Faculty Bibliography

We propose a fused lasso logistic regression to analyze callosal thickness profiles. The fused lasso regression imposes penalties on both the l1-norm of the model coefficients and their successive differences, and finds only a small number of non-zero coefficients which are locally constant. An iterative method of solving logistic regression with fused lasso regularization is proposed to make this a practical procedure. In this study we analyzed callosal thickness profiles sampled at 100 equal intervals between the rostrum and the splenium. The method was applied to corpora callosa of elderly normal controls (NCs) and patients with very mild or mild Alzheimer's disease (AD) from the Open Access Series of Imaging Studies (OASIS) database. We found specific locations in the genu and splenium of AD patients that are proportionally thinner than those of NCs. Callosal thickness in these regions combined with the Mini Mental State Examination scores differentiated AD from NC with 84% accuracy.

Tourette syndrome (TS) is characterized by tics, sensorimotor gating deficiencies, and abnormalities of cortico-basal ganglia circuits. A mutation in histidine decarboxylase (Hdc), the key enzyme for the biosynthesis of histamine (HA), has been implicated as a rare genetic cause. Hdc knockout mice exhibited potentiated tic-like stereotypies, recapitulating core phenomenology of TS; these were mitigated by the dopamine (DA) D2 antagonist haloperidol, a proven pharmacotherapy, and by HA infusion into the brain. Prepulse inhibition was impaired in both mice and humans carrying Hdc mutations. HA infusion reduced striatal DA levels; in Hdc knockout mice, striatal DA was increased and the DA-regulated immediate early gene Fos was upregulated. DA D2/D3 receptor binding was altered both in mice and in humans carrying the Hdc mutation. These data confirm histidine decarboxylase deficiency as a rare cause of TS and identify HA-DA interactions in the basal ganglia as an important locus of pathology.

Neurocognitive aging studies have focused on age-related changes in neural activity or neural structure but few studies have focused on relationships between the two. The present study quantitatively reviewed 24 studies of age-related changes in fMRI activation across a broad spectrum of executive function tasks using activation likelihood estimation (ALE) and 22 separate studies of age-related changes in gray matter using voxel-based morphometry (VBM). Conjunction analyses between functional and structural alteration maps were constructed. Overlaps were only observed in the conjunction of dorsolateral prefrontal cortex (DLPFC) gray matter reduction and functional hyperactivation but not hypoactivation. It was not evident that the conjunctions between gray matter and activation were related to task performance. Theoretical implications of these results are discussed.

We propose a penalized spline approach to performing large numbers of parallel non-parametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naively performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70000 brain locations. Supplementary materials, including an appendix and an R package, are available online.

OBJECTIVE: The purpose of this study was to examine racial-ethnic differences in use of mental health treatment for a comprehensive range of specific disorders over time. METHODS: Data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey were used to examine adult outpatient mental health visits to U.S. physicians from 1993 to 2008 (N=754,497). Annual visit prevalence for three racial-ethnic groups was estimated as the number of visits divided by the group's U.S. population size. Visit prevalence ratios (VPRs) were calculated as the minority group's prevalence divided by the non-Hispanic white prevalence. Analyses were stratified by diagnosis, physician type, patient characteristics, and year. RESULTS: VPRs for any disorder were .60 (95% confidence interval [CI]=.52-.68) for non-Hispanic blacks and .58 (CI=.50-.67) for Hispanics. Non-Hispanic blacks were treated markedly less frequently than whites for obsessive-compulsive, generalized anxiety, attention-deficit hyperactivity, personality, panic, and nicotine use disorders but more frequently for psychotic disorders. Hispanics were treated far less frequently than whites for bipolar I, impulse control, autism spectrum, personality, obsessive-compulsive, and nicotine use disorders but more frequently for drug use disorders. Racial-ethnic differences in visits to psychiatrists were generally greater than for visits to nonpsychiatrists. Differences declined with increasing patient age and appear to have widened over time. CONCLUSIONS: Racial-ethnic differences in receipt of outpatient mental health treatment from U.S. physicians varied substantially by disorder, provider type, and patient age. Most differences were large and did not show improvement over time.

Early detection and treatment of psychiatric disorders on children has shown significant impact in their subsequent development and quality of life. The assessment of psychopathology in childhood is commonly carried out by performing long comprehensive interviews such as the widely used Preschool Age Psychiatric Assessment (PAPA). Unfortunately, the time required to complete a full interview is too long to apply it at the scale of the actual population at risk, and most of the population goes undiagnosed or is diagnosed significantly later than desired. In this work, we aim to learn from unique and very rich previously collected PAPA examples the inter-correlations between different questions in order to provide a reliable risk analysis in the form of a much shorter interview. This helps to put such important risk analysis at the hands of regular practitioners, including teachers and family doctors. We use for this purpose the alternating decision trees algorithm, which combines decision trees with boosting to produce small and interpretable decision rules. Rather than a binary prediction, the algorithm provides a measure of confidence in the classification outcome. This is highly desirable from a clinical perspective, where it is preferable to abstain a decision on the low-confidence cases and recommend further screening. In order to prevent over-fitting, we propose to use network inference analysis to predefine a set of candidate question with consistent high correlation with the diagnosis. We report encouraging results with high levels of prediction using two independently collected datasets. The length and accuracy of the developed method suggests that it could be a valuable tool for preliminary evaluation in everyday care.

While it is common to think that neuroscientists are proponents of basic emotions theory, this is not necessarily the case. My ideas, for example are more aligned with cognitive than basic emotions theories.