A meta-analysis was performed on quantitative EEG (QEEG) studies that evaluated attention-deficit hyperactivity disorder (ADHD) using the criteria of the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition). The nine eligible studies (N = 1498) observed QEEG traits of a theta power increase and a beta power decrease, summarized in the theta/beta ratio with a pooled effect size of 3.08 (95% confidence interval, 2.90, 3.26) for ADHD versus controls (normal children, adolescents, and adults). By statistical extrapolation, an effect size of 3.08 predicts a sensitivity and specificity of 94%, which is similar to previous results 86% to 90% sensitivity and 94% to 98% specificity. It is important to note that the controlled group studies were often with retrospectively set limits, and that in practice the sensitivity and specificity results would likely be more modest. The literature search also uncovered 32 pre-DSM-IV studies of ADHD and EEG power, and 29 of the 32 studies demonstrated results consistent with the meta-analysis. The meta-analytic results are also supported by the observation that the theta/beta ratio trait follows age-related changes in ADHD symptom presentation (Pearson correlation coefficient, 0.996, P = 0.004). In conclusion, this meta-analysis supports that a theta/beta ratio increase is a commonly observed trait in ADHD relative to normal controls. Because it is known that the theta/beta ratio trait may arise with other conditions, a prospective study covering differential diagnosis would be required to determine generalizability to clinical applications. Standardization of the QEEG technique is also needed, specifically with control of mental state, drowsiness, and medication.

The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer’s disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility as well as an unclear path for moving basic discovery towards clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state-of-the-art. Additionally, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and towards clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional hand-off model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases.

Background: It was recently demonstrated that the Clinical Dementia Rating scale Sum of Boxes (CDR-SB) score can be used to accurately stage severity of Alzheimer dementia and mild cognitive impairment (MCI). However, to our knowledge, the utility of those interpretive guidelines has not been cross-validated or applied to a heterogeneous sample of dementia cases.Objective: To cross-validate the staging guidelines proposed in a previous study using the National Alzheimer's Coordinating Center (NACC) database. Design:The previously published cut scores were applied to the NACC sample and diagnostic accuracy estimates obtained. Next, analyses were restricted to NACC participants with a CDR global score (CDR-GS) of 0.5 and receiver operating characteristic curves generated to determine optimal CDR-SB cut scores for distinguishing MCI from very early dementia. Setting:The 2008 NACC uniform data set.Participants: There were 12 462 participants (5115 controls; 2551 patients with MCI; 4796 patients with de-mentia, all etiologies) in the NACC data set used for the current analysis.Main Outcome Measure: Accurate prediction of diagnoses (MCI or dementia) using the CDR-SB score. Results:The previously proposed CDR-SB ranges successfully classified the vast majority of patients across all impairment ranges with a of 0.91 and 94% overall correct classification rate. Additionally, the CDR-SB score discriminated between patients diagnosed with MCI and dementia when CDR-GS was restricted to 0.5 (overall area under the curve=0.83).Conclusions: These findings cross-validate the previously published CDR-SB interpretative guidelines for staging dementia severity and extend those findings to a large heterogeneous sample of patients with dementia. Additionally, the CDR-SB scores distinguished MCI from dementia in patients with reasonable accuracy when CDR-GS was restricted to 0.5.

Forty-three college students suffering from recurrent tension headache were randomly assigned to 1 of 4 elec-tromyographic (EMG) biofeedback training conditions. Although all subjects were led to believe they were learning to decrease frontal EMG activity, actual feedback was contingent on decreased EMG activity for half of the subjects and increased EMG activity for the other half. Within these 2 groups, subjects also viewed bogus video displays designed to convince them they were achieving large (high success) or small (moderate success) reductions in EMG activity. Regardless of actual changes in EMG activity, subjects receiving high-success feedback showed substantially greater improvement in headache activity (53%) than subjects receiving moderate success feedback (26%). Performance feedback was also related to changes in locus of control and self-efficacy. Changes in these 2 cog-nitive variables during biofeedback training were also correlated with reductions in headache activity following treatment, whereas changes in EMG activity exhibited during training were uncorrelated with outcome. These results suggest that the effectiveness of EMG biofeedback training with tension headache may be mediated by cog-nitive changes induced by performance feedback and not primarily by reductions in EMG activity. My favorite headache article remains the 1984 article by Holroyd, Penzien, and colleagues. This influential study was the first to demonstrate that the effectiveness of bio-feedback may be mediated by cognitive changes induced through biofeedback training rather than primarily by learned physiological control. In the 1970s and early 1980s, the rationale for biofeedback training as an intervention for recurrent headache was derived from the widely accepted notion that migraine was a vascular phenomenon and tension-type headache was a musculo-skeletal phenomenon. Accordingly, thermal and elec-tromyographic (EMG) biofeedback targeted the supposed physiological responses involved in migraine and tension-type headache, respectively. The 1984 study manipulated both the contingency of the feedback in EMG biofeedback training and patients' perceptions of their success with biofeedback, using a 2 (EMG increase vs EMG decrease) ¥ 2 (high vs moderate success) experimental design. Results demonstrated headache improvement with biofeedback regardless of whether patients had been trained to decrease or to increase EMG activity. Furthermore, superior headache improvement was achieved by the group who received the "high success" condition, regardless of biofeedback training. Headache improvements instead correlated with cognitive changes in self-efficacy and locus of control. The exemplary study challenged the popular beliefs of the day about mechanisms of biofeedback, and moreover raised questions for the prevailing notions concerning basic pathophysiology of so-called muscle contraction headache. Although previous studies had questioned the mechanisms of biofeedback with altered-contingency control conditions, this study surpa...

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Background There is a significant need for rapid and cost-effective biomarkers of Alzheimer’s disease (AD) for advancement of clinical practice and therapeutic trials. Objective The aim of the current study was to cross-validate our previously published serum-based algorithm on an independent assay platform as well as validate across tissues and species. Preliminary analyses were conducted to examine the utility in distinguishing AD from non-AD neurological disease (Parkinson’s Disease). Methods Serum proteins from our previously published algorithm were quantified from 150 AD cases and 150 controls on the Meso Scale Discovery (MSD) platform. Serum samples were analyzed from 49 Parkinson’s disease (PD) cases and compared to a random sample of 51 AD cases and 62 controls. Support vector machines (SVM) were used to discriminate PD vs. AD vs. NC. Human and AD mouse model microvessel images were quantified with HAMAMATSU imaging software. Mouse serum biomarkers were assayed via MSD. Results Analysis of 21 serum proteins from 150 AD cases and 150 controls yielded an algorithm with sensitivity and specificity of 0.90 for correctly classifying AD. This multi-marker approach was then validated across species and tissue. Assay of the top proteins in human and AD mouse model brain microvessels correctly classified 90–100% of the samples. SVM analyses were highly accurate at distinguishing PD vs. AD vs. NC. Conclusions This serum-based biomarker panel should be tested in a community-based setting to determine its utility as a first-line screen for AD and non-AD neurological diseases for primary care providers.

IntroductionThis study combined data across four independent cohorts to examine the positive and negative predictive values of an Alzheimer's disease (AD) blood test if implemented in primary care.MethodsBlood samples from 1329 subjects from multiple independent, multiethnic, community-based, and clinic-based cohorts were analyzed. A “locked-down” referent group of 1128 samples was generated with 201 samples randomly selected for validation purposes. Random forest analyses were used to create the AD blood screen. Positive (PPV) and negative (NPV) predictive values were calculated.ResultsIn detecting AD, PPV was 0.81, and NPV was 0.95 while using the full AD blood test. When detecting mild cognitive impairment, PPV and NPV were 0.74 and 0.93, respectively. Preliminary analyses were conducted to detect any “neurodegenerative disease”. The full 21-protein AD blood test yielded a PPV of 0.85 and NPV of 0.94.DiscussionThe present study creates the first-ever multiethnic referent sample that spans community-based and clinic-based populations for implementation of an AD blood screen.

Background While a great deal of literature has focused on risk factors for Mild Cognitive Impairment (MCI), little published work examines risk for MCI among Mexican Americans. Methods Data from 1628 participants (non-Hispanic n= 1002; Mexican American n=626) were analyzed from two ongoing studies of cognitive aging and Alzheimer’s disease, Project FRONTIER and TARCC. Results When looking at the full cohorts (non-Hispanic and Mexican American), age, education, APOE ε4 status and gender were consistently related to MCI diagnosis across the two cohorts. However, when split by ethnicity advancing age was the only significant risk factor for MCI among Mexican Americans across both cohorts. Conclusions The current data suggests that many of the previously established risk factors for MCI among non-Hispanic cohorts may not be predictive of MCI among Mexican Americans and point to the need for additional work aimed at understanding factors related to cognitive aging among this underserved segment of the population.