Faculty Bibliography

It has been suggested that neuronal hyperexcitability contributes to Alzheimer's disease (AD), so we asked how hyperexcitability develops in a common mouse model of beta-amyloid neuropathology - Tg2576 mice. Using video-EEG recordings, we found synchronized, large amplitude potentials resembling interictal spikes (IIS) in epilepsy at just 5 weeks of age, long before memory impairments or beta-amyloid deposition. Seizures were not detected, but they did occur later in life, suggesting that IIS are possibly the earliest stage of hyperexcitability. Interestingly, IIS primarily occurred during rapid-eye movement (REM) sleep, which is notable because REM is associated with increased cholinergic tone and cholinergic impairments are implicated in AD. Although previous studies suggest that cholinergic antagonists would worsen pathophysiology, the muscarinic antagonist atropine reduced IIS frequency. In addition, we found IIS occurred in APP51 mice which overexpress wild type (WT)-APP, although not as uniformly or as early in life as Tg2576 mice. Taken together with results from prior studies, the data suggest that surprising and multiple mechanisms contribute to hyperexcitability. The data also suggest that IIS may be a biomarker for early detection of AD.

The aim of this study was to examine working memory (WM) modalities (visual-spatial and auditory-verbal) and processes (maintenance and manipulation) in children with and without attention-deficit/hyperactivity disorder (ADHD). The sample consisted of 63 8-year-old children with ADHD and an age- and sex-matched non-ADHD comparison group (N=51). Auditory-verbal and visual-spatial WM were assessed using the Digit Span and Spatial Span subtests from the Wechsler Intelligence Scale for Children Integrated - Fourth Edition. WM maintenance and manipulation were assessed via forward and backward span indices, respectively. Data were analyzed using a 3-way Group (ADHD vs. non-ADHD)xModality (Auditory-Verbal vs. Visual-Spatial)xCondition (Forward vs. Backward) Analysis of Variance (ANOVA). Secondary analyses examined differences between Combined and Predominantly Inattentive ADHD presentations. Significant GroupxCondition (p=.02) and GroupxModality (p=.03) interactions indicated differentially poorer performance by those with ADHD on backward relative to forward and visual-spatial relative to auditory-verbal tasks, respectively. The 3-way interaction was not significant. Analyses targeting ADHD presentations yielded a significant GroupxCondition interaction (p=.009) such that children with ADHD-Predominantly Inattentive Presentation performed differentially poorer on backward relative to forward tasks compared to the children with ADHD-Combined Presentation. Findings indicate a specific pattern of WM weaknesses (i.e., WM manipulation and visual-spatial tasks) for children with ADHD. Furthermore, differential patterns of WM performance were found for children with ADHD-Predominantly Inattentive versus Combined Presentations. (JINS, 2016, 22, 1-11).

BACKGROUND: Distinguishing pediatric bipolar disorder (BD) from attention-deficit hyperactivity disorder (ADHD) can be challenging. Hyperactivity is a core feature of both disorders, but severely disturbed sleep and circadian dysregulation are more characteristic of BD, at least in adults. We tested the hypothesis that objective measures of activity, sleep, and circadian rhythms would help differentiate pediatric subjects with BD from ADHD and typically developing controls. METHODS: Unmedicated youths (N = 155, 97 males, age 5-18) were diagnosed using DSM-IV criteria with Kiddie-SADS PL/E. BD youths (n = 48) were compared to typically developing controls (n = 42) and children with ADHD (n = 44) or ADHD plus comorbid depressive disorders (n = 21). Three-to-five days of minute-to-minute belt-worn actigraph data (Ambulatory Monitoring Inc.), collected during the school week, were processed to yield 28 metrics per subject, and assessed for group differences with analysis of covariance. Cross-validated machine learning algorithms were used to determine the predictive accuracy of a four-parameter model, with measures reflecting sleep, hyperactivity, and circadian dysregulation, plus Indic's bipolar vulnerability index (VI). RESULTS: There were prominent group differences in several activity measures, notably mean 5 lowest hours of activity, skewness of diurnal activity, relative circadian amplitude, and VI. A predictive support vector machine model discriminated bipolar from non-bipolar with mean accuracy of 83.1 +/- 5.4%, ROC area of 0.781 +/- 0.071, kappa of 0.587 +/- 0.136, specificity of 91.7 +/- 5.3%, and sensitivity of 64.4 +/- 13.6%. CONCLUSIONS: Objective measures of sleep, circadian rhythmicity, and hyperactivity were abnormal in BD. Wearable sensor technology may provide bio-behavioral markers that can help differentiate children with BD from ADHD and healthy controls.

In the present study, we tested whether the volatile metabolome was altered by mutations of the Alzheimer's disease (AD)-implicated amyloid precursor protein gene (APP) and comprehensively examined urinary volatiles that may potentially serve as candidate biomarkers of AD. Establishing additional biomarkers in screening populations for AD will provide enhanced diagnostic specificity and will be critical in evaluating disease-modifying therapies. Having strong evidence of gross changes in the volatile metabolome of one line of APP mice, we utilized three unique mouse lines which over-express human mutations of the APP gene and their respective non-transgenic litter-mates (NTg). Head-space gas chromatography/mass spectrometry (GC/MS) of urinary volatiles uncovered several aberrant chromatographic peak responses. We later employed linear discrimination analysis and found that the GC/MS peak responses provide accurate (>84%) genotype classification of urinary samples. These initial data in animal models show that mutant APP gene expression entails a uniquely identifiable urinary odor, which if uncovered in clinical AD populations, may serve as an additional biomarker for the disease.

Androgens have profound effects on hippocampal structure and function, including induction of spines and spine synapses on the dendrites of CA1 pyramidal neurons, as well as alterations in long-term synaptic plasticity (LTP) and hippocampally dependent cognitive behaviors. How these effects occur remains largely unknown. Emerging evidence, however, suggests that one of the key elements in the response mechanism may be modulation of brain-derived neurotrophic factor (BDNF) in the mossy fiber (MF) system. In male rats, orchidectomy increases synaptic transmission and excitability in the MF pathway. Testosterone reverses these effects, suggesting that testosterone exerts tonic suppression on MF BDNF levels. These findings suggest that changes in hippocampal function resulting from declining androgen levels may reflect the outcome of responses mediated through normally balanced, but opposing, mechanisms: loss of androgen effects on the hippocampal circuitry may be compensated, at least in part, by an increase in BDNF-dependent MF plasticity.

OBJECTIVES: To determine safety and efficacy of the 5HT1A serotonin partial agonist buspirone on core autism and associated features in children with autism spectrum disorder (ASD). STUDY DESIGN: Children 2-6 years of age with ASD (N = 166) were randomized to receive placebo or 2.5 or 5.0 mg of buspirone twice daily. The primary objective was to evaluate the effects of 24 weeks of buspirone on the Autism Diagnostic Observation Schedule (ADOS) Composite Total Score. Secondary objectives included evaluating the effects of buspirone on social competence, repetitive behaviors, language, sensory dysfunction, and anxiety and to assess side effects. Positron emission tomography measures of tryptophan metabolism and blood serotonin concentrations were assessed as predictors of buspirone efficacy. RESULTS: There was no difference in the ADOS Composite Total Score between baseline and 24 weeks among the 3 treatment groups (P = .400); however, the ADOS Restricted and Repetitive Behavior score showed a time-by-treatment effect (P = .006); the 2.5-mg buspirone group showed significant improvement (P = .003), whereas placebo and 5.0-mg buspirone groups showed no change. Children in the 2.5-mg buspirone group were more likely to improve if they had fewer foci of increased brain tryptophan metabolism on positron emission tomography (P = .018) or if they showed normal levels of blood serotonin (P = .044). Adverse events did not differ significantly among treatment groups. CONCLUSIONS: Treatment with 2.5 mg of buspirone in young children with ASD might be a useful adjunct therapy to target restrictive and repetitive behaviors in conjunction with behavioral interventions. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00873509.

BACKGROUND AND SCOPE: Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. FINDINGS: A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. CONCLUSION: We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.