Faculty Bibliography

BACKGROUND:Alcohol use disorder (AUD) is known to have adverse effects on brain structure and function. Multimodal assessments investigating volumetric, diffusion, and cognitive characteristics may facilitate understanding of the consequences of long-term alcohol use on brain circuitry, their structural impairment patterns, and their impact on cognitive function in AUD. METHODS:Voxel- and surface-based volumetric estimations, diffusion tensor imaging (DTI), and neuropsychological tests were performed on 60 individuals: 30 abstinent individuals with AUD (DSM-IV) and 30 healthy controls. Group differences in the volumes of cortical and subcortical regions, fractional anisotropy (FA), axial and radial diffusivities (AD and RD, respectively), and performance on neuropsychological tests were analyzed, and the relationship among significantly different measures was assessed using canonical correlation. RESULTS:AUD participants had significantly smaller volumes in left pars orbitalis, right medial orbitofrontal, right caudal middle frontal, and bilateral hippocampal regions, lower FA in 9 white matter (WM) regions, and higher FA in left thalamus, compared to controls. In AUD, lower FA in 6 of 9 WM regions was due to higher RD and due to lower AD in the left external capsule. AUD participants scored lower on problem-solving ability, visuospatial memory span, and working memory. Positive correlations of prefrontal cortical, left hippocampal volumes, and FA in 4 WM regions with visuospatial memory performance and negative correlation with lower problem-solving ability were observed. Significant positive correlation between age and FA was observed in bilateral putamen. CONCLUSIONS:Findings showed specific structural brain abnormalities to be associated with visuospatial memory and problem-solving ability-related impairments observed in AUD. Higher RD in 6 WM regions suggests demyelination, and lower AD in left external capsule suggests axonal loss in AUD. The positive correlation between FA and age in bilateral putamen may reflect accumulation of iron depositions with increasing age.

Importance/UNASSIGNED:Duration of untreated psychosis (DUP) has been associated with poor outcomes in schizophrenia, but the mechanism responsible for this association is not known. Objectives/UNASSIGNED:To determine whether hippocampal volume loss occurs during the initial 8 weeks of antipsychotic treatment and whether it is associated with DUP, and to examine molecular biomarkers in association with hippocampal volume loss and DUP. Design, Setting, and Participants/UNASSIGNED:A naturalistic longitudinal study with matched healthy controls was conducted at Shanghai Mental Health Center. Between March 5, 2013, and October 8, 2014, 71 medication-naive individuals with nonaffective first-episode psychosis (FEP) and 73 age- and sex-matched healthy controls were recruited. After approximately 8 weeks, 31 participants with FEP and 32 controls were reassessed. Exposures/UNASSIGNED:The participants with FEP were treated according to standard clinical practice with second-generation antipsychotics. Main Outcomes and Measures/UNASSIGNED:Hippocampal volumetric integrity (HVI) (an automated estimate of the parenchymal fraction in a standardized hippocampal volume of interest), DUP, 13 peripheral molecular biomarkers, and 14 single-nucleotide polymorphisms from 12 candidate genes were determined. Results/UNASSIGNED:The full sample consisted of 71 individuals with FEP (39 women and 32 men; mean [SD] age, 25.2 [7.7] years) and 73 healthy controls (40 women and 33 men; mean [SD] age, 23.9 [6.4] years). Baseline median left HVI was lower in the FEP group (n = 57) compared with the controls (n = 54) (0.9275 vs 0.9512; difference in point estimate, -0.020 [95% CI, -0.029 to -0.010]; P = .001). During approximately 8 weeks of antipsychotic treatment, left HVI decreased in 24 participants with FEP at a median annualized rate of -.03791 (-4.1% annualized change from baseline) compared with an increase of 0.00115 (0.13% annualized change from baseline) in 31 controls (difference in point estimate, -0.0424 [95% CI, -0.0707 to -0.0164]; P = .001). The change in left HVI was inversely associated with DUP (r = -0.61; P = .002). Similar results were found for right HVI, although the association between change in right HVI and DUP did not achieve statistical significance (r = -0.35; P = .10). Exploratory analyses restricted to the left HVI revealed an association between left HVI and markers of inflammation, oxidative stress, brain-derived neurotrophic factor, glial injury, and markers reflecting dopaminergic and glutamatergic transmission. Conclusions and Relevance/UNASSIGNED:An association between longer DUP and accelerated hippocampal atrophy during initial treatment suggests that psychosis may have persistent, possibly deleterious, effects on brain structure. Additional studies are needed to replicate these exploratory findings of molecular mechanisms by which untreated psychosis may affect hippocampal volume and to determine whether these effects account for the known association between longer DUP and poor outcome.

Electrical stimulation of subthalamic nuclei (STN) is a widely used therapy in Parkinson's disease (PD). While deep brain stimulation (DBS) of the STN alters the neurophysiological activity in basal ganglia, the therapeutic mechanism has not been established. A positron emission tomography (PET) study of cerebral blood flow (CBF) during speech production in PD subjects treated with STN-DBS found significant increases in global (whole-brain) CBF.¹ That study utilized a series of whole-slice regions of interest to obtain global CBF values. The present study examined this effect using a voxel-based principal component analysis (PCA) combined with Fisher's linear discriminant analysis (FLDA) to classify STN-DBS on versus STN-DBS off whole-brain images. The approach yielded wide-spread CBF changes that classified STN-DBS status with accuracy, sensitivity, and specificity approaching 90%. The PCA component of the analysis supported the observation of a global CBF change during STN-DBS. The FLDA component demonstrated wide-spread multi-focal CBF changes. Further, CBF measurements related to a number of subject characteristics when STN-DBS was off, but not when it was on, suggesting that the normal relationship between CBF and behavior may be disrupted by this form of neuromodulation.

RATIONALE AND OBJECTIVES: Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. MATERIALS AND METHODS: Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change. RESULTS: The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%). CONCLUSION: Short-term longitudinal assessments significantly enhance the performance of AD prediction models.

BACKGROUND: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. OBJECTIVE: To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. METHODS: We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. RESULTS: The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. CONCLUSION: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

The risk of Alzheimer's disease can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus with a novel measure of hippocampal integrity (HI) derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 years or older at baseline, and were tested twice, roughly 3 years apart. Participants had been recruited for a study on late-life major depression (LLMD) and were evenly split between depressed patients and controls. Linear regression models were applied to the data with a cognitive composite score as the outcome, and HI and volume, together or separately, as predictors. Subsequent cognitive performance was predicted well by models that included an interaction between HI and LLMD status, such that lower HI scores predicted more cognitive decline in depressed patients. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.

BACKGROUND: Abnormal white matter integrity has been reported among first episode schizophrenia patients. However, findings on whether it can be reversed by short-term antipsychotic medications are inconsistent. METHOD: Diffusion tensor imaging (DTI) was obtained from 55 drug-naive first episode schizophrenia patients and 61 healthy controls, and was repeated among 25 patients and 31 controls after 8weeks during which patients were medicated with antipsychotics. White matter integrity is measured using fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). These measures showing a group difference by Tract-based spatial statistics (TBSS) at baseline were extracted for longitudinal comparisons. RESULTS: At baseline, patients exhibited lower FA, higher MD and higher RD versus controls in forceps, left superior longitudinal fasciculus, inferior fronto-occipital fasciculus, left corticospinal tract, left uncinate fasciculus, left anterior thalamic radiation, and bilateral inferior longitudinal fasciculi. FA values of schizophrenia patients correlated with their negative symptoms (r=-0.412, P=0.002), working memory (r=0.377, P=0.005) and visual learning (r=0.281, P=0.038). The longitudinal changes in DTI indices in these tracts did not differ between patients and controls. However, among the patients the longitudinal changes in FA values in left superior longitudinal fasciculus correlated with the change of positive symptoms (r=-0.560, p=0.004), and the change of processing speed (r=0.469, p=0.018). CONCLUSIONS: White matter deficits were validated in the present study by a relatively large sample of medication naive and first episode schizophrenia patients. They could be associated with negative symptoms and cognitive impairment, whereas improvement in white matter integrity of left superior longitudinal fasciculus correlated with improvement in psychosis and processing speed. Further examination of treatment-related changes in white matter integrity may provide clues to the mechanism of antipsychotic response and provide a biomarker for clinical studies.

BACKGROUND: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. OBJECTIVE: To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. METHODS: We used a novel algorithm to compute an index of hippocampal (structural) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. RESULTS: The HI was significantly reduced in the AD group (p < 10-20). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p < 10-13). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. CONCLUSION: In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.

INTRODUCTION: We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (+/-SD) follow-up of 5.46 (+/-1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (+/-1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes. RESULTS: We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males. DISCUSSION: Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.