Frontolimbic atrophy is associated with agitation and aggression in mild cognitive impairment and Alzheimer's disease.

BACKGROUND: The neuroanatomy of agitation and aggression in Alzheimer's disease is not well understood. METHODS: We analyzed 24 months of Alzheimer's Disease Neuroimaging Initiative data for patients with Alzheimer's disease, mild cognitive impairment-stable, and mild cognitive impairment-converter (n = 462) using the Neuropsychiatric Inventory Questionnaire Agitation and Aggression subscale. Magnetic resonance imaging regions of interest that correlated with Neuropsychiatric Inventory Questionnaire Agitation and Aggression subscale raw scores were included in mixed-model, repeated-measures analyses of agitation and aggression over time with age, sex, apolipoprotein E ε4 status, education, and Mini-Mental State Examination score as covariates. RESULTS: Neuropsychiatric Inventory Questionnaire Agitation and Aggression subscale scores worsened in patients with Alzheimer's disease and in mild cognitive impairment-converter (P < .05; trend for mild cognitive impairment, P = .0518). Greater agitation and aggression severity was associated with greater atrophy of frontal, insular, amygdala, cingulate, and hippocampal regions of interest (P < .05). Mini-Mental State Examination score was significant in mixed-effect model repeated measures only in mild cognitive impairment-converters for posterior regions of interest. Demographics and apolipoprotein ε4 were not associated with agitation and aggression. CONCLUSIONS: Agitation and aggression in Alzheimer's disease and mild cognitive impairment is associated with neurodegeneration affecting the anterior salience network that may reduce capacity to process and regulate behaviors properly.

Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state.

BACKGROUND: This study examined whether hierarchical clustering could be used to detect cell states induced by treatment combinations that were generated through automation and high-throughput (HT) technology. Data-mining techniques were used to analyze the large experimental data sets to determine whether nonlinear, non-obvious responses could be extracted from the data. METHODS: Unary, binary, and ternary combinations of pharmacological factors (examples of stimuli) were used to induce differentiation of HL-60 cells using a HT automated approach. Cell profiles were analyzed by incorporating hierarchical clustering methods on data collected by flow cytometry. Data-mining techniques were used to explore the combinatorial space for nonlinear, unexpected events. Additional small-scale, follow-up experiments were performed on cellular profiles of interest. RESULTS: Multiple, distinct cellular profiles were detected using hierarchical clustering of expressed cell-surface antigens. Data-mining of this large, complex data set retrieved cases of both factor dominance and cooperativity, as well as atypical cellular profiles. Follow-up experiments found that treatment combinations producing "atypical cell types" made those cells more susceptible to apoptosis. CONCLUSIONS Hierarchical clustering and other data-mining techniques were applied to analyze large data sets from HT flow cytometry. From each sample, the data set was filtered and used to define discrete, usable states that were then related back to their original formulations. Analysis of resultant cell populations induced by a multitude of treatments identified unexpected phenotypes and nonlinear response profiles.