Andreia V. Faria

YNICL Journal 2024 Journal Article

A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry

• Kyle M. Jensen
• Vince D. Calhoun
• Zening Fu
• Kun Yang
• Andreia V. Faria
• Koko Ishizuka
• Akira Sawa
• Pablo Andrés-Camazón

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

YNICL Journal 2023 Journal Article

Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease

• Beini Hu
• Laurent Younes
• Xuan Bu
• Chin-Fu Liu
• J. Tilak Ratnanather
• Jane Paulsen
• Nellie Georgiou-Karistianis
• Michael I. Miller

Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics.

YNIMG Journal 2019 Journal Article

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

• Xu Li
• Lin Chen
• Kwame Kutten
• Can Ceritoglu
• Yue Li
• Ningdong Kang
• John T. Hsu
• Ye Qiao

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0. 77 ± 0. 09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0. 79 ± 0. 07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0. 79 ± 0. 08 to manual labels and 0. 89 ± 0. 04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0. 97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www. mricloud. org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.

YNICL Journal 2016 Journal Article

Linking white matter and deep gray matter alterations in premanifest Huntington disease

• Andreia V. Faria
• J. Tilak Ratnanather
• Daniel J. Tward
• David Soobin Lee
• Frieda van den Noort
• Dan Wu
• Timothy Brown
• Hans Johnson

Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.

YNICL Journal 2015 Journal Article

Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

• Andreia V. Faria
• Kenichi Oishi
• Shoko Yoshida
• Argye Hillis
• Michael I. Miller
• Susumu Mori

Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.

YNIMG Journal 2014 Journal Article

Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): Application to normal and Alzheimer's populations

• Yajing Zhang
• Jiangyang Zhang
• Johnny Hsu
• Kenichi Oishi
• Andreia V. Faria
• Marilyn Albert
• Michael I. Miller
• Susumu Mori

MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i. e. , Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population.

YNIMG Journal 2014 Journal Article

Tools for multiple granularity analysis of brain MRI data for individualized image analysis

• Aigerim Djamanakova
• Xiaoying Tang
• Xin Li
• Andreia V. Faria
• Can Ceritoglu
• Kenichi Oishi
• Argye E. Hillis
• Marilyn Albert

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i. e. , each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n =29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group.

YNICL Journal 2013 Journal Article

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI

• Yuan-Yuan Qin
• Johnny T. Hsu
• Shoko Yoshida
• Andreia V. Faria
• Kumiko Oishi
• Paul G. Unschuld
• Graham W. Redgrave
• Sarah H. Ying

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

YNIMG Journal 2013 Journal Article

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: Application to determine iron content in deep gray matter structures

• Issel Anne L. Lim
• Andreia V. Faria
• Xu Li
• Johnny T.C. Hsu
• Raag D. Airan
• Susumu Mori
• Peter C.M. van Zijl

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a “deep gray matter parcellation map” (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established “white matter parcellation map” (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the “Everything Parcellation Map in Eve Space, ” also known as the “EvePM. ” It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0. 85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0. 89 for the human raters, suggesting “almost perfect” agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0. 88 between automated and manual segmentation, and 0. 85 and 0. 86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM.

YNIMG Journal 2012 Journal Article

Atlas-based analysis of resting-state functional connectivity: Evaluation for reproducibility and multi-modal anatomy–function correlation studies

• Andreia V. Faria
• Suresh E. Joel
• Yajing Zhang
• Kenichi Oishi
• Peter C.M. van Zjil
• Michael I. Miller
• James J. Pekar
• Susumu Mori

Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test–retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined.

YNIMG Journal 2011 Journal Article

Erratum to “Quantitative analysis of brain pathology based on MRI and brain atlases—Applications for cerebral palsy” [NeuroImage 54/3 (2011) 1854–1861]

• Andreia V. Faria
• Alexander Hoon
• Elaine Stashinko
• Xin Li
• Hangyi Jiang
• Ameneh Mashayekh
• Kazi Akhter
• John Hsu

YNIMG Journal 2011 Journal Article

Quantitative analysis of brain pathology based on MRI and brain atlases—Applications for cerebral palsy

• Andreia V. Faria
• Alexander Hoon
• Elaine Stashinko
• Xin Li
• Hangyi Jiang
• Ameneh Mashayekh
• Kazi Akhter
• John Hsu

We have developed a new method to provide a comprehensive quantitative analysis of brain anatomy in cerebral palsy patients, which makes use of two techniques: diffusion tensor imaging and automated 3D whole brain segmentation based on our brain atlas and a nonlinear normalization technique (large-deformation diffeomorphic metric mapping). This method was applied to 13 patients and normal controls. The reliability of the automated segmentation revealed close agreement with the manual segmentation. We illustrate some potential applications for individual characterization and group comparison. This technique also provides a framework for determining the impact of various neuroanatomic features on brain functions.

YNIMG Journal 2010 Journal Article

Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection

• Andreia V. Faria
• Jiangyang Zhang
• Kenichi Oishi
• Xin Li
• Hangyi Jiang
• Kazi Akhter
• Laurent Hermoye
• Seung-Koo Lee

Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported a structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated.

YNIMG Journal 2010 Journal Article

Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy

• Yajing Zhang
• Jiangyang Zhang
• Kenichi Oishi
• Andreia V. Faria
• Hangyi Jiang
• Xin Li
• Kazi Akhter
• Pedro Rosa-Neto

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification.

YNIMG Journal 2008 Journal Article

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

• Susumu Mori
• Kenichi Oishi
• Hangyi Jiang
• Li Jiang
• Xin Li
• Kazi Akhter
• Kegang Hua
• Andreia V. Faria

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.