About
65
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Introduction
As a statistician, my research is motived by concrete applications such as algorithms for classification and diagnosis of Alzheimer’s disease, Schizophrenia, and IBS using fMRI scans. I create methods to improve statistical power and lower the necessary sample sizes within clinical trials, both by measuring the physiological footprint of the placebo-effect using brain imaging, and by creating new statistical projections of psychosis ratings in Schizophrenia. http://ariana82.bol.ucla.edu
Additional affiliations
March 2009 - present
Publications
Publications (65)
Background
Mitigating rating inconsistency can improve measurement fidelity and detection of treatment response.
Methods
The International Society for CNS Clinical Trials and Methodology convened an expert Working Group that developed consistency checks for ratings of the Hamilton Anxiety Rating Scale (HAM-A) and Clinical Global Impression of Seve...
Independently, EEG and fMRI offer either particularly rich temporal (EEG) or spatial (fMRI) information related to the neural dynamics in the brain. In theory, the two modalities could be analyzed collectively to harness their complementary strengths. However, principled data fusion attempts have been relatively sparse, primarily because it is uncl...
Brain mechanisms underlying creativity are largely unknown and few studies have involved exceptionally creative individuals. We examined functional MRI (fMRI) connectivity in a “smart comparison group” (SCG; n = 24), and in exceptionally creative (“Big C”) visual artists (VIS; n = 21) and scientists (SCI; n = 21). Groups were matched on age, sex, a...
Background
Symptom manifestations in mood disorders can be subtle. Cumulatively, small imprecisions in measurement can limit our ability to measure treatment response accurately. Logical and statistical consistency checks between item responses (i.e., cross-sectionally) and across administrations (i.e., longitudinally) can contribute to improving m...
The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response...
International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled consistency/inconsistency flags for the Personal and Social Performance Scale (PSP). One hundred and forty seven flags were identified, 16 flag errors in deriving the PSP decile (i.e., total) score from the four individual domain scores, 74...
Dementia is characterized by a significant decline in one of several cognitive domains such as memory, language and executive function, affecting independence and representing a significant deterioration from a previous level of functioning (1). Alzheimer’s Disease (AD) represents the most common form of dementia and contributes up to 70% of the al...
Changes in neurovascular coupling are associated with both Alzheimer’s disease and vascular dementia in later life, but this may be confounded by cerebrovascular risk. We hypothesized that hemodynamic latency would be associated with reduced cognitive functioning across the lifespan, holding constant demographic and cerebrovascular risk. In 387 adu...
Background: Agonism of protease-activated receptor 1 (PAR1) potently protects neurons and vasculature in the central nervous system during stroke. We evaluated the effects of 3K3A-APC, a recombinant variant of activated protein C active at PAR1, in acute ischemic stroke patients during conventional recanalization with thrombolysis or thrombectomy o...
Background and Purpose—
The National Institutes of Health Stroke Scale, designed and validated for use in clinical stroke trials, is now required for all patients with stroke at hospital admission. Recertification is required annually but no data support this frequency; the effect of mandatory training before recertification is unknown.
Methods—
T...
Attention-deficit/ hyperactivity disorder (ADHD) is the most common neurodevelopment disorder in children, and many genetic markers have been linked to the behavioral phenotypes of this highly heritable disease. The neuroimaging correlates are similarly complex, with multiple combinations of structural and functional alterations associated with the...
After initial enthusiasm for mild therapeutic hypothermia (TH) treatment after brain injuries, including global cerebral ischemia after cardiac arrest, subsequent trials suggested similar benefit using only targeted temperature management (TTM), with fewer side effects. Globally, effective treatment of brain ischemia with TH has declined. Recent da...
To characterize acoustic features of an infant’s cry and use machine learning to provide an objective measurement of behavioral state. To apply the algorithm to colic hypothesizing that these cries sound painful.
Assessment of 1000 cries in a mobile app (ChatterBabyTM). Training an algorithm by evaluating >6000 acoustic features to predict whether...
Decoding and encoding models are popular multivariate approaches used to study representations in functional neuroimaging data. Encoding approaches seek to predict brain activation patterns using aspects of the stimuli as features. Decoding models, in contrast, utilize measured brain responses as features to make predictions about experimental mani...
Background
22q11.2 copy number variants (CNVs) are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear.
Methods
Using a functional genomics approach, we integr...
Background
22q11.2 copy number variants (CNVs) are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear.
Methods
Using a functional genomics approach, we integr...
The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a...
This study used social network analysis to evaluate whether sex heterophily, the degree to which peers are different in sex, between 126 children with autism (ages 5–12 years) and their peers affected social network connectivity. Results indicate that: (1) the quantity and sex of friends were more important in predicting social network connectivity...
Objective:
HIV-associated neurocognitive disorder (HAND) occurs in a significant percentage of HIV-infected (HIV+) adults. Increased intraindividual variability (IIV) in cognitive function may be an early marker of emerging neurocognitive disorder, which suggests that IIV may be a sensitive measure of neurologic compromise in HIV. In the current s...
Ideally, decoding models could be used for the dual purpose of prediction and neuroscientific knowledge gain. However, interpreting even linear decoding models beyond classification accuracies is difficult. Haufe and colleagues suggested projecting feature weights onto activation maps may enhance interpretability. Here we show that redundancy and n...
Objective:
Common genetic variation spans schizophrenia, schizoaffective and bipolar disorders, but historically, these syndromes have been distinguished categorically. A symptom dimension shared across these syndromes, if such a general factor exists, might provide a clearer target for understanding and treating mental illnesses that share core b...
Introduction
Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly w...
Objective: Total scale scores derived by summing ratings from the 30-item PANSS are commonly used in clinical trial research to measure overall symptom severity, and percentage reductions in the total scores are sometimes used to document the efficacy of treatment. Acknowledging that some patients may have substantial changes in PANSS total scores...
Objective: The Theta-Alpha ratio (TAR) is known to differ based upon age and cognitive ability, with pathological electroencephalography (EEG) patterns routinely found within neurodegenerative disorders of older adults. We hypothesized that cognitive ability would predict EEG metrics differently within healthy young and old adults, and that healthy...
Although the Positive and Negative Syndrome Scale (PANSS) was developed for use in schizophrenia (SZ), antipsychotic drug trials use the PANSS to measure symptom change also for bipolar (BP) and schizoaffective (SA) disorder, extending beyond its original indications. If the dimensions measured by the PANSS are different across diagnoses, then the...
22q11.2 Deletion syndrome (22q11DS) is a genetic disorder associated with numerous phenotypic consequences and is one of the greatest known risk factors for psychosis. We investigated intrinsic-connectivity-networks (ICNs) as potential biomarkers for patient and psychosis-risk status in 2 independent cohorts, UCLA (33 22q11DS-participants, 33 demog...
International Society for CNS Clinical Trials and Methodology convened an expert working-group that assembled consistency/inconsistency flags for the Positive and Negative Syndrome Scale (PANSS). Twenty-four flags were identified and divided based on extent to which they represent error (Possibly, Probably, Very probably or definitely). The flags w...
Background:
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse cod...
Although children with autism spectrum disorder are frequently included in mainstream classrooms, it is not known how their social networks change compared to typically developing children and whether the factors predictive of this change may be unique. This study identified and compared predictors of social connectivity of children with and withou...
Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain's neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion te...
Objectives:
An estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records...
The Positive and Negative Syndrome Scale (PANSS) is frequently described with five latent factors, yet published factor models consistently fail to replicate across samples and related disorders. We hypothesize that (1) a subset of the PANSS, instead of the entire PANSS scale, would produce the most replicable five-factor models across samples, and...
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience....
The current study explores relationships between mindfulness, emotional regulation, impulsivity, and stress proneness in a sample of participants recruited in a Diagnostic and Statistical Manual of Mental Disorder Fifth Edition Field Trial for Hypersexual Disorder and healthy controls to assess whether mindfulness attenuates symptoms of hypersexual...
In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact ch...
Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connec...
The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independe...
The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice...
Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided dia...
Although the placebo effect is known to have a strong impact on the outcomes of clinical trials, methods for measuring it are limited to physiological observations. We propose a method of localizing, identifying and measuring placebo and treatment-induced networks in the brain using functional neuroimaging. Measuring the relative activation of thes...
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both s...
Developing EEG-based computer aided diagnostic (CAD) tools would allow identification of epilepsy in individuals who have experienced possible seizures, yet such an algorithm requires efficient identification of meaningful features out of potentially more than 35,000 features of EEG activity. Mutual information can be used to identify a subset of m...
Machine Learning (ML) methods applied to real-time functional MRI (rt-fMRI) data provide the ability to predict and detect online any changes in cognitive states. Applications based on rt-fMRI require appropriate selection of features, preprocessing routines, and models in order to both be practical to implement and deliver interpretable results. I...
Independent component analysis (ICA) is a popular method for the analysis of functional magnetic resonance imaging (fMRI) signals that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whe...
Machine learning methods have been applied to classifying fMRI scans by studying locations in the brain that exhibit temporal intensity variation between groups, frequently reporting classification accuracy of 90% or better. Although empirical results are quite favorable, one might doubt the ability of classification methods to withstand changes in...
The analysis of fMRI data is challenging because they consist generally of a relatively modest signal contained in a high-dimensional space: a single scan can contain millions of voxel recordings over space and time. We present a method for classification and discrimination among fMRI that is based on modeling the scans as distance matrices, where...
Introduction: Machine learning classification methods have been successfully applied to fMRI by identifying localized regions of interest (ROI) that exhibit temporal intensity variation between groups [Fu2008; Davatzikos2005; Norman2006]. We present a machine-learning classifier which identifies population-wide whole-brain spatial activity maps tha...
Nocturnal reflux is a largely undiagnosed and unmanaged condition predisposing to multiple esophageal complications. We evaluated the effects of rabeprazole and pantoprazole on nocturnal intragastric pH and gastric acid output during Day 1 of therapy following the consumption of standard meals.
The study had a double-blinded, randomized, two-way cr...
Independent components analysis (ICA) is a popular method for the analysis of functional MRI (fMRI) signals, that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms...