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*

Ketamine, a noncompetitive NMDA receptor antagonist, has been exclusively used as an anesthetic in medicine and has led to new insights into the pathophysiology of neuropsychiatric disorders. Clinical studies have shown that low subanesthetic doses of ketamine produce antidepressant effects for individuals with depression. However, its use as a treatment for psychiatric disorders has been limited due to its reinforcing effects and high potential for diversion and misuse. Preclinical studies have focused on understanding the molecular mechanisms underlying ketamine's antidepressant effects, but a precise mechanism had yet to be elucidated. Here we review different hypotheses for ketamine's mechanism of action including the direct inhibition and disinhibition of NMDA receptors, AMPAR activation, and heightened activation of monoaminergic systems. The proposed mechanisms are not mutually exclusive, and their combined influence may exert the observed structural and functional neural impairments. Long term use of ketamine induces brain structural, functional impairments, and neurodevelopmental effects in both rodents and humans. Its misuse has increased rapidly in the past 20 years and is one of the most common addictive drugs used in Asia. The proposed mechanisms of action and supporting neuroimaging data allow for the development of tools to identify 'biotypes' of ketamine use disorder (KUD) using machine learning approaches, which could inform intervention and treatment. 

Ketamine use disorder: preclinical, clinical, and neuroimaging evidence to support proposed mechanisms of actions.

Foreword. Substance use and abuse among adolescents.

Dopamine Deficiency, Eating, and Body Weight

Abstract Seasonality patterns are reported in various psychiatric disorders. The current paper summarizes findings on brain adaptations associated with seasonal changes, factors that contribute to individual differences and their implications for psychiatric disorders. Changes in circadian rhythms are likely to prominently mediate these seasonal effects since light strongly entrains the internal clock modifying brain function. Inability of circadian rhythms to accommodate to seasonal changes might increase the risk for mood and behavior problems as well as worse clinical outcomes in psychiatric disorders. Understanding the mechanisms that account for inter-individual variations in seasonality is relevant to the development of individualized prevention and treatment for psychiatric disorders. Despite promising findings, seasonal effects are still understudied and only controlled as a covariate in most brain research. Rigorous neuroimaging studies with thoughtful experimental designs, powered sample sizes and high temporal resolution alongside deep characterization of the environment are needed to better understand the seasonal adaptions of the human brain as a function of age, sex, and geographic latitude and to investigate the mechanisms underlying the alterations in seasonal adaptation in psychiatric disorders. 

/pdf/seasonality-of-brain-function-role-in-psychiatric-disorders-104vxqxk.pdf

Seasonality of brain function: role in psychiatric disorders

Key Points Question Did buprenorphine-involved overdose deaths change after implementing prescribing flexibilities during the COVID-19 pandemic? Findings In this cross-sectional study including 74 474 opioid-involved overdose deaths, buprenorphine was involved in 2.6% of opioid-involved overdose deaths during July 2019 to June 2021. Although monthly opioid-involved overdose deaths increased, the proportion involving buprenorphine fluctuated but did not increase. Meaning These findings suggest that actions to facilitate access to buprenorphine-based treatment for opioid use disorder during the COVID-19 pandemic were not associated with an increased proportion of overdose deaths involving buprenorphine; efforts are needed to expand more equitable and culturally competent access to and provision of buprenorphine-based treatment.

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2800689/tanz_2023_oi_221477_1673544642.2046.pdf

Nora D. Volkow

Papers

Ketamine use disorder: preclinical, clinical, and neuroimaging evidence to support proposed mechanisms of actions.

Foreword. Substance use and abuse among adolescents.

Dopamine Deficiency, Eating, and Body Weight

Seasonality of brain function: role in psychiatric disorders

Trends and Characteristics of Buprenorphine-Involved Overdose Deaths Prior to and During the COVID-19 Pandemic