We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.

Control of goal-directed and stimulus-driven attention in the brain

Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.

Chapter 11 Working memory

https://surfer.nmr.mgh.harvard.edu/ftp/articles/desikan06-parcellation.pdf

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

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

Using functional magnetic resonance imaging (fMRI), we found an area in the fusiform gyrus in 12 of the 15 subjects tested that was significantly more active when the subjects viewed faces than when they viewed assorted common objects. This face activation was used to define a specific region of interest individually for each subject, within which several new tests of face specificity were run. In each of five subjects tested, the predefined candidate “face area” also responded significantly more strongly to passive viewing of (1) intact than scrambled two-tone faces, (2) full front-view face photos than front-view photos of houses, and (in a different set of five subjects) (3) three-quarter-view face photos (with hair concealed) than photos of human hands; it also responded more strongly during (4) a consecutive matching task performed on three-quarter-view faces versus hands. Our technique of running multiple tests applied to the same region defined functionally within individual subjects provides a solution to two common problems in functional imaging: (1) the requirement to correct for multiple statistical comparisons and (2) the inevitable ambiguity in the interpretation of any study in which only two or three conditions are compared. Our data allow us to reject alternative accounts of the function of the fusiform face area (area “FF”) that appeal to visual attention, subordinate-level classification, or general processing of any animate or human forms, demonstrating that this region is selectively involved in the perception of faces.

/pdf/the-fusiform-face-area-a-module-in-human-extrastriate-cortex-2xuspzx096.pdf

The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception

Principal component analysis of ERPS: Further simulation studies

Evaluation of frontal and parietal association cortex contributions to attention using fMRI

Achieving a mechanistic explanation of brain function requires understanding causal relationships among regions. A relatively new technique to assess effective connectivity in fMRI data is Dynamic Causal Modeling (DCM). As DCM is more frequently used, it becomes increasingly important to further validate the technique and understand its limitations. With DCM, Bayesian Model Selection (BMS) is used to select the most likely causal model. We conducted simulations to test the degree to which BMS is robust to two types of challenges when applied to DCMs, those inherent to data (Category 1) and those inherent to model space (Category 2). Category 1 challenges tested properties of the data (low signal-to-noise, different response magnitudes and shapes across regions) that could either blur the distinction between models or potentially bias model selection. These challenges are impossible or difficult to measure and control in real data, so investigating their effect upon BMS through simulation is critical. Category 2 challenges tested properties of model space that create subsets of confusable models. Our results suggest that given data that conform to the prior assumptions of DCM, BMS is robust to challenges from Category 1. However, in the face of Category 2 challenges (when a more homogenous model space was tested) the false positive rate rose above an acceptable level. We show that such errors are neither trivial nor easily avoided with existing approaches. However, we argue that it is possible to detect Category 2 challenges, and avoid inappropriate interpretations by conducting simulations prior to applying DCM.

https://www.biorxiv.org/content/biorxiv/early/2017/01/22/102293.full.pdf

Challenges for Bayesian Model Selection of Dynamic Causal Models

A functional brain system for face processing revealed by event-related potentials and functional MRI

Rapid identification of a familiar face requires an image-invariant representation of person identity. A varying sample of familiar faces is necessary to disentangle image-level from person-level p...

Gregory McCarthy

Papers

Principal component analysis of ERPS: Further simulation studies

Evaluation of frontal and parietal association cortex contributions to attention using fMRI

Challenges for Bayesian Model Selection of Dynamic Causal Models

A functional brain system for face processing revealed by event-related potentials and functional MRI

Perceptual and Semantic Phases of Face Identification Processing: A Multivariate Electroencephalography Study.