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

An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

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

The WU-Minn Human Connectome Project: An Overview

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

A multi-modal parcellation of human cerebral cortex

Estimated by Functional Connectivity

Synchronization across different brain regions is suggested to be a possible mechanism for functional integration. Analyzing the synchronization of cortical activity is possible if the source activity can be estimated by solving the inverse problem. RAP-MUSIC and R-MUSIC are well-known inverse algorithms that employ an independent topography (IT) source model which allows for synchronous sources. However, these two algorithms have difficulty distinguishing highly correlated sources because of their dependence on a correlation threshold. In this paper we modify RAP-MUSIC to a multi-stage process which analyzes the correlation of candidate sources and searches ITs among pre-correlated groups. This algorithm is not dependent on a subspace correlation threshold to identify synchronous sources and also avoids an exhaustive multidimensional search for such sources among numerous possible combinations. A comparative study was carried out on simulated data, and the results demonstrate superior performance with the modified algorithm compared to the original RAP-MUSIC in recovering synchronous sources. The modified algorithm thus has potential in the study of brain synchronization

Localizing Synchronized Dipole Sources Using a Modified RAP-MUSIC Algorithm

In binocular rivalry, a subject views two incongruent stimuli through each eye but consciously perceives only one stimulus at a time, with a switch in perceptual dominance every a few seconds. To locate the fusiform face area (FFA) which is a face-selective region, thirteen subjects are recorded with a 64-channel electroencephalograph while experiencing binocular rivalry. A face image flickering at one frequency is presented to one eye and a non-face image flickering at the same frequency is presented to the other eye. Steady state evoked potential (SSVEP) at the frequency is used as tags for the two stimuli. This paper uses an algorithm called standardized shrinking LORETA-FOCUSS (SSLOFO) to reconstruct face-selective sources from the EEG data. The sources are selected by comparing signal strength at the stimulus frequency during face dominance and face suppression. The results demonstrate that the face-selective region identified in this paper is consistent with FFA, as has been confirmed to be activated about twice as strongly in fMRI experiments when people view faces as when they view other kinds of objects. The present study also suggests that the method has the potential advantage of investigating neural correlates.

Localization of FFA using SSVEP-based binocular rivalry.

Optimizing rTMS for Transdiagnostic Symptom and Circuitry Profiles

A system and methods for functional brain mapping is provided. The method includes providing a set of time-series functional magnetic resonance imaging (fMRI) data acquired from a brain of a subject while performing a functional task and decomposing the set of time-series fMRI data into a set of task signals and a set of non-task signals using a model related to the functional task performed by the subject. The method also includes generating a task activity map using the set of task signals and generating a non-task activity map using the set of non-task signals. The method further includes producing a combination map by selectively weighting the task activity map and non-task activity map, and combining the selectively weighted maps, using a statistical parameter, in dependence of a threshold.

System and Methods For Combined Functional Brain Mapping

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