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A principled multivariate intersubject analysis of Generalized Partial Directed Coherence with Dirichlet Regression: application to healthy aging in areas exhibiting cortical thinning

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Abstract

Background Generalized Partial Directed Coherence (GPDC) is a multivariate measure of predictability between functional timeseries defined in the frequency domain. However, analysis has often been constrained by its compositional nature. Specifically, the squared GPDC from a node region to all nodes in any given frequency must sum to one. New method When analyzing GPDC spectra, it is imperative to consider that squared GPDC from a source timeseries sums to one over its target timeseries. Dirichlet Regression allows the modeling of compositional data and, therefore, becomes a principled choice for the multivariate analysis of GPDC on arbitrary subject-level variables. Results Eleven resting-state fMRI connections underwent age-related alterations, with two decreases in squared GPDC from a region to itself in two frequencies, signaling increased integration with the rest, and nine increases in squared GPDC, one involving different regions. All frequencies had at least one alteration due to age. Comparison with existing method(s) Our methodology identifies alterations in GPDC in more connections than a naïve approach based on linear regression and centered log-ratio analysis. We also studied alternative connectivity indices between the same ROIs, uncovering no effect of age on the time-domain predictive-causality metrics for any connection, while for Pearson correlation five connections displayed significant effects of age, with parallels to the results pertaining to GPDC. Conclusions Dirichlet Regression allows the study of continuous or discrete variables as predictors for the analysis of GPDC, enabling a wider adoption of this measure of connectivity.

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... Firstly, it reflects the relationships between different brain regions, and secondly, it establishes causal pathways. Building on GPC, two methods have been developed: Generalized Partial Directed Coherence (g-PDC) and Generalized Orthogonalized Partial Directed Coherence (g-OPDC) (81,82). These methods support the study of brain connectivity during cognitive activities, reducing the risk of volume conduction artifacts and ensuring that prediction results more closely mirror actual conditions. ...
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... This pre-processing pipeline has been reported elsewhere (Vieira et al., 2020;Vieira and Salmon, 2019) and is partially reproduced below. ...
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... The methods and data were partially described in Vieira and Garrido Salmon (Vieira and Garrido Salmon, 2019), which we reproduce with small adjustments below. The NKI-RS Phase II received express authorization by their respective Institutional Review Board at the Nathan Kline Institute (#239708) and at the Montclair State University (#000983B). ...
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... Salmon [44], which we reproduce with small adjustments below. The NKI- ...
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GNU Parallel is a shell tool for executing jobs in parallel using one or more computers. A job is can be a single command or a small script that has to be run for each of the lines in the input. The typical input is a list of files, a list of hosts, a list of users, a list of URLs, or a list of tables. A job can also be a command that reads from a pipe. GNU Parallel can then split the input and pipe it into commands in parallel. If you use xargs and tee today you will find GNU Parallel very easy to use as GNU Parallel is written to have the same options as xargs. If you write loops in shell, you will find GNU Parallel may be able to replace most of the loops and make them run faster by running several jobs in parallel. GNU Parallel can even replace nested loops. GNU Parallel makes sure output from the commands is the same output as you would get had you run the commands sequentially. This makes it possible to use output from GNU Parallel as input for other programs. You can find more about GNU Parallel at: http://www.gnu.org/s/parallel/ You can install GNU Parallel in just 10 seconds with: (wget -O - pi.dk/3 || curl pi.dk/3/) | bash Watch the intro video on http://www.youtube.com/playlist?list=PL284C9FF2488BC6D1 Walk through the tutorial (man parallel_tutorial). Your commandline will love you for it. When using programs that use GNU Parallel to process data for publication please cite: O. Tange (2011): GNU Parallel - The Command-Line Power Tool, ;login: The USENIX Magazine, February 2011:42-47. If you like GNU Parallel: Give a demo at your local user group/team/colleagues Post the intro videos on Reddit/Diaspora*/forums/blogs/ Identi.ca/Google+/Twitter/Facebook/Linkedin/mailing lists Get the merchandise https://www.gnu.org/s/parallel/merchandise.html Request or write a review for your favourite blog or magazine Request or build a package for your favourite distribution (if it is not already there) Invite me for your next conference If you use GNU Parallel for research: Please cite GNU Parallel in you publications (use --bibtex) If GNU Parallel saves you money: (Have your company) donate to FSF https://my.fsf.org/donate/
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Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of ``scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
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The discovery that spontaneous fluctuations in BOLD (blood oxygen level dependent) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of approximately one second. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open versus closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.
Article
The recent advancement of simultaneous multi-slice imaging using multiband excitation has dramatically reduced the scan time of the brain. The evolution of this parallel imaging technique began over a decade ago and through recent sequence improvements has reduced the acquisition time of multi-slice EPI by over ten fold. This technique has recently become extremely useful for (i) functional MRI studies for improving the statistical definition of neuronal networks, and (ii) diffusion based fiber tractography for improving the ability to visualize structural connections in the human brain. Several applications and evaluations are underway which show promise for this family of fast imaging sequences.
Article
Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have also demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.
Article
Three-dimensional (3D) MP-RAGE (magnetization-prepared rapid gradient-echo) imaging was evaluated as a high-resolution 3D T1-weighted brain imaging technique for patients with suspected neurologic disease. Fourteen patients were studied. In five, 3D MP-RAGE images were compared with 3D FLASH (fast low-angle shot) images. Signal difference-to-noise ratios and T1 contrast were not statistically different for 3D MP-RAGE images as opposed to 3D FLASH images. Advantages intrinsic to the application of 3D MP-RAGE sequences include decreased imaging time and decreased motion artifact. With this technique, it is possible to perform a relatively motion-insensitive, T1-weighted screening brain study with voxel resolution of 1.0 × 1.4 × 2.0 mm or smaller, in an imaging time of 5.9 minutes or less-permitting offline (poststudy) reconstruction of high-resolution images in any desired plane.
Article
An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (<0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10−3) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
Article
The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%-70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%-19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.
Article
This contribution presents a new R package, called “compositions”. It provides tools to analyze amount or compositional data sets in four different geometries, each one associated with an R class: rplus (for amounts, or open compositions, in a real, classical geometry), aplus (for amounts in a logarithmic geometry), rcomp (for closed compositions in a real geometry) and acomp (for closed compositions in a logistic geometry, following a log-ratio approach). The package allows to compare results obtained with these four approaches, since an analogous analysis can be performed according to each geometry, with minimal and straightforward modifications of the instructions. Beside these grounding classes, the package also includes: the most-basic features such as data transformations (e.g. logarithm, or additive logistic transform), basic statistics (both the classical ones, and those developed in the log-ratio framework of compositional analysis), high-level graphics (like ternary diagram matrix and scatter-plots) and high-level analysis (e.g. principal components or cluster analysis). Results of these functions and analysis are also provided in a consistent way among the four geometries, to ease their comparison.
Article
FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.
Article
Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.
Article
Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.
Article
The insula is a brain structure implicated in disparate cognitive, affective, and regulatory functions, including interoceptive awareness, emotional responses, and empathic processes. While classically considered a limbic region, recent evidence from network analysis suggests a critical role for the insula, particularly the anterior division, in high-level cognitive control and attentional processes. The crucial insight and view we present here is of the anterior insula as an integral hub in mediating dynamic interactions between other large-scale brain networks involved in externally oriented attention and internally oriented or self-related cognition. The model we present postulates that the insula is sensitive to salient events, and that its core function is to mark such events for additional processing and initiate appropriate control signals. The anterior insula and the anterior cingulate cortex form a "salience network" that functions to segregate the most relevant among internal and extrapersonal stimuli in order to guide behavior. Within the framework of our network model, the disparate functions ascribed to the insula can be conceptualized by a few basic mechanisms: (1) bottom-up detection of salient events, (2) switching between other large-scale networks to facilitate access to attention and working memory resources when a salient event is detected, (3) interaction of the anterior and posterior insula to modulate autonomic reactivity to salient stimuli, and (4) strong functional coupling with the anterior cingulate cortex that facilitates rapid access to the motor system. In this manner, with the insula as its integral hub, the salience network assists target brain regions in the generation of appropriate behavioral responses to salient stimuli. We suggest that this framework provides a parsimonious account of insula function in neurotypical adults, and may provide novel insights into the neural basis of disorders of affective and social cognition.
Article
Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.
Article
Cerebral volume loss has long been associated with normal aging, but whether this is due to aging itself or to age-related diseases, including incipient Alzheimer disease, is uncertain. To understand the changes that occur in the aging brain, we examined the cerebral cortex of 27 normal individuals ranging in age from 56 to 103 years. None fulfilled the criteria for the neuropathologic diagnosis of Alzheimer disease or other neurodegenerative disease. Seventeen of the elderly participants had cognitive testing an average of 6.7 months prior to death. We used quantitative approaches to analyze cortical thickness, neuronal number, and density. Frontal and temporal neocortical regions had clear evidence of cortical thinning with age, but total neuronal numbers in frontal and temporal neocortical regions remained relatively constant during a 50-year age range. These data suggest that loss of neuronal and dendritic architecture, rather than loss of neurons, underlies neocortical volume loss with increasing age in the absence of Alzheimer disease.
Article
A new three-dimensional imaging technique which is applicable for 3D MR imaging throughout the body is introduced. In our preliminary investigations we have acquired high-quality 3D image sets of the abdomen showing minimal respiratory artifacts in just over 7 min (voxel size 2.7 X 2.7 X 2.7 mm3), and 3D image sets of the head showing excellent gray/white contrast in less than 6 min (voxel size 1.0 X 2.0 X 1.4 mm3).
Article
The coherence function quantifies the association between pairs of signals as a function of frequency and has been shown to be useful for measuring changes in EEG topography related to cognitive tasks, psychopathology, and other aspects of brain organisation. For a narrow frequency band, its magnitude is analogous to the correlation coefficient between the signals limited by that band, but its value may differ because of the way that smoothing over frequency is achieved. The coherence function is described in physical terms using simple waveforms.
Article
A method to encode multiple two-dimensional Fourier transform (2D FT) images within a single echo train is presented. This new method, simultaneous echo refocusing (SER), is a departure from prior echo planar image (EPI) sequences which use repeated single-shot echo trains for multislice imaging. SER simultaneously acquires multiple slices in a single-shot echo train utilizing a shared refocusing process. The SER technique acquires data faster than conventional multislice EPI since it uses fewer gradient switchings and fewer preparation pulses such as diffusion gradients. SER introduces a new capability to simultaneously record multiple spatially separated sources of physiologic information in subsecond image acquisitions, which enables several applications that are dependent on temporal coherence in MRI data including velocity vector field mapping and brain activation mapping.
Article
Since its development about 15 years ago, functional magnetic resonance imaging (fMRI) has become the leading research tool for mapping brain activity. The technique works by detecting the levels of oxygen in the blood, point by point, throughout the brain. In other words, it relies on a surrogate signal, resulting from changes in oxygenation, blood volume and flow, and does not directly measure neural activity. Although a relationship between changes in brain activity and blood flow has long been speculated, indirectly examined and suggested and surely anticipated and expected, the neural basis of the fMRI signal was only recently demonstrated directly in experiments using combined imaging and intracortical recordings. In the present paper, we discuss the results obtained from such combined experiments. We also discuss our current knowledge of the extracellularly measured signals of the neural processes that they represent and of the structural and functional neurovascular coupling, which links such processes with the hemodynamic changes that offer the surrogate signal that we use to map brain activity. We conclude by considering applications of invasive MRI, including injections of paramagnetic tracers for the study of connectivity in the living animal and simultaneous imaging and electrical microstimulation.
Article
The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.
Article
A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.