Serial Neuropsychological Assessment and Magnetic Resonance Imaging Analysis in Multiple Sclerosis

Hohol, Marika; Guttmann, Charles R.G.; Orav, John; Mackin, Glenn A.; Kikinis, R.; Khoury, Samia J.; Jólesz, Ferenc A.; Weiner, Howard L.

doi:10.1001/archneur.1997.00550200074013

Cited by 192 publications

(128 citation statements)

References 38 publications

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“…Charcot first observed cognitive slowing as a feature of MS in the nineteenth century (Charcot, 1877). Slowed information processing speed is widely reported in the literature as the primary neuropsychological feature of MS (Achiron et al, 2005;Archibald and Fisk, 2000;Brassington and Marsh, 1998;De Sonneville et al, 2002;DeLuca et al, 2004;Marrie, 2004;Parmenter et al, 2007;Rao et al, 1989;Rao, 1996;Zakzanis, 2000).The neuropathological mechanism is thought to involve widespread cortical and subcortical lesions; total lesion load has been found to be positively correlated with attentional, memory and executive functioning impairments (Hohol et al, 1997;Kieseier et al, 2005;Walker et al, 2001). White matter lesion prevalence is thought to potentially account for all of the age-related variance between individuals in tests of speed and executive ability (Rabbitt et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Stroop performance in multiple sclerosis: Information processing, selective attention, or executive functioning?

Macniven

Davis

et al. 2008

J Int Neuropsychol Soc

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Cognitive impairments in information processing speed, attention and executive functioning are widely reported in patients with multiple sclerosis (MS). Several studies have identified impaired performance on the Stroop test in people with MS, yet uncertainty remains over the cause of this phenomenon. In this study, 25 patients with MS were assessed with a neuropsychological test battery including a computerized Stroop test and a computerized test of information processing speed, the Graded Conditional Discrimination Tasks (GCDT). The patient group was compared with an individually age, sex and estimated premorbid IQ-matched healthy control group. The patients' reaction times (RTs) were significantly longer than those of the controls on all Stroop test trials and there was a significantly enhanced absolute (RT incongruent -RT neutral ) and relative (100{[RT incongruent -RT neutral ]0RT neutral ) Stroop interference effect for the MS group. The linear function relating RT to stimulus complexity in the GCDT was significantly steeper in the patient group, indicating slowed information processing. The results are discussed with reference to the difference engine model, a theory of diversity in speeded cognition. It is concluded that, in the assessment of people with MS, great caution must be used in the interpretation of performance on neuropsychological tests which rely on RT as the primary measure. (JINS, 2008, 14, 805-814.)

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Section: Introductionmentioning

confidence: 99%

Stroop performance in multiple sclerosis: Information processing, selective attention, or executive functioning?

Macniven

Davis

et al. 2008

J Int Neuropsychol Soc

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“…Examples include schizophrenia [Gaser et al, 1999], Alzheimer disease [Jack et al, 1992[Jack et al, , 1997], Huntington's disease [Aylward et al, 1997[Aylward et al, , 1998Jernigan et al, 1991], and attention-defecit hyperactivity disorder [Giedd et al, 1994;Hynd et al, 1991]. Furthermore, symptoms detected by neuroimaging can frequently be correlated with behavioral manifestations of disease [Backman et al, 1997;Kwon et al, 2003;Hohol et al, 1997;Zivadinov et al, 2001]. Importantly, this can provide researchers with insights into disease pathogenesis, particularly in the beginning stages of investigation.…”

Section: Introductionmentioning

confidence: 99%

Mouse behavioral mutants have neuroimaging abnormalities

Nieman

Lerch

Bock

et al. 2007

Human Brain Mapping

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Impaired cognitive, memory, or motor performance is a distinguishing characteristic of neurological diseases. Although these symptoms are frequently the most evident in human patients, additional markers of disease are critical for proper diagnosis and staging. Noninvasive neuroimaging methods have become essential in this capacity and provide means of evaluating disease and tracking progression. These imaging methods are also becoming available to scientists in the research laboratory for assessment of animal models of neurological disease. Imaging in mouse models of neurological disease is of particular interest, owing to the availability of inbred strains and genetic manipulation tools that permit detailed investigation of the roles of various genes and gene products in disease pathogenesis. However, the relative prevalence of neuroimaging abnormalities in mice exhibiting neurological symptoms has not been reported. This prevalence has both theoretical and practical value because it is influenced by both the sensitivity of macroscopic anatomical measures to underlying genetic and disease processes and by the efficiency of neuroimaging in detecting and characterizing these effects. In this paper, we describe a meta-analysis of studies involving behavioral mouse mutants at our laboratory. In summary, we have evaluated 15 different mutant genotypes, of which 13 showed abnormal neuroimaging findings. This indicates a surprisingly high prevalence of neuroimaging abnormalities (87%) and suggests that disease processes affecting behavior generally alter neuroanatomy as well. As a consequence, neuroimaging provides a highly sensitive marker of neurological disease in mice exhibiting abnormal behavior. Hum Brain Mapp 28: [567][568][569][570][571][572][573][574][575] 2007. V V C 2007 Wiley-Liss, Inc.

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“…Increasing recognition of the functional relevance and prognostic role of MS cognitive impairments has resulted in focused attention on MR correlates of cognitive deficit. Assessment of the global and regional white matter (WM) lesion burden, derived from T2-or T1-weighted MRI shows modest association with cognitive status (Arnett et al, 1994;Hohol, et al, 1997;Patti et al, 2009;Rao et al, 1989;Rovaris et al, 2001;Swirsky-Sacchetti et al, 1992). A stronger relationship has been obtained with quantification of the whole and…”

Section: Introductionmentioning

confidence: 99%