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Show ORIGINAL CONTRIBUTION The Effects of Auditory Distraction on Visual Cognitive Performance in Multiple Sclerosis Leonard L. LaPointe, PhD, Charles G Maitland, MD, Adrienne A. Blanchard, MS, Brett E. Kemker, PhD, Julie A. G. Stierwalt, PhD, and Gary R. Heald, PhD Background: A subset of individuals with multiple sclerosis ( MS) endures degradation of cognitive function during disease progression. The purpose of this study was to compare visual cognitive reaction time performance during three conditions of auditory distraction ( four- talker babble; word repetition; babble combined with word repetition) to a quiet, undistracted condition. Methods: Twenty- two patients with mild relapsing-remitting MS ( Expanded Disability Status Scale mean of 3.0) and 17 age- matched and education- matched control subjects free of neurologic disease were tested on four cognitive visual processing subtests of simple reaction time, choice reaction time, and visual working memory for same and sequential digits concurrently during three conditions of auditory distraction. Results: When reaction times for MS and control participants were pooled across all four cognitive tests, the scores of the MS patients in quiet ( 528 ms) were significantly slower than those of the control subjects ( 459 ms). The auditory distraction condition of word repetition combined with four- talker babble degraded cognitive performance more than most of the other distraction conditions in both groups. Conclusions: Even in mild MS, subtle visual cognitive processing deficits may be elicited by auditory distraction. ( J Neuro- Ophthalmol 2005; 25: 92- 94) An estimated 2,500,000 people in the world have multiple sclerosis ( MS) and approximately 50% will have some form of cognitive dysfunction as a result of this disorder ( 1- 4). Although cognitive dysfunction is more common among MS patients who have had the disease for a long NeuxoCom- NeuroCog Research Laboratory, Department of Communication Disorders, Florida State University, Tallahassee, FL. Manuscript of paper presented at XVth Meeting of International Neuro- Ophthalmology Society, July 18- 22, 2004, Geneva, Switzerland. Address correspondence to Leonard L. LaPointe, PhD, 301 Regional Rehab Center, Florida State University, Tallahassee, FL 32306- 1200; E- mail: llapoint@ mailer. fsu. edu time, little is known about the presence or severity of cognitive impairment in the early stages or in the milder severity levels of the disease. Behavioral concerns and reports on cognitive dysfunction in MS are supported by recent findings of brain atrophy in early MS ( 5,6). The theoretical groundings of this research can be found in cognitive systems models of signal extraction from interference, competition, and distraction as outlined by Endsley ( 7) and by Rapp and Hendel ( 8). Further, the cognitive resource allocation model of Kahneman ( 9) offers explanatory power to studies of attention that have used divided attention and distraction paradigms to investigate cognitive degradation during interference. This model holds that a fixed cognitive resource capacity in humans requires either conscious or unconscious allocation of attention to competing stimuli. Therefore, if simultaneous cognitive task demands impinge on an array of human behaviors, some of these behaviors may be performed at less than optimal levels. Cognitive resource capacity can be allocated variably depending on levels of arousal, motivation, effort, task demands, and nervous system integrity. These theoretical concepts form the basis for the generation of our hypotheses regarding the effects of distraction on visual cognitive processing and performance in MS. The purposes of this study are: ( 1) to determine the effects of different types of ecologically valid auditory distractions on visual processing and visual cognitive performance measures; and ( 2) to attempt to clarify the role of distractions on attention and cognitive resource allocation in MS. METHODS The MS group consisted of 22 patients ( 5 men, 17 women) with relapsing- remitting MS, ranging in age from 28 to 67 years ( mean 49 years). Expanded Disability Status Scale ( 10) scores ranged from 0 to 4.5 ( mean 3.0). The control group consisted of 17 subjects ( 8 men, 9 women) matched in age and education to the MS group, and ranging in age from 18 to 84 years ( mean 48 years). No statistically 92 J Neuro- Ophthalmol, Vol. 25, No. 2, 2005 Auditory Distraction in Multiple Sclerosis J Neuro- Ophthalmol, Vol. 25, No. 2, 2005 significant differences existed in the group means for age and education. Cognitive Measures Four subtests from the California Computerized Assessment Package ( 11) were used to assess visual processing and visual cognitive function. This test measures reaction time and accuracy during the identification of visual stimuli precisely controlled in exposure duration time and in-terstimulus interval and presented on a 15- inch computer monitor. The subtests used for this study included: 1. Simple reaction time (" Press the space bar when you see any number on the screen.") 2. Choice reaction time (" Press the space bar only when you see the number ' 7' on the screen.") 3. Working memory, same number (" Press the space bar when you see two numbers in a row, e. g., ' 5' followed by ' 5'.") 4. Working memory, sequence (" Press the space bar when you see two numbers in incremental sequence, e. g., ' 5' followed by ' 6'.") All participants had to pass a training sequence of trials to assure that they understood the instructions and were able to complete the computerized subtests. The four computerized subtests require approximately 8 to 10 minutes for completion. Distraction Conditions Three auditory distraction conditions were used for comparison of performance in the control quiet condition. These distraction conditions included: 1. Four- talker babble: four speakers ( two men, two women) simultaneously read different passages of emotionally neutral informative material. This standardized auditory distraction material is contained on a CD and produced according to acoustically standardized conditions at AudiTec of St. Louis acoustic laboratories. 2. Word repetition: an examiner interjected words that the participant was asked to repeat. These words were taken from the Northwestern 6 word lists for testing auditory speech reception thresholds. Words were single syllable, phonetically balanced, and relatively frequently occurring in spoken English ( eg, " fat," " whip," " goose"). 3. Combined four- talker babble with word repetition: a four- talker CD was played while the patients and subjects repeated words said by the examiner. Before presentation of the distraction conditions, each participant's hearing was screened. Auditory distraction levels were presented at 40 decibels ( dB) above auditory threshold or 40 dB above the passed screening level of 25 dB. All distraction level conditions were presented concurrently during presentation of the cognitive tasks and were counterbalanced to distribute any order effect of learning or fatigue equally across all conditions. Examiners were not blinded as to whether participants belonged to the MS or control group. Reaction times and accuracy scores were analyzed statistically using repeated measures between- group and within- group ANOVAs, with Bonferroni correction. Power analysis revealed an acceptable effect size for use of the statistical procedures selected for analysis. RESULTS Table 1 lists the results for both groups on each visual cognitive subtest in the quiet condition and in three distraction conditions. When reaction times for MS and control participants were pooled across all four cognitive tests, the scores of the MS patients in quiet ( 528 ms) were significantly slower than those of the control subjects ( 459 ms). These differences reached significance for the pooled distraction conditions as well ( MS = 551 ms versus controls^ 483 ms; ANOVA F = 8.432, df= l. 38, P = 0.006). When between- group differences across all visual cognitive subtests were analyzed across distraction conditions of quiet, four- speaker babble, word repetition, and TABLE 1. Reaction times ( in ms) on visual cognitive subtests across quiet and distraction conditions for MS patients ( N = 22) and control subjects ( N = 17) Quiet Babble Word repetition Combination Control MS Control MS Control MS Control MS Simple Choice Same Sequential 367 ( 65)* 423 ( 45) 507 ( 94) 566 ( 115) 394 ( 65) 477 ( 66) 601 ( 125) 640 ( 119) 345 ( 53) 421 ( 50) 505 ( 107) 578 ( 99) 382 ( 67) 468 ( 60) 581 ( 100) 633 ( 105) 419 ( 58) 455 ( 60) 533 ( 124) 602 ( 80) 477 ( 108) 490 ( 73) 607 ( 96) 665 ( 96) 401 ( 55) 458 ( 75) 527 ( 107) 596 ( 87) 486 ( 162) 507 ( 98) 621 ( 108) 692 ( 128) * A11 numbers in parentheses refer to standard deviation. 93 J Neuro- Ophthalmol, Vol. 25, No. 2, 2005 LaPointe et al word repetition and four- talker babble, MS performance was found to be significantly slower in all cases except for the easiest condition of simple reaction time in the quiet condition. Among the distraction conditions, word repetition combined with four- speaker babble produced the slowest reaction times for both groups. DISCUSSION Our study found significant differences in reaction times for four visual processing and visual cognitive computerized subtests between MS patients and matched control subjects. This provides evidence to support the many previous studies that have found cognitive slowing or impairment in individuals with MS ( 1- 4) but extends these findings to a sample of MS patients who are only mildly disabled by their MS ( Expanded Disability Status Scale mean of 3.0). Additionally, the presence of auditory competition and distraction appears to add to the cognitive resource allocation load and diminish performance even more. We expect that these effects would be reciprocal across modalities and tasks. That is, we anticipate that visual distraction would affect auditory performance as well, a phenomenon yet to be elucidated. Distraction may be a means of taxing the cognitive system, particularly in the ability to efficiently allocate attentional resources that allows earlier detection of potential cognitive slowing. This suggests that evidence of impaired cognitive function may be a characteristic that is apparent early in the course of the disease progression if adequate measures of visual cognitive function are used, particularly within conditions of interference, competition, and distraction. We continue our investigation of longitudinal characteristics of decline and/ or remission in MS, as well as our search for the distraction environments that differentially affect cognitive reaction time and accuracy. REFERENCES 1. NIH - NINDS. Bethesda, MD: Neurological disorders. Available at: http:// www. ninds. nih. gov/ health_ and_ medical/ disorder_ index. htm. Accessed June 11, 2004. 2. Piras MR, Magnano I, Canu ED, et al. Longitudinal study of cognitive dysfunction in multiple sclerosis: neuropsychologic, neuro-radiologic, and neurophysiological findings. J Neurol Neurosurg Psychiatry 2003; 74: 878- 85. 3. Rao SM, St. Aubin- Faubert P, Leo GJ. Information processing speed in patients with multiple sclerosis. J Clin Exp Neuropsychol 1989; 11: 471- 7. 4. Jones SJ, Sprague L, Vaz Pato M. Electrophysiological evidence for a defect in the processing of temporal sound patterns in multiple sclerosis. J Neurol Neurosurg Psychiatry 2002; 73: 561- 7. 5. Chard DT, Griffin CM, Parker GJ, et al. Brain atrophy in clinically early relapsing- remitting multiple sclerosis. Brain 2002; 125: 327- 37. 6. Edwards SG, Liu C, Blumhardt LD. Cognitive correlates of supra-tentorial atrophy on MRI in multiple sclerosis. Acta Neurol Scand 2001; 104: 214- 23. 7. Endsley MR, Kaber DB. Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics 1999; 42: 462- 92. 8. Rapp B, Hendel SK. Principles of cross- modal competition: evidence from deficits of attention. Psychon Bull Rev 2003; 10: 210- 9. 9. Kahneman D. Attention and effort. Englewood Cliffs, NJ: Prentice- Hall; 1973. 10. Kurtzke JF, Rating neurologic impairment in multiple sclerosis: an expanded disability status scale ( EDSS). Neurology 1983; 33: 1444- 52. 11. Miller, EN. The California Computerized Assessment Package ( CalCAP ® ), 2nd Ed. Los Angeles: UCLA; 1999. 94 © 2005 Lippincott Williams & Wilkins |