugs, vaccines, and diagnostics. The growth in the number of molecular entities entering the drug development pipeline has accelerated as a consequence of powerful discovery and screening technologies such as combinatorial chemistry, mass spectrometry, high throughput screening, celland tissue-based DNA microarrays, and proteomic approaches.1 As a consequence, there is an escalating number of therapeutic candidates, which has caused the need for new technologies and strategies to streamline the process to make safe and effective therapies available to patients. One approach to the achievement of more expeditious and informative therapeutic research is the use of precise clinical measurement tools to determine disease progression and the effects of interventions (drugs, surgery, and vaccines). For example, gene-based approaches such as single nucleotide polymorphism maps are now being developed to distinguish the molecular and cellular basis for variations in clinical response to therapy.2 Another approach is the use of a wide array of analytical tools to assess biological parameters, which are referred to as biomarkers. Biomarker measurements can help explain empirical results of clinical trials by relating the effects of interventions on molecular and cellular pathways to clinical responses. In doing so, biomarkers provide an avenue for researchers to gain a mechanistic understanding of the differences in clinical response that may be influenced by uncontrolled variables (for example, drug metabolism). There are a variety of ways that biomarker measurements can aid in the development and evaluation of COMMENTARY

Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework*

Dopamine (DA) neurotransmission is critical in the neurobiology of reward and aversion, but its contribution to the aversive state of opioid withdrawal remains unknown in humans. To address this, we used updated voxelwise methods and retrospectively analyzed a [11C]raclopride-PET dataset to measure D2/3 receptor availability and relative cerebral blood flow (R1) in male opioid use disorder (OUD) participants (n = 10) during placebo and acute opioid withdrawal conditions. We found that acute withdrawal precipitated by the opioid antagonist naloxone significantly increased dorsal striatal DA release in OUD participants (pFWE < 0.05). Net changes in striatal DA were significantly correlated with a subjective index of withdrawal aversion such that greater DA increases were associated with more aversive responses (r(8) = 0.82, p < 0.005). Withdrawal also affected brain function, as indexed by increases in relative cerebral blood flow in the insula and putamen (pFWE < 0.05). Our findings are different from preclinical studies that have primarily reported decreases in ventral striatal DA during naloxone precipitated withdrawal, whereas this effect was not significant in OUD participants (p = 0.79). In sum, we provide evidence for the contribution of increases in dorsal striatal DA to the aversive state of naloxone precipitated withdrawal in humans.

/pdf/naloxone-precipitated-withdrawal-increases-dopamine-release-pmwfyc7wlz.pdf

Naloxone precipitated withdrawal increases dopamine release in the dorsal striatum of opioid dependent men.

Overlapping Neuronal Circuits in Addiction and Obesity

Positron emission tomography (PET) makes possible the quantitation of the regional distribution of positron-emitting radioisotopes in a selected organ (Ter Pogossian et al.: Radiology 114:89-98, 1975). Since a large variety of physiologic compounds can be labeled with positron emitters (Wolf: Seminars in Nuclear Medicine 11:2-12, 1981; Fowler and Wolf: Nuclear Science Series, 1982), the technique can be used to monitor various biochemical and physiological processes of living tissues (Phelps et al.: Annals of Internal Medicine 98:339-359, 1983; Brodie et al.: Handbook of Neurochemistry, 1983). Indeed, research in the clinical application of PET has expanded considerably over the past 5 years and PET has been used in a variety of clinical problems in psychiatric, neurologic, and oncologic disorders (Phelps and Mazziotta: Science 228:799-809, 1985; Patronas et al.: Journal of Neurosurgery 62:816-822, 1985; Volkow and Tancredi: International Journal of Technology Assessment in Health Care 2:577-594, 1986). Interpretation of the findings by the clinician has sometimes been confounded by the complexity of the technique and the many parameters which can influence the data. A simple knowledge of the methodological background of PET is needed in order to appreciate the potential applications and the limitations of this technique. There are three basic components in PET: instrumentation, radiopharmaceuticals, and mathematical modeling. A number of reviews address varied aspects of the last two components (Fowler and Wolf: Positron Emission Tomography and Autoradiography: Principals and Application of the Brain and Heart, 1986; Karlstrom and Christman: Brookhaven National Laboratory Associated Universities, Inc., 1983; Huang and Phelps: Positron Emission Tomography and Autoradiography: Principles and Applications for the Brain and Heart, 1986), and in this paper we will focus on PET instrumentation as it pertains to its clinical application. The aim of this article is to give an outline of the basic principles behind PET instrumentation, such as positron detection, resolution, quantitation, sensitivity, and image reconstruction as it affects the data presented to the clinician.

Positron emission tomography instrumentation: an overview.

High prescribing rates have probably contributed to recent increases in neonatal abstinence syndrome

https://www.bmj.com/content/bmj/352/bmj.i19.full.pdf

Nora D. Volkow

Papers

Naloxone precipitated withdrawal increases dopamine release in the dorsal striatum of opioid dependent men.

Overlapping Neuronal Circuits in Addiction and Obesity

Positron emission tomography instrumentation: an overview.

Opioids in pregnancy.

Hemodynamic and neuronal responses to cocaine differ in awake versus anesthetized animals: Optical brain imaging study