scholarly journals Modular preprocessing pipelines can reintroduce artifacts into fMRI data

2018 ◽  
Author(s):  
Martin A. Lindquist ◽  
Stephan Geuter ◽  
Tor D. Wager ◽  
Brian S. Caffo
Keyword(s):  
Fmri Data ◽  
Linear Filter ◽  
Linear Filtering ◽  
Linear Filters ◽  
Data Set ◽  
Temporal Filtering ◽  
Global Signal ◽  
In Series ◽  
Band Pass ◽  
Fmri Analysis

AbstractThe preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps—including motion regression, scrubbing, component-based correction, global signal regression, and temporal filtering—are performed sequentially. In this work we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.

scholarly journals The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 787
Author(s):  
Antonio Dávalos ◽  
Meryem Jabloun ◽  
Philippe Ravier ◽  
Olivier Buttelli
Keyword(s):  
Moving Average ◽  
Permutation Entropy ◽  
Linear Filter ◽  
Type I ◽  
Linear Filtering ◽  
Linear Filters ◽  
Ordinal Information ◽  
Specific Frequency ◽  
The Impact ◽  
Specific Contribution

Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.

A single digital linear filter for computations in electrical methods—A unifying approach

Geophysics ◽  
1984 ◽  
Vol 49 (7) ◽  
pp. 1115-1118 ◽  
Author(s):  
U. C. Das
Keyword(s):  
Linear Filter ◽  
Linear Filtering ◽  
Electrical Measurements ◽  
Domain Integral ◽  
Electrical Methods ◽  
Single Formula

A major contribution to the interpretation of electrical measurements was made with the application of digital linear filtering introduced by Ghosh (1970, 1971a, b). This rendered the computations easy and fast. In a recent publication, I showed (Das, 1982) that the filters for computing responses for any electrode or coil configurations employed in electrical methods could be derived easily from stored basic spectra of the two filter functions, namely, [Formula: see text] and [Formula: see text]. One has to multiply the stored spectra by simple factors to arrive at the required spectra. I show here that a simple mathematical manipulation transforms a [Formula: see text] domain integral into its corresponding [Formula: see text] domain integral, thereby leading to the use of a single [Formula: see text] filter for a variety of computations in electrical methods.

scholarly journals Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data

2018 ◽  
Vol 30 (3) ◽  
pp. 670-707 ◽  
Author(s):  
Dorian Florescu ◽  
Daniel Coca
Keyword(s):  
Sensory Processing ◽  
Neuron Model ◽  
Linear Filter ◽  
Spiking Neuron ◽  
Neuron Models ◽  
Integrate And Fire ◽  
Spiking Neuron Model ◽  
In Series ◽  
Linear And Nonlinear ◽  
Model Equations

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

scholarly journals Neuro-Current Response Functions: A Unified Approach to MEG Source Analysis under the Continuous Stimuli Paradigm

2019 ◽  
Author(s):  
Proloy Das ◽  
Christian Brodbeck ◽  
Jonathan Z. Simon ◽  
Behtash Babadi
Keyword(s):  
Auditory Processing ◽  
Source Analysis ◽  
High Temporal Resolution ◽  
Linear Filter ◽  
Current Response ◽  
Response Functions ◽  
Continuous Speech ◽  
Spatiotemporal Resolution ◽  
Linear Filters ◽  
Neural Processes

AbstractCharacterizing the neural dynamics underlying sensory processing is one of the central areas of investigation in systems and cognitive neuroscience. Neuroimaging techniques such as magnetoencephalography (MEG) and Electroencephalography (EEG) have provided significant insights into the neural processing of continuous stimuli, such as speech, thanks to their high temporal resolution. Existing work in the context of auditory processing suggests that certain features of speech, such as the acoustic envelope, can be used as reliable linear predictors of the neural response manifested in M/EEG. The corresponding linear filters are referred to as temporal response functions (TRFs). While the functional roles of specific components of the TRF are well-studied and linked to behavioral attributes such as attention, the cortical origins of the underlying neural processes are not as well understood. In this work, we address this issue by estimating a linear filter representation of cortical sources directly from neuroimaging data in the context of continuous speech processing. To this end, we introduce Neuro-Current Response Functions (NCRFs), a set of linear filters, spatially distributed throughout the cortex, that predict the cortical currents giving rise to the observed ongoing MEG (or EEG) data in response to continuous speech. NCRF estimation is cast within a Bayesian framework, which allows unification of the TRF and source estimation problems, and also facilitates the incorporation of prior information on the structural properties of the NCRFs. To generalize this analysis to M/EEG recordings which lack individual structural magnetic resonance (MR) scans, NCRFs are extended to free-orientation dipoles and a novel regularizing scheme is put forward to lessen reliance on fine-tuned coordinate co-registration. We present a fast estimation algorithm, which we refer to as the Champ-Lasso algorithm, by leveraging recent advances in optimization, and demonstrate its utility through application to simulated and experimentally recorded MEG data under auditory experiments. Our simulation studies reveal significant improvements over existing methods that typically operate in a two-stage fashion, in terms of spatial resolution, response function reconstruction, and recovering dipole orientations. The analysis of experimentally-recorded MEG data without MR scans corroborates existing findings, but also delineates the distinct cortical distribution of the underlying neural processes at high spatiotemporal resolution. In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.

scholarly journals A Novel Image Highly Compression Technique With Spread Parameter

2013 ◽  
pp. 196-199
Author(s):  
E. Wilvathi ◽  
M. KOTESWARA RAO
Keyword(s):  
Artifact Reduction ◽  
Fuzzy Filter ◽  
Linear Filtering ◽  
Linear Filters ◽  
Compression Technique ◽  
Jpeg Images ◽  
Compressed Images ◽  
Non Linear ◽  
Edge Based ◽  
Filtering Techniques

A novel image highly compressed technique has been introduced to reduce the artifacts in compressed JPEG images. In order to reduce the noise, non-linear filtering techniques are often employed than linear filters and don’t degrade the edges. A new metric has been introduced to reduce the artifacts occurred in colored images along the sharp transitions using directional spread parameter. Simulations on compressed images show improvement in artifact reduction by using edge based directional fuzzy filter when compared to the non-linear filters.

scholarly journals Salamander retinal ganglion cell responses to rich stimuli

2020 ◽  
Author(s):  
Kolia Sadeghi ◽  
Michael J. Berry
Keyword(s):  
Ganglion Cell ◽  
Retinal Ganglion Cells ◽  
Spatial Information ◽  
Ganglion Cells ◽  
Light Stimulus ◽  
Linear Filter ◽  
Retinal Ganglion ◽  
Linear Filters ◽  
Cell Responses ◽  
Random Stimulus

AbstractThe retina’s phenomenological function is often considered to be well-understood: individual retinal ganglion cells are sensitive to a projection of the light stimulus movie onto a classical center-surround linear filter. Recent models elaborating on this basic framework by adding a second linear filter or spike histories, have been quite successful at predicting ganglion cell spikes for spatially uniform random stimuli, and for random stimuli varying spatially with low resolution. Fitting models for stimuli with more finely grained spatial variations becomes difficult because of the very high dimensionality of such stimuli. We present a method of reducing the dimensionality of a fine one dimensional random stimulus by using wavelets, allowing for several clean predictive linear filters to be found for each cell. For salamander retinal ganglion cells, we find in addition to the spike triggered average, 3 identifiable types of linear filters which modulate the firing of most cells. While some cells can be modeled fairly accurately, many cells are poorly captured, even with as many as 4 filters. The new linear filters we find shed some light on the nonlinearities in the retina’s integration of temporal and fine spatial information.

scholarly journals Template-based prediction of vigilance fluctuations in resting-state fMRI

2017 ◽  
Author(s):  
Maryam Falahpour ◽  
Catie Chang ◽  
Chi Wah Wong ◽  
Thomas T. Liu
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Eyes Closed ◽  
Vigilance State ◽  
Global Signal ◽  
Significant Difference ◽  
Eyes Open ◽  
Positive Correlations

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

scholarly journals Measuring and Estimating Pressure-Saturation Curves on Undisturbed Soil Samples by Using Water and NAPL

2002 ◽  
Vol 51 (1-2) ◽  
pp. 27-36 ◽  
Author(s):  
Csaba Makó
Keyword(s):  
Humus Content ◽  
Estimation Method ◽  
Soil Samples ◽  
Direct Result ◽  
Organic Liquids ◽  
Soil Parameters ◽  
Retention Data ◽  
Data Set ◽  
Undisturbed Soil ◽  
In Series

In order to expand the experimental data set of models describing the movement of organic liquids polluting the soils, a series of experiments was set up in which the fluid retention (pressure- s aturation curves of the soils) were measured using water and NAPL (DUNASOL 180/220, a non aromatic petroleum product). Measurements were carried out on undisturbed soil samples originating from 35 different horizons of 12 characteristic Hungarian soils. The P-S curves with NAPL were determined in series, by a modified pressure cell apparatus - designed and constructed in the laboratory of our department - containing oil-resistant (silicon rubber, Teflon) components.   The applied methodology and the statistical analysis of the measured data are presented. The results show that the commonly used Leverett-type scaling of the water retention data provides inadequate estimation of the NAPL retention in some cases. This deviation may be a direct result of changes in clay volume and soil aggregation when saturation with different fluids was performed.  According to the analysis, however, with the easily measurable soil parameters (bulk density, particle size distribution and humus content) a better estimation of NAPL retention can be given. This estimation method (after extending the database) can be useful for modelling the fate and migration of NAPL or mapping the organic contaminant sensitivity of the soils. 

Effect of Word-Line Bias on Linearity of Multi-Level Conductance Steps for Multi-Layer Neural Networks Based on NAND Flash Cells

2020 ◽  
Vol 20 (7) ◽  
pp. 4138-4142
Author(s):  
Sung-Tae Lee ◽  
Suhwan Lim ◽  
Nagyong Choi ◽  
Jong-Ho Bae ◽  
Dongseok Kwon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Flash Memory ◽  
Storage Capacity ◽  
High Density ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
Data Set ◽  
Source Line ◽  
In Series ◽  
Multi Level

NAND flash memory which is mature technology has great advantage in high density and great storage capacity per chip because cells are connected in series between a bit-line and a source-line. Therefore, NAND flash cell can be used as a synaptic device which is very useful for a high-density synaptic array. In this paper, the effect of the word-line bias on the linearity of multi-level conductance steps of the NAND flash cell is investigated. A 3-layer perceptron network (784×200×10) is trained by a suitable weight update method for NAND flash memory using MNIST data set. The linearity of multi-level conductance steps is improved as the word line bias increases from Vth −0.5 to Vth +1 at a fixed bit-line bias of 0.2 V. As a result, the learning accuracy is improved as the word-line bias increases from Vth −0.5 to Vth+1.

scholarly journals Computational approaches to the neuroscience of social perception

2020 ◽  
Author(s):  
Jeffrey A Brooks ◽  
Ryan M Stolier ◽  
Jonathan B Freeman
Keyword(s):  
Social Perception ◽  
Impression Formation ◽  
Social Information ◽  
Brain Regions ◽  
Neural Representation ◽  
Fmri Data ◽  
Psychological Models ◽  
Fmri Analysis ◽  
Multivariate Pattern ◽  
Multiple Domains

Abstract Across multiple domains of social perception - including social categorization, emotion perception, impression formation, and mentalizing - multivariate pattern analysis (MVPA) of fMRI data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.

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