Murali Doraiswamy

Position Statement of the American Association for Geriatric Psychiatry Regarding Principles of Care for Patients With Dementia Resulting From Alzheimer Disease

There exists currently an effective, systematic care/treatment model for patients with dementia resulting from Alzheimer disease (AD). This consists of a series of therapeutic interventions—pharmacologic and nonpharmacologic—targeted at patients with AD and their caregivers. Although these interventions do not produce a cure of the underlying disease and do not appear to stop its progression, they have been shown to produce benefits for patients and their caregivers. The aims of this care model, often referred to as “Dementia Care,” are to delay disease progression, delay functional decline, improve quality of life, support dignity, control symptoms, and provide comfort at all stages of AD. This evolving model is based on scientific evidence of beneficial outcomes, with acceptable risks, and is increasingly targeted at an improving pathophysiological understanding of the biology of AD. Although the evidence is limited, the existing evidence, coupled with clinical experience and common sense, is adequate to produce a minimal set of care principles. In this context, the American Association for Geriatric Psychiatry (AAGP) affirms that there now exists a minimal set of care principles for patients with AD and their caregivers. Consequently, the detection and treatment of AD must now be considered part of the typical care practices for any physician and other licensed clinicians who interact with patients with this disease. This document articulates these principles of care.

Psychosis in parkinson's disease: Diagnosis and treatment

1. This article reviews the prevalence, diagnosis, pathophysiology and management of psychosis in Parkinsons disease. 2. Psychosis in Parkinsons disease has been associated with all antiparkinsonian medications. The most common symptoms are vivid disturbing dreams, visual hallucinations and paranoid delusions. 3. The emergence of psychosis reduces the patients functional capacity and increases caregiver burden. It also poses a therapeutic dilemma because effective treatment of psychotic symptoms may result in worsening of motor symptoms and vice versa. 4. Increased physician awareness is essential for proper diagnosis and management. Withdrawal of anticholinergic medications and amantadine followed by levodopa dose adjustment is effective in many patients. 5. Atypical neuroleptics, in low doses, may be successful when other measures have failed. However, these agents are not approved for treating Parkinsonian psychosis and must be considered as investigational therapies.

A randomized, controlled, multicenter, international study of the impact of florbetapir (18F) PET amyloid imaging on patient management and outcome

new datasets), increasing our discovery sample to 21,433 cases and 44,340 controls. Methods: All datasets were imputed to a 1000 Genomes reference panel (Phase 1 v3, March 2012) of over 37 million variants, many of which are low-frequency single nucleotide variants (SNV) and indels. Single-variant-based association analysis was conducted adjusting for age, sex and population substructure. Individual datasets were analyzed with the score test for case-control datasets and general estimating equations (with generalized linear mixed model for rare variants) for family-based analyses. Within-study results were meta-analyzed in METAL. Gene-based testing was conducted on summary statistics using VEGAS. Results: Imputation produced approximately nine million high-quality low-frequency variants for analyses. Twenty-five loci were genomewide significant at P 5310-8, including five novel loci. Three of these novel loci are driven by significant low-frequency variants, while two are associations of common intergenic variants between the genes USP6NL and ECHDC3 at Chr10: 10:11720308 (P1⁄42.91x10) and the genes CYYR1 and ADAMTS1 at Chr21: 28,156,856 (P1⁄41.44x10). Previously reported rare and low-frequency variants in TREM2 and SORL1 were also significantly associated, while low-frequency SNVs in the common loci BIN1 (MAF1⁄40.026) and CLU (MAF1⁄40.029) show suggestive significance (P 5310-7). Twelve additional loci produced signals with suggestive significance, seven driven by low-frequency or rare variants and five driven by common variants. Genotyping to confirm imputation quality, and replication genotyping using the Sequenom MassArray are underway. Gene-based analyses identified 13 significantly associated genes (Bonferroni P 2.83x10-6), four of which are novel loci driven by nominally significant low-frequency variants. Conclusions: Using an imputation set with a large number of rare variants we identified several novel candidate loci for LOAD, giving support to the hypothesis that rare and low-frequency variant imputation can identify novel associations with disease.

Memory-efficient query-driven community detection with application to complex disease associations

Community detection in real-world graphs presents a number of challenges. First, even if the number of detected communities grows linearly with the graph size, it becomes impossible to manually inspect each community for value added to the application knowledge base. Mining for communities with query nodes as knowledge priors could allow for filtering out irrelevant information and for enriching end-users knowledge associated with the problem of interest, such as discovery of genes functionally associated with the Alzheimer’s (AD) biomarker genes. Second, the data-intensive nature of community enumeration challenges current approaches that often assume that the input graph and the detected communities fit in memory. As computer systems scale, DRAM memory sizes are not expected to increase linearly, while technologies such as SSD memories have the potential to provide much higher capacities at a lower power-cost point, and have a much lower latency than disks. Out-of-core algorithms and/or databaseinspired indexing could provide an opportunity for different design optimizations for query-driven community detection algorithms tuned for emerging architectures. Therefore, this work addresses the need for query-driven and memory-efficient community detection. Using maximal cliques as the community definition, due to their high signalto-noise ratio, we propose and systematically compare two contrasting methods: indexed-based and out-of-core. Both methods improve peak memory efficiency as much as 1000X compared to the state-of-the-art. However, the index-based method, which also has a 10-to-100-fold run time reduction, outperforms the out-of-core algorithm in most cases. The achieved scalability enables the discovery of diseases that are known to be or likely associated with Alzheimer’s when the genome-scale network is mined with AD biomarker genes as knowledge priors.

Computational Modeling of the Dynamic Biomarker Cascade in Alzheimer\'s Disease

Background The heterogeneity and complexity of Alzheimer’s disease (AD) lends itself to dynamic causal modeling, a computational systems biology approach to simulate complex systems. Methods We implemented a computational dynamic causal model (DCM) to test the common descriptive assumptions of biomarker evolution in AD. We modeled beta-amyloid, tau, neuro-degeneration and cognitive impairment as first order non-linear differential equations to include beta-amyloid dependent and non-dependent neurodegenerative cascades. We tested the DCM, by adjusting its parameters to simulate three specific simulation scenarios in early-onset autosomal dominant AD and late-onset AD, to determine whether computed biomarker trajectories agreed with current assumptions of AD progression. We also simulated the effects of anti-amyloid therapy in late-onset AD. Results The computational model of early-onset “pure AD” demonstrated the initial appearance of amyloid, followed by other biomarkers of tau and neurodegeneration, followed by the onset of cognitive decline as predicted by prior literature. The model also showed the ability to vary onset of cognitive decline based on cognitive reserve. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid-related or non-amyloid-related tauopathy, depending on the magnitude of age-related comorbid pathology, and also closely matched the cascade predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any benefit on cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions To our knowledge, this is the first computational model of the dynamic biomarker cascade in autosomal dominant and late-onset AD. Such models, with refinement using actual molecular, clinical and genomic data, are an important tool towards developing a systems biology precision medicine approach for understanding and preventing AD.

Psychometric properties of a mobile neurocognitive assessment tool

for Radiation Research (PBB3) and with Tohoku University (THK 5351). BBAR is the pathology core of J-ADNI, J-DIAN and CJD-surveillance. Since the brain banking in Japan is legally based on autopsy, we have a system for preregistration to autopsy by patients or their relatives as an alsternative gateway to brain banking. In 2014, this gate welcomed four patients with glioblastoma, CNS lymphoma, CJD and control who died of systemic malignancy. We also conducted lectures open to the public to promote brain donation. We also teach neuroscientists our method of brain banking and how to conduct neuroscience research using our resource. Conclusions: Our culture esteems human dead body, especially brain, but considering racial difference in human disease, JBBNNR should work as the infrastructure for neuroscience research in Asia.

Neuropsychological correlates of erp cognitive measures in Alzheimer’s disease

Marco Cecchi, Dennis Moore, Carl H. Sadowsky, Paul Solomon, Murali Doraiswamy, Charles D. Smith, Gregory A. Jicha, Andrew Budson, Steven E. Arnold, Neuronetrix, Louisville, KY, USA; Nova SE University, West Palm Beach, FL, USA; Boston Center for Memory, Newton, MA, USA; Duke University, Durham, NC, USA; University of Kentucky, Lexington, KY, USA; Department of Cognitive & Behavioral Neurology, VA Boston Healthcare System, Boston, MA, USA; University of Pennsylvania, Philadelphia, PA, USA. Contact e-mail: mcecchi@neuronetrix.com

Multi-modality fusion of neuroimaging data in predicting abnormal cognitive decline in aging

between the two groups. Results: Regional analyses were performed and direct comparisons of the Jacobian maps revealed that the AAMI group demonstrated significantly greater longitudinal atrophy in the right frontal and right temporal lobes as well as the anterior and posterior cingulate gyrus relative to the NC group (p<0.05; corrected for multiple comparisons using permutation tests). In contrast, the NC group did not show any area of greater brain volume loss relative to the AAMI group. Conclusions: The diagnosis of AAMI was associated with increased rate of brain atrophy relative to NC subjects in regions that are affected in Alzheimer’s disease (AD). AAMI may be diagnostically useful in identifying individuals with early signs of underlying pathology who may be in the prodromal stages of AD.

ERP as a biomarker for Alzheimer's disease: The cognision system

Background: Biomarker research has led to development of promising markers (e.g., amyloid imaging, cerebrospinal fluid analysis). However, the causal link between the pathologic process and cognitive decline remains unclear, contributing to uncertainty in early AD diagnosis as well as monitoring progression. Recent consensus statements suggest that AD diagnosis should involve evidence of pathological biochemical process as well as cognitive malfunction, with progressive cognitive decline as the core criterion. The FDA has stated that a treatment must not only show the desired pharmacodynamic effect (as demonstrated by biomarkers) but also prove efficacy in a cognitive domain. This requires the development of a reliable cognitive biomarker. The limitations of psychometric testing are well known. Event-related potentials (ERP) have potential as a cognitive biomarker for early AD as well as evaluating drug efficacy. Methods: ERP studies demonstrate its utility in diagnosis. Our portable, easy-to-use ERP system enables standardized data collection in outpatient settings. This device is being used in a multi-center trial (100 well-characterized AD and 100 age-matched controls to validate ERP as a cognitive biomarker). Results: ERP and other biomarker data will be analyzed to address following specific aims: 1) To train an ensemble-of-classifiers neural network system with time and frequency based features of ERP and to determine whether the sensitivity, specificity, and positive likelihood ratio (PLR) in detecting early AD can meet the performance of community clinic physicians. 2) To test, within the dementia cohort, how well ERP biomarkers correlate with CSF biomarkers and test whether combining ERP data with MRI data in a decision-fusion classifier boosts the classification accuracy of differential diagnosis (AD vs. non-AD dementia). 3) To compare statistically, within the dementia cohort, ERP biomarkers with ADAS-Cog scores to assess the utility of ERP biomarkers in monitoring efficacy of AD drugs. Conclusions: Clinical trials are currently underway and preliminary results will be presented.

IC-101-06

within parietal and posterior cingulate regions, often referred to as the ‘default network.’ Objective: To elucidate the relationship between hippocampal memory-related activation and default network deactivation across the continuum of normal aging, mild cognitive impairment (MCI) and AD. Methods: We utilized independent component analyses (ICA) to analyze fMRI data acquired during a face-name associative memory paradigm in 52 older individuals: 15 older controls (OC; CDR 0.0); 10 patients with mild probable AD (CDR 1.0); and 27 subjects with MCI (defined here as CDR 0.5). We then further subdivided the MCI subjects on the basis of their CDR Sum of Box (CDR-SB) score into Low-SB-MCI (CDR-SB 0.5-1.5; n 15) and High-SB-MCI (2.03.5; n 12). Results: Comparing across groups, we found evidence of a non-linear trajectory of fMRI activation across the continuum of impairment. Low-SB-MCI showed paradoxical hippocampal hyperactivation compared to OC (p 0.001), while High-SB-MCI subjects demonstrated significant hypoactivation, similar to AD. The pattern of task-related deactivation in parietal regions showed a reciprocal pattern to that of hippocampal activation across the groups, with Low-SB-MCI exhibiting hyper-deactivation and High-SB-MCI and mild AD demonstrating significantly decreased deactivation (p 0.001). Moreover, across all 52 subjects, extent of hippocampal activation was strongly related to extent of deactivation in lateral parietal regions (R 0.90; p 0.0001); posterior cingulate (R 0.57; p 0.001) and precuneus (R 0.78; p 0.0001). Conclusions: Our findings suggest that dysfunction of medial temporal lobe based memory systems is strongly related to failure of deactivation in the default network over the course of MCI and AD. These findings support the hypothesis that there is a coordinated relationship between these brain systems, and provide evidence of a functional interaction between medial temporal lobe and neocortical pathology in early AD.