Neurometrics: Computer-Assisted Differential Diagnosis of Brain Dysfunctions

John, E. Roy; Prichep, Leslie S.; Fridman, J; Easton, Paul A.

doi:10.1126/science.3336779

Cited by 521 publications

(261 citation statements)

References 11 publications

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“…The evolution of EEG frequency bands as a function of age in children and young adults is wellknown (Matousek and Petersén, 1973;John et al, 1980). Subsequent investigations (John et al, 1988) allowed the generalisation of the so-called 'developmental equations' in middle-aged and elderly individuals. More importantly, the spectral evolution with age has been previously compared with the evolution of complexity values.…”

Section: Discussionmentioning

confidence: 99%

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Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

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Objective: Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. Method:This study examines changes in resting-state magnetoencephalogram (MEG) complexity -quantified with the Lempel-Ziv complexity (LZC) algorithm -due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years.Results: A significant quadratic (curvilinear) relationship (p < 0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life.Once that peak was crossed, complexity values slowly decreased until late senescence.Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p < 0.05). Conclusions:These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm.Significance: Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not 3 only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.Keywords: Life Span, Ageing, Complexity, Brain Development, White Matter Development Highlights 1. A significant quadratic (curvilinear) relationship between age and oscillatory complexity exists, with complexity maxima reached by the sixth decade of life.2. As in previous studies, females exhibit higher complexity values than males, at least in some brain regions.3. The evolution of oscillatory complexity across the life span is interpreted as a physiological rhythm which is altered by several brain pathologies.

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Section: Discussionmentioning

confidence: 99%

“…Interestingly, during this new substitution process, low-frequency bands also increase 5 their topographic location, following a posterior-to-anterior tendency (see John et al, 1988, and the review by Rossini et al, 2007). …”

Section: Introductionmentioning

confidence: 98%

Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

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“…This normative database consists a sample of 250 normal adults between the ages of 17 and 90 years who were screened for the presence of psychiatric, neurologic, or medical conditions known to affect the EEG (25).…”

Section: Methodsmentioning

confidence: 99%

“…This technique utilizes parametric statistics to compare EEG measures obtained from an individual patient with those obtained from an ageregressed database of normal individuals, and is free of cultural and ethnic bias (21)(22)(23). QEEG has been shown to be a sensitive indicator of cortical electrophysiologic dysfunction in patients with a diverse range of neurologic and psychiatric disorders (24)(25)(26). The purpose of the present study was to determine the utility of QEEG for the diagnosis and treatment of NPSLE.…”

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confidence: 99%

Quantitative electroencephalography: A new approach to the diagnosis of cerebral dysfunction in systemic lupus erythematosus

Ritchlin

Chabot

Alper

et al. 1992

Arthritis & Rheumatism

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Objective. Neuropsychiatric manifestations are common in patients with systemic lupus erythematosus (SLE), but accurate diagnosis is often difficult. We conducted a prospective study to determine the utility of neurometric quantitative electroencephalography (QEEG) as an indicator of cerebral dysfunction in SLE patients. Methods. Fifty-two SLE patients were divided into 4 groups based on the results of neuropsychiatric evaluations. These included patients with objective evidence of neuropsychiatric SLE (NPSLE), patients with neuropsychiatric symptoms, patients with no evidence of NPSLE, and patients with a prior history of NPSLE. All QEEG findings were compared with data in an age-regressed normative database and with findings in an independent sample of normal subjects.Results. QEEG sensitivity was 87%, and specificity was 75%. QEEG results were abnormal in 74% of the SLE patients with neuropsychiatric symptoms and in 28% of the patients with no evidence of active NPSLE. QEEG profiles varied as a function of the severity and type of neuropsychiatric manifestation present. Within this patient population, QEEG was more sensitive than magnetic resonance imaging, computed tomography scanning, or conventional EEG.Conclusion. Neurometric QEEG may be a sensitive indicator of cerebral dysfunction in patients with NPSLE and can differentiate patients with diverse neuropsychiatric manifestations. When combined with a careful clinical history and evaluation, QEEG provides information that may be useful for the early detection of NPSLE and for serial evaluation of disease activity and treatment efficacy.Neurologic and psychiatric manifestations of systemic lupus erythematosus (SLE) are common, occurring at any time during the illness, and often in the absence of generalized disease activity. Estimates of the prevalence of neuropsychiatric SLE (NPSLE) vary between 24% and 75% (1-7). This variability is partially due to the lack of uniform diagnostic criteria for determining central nervous system (CNS) involve-

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“…It should be noted that these z-scores are based on the average power in each band for all electrodes within a given QEEG recording, and are not z-scores referenced to some normative population (e.g., as in the "neurometrics" approach, (cf., John et al 1988)). The normalization process places absolute and relative power values into a common unit (standard deviation or zscore units) which allows them to be combined.…”

Section: Cordance Calculationsmentioning

confidence: 99%

Early Changes in Prefrontal Activity Characterize Clinical Responders to Antidepressants

Cook¹,

Leuchter

Morgan

et al. 2002

Neuropsychopharmacology

179

169

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Previous studies have shown that changes in brain functionClinicians long have observed a lag time of several weeks between the initiation of antidepressant treatment and clinical response for many patients (Hyman and Nestler 1996;Katz et al. 1996). Some individuals do have early symptomatic improvement, and this has been reported to predict further improvement over the next several weeks (Nierenberg et al. 1995). Reports have suggested that some physiologic changes are seen shortly after initiation of treatment (Sulser 1989;Beck 1995;Dahmen et al. 1997). No clinically practical physiologic predictor of treatment response has yet been identified with these techniques, however, and the relationship of early physiologic changes to eventual clinical outcome remains incompletely understood.Quantitative electroencephalography (QEEG) has been used as a physiologic measure in efforts to address these questions. Prior work with "pharmaco-EEG" techniques has shown that the administration of antidepressant compounds yields reproducible changes in EEG activity in healthy control subjects within a few hours of dosing (Saletu et al. 1982(Saletu et al. , 1983(Saletu et al. , 1986(Saletu et al. , 1987(Saletu et al. , 1987 Grunberger 1985, 1988;Sannita et al. 1983;Sannita 1990; Early Prefrontal Changes in Depression 121Itil et al. 1984;Herrmann et al. 1991;Luthringer et al. 1996). The relationship of these immediate EEG changes in control subjects to eventual clinical response in a depressed population is unclear. Other QEEG work with depressed subjects has found that changes from baseline in theta power early in the course of treatment may characterize groups of depressed patients who are more likely to respond to antidepressant treatment (Ulrich et al. 1994). Unfortunately, the overlap in the value of these changes between responder and nonresponder groups precluded the use of this measure in response prediction for individual subjects, and prior research did not indicate how to relate changes in theta power to other measures of regional brain activity (e.g., regional cerebral blood flow or metabolism). We previously have shown that absolute and relative power are complementary measures of brain activity (Leuchter et al. 1993). A relatively new QEEG measure, "cordance," combines information from both absolute and relative power measures (Leuchter et al. 1994a(Leuchter et al. , 1994b. The algorithm yields two indicators: a categorical value ("concordant" or "discordant" state) and a numerical value for each electrode. In an earlier report with the categorical measure , we observed that depressed subjects exhibiting the concordant state prior to treatment had better treatment outcomes when treated with fluoxetine than did subjects with the discordant state. In this report, we use the num*erical values of cordance, because they allow examination of changes in regional brain activity with treatment. In validation against data collected simultaneously with [H 2 15O]-positron emission tomography (PET), cordance values in the the...

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Neurometrics: Computer-Assisted Differential Diagnosis of Brain Dysfunctions

Cited by 521 publications

References 11 publications

Brain oscillatory complexity across the life span

Brain oscillatory complexity across the life span

Quantitative electroencephalography: A new approach to the diagnosis of cerebral dysfunction in systemic lupus erythematosus

Early Changes in Prefrontal Activity Characterize Clinical Responders to Antidepressants

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