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

/pdf/an-integrative-theory-of-prefrontal-cortex-function-2j9unprn5f.pdf

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*

Translating Opioid Pharmacology From Bench to Bedside, and Back.

http://ieeexplore.ieee.org/iel4/1015/7498/00684478.pdf

A Multimodal Reconstruction Algorithm For Pet Brain Images

Eye-blinking has been implicated in arousal and attention. Here we test the hypothesis that blinking-moments represent arousal surges associated with activation of the ascending arousal network (AAN) and its thalamic projections. For this purpose, we explored the temporal relationship between eye-blinks and fMRI BOLD activity in AAN and thalamic nuclei, as well as whole brain cluster corrected activations during eyes-open, resting-state fMRI scanning. We show that BOLD activations in the AAN nuclei peaked prior to the eye blinks and in thalamic nuclei peaked prior to and during the blink, consistent with the role of eye blinking in arousal surges. Additionally, we showed visual cortex peak activation prior to the eye blinks, providing further evidence of the visual cortex's role in arousal, and document cerebellar peak activation post eye blinks, which might reflect downstream engagement from arousal surges.

/pdf/activation-of-brain-arousal-networks-coincident-with-eye-2qc2ms6g.pdf

Activation of brain arousal networks coincident with eye blinks during resting state.

Presents a method to make dynamic image analysis by image fusion. This method is unsupervised and does not require previous knowledge of the number of relevant parameters as in statistical methods. It uses set operations to segment recursively a given region if at any following time this region appears not to be homogeneous. This algorithm has been tested with success on two sets of positron emission tomography images (a set of 10 images of a phantom and a set of 22 images of a human brain). It allows an automatic region of interest detection and it displays a kinetic image that could help image interpretation.

Temporal image fusion

Clinical observation and research discovery each can give rise to treatments that prove effective in clinical trials and, accordingly, are considered evidence-based. For example, motivational interviewing draws on, and addresses, clinicians’ recognition of patient ambivalence, and contingency management emerged from research on the effects of rewards on human behavior.

Whether they originate in the clinic or the research setting, evidence-based treatments provide clinicians with tools to treat patients more effectively while still treating each as a unique individual. Thus, motivational interviewing, by moving clients past ambivalence, clears the way to addressing other issues that are particular to the individual. Contingency management, similarly, can increase clinicians’ ability to address clients’ individual issues by motivating attendance in sessions and other desirable behaviors.

The future of substance abuse treatment is the development of an increasing array of evidence-based treatments that clinicians will use to address patients’ varied strengths, needs, and circumstances. We have already begun to see the emergence of evidence-based treatments for patients with certain co-dependencies and co-occurring disorders. Research reviewed in this issue of Addiction Science & Clinical Practice suggests that interventions might be developed for patients in specific stages of addiction, with different types and degrees of cognitive impairment, or with particular genotypes.

Far from reducing the role of clinicians and the importance of the therapeutic relationship, evidence-based treatments rely heavily on clinical skills and empathy. Increasingly, clinicians will need to be familiar with the variety of available evidence-based treatments, to identify the right one—or combination or sequence—for each patient and to administer a range of assessments and interventions with fidelity. For substance abuse clinicians, the tools, the challenges, and the potential for success increase in concert.

Nora D. Volkow

Papers

Translating Opioid Pharmacology From Bench to Bedside, and Back.

A Multimodal Reconstruction Algorithm For Pet Brain Images

Activation of brain arousal networks coincident with eye blinks during resting state.

Temporal image fusion

Toward individualized treatment for substance abuse.