Faculty Bibliography

OBJECTIVES/OBJECTIVE:Disengagement from treatment is common in first episode schizophrenia (FES) and is associated with poor outcomes. Our aim was to determine whether hippocampal subfield volumes predict disengagement during maintenance treatment of FES. METHODS:FES patients were recruited from sites in Boston, New York, Shanghai, and Changsha. After stabilization on antipsychotic medication, participants were randomized to add-on citalopram or placebo and followed for 12 months. Demographic, clinical and cognitive factors at baseline were compared between completers and disengagers in addition to volumes of hippocampal subfields. RESULTS:Baseline data were available for 95 randomized participants. Disengagers (n = 38, 40%) differed from completers (n = 57, 60%) by race (more likely Black; less likely Asian) and in more alcohol use, parkinsonism, negative symptoms and more impairment in visual learning and working memory. Bilateral dentate gyrus (DG), CA1, CA2/3 and whole hippocampal volumes were significantly smaller in disengagers compared to completers. When all the eight volumes were entered into the model simultaneously, only left DG volume significantly predicted disengagement status and remained significant after adjusting for age, sex, race, intracranial volume, antipsychotic dose, duration of untreated psychosis, citalopram status, alcohol status, and smoking status (P < .01). Left DG volume predicted disengagement with 57% sensitivity and 83% specificity. CONCLUSIONS:Smaller left DG was significantly associated with disengagement status over 12 months of maintenance treatment in patients with FES participating in a randomized clinical trial. If replicated, these findings may provide a biomarker to identify patients at risk for disengagement and a potential target for interventions.

Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.

BACKGROUND:-weighted three-dimensional MRI scans of the human brain. This is a fundamental processing step in computational neuroimaging. NEW METHOD/METHODS:The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark detection algorithm is introduced. An automatic algorithm is presented for identification of LOOC landmarks on MRI scans. Multiple sets of LOOC landmarks are identified on each volume and a Generalized Orthogonal Procrustes Analysis of the landmarks is used to find a rigid-body transformation of each volume into a common space where the volumes are aligned precisely. RESULTS:rotation. COMPARISON WITH EXISTING METHOD(S)/CONCLUSIONS:In comparison with existing methods, ATRA does not require any image preprocessing (e.g., skull-stripping or intensity normalization) and can handle conditions where rigid-body motion assumptions are not true (e.g., movement in eyes, jaw, neck) and brain tissue loss over time in neurodegenerative diseases. In a systematic comparison with the FSL FLIRT algorithm, ATRA provided faster and more accurate registrations. CONCLUSIONS:The algorithm is symmetric, in the sense that any permutation of the input volumes does not change the transformation matrices, and unbiased, in that all volumes undergo exactly one interpolation operation, which precisely aligns them in a common space. There is no interpolation bias and no reference volume. All volumes are treated exactly the same. The algorithm is fast and highly accurate.

BACKGROUND:Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE:The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS:We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS:In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION/CONCLUSIONS:Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

Reports an error in "Association of aripiprazole with reduced hippocampal atrophy during maintenance treatment of first-episode schizophrenia" by John Wang, Kamber L. Hart, Wei Qi, Babak A. Ardekani, Chenxiang Li, Julia Marx, Oliver Freudenreich, Corinne Cather, Daphne Holt, Iruma Bello, Erica D. Diminich, Yingying Tang, Michelle Worthington, Botao Zeng, Renrong Wu, Xiaoduo Fan, Jingping Zhao, Jijun Wang and Donald C. Goff (Journal of Clinical Psychopharmacology, 2021[May-Jun], Vol 41[3], 244-249). In the originally published article, there were errors in the affiliations for two authors. The correct affiliations are provided in the erratum and appear in this record. In addition, in the footnotes to Table 3, the words "Bonferroni correction" should have been deleted after the P value of 0.05. (The following abstract of the original article appeared in record 2021-44706-004). Purpose/Background: Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation. Methods/Procedures: Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2). Findings/Results: Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES. Implications/Conclusions: These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

PURPOSE/BACKGROUND/OBJECTIVE:Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation. METHODS/PROCEDURES/UNASSIGNED:Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2). FINDINGS/RESULTS/UNASSIGNED:Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES. IMPLICATIONS/CONCLUSIONS/UNASSIGNED:These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established.

Hippocampal volume loss is prominent in first episode schizophrenia (FES) and has been associated with poor clinical outcomes and with BDNF genotype; antidepressants are believed to reverse hippocampal volume loss via release of BDNF. In a 12-month, placebo-controlled add-on trial of the antidepressant, citalopram, during the maintenance phase of FES, negative symptoms were improved with citalopram. We now report results of structural brain imaging at baseline and 6 months in 63 FES patients (34 in citalopram group) from the trial to assess whether protection against hippocampal volume loss contributed to improved negative symptoms with citalopram. Hippocampal volumetric integrity (HVI) did not change significantly in the citalopram or placebo group and did not differ between treatment groups, whereas citalopram was associated with greater volume loss of the right CA1 subfield. Change in cortical thickness was associated with SANS change in 4 regions (left rostral anterior cingulate, right frontal pole, right cuneus, and right transverse temporal) but none differed between treatment groups. Our findings suggest that minimal hippocampal volume loss occurs after stabilization on antipsychotic treatment and that citalopram's potential benefit for negative symptoms is unlikely to result from protection against hippocampal volume loss or cortical thinning.

Early detection of Alzheimer's disease (AD) is important for timely interventions and developing new treatments. Hippocampus atrophy is an early biomarker of AD. The hippocampal parenchymal fraction (HPF) is a promising measure of hippocampal structural integrity computed from structural MRI. It is important to characterize the dependence of HPF on covariates such as age and sex in the normal population to enhance its utility as a disease biomarker. We measured the HPF in 4239 structural MRI scans from 340 cognitively normal (CN) subjects aged 59-89 years from the AD Neuroimaging Initiative database, and studied its dependence on age, sex, apolipoprotein E (APOE) genotype, brain hemisphere, intracranial volume (ICV), and education using a linear mixed-effects model. In this CN cohort, HPF was inversely associated with ICV; was greater on the right hemisphere compared to left in both sexes with the degree of right > left asymmetry being slightly more pronounced in men; declined quadratically with age and faster in APOE ϵ4 carriers compared to non-carriers; and was significantly associated with cognitive ability. Consideration of HPF as an AD biomarker should be in conjunction with other subject attributes that are shown in this research to influence HPF levels in CN older individuals.