Advantages of Task Conditions in QEEG Assessment

Advantages of Task Conditions

Some workers in the field of QEEG have expressed the opinion that the EEG is destabilized or otherwise corrupted by the use of recordings obtained during cognitive task conditions (Thatcher, 1998). However, many careful studies from respected laboratories have reported just the opposite. For example, a recent paper from McEvoy et al., (2000) has demonstrated greater stability in quantitative EEG values collected during cognitive task conditions (r = 0.9) than during the eyes closed condition (r = 0.7). This fact becomes self-evident to anyone who bothers to look at the data. Certainly, tasks involving gross head or body movements are not recommended or included in this assessment. But cognitive tasking, by its very nature, acts to focus attention and reduce variability in state, and thereby in EEG characteristics. It should also be pointed out that tasks involving signal detection, memory, or mathematical calculations have disclosed significant frontal EEG changes of critical importance to the evaluation of both performance and clinical status (Gevins et al., 1997: Klimesch et al., 1994: Sterman, 1999, 2000; Sterman et al., 1996).

While important in all contexts, the utility of task conditions in the QEEG evaluation of Attention Deficit Disorder is particularly relevant. Despite present day resistance to the significance of the EEG in this area (see for example the DMS IV, which relies exclusively on behavioral observations and history for diagnosis), evidence of EEG abnormality as a possible marker for ADD has existed for many years. Using labels such as "Minimal Brain Damage" and "Childhood Behavior Disorder", neurologists in the early part of the 20th century described a syndrome that included hyperactive behavior, short attention span, frequent mood shifts, and various minor perceptual disturbances. The possibility of a physiological disturbance was recognized almost at the outset by the discovery of a high incidence of unusually slow EEG brain wave patterns in these children (Lindsley and Cutts, 1940; Solomon, Bradley & Jasper 1938). In fact, this finding originally suggested that these children were under-aroused, and contributed to the initial exploration of stimulants and other centrally acting medications as a therapy (Lindsley and Henry, 1937; Walker and Kirkpatric, 1947). By mid-century, however, the lack of evidence for gross neurological damage or deficits associated with this syndrome led to a change in accepted terminology, and adoption of the new label "Minimal Cerebral Dysfunction" (MCD). EEG studies during this period also found that a high percentage of children with a diagnosis of MCD showed diffuse abnormal EEG slow activity (Capute et al, 1968; Klinkerfuss et al, 1965; Small and Milstein, 1978). This diagnosis became ADD with the DMS II.

Today, using contemporary QEEG methods, a growing number of comprehensive scientific reports strongly support the importance of EEG markers in ADD. Recent papers, applying careful diagnostic criteria to the study of large groups of children, have shown conclusively that more than 90% of those with ADD show QEEG findings indicating disturbances in neurophysiological regulation (see for example Chabot et al., 1996; 1999; Hughes and John, 1999; Suffin and Emory, 1997). Moreover, a convergence of EEG findings with metabolic, imaging, and genetic studies of the brain in ADD patients is providing compelling evidence for the existence of a family of more-or-less related pathophysiologies in ADD (see Sterman, 2000 for review)

Collectively, these studies have illuminated two important facts. First, that there are a number of different patterns of disturbance seen in this population, and second, that these disturbances are reliably increased during cognitive challenge. It is somewhat puzzling, therefore, that relatively fixed or standardized protocols are typically used in the treatment of ADD with neurofeedback, and that the clinical evaluation of ADD using QEEG has been based exclusively on data obtained from the eyes closed, resting state.

The SKIL Topometric QEEG analysis program addresses both of these issues. It was designed to provide an expanded, data-driven approach to both evaluation and neurofeedback training. It uses a number of new and novel metrics, including a standardized four-condition analysis protocol. These include 1) eyes closed, 2) eyes open, 3) an information intake task, usually reading at age level, and 4) a high-level cognitive processing task. One of the options for the high-level processing task is a rather simple math test that is graduated in difficulty. This test has proven to be particularly valuable in the disclosure of pathology in a large subset of clients diagnosed as ADD.

One of the strong features of the SKIL program is the ability to select the particular frequencies band or bands that prove significant in a given individual. These can be determined by both magnitude and statistical (database comparison) analysis, in single hertz intervals from 1 to 23 Hz. Relevant bandwidths can then be selected and compared topographically across the four basic state conditions. An example of this comparison, derived from the SKIL database, is shown in figure 1 for the relevant 5-7 Hz band. Many of the children and adults carrying the behavioral diagnosis of ADD show significantly altered state comparison patterns in this band. The most common of these is shown in figure 2, from a subject with attentional, affective, and performance problems. While the eyes open and reading conditions produced the expected suppression of 5-7 Hz activity across all sites when compared to eyes closed, the math test resulted in a profound elevation of values recorded at frontal and particularly pre-frontal cortex.

Figure 1. Mean spectral magnitude values from database for 5-7 Hz band during four test conditions including eyes closed, eyes open, reading (T1), and math (T2).

Figure 2. Topographic distribution of EEG spectral densities in an 18 year old male subject with ADD. Graph at top compares subject (red) during reading performance with database means for this condition (dark black) and +/- 2 standard deviations in the 5-7 Hz frequency band. Graph at bottom shows same comparison during math performance.

The reliability of this finding is demonstrated in figure 3, where group data from six of these ADD types is compared with six non-ADD controls within the same age range. This figure focuses on the standard 4-8 Hz theta band in the eyes closed and math test conditions. Since some of the ADD data were collected on a 16 channel recording system lacking midline sites, the designation of electrode sites on the abscissa also lacks these locations. The generalized suppression of this activity seen in the control group is in stark contrast to the statistically significant frontal elevation seen in the ADD subgroup, despite the fact that eyes closed values are generally similar. Activity in the pre-frontal cortex is only slightly elevated in the ADD subgroup with eyes closed but is dramatically elevated during the math test. These findings emphasize the significant contribution of multi-state comparison in the QEEG evaluation of ADD. Further, this expanded perspective has disclosed a number of distinct patterns of EEG deviance in the ADD population, a fact that can only help to clarify diagnosis and direct optimal neurofeedback treatment for this population.

Figure 3. Comparison of mean topographic EEG spectral densities in two groups of subjects between the ages of 10 and 18 years during eyes closed and math test performance conditions. Top: non-ADD controls. Bottom: ADD group designated as Type I.

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