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Howard J. Aizenstein

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14 papers

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YNIMG Journal 2021 Journal Article

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

Mahbaneh Eshaghzadeh Torbati
Davneet S. Minhas
Ghasan Ahmad
Erin E. O’Connor
John Muschelli
Charles M. Laymon
Zixi Yang
Ann D. Cohen

Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.

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YNICL Journal 2021 Journal Article

Analysis of hippocampal subfields in sickle cell disease using ultrahigh field MRI

Tales Santini
Minseok Koo
Nadim Farhat
Vinicius P. Campos
Salem Alkhateeb
Marcelo A.C. Vieira
Meryl A. Butters
Caterina Rosano

Sickle cell disease (SCD) is an inherited hemoglobinopathy that causes organ dysfunction, including cerebral vasculopathy and neurological complications. Hippocampal segmentation with newer and advanced 7 Tesla (7T) MRI protocols has revealed atrophy in specific subregions in other neurodegenerative and neuroinflammatory diseases, however, there is limited evidence of hippocampal involvement in SCD. Thus, we explored whether SCD may be also associated with abnormalities in hippocampal subregions. We conducted 7T MRI imaging in individuals with SCD, including the HbSS, HbSC and HbS/beta thalassemia genotypes (n = 53), and healthy race and age-matched controls (n = 47), using a customized head coil. Both T1- and T2-weighted images were used for automatic segmentation of the hippocampal subfields. Individuals with SCD had, on average, significantly smaller volume of the region including the Dentate Gyrus and Cornu Ammonis (CA) 2 and 3 as compared to the control group. Other hippocampal subregions also showed a trend towards smaller volumes in the SCD group. These findings support and extend previous reports of reduced volume in the temporal lobe in SCD patients. Further studies are necessary to investigate the mechanisms that lead to structural changes in the hippocampus subfields and their relationship with cognitive performance in SCD patients.

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YNICL Journal 2019 Journal Article

Low-dose augmentation with buprenorphine increases emotional reactivity but not reward activity in treatment resistant mid- and late-life depression

Chemin Lin
Helmet T. Karim
Marta Pecina
Howard J. Aizenstein
Eric J. Lenze
Daniel M. Blumberger
Benoit H. Mulsant
Evan D. Kharasch

Buprenorphine is currently being studied for treatment-resistant depression because of its rapid effect, relative safety, and unique pharmacodynamics. To understand the neural impact of buprenorphine in depression, we examined acute limbic and reward circuit changes during an intervention with low-dose buprenorphine augmentation pharmacotherapy. Mid and late-life adults with major depression (N = 31) who did not completely respond to an adequate trial of venlafaxine were randomized to augmentation with low-dose buprenorphine or matching placebo. We investigated early neural changes using functional magnetic resonance imaging (fMRI) from pre-randomization to 3 weeks using both an emotional reactivity task and a gambling task. We tested if: 1) there were significant neural changes acutely per intervention group, and 2) if acute neural changes were associated with depressive symptom change over 8 weeks using both the total score and the dysphoria subscale of the Montgomery Asberg Depression Rating Scale. Participants in both the buprenorphine and placebo groups showed similar changes in depressive symptoms. Neither the emotional reactivity nor gambling task resulted in significant neural activation changes from pre-randomization to 3-weeks. In both groups, increases in rostral anterior cingulate (rACC) and ventromedial prefrontal cortex (vmPFC) activation during the emotional reactivity task were associated with overall symptom improvement. In the buprenorphine but not the placebo group, increased activation in left anterior insula (aINS) and bilateral middle frontal gyrus (MFG) was associated with improvement on the dysphoria subscale. Activation changes in the reward task were not associated with buprenorphine. This is the first study to show an association between acute neural changes during emotion reactivity and changes in depression severity with buprenorphine treatment.

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YNICL Journal 2018 Journal Article

Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression

Helmet T. Karim
Maxwell Wang
Carmen Andreescu
Dana Tudorascu
Meryl A. Butters
Jordan F. Karp
Charles F. Reynolds
Howard J. Aizenstein

Pharmacological treatment of major depressive disorder (MDD) typically involves a lengthy trial and error process to identify an effective intervention. This lengthy period prolongs suffering and worsens all-cause mortality, including from suicide, and is typically longer in late-life depression (LLD). Our group has recently demonstrated that during an open-label venlafaxine (serotonin-norepinephrine reuptake inhibitor) trial, significant changes in functional resting state connectivity occurred following a single dose of treatment, which persisted until the end of the trial. In this work, we propose an analysis framework to translate these perturbations in functional networks into predictors of clinical remission. Participants with LLD (N = 49) completed 12-weeks of treatment with venlafaxine and underwent functional magnetic resonance imaging (fMRI) at baseline and a day following a single dose of venlafaxine. Data was collected at rest as well as during an emotion reactivity task and an emotion regulation task. Remission was defined as a Montgomery-Asberg Depression Rating Scale (MADRS) ≤10 for two weeks. We computed eigenvector centrality (whole brain connectivity) and activation during the emotion regulation and emotion reactivity tasks. We employed principal components analysis, Tikhonov-regularized logistic classification, and least angle regression feature selection to predict remission by the end of the 12-week trial. We utilized ten-fold cross-validation and Receiver Operator Curves (ROC) curve analysis. To determine task-region pairs that significantly contributed to the algorithm's ability to predict remission, we used permutation testing. Using the fMRI data at both baseline and after the first dose of treatment yielded a sensitivity of 72% and a specificity of 68% (AUC = 0.77), a 15% increase in accuracy over baseline MADRS. In general, the accuracy at baseline was further improved by using the change in activation following a single dose. Activation of the frontal cortex, hippocampus, parahippocampus, caudate, thalamus, medial temporal cortex, middle cingulate, and visual cortex predicted treatment remission. Acute, dynamic trajectories of functional imaging metrics in response to a pharmacological intervention are a valuable tool for predicting treatment response in late-life depression and elucidating the mechanism of pharmacological therapies in the context of the brain's functional architecture.

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YNIMG Journal 2018 Journal Article

Insulin sensitivity predicts brain network connectivity following a meal

John P. Ryan
Helmet T. Karim
Howard J. Aizenstein
Nicole L. Helbling
Frederico G.S. Toledo

There is converging evidence that insulin plays a role in food-reward signaling in the brain and has effects on enhancing cognition. Little is known about how these effects are altered in individuals with insulin resistance. The present study was designed to identify the relationships between insulin resistance and functional brain connectivity following a meal. Eighteen healthy adults (7 male, 11 female, age: 41-57 years-old) completed a frequently-sampled intravenous glucose tolerance test to quantify insulin resistance. On separate days at least one week apart, a resting state functional magnetic resonance imaging scan was performed: once after a mixed-meal and once after a 12-h fast. Seed-based resting state connectivity of the caudate nucleus and eigenvector centrality were used to identify relationships between insulin resistance and functional brain connectivity. Individuals with greater insulin resistance displayed stronger connectivity within reward networks following a meal suggesting insulin was less able to suppress reward. Insulin resistance was negatively associated with eigenvector centrality in the dorsal anterior cingulate cortex following a meal. These data suggest that individuals with less sensitivity to insulin may fail to shift brain networks away from reward and toward cognitive control following a meal. This altered feedback loop could promote overeating and obesity.

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YNICL Journal 2016 Journal Article

Studying depression using imaging and machine learning methods

Meenal J. Patel
Alexander Khalaf
Howard J. Aizenstein

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

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YNICL Journal 2015 Journal Article

Comparison of qualitative and quantitative imaging characteristics of [11C]PiB and [18F]flutemetamol in normal control and Alzheimer's subjects

James M. Mountz
Charles M. Laymon
Ann D. Cohen
Zheng Zhang
Julie C. Price
Sanaa Boudhar
Eric McDade
Howard J. Aizenstein

INTRODUCTION: Neuritic amyloid plaques and neurofibrillary tangles, the hallmark pathologic lesions of Alzheimer's disease, are thought to develop before the symptoms of brain failure are clinically detectable. Imaging methods capable of detecting the presence of neuritic amyloid plaques should improve a clinician's ability to identify Alzheimer's disease during the earliest symptomatic phase and to identify at-risk individuals presymptomatically. Currently the best studied amyloid imaging ligand is [(11)C]Pittsburgh Compound B ([(11)C]PiB). However, the 20-minute half-life of this radiotracer limits its use. This study is designed to evaluate the performance characteristics of [(18)F]flutemetamol and to independently compare results to [(11)C]PiB in the same subjects. METHODS: Twenty-three subjects, 15 cognitively normal (NL) and 8 with a clinical diagnosis of Alzheimer's Dementia (AD), underwent [(11)C]PiB and [(18)F]flutemetamol PET scans within 28 days of study enrollment. We studied both normal and AD subjects to assess the uptake characteristics across a range of amyloid positivity. Blinded visual reads were conducted by five raters. Correlation analyses were performed between cortical SUVR for the two tracers and also between rater scores and SUVR for each tracer. Overall reader accuracy for classifying scans as amyloid positive or negative was determined for each tracer using SUVR classification as the standard. RESULTS: The linear correlation coefficient between global cortical SUVR for the two tracers was R(2) = 0.85, indicating that both tracers have similar retention characteristics. The two tracers were well correlated for rater-determined AD-like positivity (Cohen κ = 0.82). Averaged visual ratings and global cortical SUVR disagreed on their classification in 2/23 [(11)C]PiB scans and 4/23 [(18)F]flutemetamol scans. CONCLUSIONS: [(11)C]PiB and [(18)F]flutemetamol have similar retention characteristics across a range of amyloid negative to positive subjects. Both tracers performed similarly when a standardized visual read technique was used to classify scans as amyloid-positive or amyloid-negative and correlated well with SUVR classifications. However, care in visual interpretation of amyloid positive versus amyloid negative regions should be taken, particularly in the case of [(18)F]flutemetamol when considering cortical vs. white-matter retention.

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YNIMG Journal 2013 Journal Article

Classification of amyloid-positivity in controls: Comparison of visual read and quantitative approaches

Ann D. Cohen
Wenzhu Mowrey
Lisa A. Weissfeld
Howard J. Aizenstein
Eric McDade
James M. Mountz
Robert D. Nebes
Judith A. Saxton

An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(−)] subjects. Methods We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(−) subjects was not so distinct. Results The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. Conclusion The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers.

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YNIMG Journal 2012 Journal Article

Neuroimaging differences between older adults with maintained versus declining cognition over a 10-year period

Caterina Rosano
Howard J. Aizenstein
Anne B. Newman
Vijay Venkatraman
Tamara Harris
Jingzhong Ding
Suzanne Satterfield
Kristine Yaffe

Background Maintaining cognitive function protects older adults from developing functional decline. This study aims to identify the neuroimaging correlates of maintenance of higher global cognition as measured by the Modified Mini Mental State Test (3MS) score. Methods Repeated 3MS measures from 1997–98 through 2006–07 and magnetic resonance imaging with diffusion tensor in 2006–07 were obtained in a biracial cohort of 258 adults free from dementia (mean age 82. 9years, 56% women, 42% blacks). Participants were classified as having shown either maintenance (3MS slope>0) or decline (3MS slope<1SD below the mean) of cognition using linear mixed models. Measures of interest were white matter hyperintensity volume (WMHv) from total brain, volume of the gray matter (GMv) and microstructure (mean diffusivity, MD) for total brain and for brain areas known to be related to memory and executive control function: medial temporal area (hippocampus, parahippocampus and entorhinal cortex), cingulate cortex, dorsolateral prefrontal and posterior parietal cortex. Results Differences between cognitive maintainers (n=153) and non-maintainers (n=107) were significant for GMv of the medial temporal area (35. 8%, p=0. 004) and lower MD of the cingulate cortex (37. 9%, p=0. 008), but not for other neuroimaging markers. In multivariable regression models adjusted for age, race, WMHv and GMV from the total brain and vascular conditions, each standard deviation of GMv of the medial temporal area and each standard deviation of MD of the cingulate cortex were associated with a nearly 4 times greater probability (odds ratio [standard deviation]: 3. 80 [1. 16, 12. 44]) and a 34% lower probability (0. 66, [0. 46, 0. 97]) of maintaining cognitive function, respectively. In these models neither WMHv nor GMv from total brain were significantly associated with probability of maintaining cognitive function. Conclusions Preserving the volume of the medial temporal area and the microstructure of the cingulate cortex may contribute to maintaining cognitive function late in life.

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YNIMG Journal 2010 Journal Article

Brain activity during bladder filling is related to white matter structural changes in older women with urinary incontinence

Stasa D. Tadic
Derek Griffiths
Andrew Murrin
Werner Schaefer
Howard J. Aizenstein
Neil M. Resnick

Evidence from longitudinal studies in community-dwelling elderly links complaints of urgency and urinary incontinence with structural white matter changes known as white matter hyperintensities (WMH). How WMH might lead to incontinence remains unknown, since information about how they relate to neural circuits involved in continence control is lacking. The aim of this study was to investigate the role of WMH in altered brain activity in older women with urgency incontinence. In a cross-sectional study, we measured WMH, globally and in specific white matter tracts, and correlated them with regional brain activity measured by fMRI (combined with simultaneous urodynamic monitoring) during bladder filling and reported ‘urgency’. We postulated that increase in global WMH burden would be associated with changes (either attenuation or reinforcement) in responses to bladder filling in brain regions involved in bladder control. Secondly, we proposed that such apparent effects of global WMH burden might be specifically related to the burden in a few critical white matter pathways. The results showed that regional activations (e. g. medial/superior frontal gyrus adjacent to dorsal ACG) and deactivations (e. g. perigenual ACG adjacent to ventromedial prefrontal cortex) became more prominent with increased global WMH burden, suggesting that activity aimed at suppressing urgency was augmented. Secondary analyses confirmed that the apparent effect of global WMH burden might reflect the presence of WMH in specific pathways (anterior thalamic radiation and superior longitudinal fasciculus), thus affecting connections between key regions and suggesting possible mechanisms involved in continence control.

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YNIMG Journal 2010 Journal Article

Multivariate tensor-based morphometry on surfaces: Application to mapping ventricular abnormalities in HIV/AIDS

Yalin Wang
Jie Zhang
Boris Gutman
Tony F. Chan
James T. Becker
Howard J. Aizenstein
Oscar L. Lopez
Robert J. Tamburo

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics—these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

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YNIMG Journal 2007 Journal Article

3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry

Ming-Chang Chiang
Rebecca A. Dutton
Kiralee M. Hayashi
Oscar L. Lopez
Howard J. Aizenstein
Arthur W. Toga
James T. Becker
Paul M. Thompson

35% of HIV-infected patients have cognitive impairment, but the profile of HIV-induced brain damage is still not well understood. Here we used tensor-based morphometry (TBM) to visualize brain deficits and clinical/anatomical correlations in HIV/AIDS. To perform TBM, we developed a new MRI-based analysis technique that uses fluid image warping, and a new α-entropy-based information-theoretic measure of image correspondence, called the Jensen–Rényi divergence (JRD). Methods 3D T1-weighted brain MRIs of 26 AIDS patients (CDC stage C and/or 3 without HIV-associated dementia; 47. 2±9. 8 years; 25M/1F; CD4+ T-cell count: 299. 5±175. 7/μl; log10 plasma viral load: 2. 57± 1. 28 RNA copies/ml) and 14 HIV-seronegative controls (37. 6±12. 2 years; 8M/6F) were fluidly registered by applying forces throughout each deforming image to maximize the JRD between it and a target image (from a control subject). The 3D fluid registration was regularized using the linearized Cauchy–Navier operator. Fine-scale volumetric differences between diagnostic groups were mapped. Regions were identified where brain atrophy correlated with clinical measures. Results Severe atrophy (∼15–20% deficit) was detected bilaterally in the primary and association sensorimotor areas. Atrophy of these regions, particularly in the white matter, correlated with cognitive impairment (P =0. 033) and CD4+ T-lymphocyte depletion (P =0. 005). Conclusion TBM facilitates 3D visualization of AIDS neuropathology in living patients scanned with MRI. Severe atrophy in frontoparietal and striatal areas may underlie early cognitive dysfunction in AIDS patients, and may signal the imminent onset of AIDS dementia complex.

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YNIMG Journal 2007 Journal Article

Optimum template selection for atlas-based segmentation

Minjie Wu
Caterina Rosano
Pilar Lopez-Garcia
Cameron S. Carter
Howard J. Aizenstein

Atlas-based segmentation of MR brain images typically uses a single atlas (e. g. , MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e. g. , for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the ‘best’ template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection method produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4. 353, p =0. 0024; right amygdala, matched paired t test t(8)>3. 175, p <0. 013; for the remaining ROIs, t(8)=4. 36, p <0. 002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses.

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YNIMG Journal 2006 Journal Article

3D mapping of ventricular and corpus callosum abnormalities in HIV/AIDS

Paul M. Thompson
Rebecca A. Dutton
Kiralee M. Hayashi
Allen Lu
Sharon E. Lee
Jessica Y. Lee
Oscar L. Lopez
Howard J. Aizenstein

Objective: 40 million people worldwide are now infected with HIV/AIDS, an illness that often leads to rapidly progressing dementia and death. Even so, little is known about how AIDS affects the brain. Using computational anatomy techniques, we mapped how AIDS impacts the corpus callosum (CC) and ventricular system, two systems that show prominent changes on MRI. We (1) identified regions with greatest differences between AIDS patients and healthy controls and (2) correlated specific 3D patterns of structural differences with measures of immune system deterioration and cognitive decline. Methods: 51 3D brain MRI scans from 30 non-demented AIDS patients (age: 43. 4 years ± 7. 6 SD) and 21 HIV-seronegative controls (age: 39. 5 years ± 12. 2) were aligned to ICBM standard space. 3D surface mesh reconstructions of the lateral ventricles and CC were spatially averaged and compared across diagnostic groups. Structural alterations were correlated with viral load, T cell counts, and cognitive impairment. Results: Statistical maps revealed the 3D profile of ventricular expansion and callosal thinning in AIDS. Specific 3D ventricular changes were linked with immune system decline (CD4+ T cell counts; P < 0. 001) and cognitive impairment (P < 0. 009), but not viral load. Frontal horn maps distinguished AIDS patients from controls better than occipital and temporal horn measures. T cell decline linked with callosal thinning in anterior regions connecting frontal areas with greatest cortical atrophy. Conclusion: These maps (1) reveal how brain changes in HIV/AIDS relate to immune decline and impaired cognition, and, after further validation and testing, (2) may offer possible neuroimaging markers for anti-viral drug trials, which gauge how well treatments oppose disease progression in the brain.

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