Biology subjects

Glen, D. R.

Publications and source records attributed to Glen, D. R..

8 recordsLinked to original sources

bioRxiv2024

Development and early life stress sensitivity of the rat cortical microstructural similarity network

Understanding how early life experiences shape brain network development is a key challenge in the neuroscience of mental health disorders. To address this, we used magnetic resonance imaging (MRI) similarity network analysis to study the effects of stress in the rat, an important animal model in neuropsychiatry. We measured magnetization transfer ratio (MTR) at each of 53 distinct cortical areas and estimated a cortical similarity network for each individual scan, in two independent experimental datasets. We first characterized normative network development in rats scanned repeatedly between postnatal days 20 (weanling) and 290 (mid-adulthood) (N=47), and then contrasted these findings with a cohort exposed to early life stress in the form of repeated maternal separation (RMS, N=40). The normative rat cortical similarity network exhibited biologically meaningful organization, aligning with prior cytoarchitectonic and tract-tracing data, and displayed complex topological features, including rich club organization. During postnatal and adolescent development, brain regions became more similar, including an early phase of fronto-hippocampal convergence. Early increases in inter-areal similarity were reversed in a later phase of fronto-hippocampal divergence in mid-adulthood. RMS exposure altered inter-areal similarity, especially between frontal and parahippocampal regions, that were also most active developmentally and in aging. Our results reveal how normative cortical network changes in the developing brain are influenced by early life stress. These findings suggest a new translational framework for elucidating how environmental risk factors lead to atypical development of cortical networks.

neuroscience↗

bioRxiv2022

Improving 3D Edge Detection for Visual Inspection of MRI Coregistration and Alignment

Detecting and visualizing edges is important in several neuroimaging and medical imaging applications. For example, it is common to use edge maps to ensure the automatic alignment of low-resolution functional MRI images to match a high-resolution structural image has been successful. Specifically, software toolboxes like FSL and AFNI generate volumetric edge maps that can be particularly useful for visually assessing the alignment of datasets, overlaying the edge map of one on the other. Therefore, edge maps play a crucial role in quality assurance. Popular methods for computing edges are based on either the first derivative of the image as in FSL, or a variation of the Canny Edge detection method as implemented in AFNI. The crucial algorithmic parameter for adjustment for each of these methods relates to the image intensity. However, image intensity is relative and can be quite variable in most neuroimaging modalities. Further, the existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. We suggest that using the second derivative (difference of Gaussian, or DoG) of the image to generate edges resolves both these issues. This method primarily operates by specifying a spatial scale of interest (which is typically known in medical imaging) rather than a contrast scale, and creates closed surfaces by definition. We describe some convenient implementation features (for both efficiency and visual quality) developed here, and we provide open source implementations of this method as both online and high performance portable code. Finally, we include this method as part of both the AFNI and FSL software packages.

neuroscience↗

bioRxiv2020

EPI Distortion Correction is Easy and Useful, and You Should Use It: A case study with toddler data

Task, resting state, and diffusion MRI data are usually acquired from subjects using echo-planar based imaging techniques. These techniques are highly susceptible to B0 homogeneity effects that result in geometric distortions in the reconstructed images. As researchers work to link the information from these scans back to various developmental stages, or to conditions and diseases in specific regions or structures of the brain, it becomes critical to have accurate correspondence between more geometrically distorted echo-planar images and less geometrically distorted anatomical images. A variety of techniques and tools have been developed to improve this correspondence. The basic premise behind most techniques used to mitigate geometric distortion is to acquire enough information to inform software tools how echo-planar images are warped, then have them undo that warping. Here, we investigate the application of two common methods: B0 correction, and reverse-polarity phase-encoding (or reverse blip) correction. We implement each of these in two separate, widely used software packages in the field: AFNI and FSL. We find that using either technique in either software package results in reduced geometric distortions in the EPI images. We discuss the practical implementations of these methods (e.g., increased scan and processing time). In general, however, both methods possess readily available data acquisition schemes, and are highly efficient to include in processing streams. Due to the overall data improvement, we strongly recommend that researchers include one of these methods in their standard protocols.

biophysics↗

bioRxiv2020

LAYNII: A software suite for layer-fMRI

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed open-source and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain layerification and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data. HighlightsO_LIA new software toolbox is introduced for layer-specific functional MRI: LayNii. C_LIO_LILayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS. C_LIO_LILayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints. C_LIO_LILayNii performs layerification in the native voxel space of functional data. C_LIO_LILayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/148080v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@e12e9org.highwire.dtl.DTLVardef@1fbb564org.highwire.dtl.DTLVardef@41da13org.highwire.dtl.DTLVardef@1545106_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience↗

bioRxiv2020

A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years

Anatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group-level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative-template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n=466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age-specific IBTs (ages 6-60 years) as well as maximum probability map (MPM) atlases for each template; for each age-groups template-atlas pair, there is both a "population-average" and a "typical" version. Validation experiments on an independent Indian structural and functional-MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior-Posterior and Inferior-Superior axes, but minimal Left-Right. For each age-group, the MPM brain atlases provide reasonably good representation of the native-space volumes in the IBT space, except in a few regions with high inter-subject variability. These findings provide evidence to support the use of age and population-specific templates in human brain mapping studies. This dataset is made publicly available (https://hollabharath.github.io/IndiaBrainTemplates). HighlightsO_LIA new set of age-specific T1w Indian brain templates for ages 6-60 yr are developed and validated. C_LIO_LIA new AFNI tool, make_template_dask.py, for the creation of group-based templates. C_LIO_LIMaximum probability map atlases are also provided for each template. C_LIO_LIResults indicate the appropriateness of Indian templates for spatial normalization of Indian brains C_LI

neuroscience↗

bioRxiv2018

One Model to Rule Them All: An Integrative Approach to Matrix-Based Analyses in Neuroimaging Connectomics

Network modeling in neuroimaging holds promise in probing the interrelationships among brain regions and potential clinical applications. Two types of matrix-based analysis (MBA) are usually seen in neuroimaging connectomics: one is the functional attribute matrix (FAM) of, for example, correlations, that measures the similarity of BOLD response patterns among a list of predefined regions of interest (ROIs). Another type of MBA involves the structural attribute matrix (SAM), e.g., describing the properties of white matter between any pair of gray-matter regions such as fractional anisotropy, mean diffusivity, axial and radial diffusivity. There are different methods that have been developed or adopted to summarize such matrices across subjects, including general linear models (GLMs) and various versions of graph theoretic analysis. We argue that these types of modeling strategies tend to be \"inefficient\" in statistical inferences and have many pitfalls, such as having strong dependence on arbitrary thresholding under conventional statistical frameworks.\n\nHere we offer an alternative approach that integrates the analyses of all the regions, region pairs (RPs) and subjects into one framework, called Bayesian multilevel (BML) modeling. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. This integrative approach avoids the multiple testing issue that typically plagues the conventional statistical analysis in neuroimaging, and it provides a principled way to quantify both the effect and its uncertainty at each region as well as for each RP. As a result, a unique feature of BML is that the effect at each region and the corresponding uncertainty can be estimated, revealing the relative strength or importance of each region; in addition, the effect at each RP is obtained along with its uncertainty as statistical evidence. Most importantly, the BML approach can be scrutinized for consistency through validation and comparisons with alternative assumptions or models. We demonstrate the BML methodology with a real dataset with 16 ROIs from 41 subjects, and compare it to the conventional GLM approach in terms of model efficiency, performance and inferences. Furthermore, we emphasize the notion of full results reporting through \"highlighting,\" instead of through the common practice of \"hiding.\" The associated program will be available as part of the AFNI suite for general use.

neuroscience↗

bioRxiv2018

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.

neuroscience↗

bioRxiv2018

FMRI processing with AFNI: Some comments and corrections on \"Exploring the Impact of Analysis Software on Task fMRI Results\"

A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use \"default\" settings of each softwares pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each packages defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a \"one-size-fits-all\" pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNIs processing pipeline design tool at the individual level, afni_proc.py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.

neuroscience↗