Author name cluster

Giovanni Schifitto

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

YNIMG Journal 2024 Journal Article

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling

Alan J. Finkelstein
Congyu Liao
Xiaozhi Cao
Merry Mani
Giovanni Schifitto
Jianhui Zhong

PURPOSE: Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods. METHODS: To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise. RESULTS: Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction. CONCLUSION: The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Details DOI

YNICL Journal 2023 Journal Article

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

Santiago Aja-Fernández
Carmen Martín-Martín
Álvaro Planchuelo-Gómez
Abrar Faiyaz
Md Nasir Uddin
Giovanni Schifitto
Abhishek Tiwari
Saurabh J. Shigwan

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Details DOI

YNICL Journal 2021 Journal Article

Increased risk for cerebral small vessel disease is associated with quantitative susceptibility mapping in HIV infected and uninfected individuals

Kyle D. Murray
Md Nasir Uddin
Madalina E. Tivarus
Bogachan Sahin
Henry Z. Wang
Meera V. Singh
Xing Qiu
Lu Wang

The aim of this study was to assess, in the context of cerebral small vessel disease (CSVD), whether cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age ± SD = 53.2 ± 10.9 years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynolds scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.

Details DOI

YNICL Journal 2021 Journal Article

Mitochondrial toxicity before and after combination antiretroviral therapy, a Magnetic Resonance Spectroscopy study

Madalina E. Tivarus
Yuchuan Zhuang
Lu Wang
Kyle D. Murray
Arun Venkataraman
Miriam T. Weber
Jianhui Zhong
Xing Qiu

The aim of this study was to quantify, via Magnetic Resonance Spectroscopy (MRS), the effect of combination antiretroviral therapy (cART) on brain metabolites and characterize any possible associations between changes in metabolites, age, blood biomarkers of neuronal damage, functional connectivity and cognitive performance. As cART has dramatically increased the life expectancy of HIV-infected (HIV + ) individuals and unmasked an increase in HIV-associated neurocognitive disorders, it is still not clear whether cART neurotoxicity contributes to these disorders. We hypothesized a bimodal effect, with early cART treatment of HIV infection decreasing inflammation as measured by MRS metabolites and improving cognitive performance, and chronic exposure to cART contributing to persistence of cognitive impairment via its effect on mitochondrial function. Basal ganglia metabolites, functional connectivity, cognitive scores, as well as plasma levels of neurofilament light chain (NfL) and tau protein were measured before and after 12 weeks, 1 year and 2 years of cART in a cohort of 50 cART-naïve HIV + subjects and 72 age matched HIV- healthy controls. Glutamate (Glu) levels were lower in the cART naïve patients than in healthy controls and were inversely correlated with plasma levels of NfL. There were no other significant metabolite differences between HIV + and uninfected individuals. Treatment improved Glu levels in HIV+, however, no associations were found between Glu, functional connectivity and cognitive performance. Stable brain metabolites and plasma levels of NfL and Tau over two-years of follow-ups suggest there are no signs of cART neurotoxicity in this relatively young cohort of HIV + individuals.

Details DOI

YNICL Journal 2018 Journal Article

Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective

Anas Z. Abidin
Adora M. DSouza
Mahesh B. Nagarajan
Lu Wang
Xing Qiu
Giovanni Schifitto
Axel Wismüller

HIV is capable of invading the brain soon after seroconversion. This ultimately can lead to deficits in multiple cognitive domains commonly referred to as HIV-associated neurocognitive disorders (HAND). Clinical diagnosis of such deficits requires detailed neuropsychological assessment but clinical signs may be difficult to detect during asymptomatic injury of the central nervous system (CNS). Therefore neuroimaging biomarkers are of particular interest in HAND. In this study, we constructed brain connectivity profiles of 40subjects (20 HIV positive subjects and 20 age-matched seronegative controls) using two different methods: a non-linear mutual connectivity analysis approach and a conventional method based on Pearson's correlation. These profiles were then summarized using graph-theoretic methods characterizing their topological network properties. Standard clinical and laboratory assessments were performed and a battery of neuropsychological (NP) tests was administered for all participating subjects. Based on NP testing, 14 of the seropositive subjects exhibited mild neurologic impairment. Subsequently, we analyzed associations between the network derived measures and neuropsychological assessment scores as well as common clinical laboratory plasma markers (CD4 cell count, HIV RNA) after adjusting for age and gender. Mutual connectivity analysis derived graph-theoretic measures, Modularity and Small Worldness, were significantly (p <0. 05, FDR adjusted) associated with the Executive as well as Overall z-score of NP performance. In contrast, network measures derived from conventional correlation-based connectivity did not yield any significant results. Thus, changes in connectivity can be captured using advanced time-series analysis techniques. The demonstrated associations between imaging-derived graph-theoretic properties of brain networks with neuropsychological performance, provides opportunities to further investigate the evolution of HAND in larger, longitudinal studies. Our analysis approach, involving non-linear time-series analysis in conjunction with graph theory, is promising and it may prove to be useful not only in HAND but also in other neurodegenerative disorders.

Details DOI

YNICL Journal 2018 Journal Article

Functional and structural connectivity of the amygdala underpins locus of control in mild cognitive impairment

Ping Ren
Benjamin Chapman
Zhengwu Zhang
Giovanni Schifitto
Feng Lin

Locus of control (LOC) is an important personality trait. LOC over cognitive competency reflects an individual's perceived control of desired cognitive outcomes, which is critical for maintaining successful cognitive aging. It is important to understand the neural substrates of LOC over cognitive competency in older adults, especially for individuals at high risk of dementia. Here, we characterized a cohesive functional and structural connectivity profile underlying LOC among 55 older adults with amnestic mild cognitive impairment (aMCI), combining resting-state functional magnetic resonance imaging and diffusion tensor imaging. The results showed that both functional and structural connectivity between the medial prefrontal cortex and amygdala were significantly correlated with external LOC. The functional connectivity mediated the correlation between structural connectivity and external LOC. In addition, aging-associated neurodegeneration moderated the relationship between structural connectivity and external LOC, showing that the structural connectivity was positively correlated with external LOC in low, but not high neurodegeneration. Our results suggest a critical role of the functional amygdala-frontal network, which may serve as a bridge between its white matter tract and LOC over cognitive competency in groups at high risk for dementia.

Details DOI

YNIMG Journal 2018 Journal Article

Mutual connectivity analysis of resting-state functional MRI data with local models

Adora M. DSouza
Anas Z. Abidin
Udaysankar Chockanathan
Giovanni Schifitto
Axel Wismüller

Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera. We demonstrate on realistic simulations of fMRI data that, at long sampling intervals, i. e. high repetition time (TR) of fMRI signals, MCA-LM performs better than or comparable to correlation-based methods and Patel's measures. However, at fast image acquisition rates corresponding to low TR, MCA-LM significantly outperforms these methods. This insight is particularly useful in the light of recent advances in fast fMRI acquisition techniques. Methods that can capture the complex dynamics of brain activity, such as MCA-LM, should be adopted to extract as much information as possible from the improved representation. Furthermore, MCA-LM works very well for simulations generated at weak neuronal interaction strengths, and simulations modeling inhibitory and excitatory connections as it disentangles the two opposing effects between pairs of regions/voxels. Additionally, we demonstrate that MCA-LM is capable of capturing meaningful directed connectivity on experimental fMRI data. Such results suggest that it introduces sufficient complexity into modeling fMRI time-series interactions that simple, linear approaches cannot, while being data-driven, computationally practical and easy to use. In conclusion, MCA-LM can provide valuable insights towards better understanding brain activity.

Details DOI

YNICL Journal 2016 Journal Article

Spatial regression analysis of serial DTI for subject-specific longitudinal changes of neurodegenerative disease

Bilan Liu
Xing Qiu
Tong Zhu
Wei Tian
Rui Hu
Sven Ekholm
Giovanni Schifitto
Jianhui Zhong

Quantitative measurement of localized longitudinal changes in brain abnormalities at an individual level may offer critical information for disease diagnosis and treatment. The voxel-wise permutation-based method SPREAD/iSPREAD, which combines resampling and spatial regression of neighboring voxels, provides an effective and robust method for detecting subject-specific longitudinal changes within the whole brain, especially for longitudinal studies with a limited number of scans. As an extension of SPREAD/iSPREAD, we present a general method that facilitates analysis of serial Diffusion Tensor Imaging (DTI) measurements (with more than two time points) for testing localized changes in longitudinal studies. Two types of voxel-level test statistics (model-free test statistics, which measure intra-subject variability across time, and test statistics based on general linear model that incorporate specific lesion evolution models) were estimated and tested against the null hypothesis among groups of DTI data across time. The implementation and utility of the proposed statistical method were demonstrated by both Monte Carlo simulations and applications on clinical DTI data from human brain in vivo. By a design of test statistics based on the disease progression model, it was possible to apportion the true significant voxels attributed to the disease progression and those caused by underlying anatomical differences that cannot be explained by the model, which led to improvement in false positive (FP) control in the results. Extension of the proposed method to include other diseases or drug effect models, as well as the feasibility of global statistics, was discussed. The proposed statistical method can be extended to a broad spectrum of longitudinal studies with carefully designed test statistics, which helps to detect localized changes at the individual level.

Details DOI

YNICL Journal 2013 Journal Article

Disrupted cerebral metabolite levels and lower nadir CD4 + counts are linked to brain volume deficits in 210 HIV-infected patients on stable treatmentpatients on stable treatment

Xue Hua
Christina P. Boyle
Jaroslaw Harezlak
David F. Tate
Constantin T. Yiannoutsos
Ron Cohen
Giovanni Schifitto
Assawin Gongvatana

Cognitive impairment and brain injury are common in people with HIV/AIDS, even when viral replication is effectively suppressed with combined antiretroviral therapies (cART). Metabolic and structural abnormalities may promote cognitive decline, but we know little about how these measures relate in people on stable cART. Here we used tensor-based morphometry (TBM) to reveal the 3D profile of regional brain volume variations in 210 HIV + patients scanned with whole-brain MRI at 1.5 T (mean age: 48.6 ± 8.4 years; all receiving cART). We identified brain regions where the degree of atrophy was related to HIV clinical measures and cerebral metabolite levels assessed with magnetic resonance spectroscopy (MRS). Regional brain volume reduction was linked to lower nadir CD4 + count, with a 1-2% white matter volume reduction for each 25-point reduction in nadir CD4 +. Even so, brain volume measured by TBM showed no detectable association with current CD4 + count, AIDS Dementia Complex (ADC) stage, HIV RNA load in plasma or cerebrospinal fluid (CSF), duration of HIV infection, antiretroviral CNS penetration-effectiveness (CPE) scores, or years on cART, after controlling for demographic factors, and for multiple comparisons. Elevated glutamate and glutamine (Glx) and lower N-acetylaspartate (NAA) in the frontal white matter, basal ganglia, and mid frontal cortex - were associated with lower white matter, putamen and thalamus volumes, and ventricular and CSF space expansion. Reductions in brain volumes in the setting of chronic and stable disease are strongly linked to a history of immunosuppression, suggesting that delays in initiating cART may result in imminent and irreversible brain damage.

Details DOI

YNIMG Journal 2011 Journal Article

Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study

Tong Zhu
Rui Hu
Xing Qiu
Michael Taylor
Yuen Tso
Constantin Yiannoutsos
Bradford Navia
Susumu Mori

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies.

Details DOI

YNIMG Journal 2004 Journal Article

Regional patterns of brain metabolites in AIDS dementia complex

Constantin T. Yiannoutsos
Thomas Ernst
Linda Chang
P. Lani Lee
Todd Richards
Christina M. Marra
Dieter J. Meyerhoff
Jeffrey G. Jarvik

The relationship of the cellular changes in the HIV-infected brain to the onset and progression of AIDS dementia complex (ADC) remains uncertain. We undertook an in vivo proton magnetic resonance spectroscopy (MRS) study and used factor analysis to identify specific cellular and regional brain changes that may serve as metabolic markers of ADC. The ratio of N-acetyl aspartate (NAA), choline (Cho), and myoinositol (MI) over creatine (Cr), markers of neuronal and glial cell metabolism, were measured in the basal ganglia, centrum semiovale, and parietal cortex from 100 subjects with and without ADC. Three metabolic patterns were identified, which we termed “inflammatory” (mainly MI/Cr elevations in all three regions plus Cho/Cr increases in the centrum semiovale and parietal cortex), “basal ganglia” (mostly NAA/Cr and Cho/Cr elevations in the basal ganglia), and “neuronal” (primarily NAA/Cr reductions in the centrum semiovale and the parietal cortex). Logistic regression analysis revealed that, adjusted for age, basal ganglia and neuronal pattern scores were strongly associated with ADC but inflammatory levels were not. We conclude that by using factor analysis, we are able to combine multiple metabolites across brain regions in a biologically plausible manner and construct a predictive model of ADC adjusting for relevant factors such as age.

Details DOI