Neuropsychological "systems efficiency" and positron emission tomography

IQ-related fMRI differences during cognitive set shifting

This event-related functional magnetic resonance imaging study compared neural correlates of executive function (cognitive set-shifting) in 28 healthy participants with either high (HIQ) or average (AIQ) intelligence. Despite comparable behavioral performance (except for slower reactions), the AIQ participants showed greater (especially prefrontal) activation during response selection; the HIQ participants showed greater activation (especially parietal) during feedback evaluation. HIQ participants appeared to engage cognitive resources to support more efficient strategies (planning during feedback in preparation for the upcoming response) which resulted in faster responses and less need for response inhibition and conflict resolution. Whether greater intelligence is associated with more or less brain activity (the “neural efficiency” debate) depends therefore on the specific component of the task being examined as well as the brain region recruited. One implication is that caution must be exercised when drawing conclusions from differences in activation between groups of individuals in whom IQ may differ (e.g., psychiatric vs. control samples).

The influence of task demand and learning on the psychophysiological response

The level of expertise of an operator may significantly influence his/her psychophysiological response to high task demand. A naive individual may invest considerable mental effort during performance of a difficult task and psychophysiological reactivity will be high compared to the psychophysiological response of a highly skilled operator. A study on multitasking performance was conducted to investigate the interaction between learning and task demand on psychophysiological reactivity. Thirty naive participants performed high and low demand versions of the Multi-attribute Task Battery (MATB) over a learning period of 64 min. High and low task demand setting were preset via a pilot study. Psychophysiological variables were collected from four channels of EEG (Cz, P3, P4, Pz), ECG, EOG and respiration rate to measure the impact of task demand and learning. Several variables were sensitive to the task demand manipulation but not time-on-task, e.g., heart rate, Theta activity at parietal sites. The sensitivity of certain variables to high demand was compromised by skill acquisition, e.g., respiration rate, suppression of alpha activity. A sustained learning effect was observed during the high demand condition only; multiple regression analyses revealed that specific psychophysiological variables predicted learning at different stages on the learning curve. The implications for the sensitivity of psychophysiological variables are discussed.

The neural bases of strategy and skill in sentence-picture verification

This experiment used functional Magnetic Resonance Imaging to examine the relation between individual differences in cognitive skill and the amount of cortical activation engendered by two strategies (linguistic vs. visual-spatial) in a sentence-picture verification task. The verbal strategy produced more activation in language-related cortical regions (e.g., Broca's area), whereas the visual-spatial strategy produced more activation in regions that have been implicated in visual-spatial reasoning (e.g., parietal cortex). These relations were also modulated by individual differences in cognitive skill: Individuals with better verbal skills (as measured by the reading span test) had less activation in Broca's area when they used the verbal strategy. Similarly, individuals with better visual-spatial skills (as measured by the Vandenberg, 1971, mental rotation test) had less activation in the left parietal cortex when they used the visual-spatial strategy. These results indicate that language and visual-spatial processing are supported by partially separable networks of cortical regions and suggests one basis for strategy selection: the minimization of cognitive workload.

Increased regional cerebral glucose metabolism and semantic memory performance in Alzheimer's disease: a pilot double blind transdermal nicotine positron emission tomography study

Nicotinic receptor dysfunction and impaired semantic memory occur early in Alzheimer's disease patients (AD). Previous research implied that nicotine's ability to enhance alertness, arousal, and cognition in a number of nonclinical populations was a function of its ability to stimulate CNS nicotinic cholinergic receptors. In this study it was hypothesized that transdermal administration of nicotine would increase both regional cerebral glucose metabolism (rCMRglc) and semantic memory (as assessed by verbal fluency). Two mild AD and two elderly controls underwent positron emission tomography scanning during a double blind nicotinic agonist verbal fluency challenge procedure. rCMRglc increases occurred in both AD patients, but not controls. In the two AD patients, verbal fluency scores increased by an average of 17%. One elderly control's verbal fluency increased, and the other decreased. These findings suggest that nicotine's effect on metabolism and verbal fluency is due to its ability to stimulate the cholinergic system.

Methodological and theoretical issues in neural network models of frontal cognitive functions

Neural network models have made significant strides in recent years toward modeling of neuropsychological data. In particular, three different research groups, including that of the present authors, have simulated in model networks some of the behavioral effects of frontal lobe damage. In this article we review these models and discuss their significance in terms of hierarchical organization in the nervous system. These models, to varying degrees, incorporate several widely used neural network principles that have also been used to model a wide range of data in other areas such as categorization, conditioning, and motor control. These principles include associative learning, competition, opponent processing, neuromodulation, and interlevel resonant feedback. Specifically, we show how combinations of these principles serve to model the attentional requirements of cognitive tasks and motor plans.

Parallel distributed processing and neuropsychology: a neural network model of Wisconsin Card Sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.

Regional glucose metabolic changes after learning a complex visuospatial/motor task: a positron emission tomographic study

Regional cerebral glucose metabolic rate (GMR) quantified with positron emission tomography (PET) with 18-fluoro-2-deoxyglucose (FDG) was measured twice in 8 young men performing a complex visuospatial/motor task (the computer game Tetris), before and after practice. After 4-8 weeks of daily practice on Tetris, GMR in cortical surface regions decreased despite a more than 7-fold increase in performance. Subjects who improved their Tetris performance the most after practice showed the largest glucose metabolic decreases after practice in several areas. These results suggest that learning may result in decreased use of extraneous or inefficient brain areas. Changes in regional subcortical glucose metabolic rate with practice may reflect changes in cognitive strategy that are a part of the learning process.

Parallel distributed processing and neural networks: origins, methodology and cognitive functions

Parallel Distributed Processing (PDP), a computational methodology with origins in Associationism, is used to provide empirical information regarding neurobiological systems. Recently, supercomputers have enabled neuroscientists to model brain behavior-relationships. An overview of supercomputer architecture demonstrates the advantages of parallel over serial processing. Histological data provide physical evidence of the parallel distributed nature of certain aspects of the human brain, as do corresponding computer simulations. Whereas sensory networks follow more sequential neural network pathways, in vivo brain imaging studies of attention and rudimentary language tasks appear to involve multiple cortical and subcortical areas. Controversy remains as to whether associative models or Artificial Intelligence symbolic models better reflect neural networks of cognitive functions; however, considerable interest has shifted towards associative models.