Ciprian M. Crainiceanu

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.

YNIMG Journal 2017 Journal Article

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

• Aaron Carass
• Snehashis Roy
• Amod Jog
• Jennifer L. Cuzzocreo
• Elizabeth Magrath
• Adrian Gherman
• Julia Button
• James Nguyen

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4. 4 time-points, and test data of fourteen subjects with a mean of 4. 4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 2 2 The Challenge Evaluation Website is: http: //smart-stats-tools. org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

YNICL Journal 2017 Journal Article

PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT

• John Muschelli
• Elizabeth M. Sweeney
• Natalie L. Ullman
• Paul Vespa
• Daniel F. Hanley
• Ciprian M. Crainiceanu

INTRODUCTION: Intracerebral hemorrhage (ICH), where a blood vessel ruptures into areas of the brain, accounts for approximately 10-15% of all strokes. X-ray computed tomography (CT) scanning is largely used to assess the location and volume of these hemorrhages. Manual segmentation of the CT scan using planimetry by an expert reader is the gold standard for volume estimation, but is time-consuming and has within- and across-reader variability. We propose a fully automated segmentation approach using a random forest algorithm with features extracted from X-ray computed tomography (CT) scans. METHODS: The Minimally Invasive Surgery plus rt-PA in ICH Evacuation (MISTIE) trial was a multi-site Phase II clinical trial that tested the safety of hemorrhage removal using recombinant-tissue plasminogen activator (rt-PA). For this analysis, we use 112 baseline CT scans from patients enrolled in the MISTE trial, one CT scan per patient. ICH was manually segmented on these CT scans by expert readers. We derived a set of imaging predictors from each scan. Using 10 randomly-selected scans, we used a first-pass voxel selection procedure based on quantiles of a set of predictors and then built 4 models estimating the voxel-level probability of ICH. The models used were: 1) logistic regression, 2) logistic regression with a penalty on the model parameters using LASSO, 3) a generalized additive model (GAM) and 4) a random forest classifier. The remaining 102 scans were used for model validation.For each validation scan, the model predicted the probability of ICH at each voxel. These voxel-level probabilities were then thresholded to produce binary segmentations of the hemorrhage. These masks were compared to the manual segmentations using the Dice Similarity Index (DSI) and the correlation of hemorrhage volume of between the two segmentations. We tested equality of median DSI using the Kruskal-Wallis test across the 4 models. We tested equality of the median DSI from sets of 2 models using a Wilcoxon signed-rank test. RESULTS: < 0.001) models. In practical terms the difference between the random forest and the logistic regression is quite small. The correlation (95% CI) between the volume from manual segmentation and the predicted volume was 0.93 (0.9,0.95) for the random forest model. These results indicate that random forest approach can achieve accurate segmentation of ICH in a population of patients from a variety of imaging centers. We provide an R package (https://github.com/muschellij2/ichseg) and a Shiny R application online (http://johnmuschelli.com/ich_segment_all.html) for implementing and testing the proposed approach.

YNICL Journal 2016 Journal Article

Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions

• Elizabeth M. Sweeney
• Russell T. Shinohara
• Blake E. Dewey
• Matthew K. Schindler
• John Muschelli
• Daniel S. Reich
• Ciprian M. Crainiceanu
• Ani Eloyan

The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

YNIMG Journal 2016 Journal Article

Removing inter-subject technical variability in magnetic resonance imaging studies

• Jean-Philippe Fortin
• Elizabeth M. Sweeney
• John Muschelli
• Ciprian M. Crainiceanu
• Russell T. Shinohara

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

YNICL Journal 2015 Journal Article

Corrigendum to “Statistical normalization techniques for magnetic resonance imaging” [NeuroImage: Clinical 6 (2014) 9–19]

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

YNIMG Journal 2015 Journal Article

Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators

• Amanda F. Mejia
• Mary Beth Nebel
• Haochang Shou
• Ciprian M. Crainiceanu
• James J. Pekar
• Stewart Mostofsky
• Brian Caffo
• Martin A. Lindquist

A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often a necessary step for defining the network nodes used in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the inherent low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage-based estimators of such measures, allowing the noisy subject-specific estimator to “borrow strength” in a principled manner from a larger population of subjects. We present several empirical Bayes shrinkage estimators and outline methods for shrinkage when multiple scans are not available for each subject. We perform shrinkage on raw inter-voxel correlation estimates and use both raw and shrinkage estimates to produce parcellations by performing clustering on the voxels. While we employ a standard spectral clustering approach, our proposed method is agnostic to the choice of clustering method and can be used as a pre-processing step for any clustering algorithm. Using two datasets — a simulated dataset where the true parcellation is known and is subject-specific and a test–retest dataset consisting of two 7-minute resting-state fMRI scans from 20 subjects — we show that parcellations produced from shrinkage correlation estimates have higher reliability and validity than those produced from raw correlation estimates. Application to test–retest data shows that using shrinkage estimators increases the reproducibility of subject-specific parcellations of the motor cortex by up to 30%.

YNIMG Journal 2015 Journal Article

Validated automatic brain extraction of head CT images

• John Muschelli
• Natalie L. Ullman
• W. Andrew Mould
• Paul Vespa
• Daniel F. Hanley
• Ciprian M. Crainiceanu

Background X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction — the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0–100Hounsfield unit (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ =1mm3) and re-thresholded to 0–100HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0. 01, 0. 1, or 0. 35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: I C V automated I C V manual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p <0. 01, uncorrected), irrespective of the FI threshold. Using an FI of 0. 01 or 0. 1 performed better than 0. 35. Thus, all reported results refer only to smoothed data using an FI of 0. 01 or 0. 1. Using an FI of 0. 01 had a higher median sensitivity (0. 9901) than an FI of 0. 1 (0. 9884, median difference: 0. 0014, p <0. 001), accuracy (0. 9971 vs. 0. 9971; median difference: 0. 0001, p <0. 001), and DSI (0. 9895 vs. 0. 9894; median difference: 0. 0004, p <0. 001) and lower specificity (0. 9981 vs. 0. 9982; median difference: −0. 0001, p <0. 001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0. 01, the mean (SD) ICV ratio was 1. 002 (0. 008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5. 2%) and the ICC estimate was high (0. 929, 95% CI: 0. 91, 0. 945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0. 01 or 0. 1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided.

YNIMG Journal 2014 Journal Article

Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI

• Haochang Shou
• Ani Eloyan
• Mary Beth Nebel
• Amanda Mejia
• James J. Pekar
• Stewart Mostofsky
• Brian Caffo
• Martin A. Lindquist

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan–rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject's connectivity than the individual's own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.

YNICL Journal 2014 Journal Article

Statistical normalization techniques for magnetic resonance imaging

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

YNIMG Journal 2013 Journal Article

Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes

• Lei Huang
• Jeff Goldsmith
• Philip T. Reiss
• Daniel S. Reich
• Ciprian M. Crainiceanu

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.

YNICL Journal 2013 Journal Article

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI

• Elizabeth M. Sweeney
• Russell T. Shinohara
• Navid Shiee
• Farrah J. Mateen
• Avni A. Chudgar
• Jennifer L. Cuzzocreo
• Peter A. Calabresi
• Dzung L. Pham

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

YNICL Journal 2013 Journal Article

Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling

• Blake C. Jones
• Govind Nair
• Colin D. Shea
• Ciprian M. Crainiceanu
• Irene C.M. Cortese
• Daniel S. Reich

Brain atrophy, measured by MRI, has been proposed as a useful surrogate marker for disease progression in multiple sclerosis (MS). However, it is conventionally assumed that the accurate quantification of brain atrophy is made difficult, if not impossible, by changes in the parameters of the MRI acquisition, which are almost inevitable over the course of a longitudinal study since MRI technology changes rapidly. This state of affairs can negatively affect clinical trial design and limit the use of historical data. Here, we investigate whether we can coherently estimate brain atrophy rates in a heterogeneous MS sample via linear mixed-effects multivariable regression, incorporating three critical assumptions: (1) using age at time of scanning, rather than time since baseline, as the regressor of interest; (2) scanning individuals with a variety of techniques; and (3) introducing a simple additive correction for major differences in MRI protocol. We fit the model to several measures of brain volume as the outcome in two MS populations: 1123 scans from 195 cases acquired for over approximately 7 years in two natural history protocols (Cohort 1), and 1331 scans from 69 cases seen for over 11 years who were primarily treated with two specific MS disease-modifying therapies (Cohort 2). We compared the mixed-effects model with additive correction for MRI acquisition parameters to a model fit without this correction and performed sample-size calculations to provide an estimate of the number of participants in an MS clinical trial that might be required to see a therapeutic effect of treatment using the approach described here. The results show that without the additive correction for T1-weighted protocol parameters, atrophy was underestimated and subject-specific estimates were more narrowly distributed about the population mean. Ventricular CSF is the most consistently estimated brain volume, with a mean of 2.8%/year increase in Cohort 1 and 4.4%/year increase in Cohort 2. An interesting observation was that gray matter volume decreased and white matter volume remained essentially unchanged in both cohorts, suggesting that changes in ventricular CSF volume are a surrogate for changes in gray matter volume. In conclusion, the mixed-effects modeling framework presented here allows effective use of heterogeneously acquired and historical data in the study of brain atrophy in MS, potentially simplifying the design of future single- and multi-site clinical trials and natural history studies.

YNIMG Journal 2011 Journal Article

Dynamics of large-scale cortical interactions at high gamma frequencies during word production: Event related causality (ERC) analysis of human electrocorticography (ECoG)

• Anna Korzeniewska
• Piotr J. Franaszczuk
• Ciprian M. Crainiceanu
• Rafał Kuś
• Nathan E. Crone

Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional–anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i. e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i. e. sites with a pattern of ERC “divergence”, were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping interfered with word production. These findings suggest that the number, strength and directionality of event-related causal interactions may help identify network nodes that are not only activated by a task but are critical to its performance.

YNIMG Journal 2011 Journal Article

Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis

• Jeff Goldsmith
• Ciprian M. Crainiceanu
• Brian S. Caffo
• Daniel S. Reich

Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a “penalized functional regression” procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a “tract abnormality score” for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6. 4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.

YNIMG Journal 2011 Journal Article

Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis

• Russell T. Shinohara
• Ciprian M. Crainiceanu
• Brian S. Caffo
• María Inés Gaitán
• Daniel S. Reich

The processes by which new white matter lesions in multiple sclerosis (MS) develop are only partially understood. Much of this understanding has come through magnetic resonance imaging (MRI) of the human brain. One of the hallmarks of new lesion development in MS is enhancement on T1-weighted MRI scans following the intravenous administration of a gadolinium-based contrast agent that shortens the longitudinal relaxation time of the tissue. Visible enhancement on the MRI results from the opening of the blood–brain barrier and reveals areas of active inflammation. The incidence and number of existing enhancing lesions are common outcome measures used in MS treatment clinical trials. Dynamic-contrast-enhanced MRI (DCE-MRI) can estimate the rate at which contrast agents pass from the plasma to MS lesions. In this paper, we develop a principal component-based framework for the analysis of these data that provides biologically meaningful quantification of blood–brain–barrier opening in new MS lesions. To accomplish this, we use functional principal components analysis to study directions of variation in the voxel-level time series of intensities both within and across subjects. The analysis reveals and allows quantification of typical spatiotemporal enhancement patterns in acute MS lesions, providing measures of magnitude, rate, shape (ring-like vs. nodular), and dynamics (centrifugal vs. centripetal). Across 10 subjects with relapsing–remitting and primary progressive MS, we found subjects to have between 0 and 12 gadolinium-enhancing lesions, the majority of which enhanced centripetally. We quantified the spatiotemporal behavior within each of these lesions using novel measures. Further application of these techniques will determine the extent to which these lesion measures can predict or track response to therapy or long-term prognosis in this disorder.

YNIMG Journal 2010 Journal Article

Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk

• Brian S. Caffo
• Ciprian M. Crainiceanu
• Guillermo Verduzco
• Suresh Joel
• Stewart H. Mostofsky
• Susan Spear Bassett
• James J. Pekar

Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with a variety of cognitive and memory impairments and dysfunction, including Alzheimer's disease. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found an indication of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls, which was not significant in the light of multiple comparisons adjustment. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles.