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

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

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

https://www.fil.ion.ucl.ac.uk/~karl/Voxel-Based%20Morphometry.pdf

Voxel-Based Morphometry—The Methods

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Recently, there has been a rapid growth in the use of 3D multi-modal correlative imaging for studies of the human brain Regional cerebral blood flow (CBF) changes indicate brain areas involved in stimulus processing These focal changes are often too small ( >

3D statistical neuroanatomical models from 305 MRI volumes

Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

Although conventional proton magnetic resonance imaging has increased our ability to detect brain tumors, it has not enhanced to nearly the same degree our ability to diagnose tumor type1. Proton magnetic resonance spectroscopy is a safe, noninvasive means of performing biochemical analyses in vivo2. Using this technique, we characterized and classified tissue from normal brains, as well as tissue from the five most common types of adult supratentorial brain tumors. These six tissue types differed in their pattern across the six metabolites measured. ‘Leaving–one–out’ linear discriminant analyses based on these resonance profiles correctly classified 104 of 105 spectra, and, whereas conventional preoperative clinical diagnosis misclassified 20 of 91 tumors, the linear discriminant analysis approach missed only 1. Thus, we have found that a pattern–recognition analysis of the biochemical information obtained from proton magnetic resonance spectroscopy can enable accurate, noninvasive diagnosis of the most prevalent types of supratentorial brain tumors.

Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy.

The use of computational approaches in the analysis of highresolution magnetic resonance images (MRI) of the human brain provides a powerful tool for in vivo studies of brain anatomy. Here, we report results obtained with a voxel-wise statistical analysis of hemispheric asymmetries in regional ‘amounts’ of gray matter, based on MRI scans obtained in 142 healthy young adults. Firstly, the voxel-wise analysis detected the well-known frontal (right > left) and occipital (left > right) petalias. Secondly, our analysis confirmed the presence of left-greater-than-right asymmetries in several posterior language areas, including the planum temporale and the angular gyrus; no significant asymmetry was detected in the anterior language regions. We also found previously described asymmetries in the cingulate sulcus (right > left) and the caudate nucleus (right > left). Finally, in some brain regions we observed highly significant asymmetries that were not reported before, such as in the anterior insular cortex (right > left). The above asymmetries were observed in men and women. Our results thus provide confirmation of the known structural asymmetries in the human brain as well as new findings that may stimulate further research of hemispheric specialization.

/pdf/structural-asymmetries-in-the-human-brain-a-voxel-based-44cyo1hysu.pdf

Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans

http://www.bic.mni.mcgill.ca/users/jason/reprints/Bernasconi2004.pdf

D.L. Collins

Papers

3D statistical neuroanatomical models from 305 MRI volumes

Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy.

Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans

Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy.