Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-08-24
2002-05-21
Jaworski, Francis J. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
Reexamination Certificate
active
06390979
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention is related to computerized systems and methods for determining general intelligence, working memory and psychomotor functions using a portable non-invasive transcranial Doppler ultrasound, microcomputer, an operatively connected cellular telephone, and a computer-aided display. In recent years development of cognitive neuroscience has sort ways to monitor mental performance beyond the conventional neuropsychological approaches. However, monitoring mental performance has not been an easy task. Currently, there is no comprehensive and universal approach for mental performance monitoring. To address this problem more effectively, it is important to understand the basic mechanisms that underlie mental performance. The determinants of human mental performance have been the subject of intense debate for over a century. Spearman (1904, 1923, 1927) suggested that measures of performance or success in diverse cognitive tests show a pattern of almost universal positive correlation. He postulated the hypothesis of a general or g factor making some contribution to success in diverse forms of cognitive activity. This means that people with high g scores will be those usually performing well, although Spearman himself avoided the term intelligence and instead used the term g to refer to the determinants of shared variance among tests of intellectual ability (Jensen, 1987).
Snow et al (1984) constructed an idealized space in which task complexity is maximal near the center and decreases toward the periphery. Psychometric tests including Raven's test and other complex reasoning tests were placed at the center, while simpler tests were placed toward the periphery. Applying this construct, irrespective of the views held by both hypotheses, the centrality of the Raven's test (Raven, 1938) emerges in either case. Furthermore, it suggests that Raven's test is a good measure of intelligence and should account for a good deal of the reasoning in other tests in the center of space (Carpenter et al., 1990). The g factor has since been interpreted as “general intelligence” and refers to a construct underlying a small range of tests, namely those at the center of space.
The concept of general intelligence must have neural anatomic structures for processing of the information. It has been argued that the necessity of keeping several conceptual formulations in mind during Raven's Progressive Matrices (RPM) is itself a working memory function (Carpenter et al., 1990) involving prefrontal cortex (Prabhakaran et al., 1997). Post-rolandic structures may be more critical for this task as shown in patients with brain lesions (Basso et al., 1973). Supportive evidence in normals using positron emission tomography studies have shown that high g tasks do not show diffuse recruitment of multiple brain regions, instead they are associated with selective recruitment of lateral prefrontal cortex in one or both hemispheres (Duncan et al., 2000).
Several recent studies have demonstrated that working memory is typically associated with activations in the prefrontal cortex (PFC), anterior cingulate, parietal and occipital regions (see review by D'Esposito, 2000). These brain areas received blood supply from the middle cerebral arteries. Two fundamental working-memory processes have been identified: the passive maintenance of information in short-term memory and the active manipulation of this information (D'Esposito, 2000).
Motor skill learning is associated with the activation of motor areas of the frontal lobes (Cabeza & Nyberg, 2000), notably the premotor and supplementary motor cortex (lateral and medial Broadman Area 6), and also parietal areas. All these brain areas receive blood supply predominantly from the middle cerebral arteries. Recently, studies using functional magnetic resonance imaging (fMRI) have examined motor skill learning of complex finger movements in piano players and non-musicians (Hund-Gerogiadis & von Cramon, 1999). As learning progressed, piano players showed increased activity in the contra-lateral hand area, whereas non-musicians showed decline in primary motor cortex activation. This may mean that practice-related changes in activity are influenced by pre-practice experience of the subjects.
A study on non-motor skill learning (Fletcher, Buchel, Josephs et al, 1999) investigated changes in brain activity during artificial grammar learning. As subjects learned grammar rules they relied less on memory for specific instances. Learning grammar activated left PFC, whereas reduced instance memory attenuated right PFC (Poldrack, Prabakharan, Seger, Gabrieli, 1999). The visual processing of letters by both hemispheres has been documented using the transcranial Doppler technique (Njemanze, 1996).
Currently, the use of imaging techniques such as positron emission tomography (PET) and fMRI cannot be used a single subject real-time mental performance monitoring under normal everyday conditions. Electrophysiological devices particularly the electroencephalography (EEG) has been used along with other physiological variables such as eye movement, scalp and facial muscle activity, heart activity, respiration and skin conductance to determine the state of mental performance. EEG by itself presents a complexity of parameters and along with other physiological variables have been applied to compare test standards to pretest values. The patent U.S. Pat. No. 5,295,491 to Gevins described a testing method and system for testing the mental performance capability of a human subject, which includes a digital computer workstation for presenting a test to the subject, such as visuomotor memory task. Simultaneously, the subject's physiological variables including brain waves, eye activity, scalp and facial muscle activity, heart activity, respiration and/or skin conductance are analyzed. Test to baseline comparison of the physiological activity and scores are made to determine if the test was passed with a passing score and, if so, whether the subject, in order to pass the test, exceeded a standard based upon the subject's normal mental effort in taking the same or similar test. Similarly, a patent U.S. Pat. No. 5,724,987 to Givens et al. described a computer-aided training system that uses electroencephalograms (EEGs) recorded from the trainee's scalp to alter the training protocol being presented by the computer, for example to present a new task to the trainee when he or she has mastered and automatized the current task. The index of the trainee's skill mastery and automatization is determined by analysis of the EEG using mathematical classification functions, which distinguish different levels of skill acquisition. The functions are computed by computer neural networks and consist of a combination of EEG and other physiological variables, which specifically characterize a trainee's level of focused attention and neurocognitive workload and his “neurocognitive strategy”. The functions are derived either for a group of trainees, or each trainee individually, performing a battery of one or more standard training tasks while wearing an EEG hat.
The term “neurocognitive” has been defined as those mental functions for which physiological indices can be measured and “neurocognitive workload” as the level of neural activation associated with mental effort as described in U.S. Pat. No. 5,724,987 to Givens et al. The “critical limit of neurocognitive workload” refers to a measurable cutoff point after which error rates on the task appear to rise dramatically in combination with change in neurocognitive strategy. The term “neurocognitive strategy” refers to the hemispheric strategy (hemisphere advantage) a subject uses to perform a task. Neurocognitive strategy is characterized by a cerebral blood flow increase in a given hemispheric region relative to the other hemisphere; it might be either in the left or right brain or bilaterally. Givens et al in U.S. Pat. No. 5,724,987 used the phrase “skill acquisition and automation” to den
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