Daniel R. Glen

A new method for improving functional-to-structural MRI alignment using local Pearson correlation

Accurate registration of Functional Magnetic Resonance Imaging (FMRI) T2-weighted volumes to same-subject high-resolution T1-weighted structural volumes is important for Blood Oxygenation Level Dependent (BOLD) FMRI and crucial for applications such as cortical surface-based analyses and pre-surgical planning. Such registration is generally implemented by minimizing a cost functional, which measures the mismatch between two image volumes over the group of proper affine transformations. Widely used cost functionals, such as mutual information (MI) and correlation ratio (CR), appear to yield decent alignments when visually judged by matching outer brain contours. However, close inspection reveals that internal brain structures are often significantly misaligned. Poor registration is most evident in the ventricles and sulcal folds, where CSF is concentrated. This observation motivated our development of an improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2- and T1-weighted images. In the absence of an alignment gold standard, we used three human observers blinded to registration method to provide an independent assessment of the quality of the registration for each cost functional. We found that LPC performed significantly better (p<0.001) than generic cost functionals including MI and CR. Generic cost functionals were very often not minimal near the best alignment, thereby suggesting that optimization is not the cause of their failure. Lastly, we emphasize the importance of precise visual inspection of alignment quality and present an automated method for generating composite images that help capture errors of misalignment.

Direct imaging of macrovascular and microvascular contributions to BOLD fMRI in layers IV-V of the rat whisker-barrel cortex

The spatiotemporal characteristics of the hemodynamic response to increased neural activity were investigated at the level of individual intracortical vessels using BOLD-fMRI in a well-established rodent model of somatosensory stimulation at 11.7 T. Functional maps of the rat barrel cortex were obtained at 150 × 150 × 500 μm spatial resolution every 200 ms. The high spatial resolution allowed separation of active voxels into those containing intracortical macro vessels, mainly vein/venules (referred to as macrovasculature), and those enriched with arteries/capillaries and small venules (referred to as microvasculature) since the macro vessel can be readily mapped due to the fast T2 decay of blood at 11.7 T. The earliest BOLD response was observed within layers IV-V by 0.8s following stimulation and encompassed mainly the voxels containing the microvasculature and some confined macrovasculature voxels. By 1.2s, the BOLD signal propagated to the macrovasculature voxels where the peak BOLD signal was 2-3 times higher than that of the microvasculature voxels. The BOLD response propagated in individual venules/veins far from neuronal sources at later times. This was also observed in layers IV-V of the barrel cortex after specific stimulation of separated whisker rows. These results directly visualized that the earliest hemodynamic changes to increased neural activity occur mainly in the microvasculature and spread toward the macrovasculature. However, at peak response, the BOLD signal is dominated by penetrating venules even at layers IV-V of the cortex.

Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and their interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoid some prevalent pitfalls that can occur when VAR and SEM are utilized separately.

Three-Dimensional Digital Template Atlas of the Macaque Brain

We present a new 3D template atlas of the anatomical subdivisions of the macaque brain, which is based on and aligned to the magnetic resonance imaging (MRI) data set and histological sections of the Saleem and Logothetis atlas. We describe the creation and validation of the atlas that, when registered with macaque structural or functional MRI scans, provides a straightforward means to estimate the boundaries between architectonic areas, either in a 3D volume with different planes of sections, or on an inflated brain surface (cortical flat map). As such, this new template atlas is intended for use as a reference standard for macaque brain research. Atlases and templates are available as both volumes and surfaces in standard NIFTI and GIFTI formats.

Metabolically stable bradykinin B2 receptor agonists enhance transvascular drug delivery into malignant brain tumors by increasing drug half-life

BackgroundThe intravenous co-infusion of labradimil, a metabolically stable bradykinin B2 receptor agonist, has been shown to temporarily enhance the transvascular delivery of small chemotherapy drugs, such as carboplatin, across the blood-brain tumor barrier. It has been thought that the primary mechanism by which labradimil does so is by acting selectively on tumor microvasculature to increase the local transvascular flow rate across the blood-brain tumor barrier. This mechanism of action does not explain why, in the clinical setting, carboplatin dosing based on patient renal function over-estimates the carboplatin dose required for target carboplatin exposure. In this study we investigated the systemic actions of labradimil, as well as other bradykinin B2 receptor agonists with a range of metabolic stabilities, in context of the local actions of the respective B2 receptor agonists on the blood-brain tumor barrier of rodent malignant gliomas.MethodsUsing dynamic contrast-enhanced MRI, the pharmacokinetics of gadolinium-diethyltriaminepentaacetic acid (Gd-DTPA), a small MRI contrast agent, were imaged in rodents bearing orthotopic RG-2 malignant gliomas. Baseline blood and brain tumor tissue pharmacokinetics were imaged with the 1st bolus of Gd-DTPA over the first hour, and then re-imaged with a 2nd bolus of Gd-DTPA over the second hour, during which normal saline or a bradykinin B2 receptor agonist was infused intravenously for 15 minutes. Changes in mean arterial blood pressure were recorded. Imaging data was analyzed using both qualitative and quantitative methods.ResultsThe decrease in systemic blood pressure correlated with the known metabolic stability of the bradykinin B2 receptor agonist infused. Metabolically stable bradykinin B2 agonists, methionine-lysine-bradykinin and labradimil, had differential effects on the transvascular flow rate of Gd-DTPA across the blood-brain tumor barrier. Both methionine-lysine-bradykinin and labradimil increased the blood half-life of Gd-DTPA sufficiently enough to increase significantly the tumor tissue Gd-DTPA area under the time-concentration curve.ConclusionMetabolically stable bradykinin B2 receptor agonists, methionine-lysine-bradykinin and labradimil, enhance the transvascular delivery of small chemotherapy drugs across the BBTB of malignant gliomas by increasing the blood half-life of the co-infused drug. The selectivity of the increase in drug delivery into the malignant glioma tissue, but not into normal brain tissue or skeletal muscle tissue, is due to the inherent porous nature of the BBTB of malignant glioma microvasculature.

A population MRI brain template and analysis tools for the macaque

ABSTRACT The use of standard anatomical templates is common in human neuroimaging, as it facilitates data analysis and comparison across subjects and studies. For non‐human primates, previous in vivo templates have lacked sufficient contrast to reliably validate known anatomical brain regions and have not provided tools for automated single‐subject processing. Here we present the “National Institute of Mental Health Macaque Template”, or NMT for short. The NMT is a high‐resolution in vivo MRI template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, we generated maps of tissue segmentation and cortical thickness. Surface reconstructions and transformations to previously published digital brain atlases are also provided. We further provide an analysis pipeline using the NMT that automates and standardizes the time‐consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single‐subject data analysis and for characterizations of neuroimaging results across subjects and studies. HIGHLIGHTSWe present an anatomical template, distilled from in vivo MRI scans of 31 monkeys.We classified various tissue types and present a novel atlas of blood vasculature.Pial, mid‐cortical, and white matter surfaces are provided for data visualization.Scripts are provided to automate segmentation and characterization of other monkeys.The template, surfaces, segmentation maps, and analysis tools are freely available.

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

FMRI data acquisition under naturalistic and continuous stimuli (e.g., watching a video or listening to music) has become popular recently due to the fact that it entails less manipulation and more realistic/complex contexts involved in the task, compared to the conventional task-based experimental designs. The synchronization or response similarities among subjects are typically measured through inter-subject correlation (ISC) between any pair of subjects. At the group level, summarizing the collection of ISC values is complicated by their intercorrelations, which necessarily lead to the violation of independence assumed in typical parametric approaches such as Students t-test. Nonparametric methods, such as bootstrapping and permutation testing, have previously been adopted for testing purposes by resampling the time series of each subject, but the quantitative validity of these specific approaches in terms of controllability of false positive rate (FPR) has never been explored before. Here we survey the methods of ISC group analysis that have been employed in the literature, and discuss the issues involved in those methods. We then propose less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level. As part of the new approaches, subject-wise (SW) resampling is adopted instead of element-wise (EW) resampling, so that exchangeability and independence assumptions are satisfied, and the patterned correlation structure among the ISC values can be more accurately captured. We examine the FPR controllability and power achievement of all the methods through simulations, as well as their performance when applied to a real experimental dataset.

Symptom index P-value and symptom sensitivity index P-value to determine symptom association between apnea and reflux in premature infants at term

The current method to determine temporal association (TA) between reflux and symptoms is the symptom association probability (SAP), but this method has limitations due to the constraints of binning and the violation of statistical principles of the Fishers exact test that lead to an invalid estimation of TA. The aim of this study is to develop improved methods of computing the TA between apneic and reflux events using simulation and permutation methods and to compare these to the SAP. TA was analyzed between polysomnographic obstructive apneas and multichannel intraluminal impedance (MII) reflux events. Three new numerical methods were compared to the SAP in four former premature infants with persistent apneas at term. The experimentally found association was compared to the association observed in simulated or permuted data consistent with the lack of association beyond what is expected by chance alone. Temporal association was computed based on symptom and symptom sensitivity indices, SI and SSI, with varying window of association (WA) times from 15 to 300 s. The three new methods estimated P-values at varying WA that generally followed the same pattern of the SAP which had a more erratic pattern. The WA that gave the lowest P-value was approximately 120 s. Each of the novel methods produced P-value results consistent with each other and the SAP yet not subject to its limitations. The variation of WA gave a temporal profile of TA providing clues to its etiology. These new metrics are called Symptom Index (SIP) and Symptom Sensitivity Index (SSIP) P-values.

A digital 3D atlas of the marmoset brain based on multi-modal MRI

ABSTRACT The common marmoset (Callithrix jacchus) is a New‐World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high‐resolution ex‐vivo MRI images, including magnetization transfer ratio (a T1‐like contrast), T2w images, and multi‐shell diffusion MRI. Based on the multi‐modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub‐regions using a connectivity‐based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus‐based fMRI. The atlas set has been integrated into the widely‐distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi‐modal template space with multi‐level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Graphical abstract Figure. No Caption available. HighlightsMRI‐based 3D digital atlas of the marmoset brain for MRI and connectome studies.Marmoset brain templates created from multi‐modal high‐resolution 3D MRI.Anatomical regions manually delineated and labeled directly on MRI dataset.Finer parcellation of the cortex obtained based on structural connectivity profiles.Fully‐featured atlas functions and integration with the Paxinos atlas.

ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids

BACKGROUND Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subjects neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. NEW METHOD ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individuals cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for ECoG electrode localization. RESULTS ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. COMPARISON WITH EXISTING METHODS A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. CONCLUSIONS The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.