Brain intrinsic magnetic susceptibility mapping depicts whole-brain functional connectivity balance of normal aging in lifespan.

We hypothesized that brain normal aging maintains a balanced whole-brain functional connectivity (FC) in lifetime: some connections decline while other connections increase or retain, in a summation balance as a result of the cancellation of positive and negative connections. We validated this hypothesis through the use of the brain intrinsic magnetic susceptibility source (denoted by χ) as reconstructed from fMRI phase data. In implementation, we first acquired brain fMRI magnitude (m) and phase (p) data from a cohort of 245 healthy subjects in an age span of 20-60 years, then sought MRI-free brain χ source data by computationally solving an inverse mapping problem, thereby obtained triple datasets {χ, m, p} as brain images in different measurements.

Computed cancer magnetic susceptibility imaging (canχ): Computational inverse mappings of cancer MRI.

Purpose: We report a new cancer imaging modality in the contrast of tissue intrinsic susceptibility property by computed inverse magnetic resonance imaging (CIMRI).

Methods: In MRI physics, an MRI signal is formed from tissue magnetism source (primarily magnetic susceptibility χ) through a cascade of MRI-introduced transformations (e.g.

Tilted quantitative susceptibility mapping at oblique MRI (tiltQSM).

Objective: If the phase image matrix was acquired from oblique MRI, it is needed to deal with the oblique effect for quantitative susceptibility mapping (QSM), as addressed in this paper.

Methods: We proposed two methods for QSM reconstruction from slice-tilted MRI phase image (tiltQSM): 1) rotData per anti-tilting phase image rotation back into the B-upright system, and 2) rotKernel per pro-tilting dipole kernel rotation into the same oblique setting as defined by the tilted phase image. Both matrix methods were implemented in an additional preprocessing subroutine to ensure that the phase image and the dipole kernel were represented in the same coordinate system (either in B-upright system or in B-tilted system); thereafter tiltQSM could be completed through a regular QSM procedure.

Signal Variability and Cognitive Function in Older Long-Term Survivors of Breast Cancer with Exposure to Chemotherapy: A Prospective Longitudinal Resting-State fMRI Study.

The purpose of this study was to assess the effect of chemotherapy on brain functional resting-state signal variability and cognitive function in older long-term survivors of breast cancer. This prospective longitudinal study enrolled women age ≥ 65 years of age who were breast cancer survivors after exposure to chemotherapy (CH), age-matched survivors not exposed to chemotherapy, and healthy controls. Participants completed resting-state functional brain MRI and neurocognitive testing upon enrollment (timepoint 1, TP1) and again two years later (timepoint 2, TP2).

Understanding the zigzags of multi-echo phase signals by numerical simulations.

Understanding the multi-echo phase zigzag signals is instrumental to assuring the quality of MRI phase data acquisition for ensuing phase exploration and exploitation. This paper provides a theoretical and computational mechanism for understanding the zigzag multi-echo phase formation that has been observed in numerical multi-echo gradient-recalled (GRE) simulations of clinical complex-valued brain MRI images. Based on intravoxel dephasing mechanism, we calculated a train of multi-GRE complex-valued voxel signals by simulating field gradient reversals under perturbations in either gradient strength (G±G) or gradient duration (Δ±Δ), as well as the simultaneous bi-variable gradient perturbations (GΔ).

Proof of linear MRI phase imaging from an internal fieldmap.

Purpose: Brain MRI phase imaging assumes a linear spatial mapping of the internal fieldmap that continues to lack theoretical proof. We herein present one proof by replacing the arithmetic mean (in MRI signal formation from the intravoxel spin precession dephasing mechanism) with the geometric mean.

Methods: By replacing the complex arithmetic mean of intravoxel dephasing isochromats with a complex geometric mean, we readily derive a linear spatial mapping of MRI phase imaging from an internal fieldmap without any restriction in phase angles.

Brain functional connectivity (FC) invariance and variability under timeseries editing (timeset operation).

Functional connectivity (FC) is defined by temporal correlations between pairwise timeseries signals, thus inheriting the correlation invariance property. In this report, we look into FC properties under versatile timeseries manipulations, as classified into cardinality-preserved or -reduced timeset operations. We show the effect of timeset operations on brain FC mapping by task-evoked and resting-state fMRI experiments through two data analysis methods: seed-based correlation analysis (SCA) and independent component analysis (ICA).

Spatiotemporal multiscale ICA could invariantly extract task (motor) modes from wavelet subbands of fMRI data.

Background And Objective: . Given a timeseries of task-evoked functional MRI (fMRI) images (4D spatiotemporal data), we can extract the task mode by statistical independent component analysis (ICA). If the 4D data are spatiotemporally decomposed into subbands (multiresolutions in both time and space), is ICA still capable of extracting the task modes at multiscales? We answer this question using the well-established fingertapping motor-task experiments at 3T and 7T.

Computed inverse MRI (CIMRI) for intrinsic brain magnetic susceptibility mapping.

In magnetic resonance imaging (MRI), tissue magnetization in the main field B is a necessary preparation for magnetic resonance signal formation that imposes an inherent dipole effect on MRI signals, which predisposes an artifact on tissue MRI. In the MRI principle, T2*-weighted MRI can be described by a cascade of data transformations: from the source of tissue magnetic susceptibility (denoted by χ) to the output of complex-valued T2* image (in a magnitude and phase pair). Under the linear approximation of the T2* phase MRI, we can computationally reconstruct the source χ by quantitative susceptibility mapping (QSM), which is an inverse solution that is modeled by computed inverse MRI (CIMRI).

Phase fMRI defines brain resting-state functional hubs within central and posterior regions.

From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping.

Effect of chemotherapy on default mode network connectivity in older women with breast cancer.

Chemotherapy may impair cognition and contribute to accelerated aging. The purpose of this study was to assess the effects of chemotherapy on the connectivity of the default mode network (DMN) in older women with breast cancer. This prospective longitudinal study enrolled women aged ≥ 60 years with stage I-III breast cancer (CTx group) and matched healthy controls (HC group).

Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.

Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.

Spinal Manipulative Therapy Alters Brain Activity in Patients With Chronic Low Back Pain: A Longitudinal Brain fMRI Study.

Spinal manipulative therapy (SMT) helps to reduce chronic low back pain (cLBP). However, the underlying mechanism of pain relief and the neurological response to SMT remains unclear. We utilized brain functional magnetic resonance imaging (fMRI) upon the application of a real-time spot pressure mechanical stimulus to assess the effects of SMT on patients with cLBP.

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model.

Regional Upstroke Tracking for Transit Time Detection to Improve the Ultrasound-Based Local PWV Estimation in Carotid Arteries.

Pulse wave velocity (PWV) is the most important index for quantifying the elasticity of an artery. The accurate estimation of the local PWV is of great relevance to the early diagnosis and effective prevention of arterial stiffness. In ultrasonic transit time-based local PWV estimation, the locations of time fiduciary point (TFP) in the upstrokes of the propagating pulse waves (PWs) are inconsistent because of the reflected waves and ultrasonic noise.

Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI.

Conventionally, brain function is inferred from the magnitude data of the complex-valued fMRI output. Since the fMRI phase image (unwrapped) provides a representation of brain internal magnetic fieldmap (by a constant scale difference), it can also be used to study brain function while providing a more direct representation of the brain's magnetic state. In this study, we collected a cohort of resting-state fMRI magnitude and phase data pairs from 600 subjects (age from 10 to 76, 346 males), decomposed the phase data by group independent component analysis (pICA), calculated the functional network connectivity (pFNC).

Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state.

A functional magnetic resonance imaging (fMRI) experiment produces complex-valued images consisting of pairwise magnitude and phase images. As different perspective on the same magnetic source, fMRI magnitude and phase data are complementary for brain function analysis. We collected 600-subject fMRI data during rest, decomposed via group-level independent component analysis (ICA) (mICA and pICA for magnitude and phase respectively), and calculated brain functional network connectivity matrices (mFC and pFC).

Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity.

Spatial smoothing is a widely used preprocessing step in functional magnetic resonance imaging (fMRI) data analysis. In this work, we report on the spatial smoothing effect on task-evoked fMRI brain functional mapping and functional connectivity. Initially, we decomposed the task fMRI data into a collection of components or networks by independent component analysis (ICA).

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping.

Purpose: To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping.

Methods: A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division.

Functional brain connectivity in resting-state fMRI using phase and magnitude data.

Background: The output of BOLD fMRI consists of a pair of magnitude and phase components. While the magnitude data has been widely accepted for brain function analysis, we can also make use of the phase data (unwrapped) since this is a good representation of the internal magnetic field. In this work, we discuss the use of fMRI phase data for brain function analysis.

Functional overestimation due to spatial smoothing of fMRI data.

Background: Pearson correlation (simply correlation) is a basic technique for neuroimage function analysis. It has been observed that the spatial smoothing may cause functional overestimation, which however remains a lack of complete understanding. Herein, we present a theoretical explanation from the perspective of correlation scale invariance.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

Background: Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map.

Nonlinear magnitude and linear phase behaviors of T2* imaging: theoretical approximation and Monte Carlo simulation.

The underlying source of brain imaging by T2*-weighted magnetic resonance imaging (T2*MRI) is the intracranial inhomogeneous tissue magnetic susceptibility (denoted by χ) that causes an inhomogeneous field map (via magnetization) in a main field. By decomposing T2*MRI into two steps, we understand that the 1st step from a χ source to a field map is a linear but non-isomorphic spatial mapping, and the 2nd step from the field map to a T2* image is a nonlinear mapping due to the trigonometric behavior of spin precession signals. The magnitude and phase calculations from a complex T2* image introduce additional nonlinearities.

Intrinsic functional brain mapping in reconstructed 4D magnetic susceptibility (χ) data space.

Background: By solving an inverse problem of T2*-weighted magnetic resonance imaging for a dynamic fMRI study, we reconstruct a 4D magnetic susceptibility source (χ) data space for intrinsic functional mapping.

New Methods: A 4D phase dataset is calculated from a 4D complex fMRI dataset. The background field and phase wrapping effect are removed by a Laplacian technique.

Susceptibility-based functional brain mapping by 3D deconvolution of an MR-phase activation map.

The underlying source of T2*-weighted magnetic resonance imaging (T2*MRI) for brain imaging is magnetic susceptibility (denoted by χ). T2*MRI outputs a complex-valued MR image consisting of magnitude and phase information. Recent research has shown that both the magnitude and the phase images are morphologically different from the source χ, primarily due to 3D convolution, and that the source χ can be reconstructed from complex MR images by computed inverse MRI (CIMRI).