The impact of T1 versus EPI spatial normalization templates for fMRI data analyses

Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

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Citizen as Sensors' Commitment in Urban Public Action

International Journal of E-Planning Research ◽

10.4018/ijepr.2019100103 ◽

2019 ◽

Vol 8 (4) ◽

pp. 42-59 ◽

Cited By ~ 1

Author(s):

Gwendoline l'Her ◽

Myriam Servières ◽

Daniel Siret

Keyword(s):

Air Quality ◽

Citizen Science ◽

Urban Air Quality ◽

Urban Air ◽

The Public ◽

Public Action ◽

Quality Issue ◽

Data Analyses ◽

The Impact

Based on a case study in Rennes, the article presents how a group of urban public actors re-uses methods and technology from citizen sciences to raise the urban air quality issue in the public debate. The project gives a group of inhabitants the opportunity to follow air quality training and proceed PM2.5µm measurements. The authors question the impact of the ongoing hybridisation between citizen science and urban public action on participants' commitment. The authors present how the use of PM2.5-sensors during 11 weeks led to a disengagement phenomenon, even if the authors observe a strong participation to workshops. These results come from an interdisciplinary methodology using observations, interviews, and data analyses.

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Individuelle Autonomie im Status Arbeitslosigkeit ohne Leistungsbezug?

Zeitschrift für Sozialreform ◽

10.1515/zsr-2017-0020 ◽

2017 ◽

Vol 63 (3) ◽

pp. 447-482

Author(s):

Sigrid Betzelt ◽

Mareike Ebach ◽

Tanja Schmidt ◽

Olga Kedenburg

Keyword(s):

Individual Autonomy ◽

Unemployment Benefits ◽

Vulnerable Group ◽

Philosophical Concept ◽

Data Analyses ◽

New Findings ◽

The Status ◽

Using Data ◽

The Impact ◽

The Welfare State

AbstractThe article discusses new findings on a vulnerable group particularly interesting for questions of individual autonomy in the welfare state: those registered unemployed who are excluded from any unemployment benefits or social assistance entitlements (currently 208,000 in Germany) or who would not take up benefits to avoid stigmatisation. Using data from interviews and panel data analyses, and referring to a relational, socio-philosophical concept of autonomy, the impact of this status on individual autonomy is examined. The findings reveal that the status as unemployed non-beneficiary is widely perceived as violating individual autonomy and feelings of social justice for various reasons.

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An Overlooked Cost of Contracting Out: Evidence From Employee Turnover Intention in U.S. Federal Agencies

Public Personnel Management ◽

10.1177/0091026020944558 ◽

2020 ◽

pp. 009102602094455

Author(s):

Gyeo Reh Lee ◽

Sergio Fernandez ◽

Shinwoo Lee

Keyword(s):

Turnover Intention ◽

Employee Turnover ◽

The United States ◽

Federal Agencies ◽

Public Employees ◽

Contracting Out ◽

Work Related ◽

Data Analyses ◽

Employee Turnover Intention ◽

The Impact

Contracting out has long been used in all levels of government in the United States, with federal contract spending increasing 8% to 9% annually since 2015. The literature on contracting out has examined the impact of this practice on the work-related attitudes and motivation of public employees who have transitioned to work for private contractors. However, we understand very little about the effects of contracting out on the overwhelming number of public employees who are not displaced. Given the importance of work-related attitudes and turnover for organizations, this study explores the potential consequences of contracting out for employee turnover intention over a period of several years. The results of panel data analyses suggest that an increase in contracting activity in federal agencies increases the employee turnover intention rate. Contracting out also impacts employee turnover intention indirectly through its influence on job satisfaction.

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The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

Computational Intelligence and Neuroscience ◽

10.1155/2019/5850830 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Renzhou Gui ◽

Tongjie Chen ◽

Han Nie

Keyword(s):

Neural Network ◽

Deep Learning ◽

Correlation Matrix ◽

Nearest Neighbor ◽

Learning Model ◽

Fmri Data ◽

Support Vector ◽

K Nearest Neighbor ◽

The Impact ◽

Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

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An Extragalactic Reference Frame From DSN VLBI Measurements–1989

Symposium - International Astronomical Union ◽

10.1017/s0074180900086915 ◽

1990 ◽

Vol 141 ◽

pp. 261-270 ◽

Cited By ~ 4

Author(s):

O. J. Sovers

Keyword(s):

Reference Frame ◽

Systematic Errors ◽

Radio Sources ◽

Dual Frequency ◽

Source Position ◽

Deep Space Network ◽

Space Network ◽

Data Analyses ◽

Accuracy Level ◽

The Impact

Assessment of the impact of recent improvements in Deep Space Network (DSN) instrumentation, as well as of joint data analyses, provide a prognosis for the accuracy level to be expected in future realizations of an inertial radio reference frame. Intercontinental dual-frequency radio interferometric measurements during 68 sessions (including two recent sessions employing Mark III instrumentation) from 1978 to 1989 using NASA's DSN stations in California, Spain, and Australia give 8900 pairs of delay and delay rate observations. Analysis yields a catalog of positions of 200 extragalactic radio sources north of —45° declination. The resulting source position formal uncertainty distributions peak below 1 milliarcsecond, with three fourths being smaller than 2 mas. Comparison with independent measurements shows some evidence for systematic errors at the milliarcsecond level.

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The impact of functional connectivity changes on support vector machines mapping of fMRI data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2008.04.008 ◽

2008 ◽

Vol 172 (1) ◽

pp. 94-104 ◽

Cited By ~ 7

Author(s):

João Ricardo Sato ◽

Janaina Mourão-Miranda ◽

Maria da Graça Morais Martin ◽

Edson Amaro ◽

Pedro Alberto Morettin ◽

...

Keyword(s):

Support Vector Machines ◽

Functional Connectivity ◽

Fmri Data ◽

Support Vector ◽

Vector Machines ◽

The Impact

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Do koalas really get the blues? Critique of ‘Aversive behaviour by koalas (Phascolarctos cinereus) during the course of a music festival in northern New South Wales, Australia'

Australian Mammalogy ◽

10.1071/am16016 ◽

2017 ◽

Vol 39 (1) ◽

pp. 108 ◽

Cited By ~ 2

Author(s):

Sean I. FitzGibbon ◽

Amber K. Gillett ◽

Ben J. Barth ◽

Brendan Taylor ◽

William A. Ellis

Keyword(s):

New South ◽

Behavioural Response ◽

Ecological Data ◽

Short Term ◽

Music Festival ◽

Data Analyses ◽

South Wales ◽

Unpublished Report ◽

The Impact ◽

Sound Data

It is imperative that the reported results of scientific studies are based on sound data analyses and unbiased interpretation, especially where they may be used to guide government policy and regulation. A recent paper by Phillips (2016) evaluated the behavioural response of radio-collared koalas to an inaugural large music festival held in 2010 in northern New South Wales. The study concluded that six of seven koalas showed an aversive response. However, we regard the paper as misleading because it contains serious errors in the examination of koala home ranges and in the subsequent assessment of ‘aversive behaviour’ during the music festival. We conclude that Phillips’ paper is based on sufficient data to state that three, not six, of the koalas he studied displayed a short-term behavioural response to the music festival. These koalas temporarily moved outside of their estimated ranges during the festival period (10–80 m). Further, Phillips fails to report crucial ecological data regarding the high level of disease and mortality he recorded, which are presented in their entirety in the author’s prior, unpublished report. For the two deaths that are reported, no mention is made that these koalas had pre-existing disease. Rather, the author raises the possibility that the mortalities may have been related to festival-induced stress. The omission of such key data is prejudicial to the interpretation of results. The reported short-term impact of the festival upon some of the study animals is indeed noteworthy; however, it is apparent that the impact has been considerably overstated due to errors of analysis and the omission of critical ecological data. Our critique highlights our concerns by drawing on the author’s unpublished report as well as our own research at the same site over the past five years. Our aim is to ensure that debate over the impact of music festivals on wildlife is based on sound data analyses and unbiased interpretation, to provide guidance to relevant regulators and land managers.

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Drug Courts and Community Crime Rates: A Nationwide Analysis of Jurisdiction-Level Outcomes

Journal of Criminology ◽

10.1155/2013/571760 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

David R. Lilley

Keyword(s):

Panel Data ◽

Drug Court ◽

Crime Rates ◽

Drug Courts ◽

Vehicle Theft ◽

Current Drug ◽

Data Analyses ◽

Violent Offenses ◽

The Impact ◽

Analysis Of Jurisdiction

Although a substantial number of studies have reported that drug courts reduced the recidivism of graduates (Wilson et al., 2006), a series of recent analyses suggested that drug courts and similar programs were associated with unintended crime outcomes in cities and counties across the nation (Lilley and Boba, 2008; Miethe et al., 2000; Peters et al., 2002; Worrall et al., 2009). Given that over 220,000 offenders participated in this alternative to incarceration and most did not successfully complete the drug court program, jurisdictional crime may have been impacted. A series of panel data analyses were conducted among more than 5,000 jurisdictions nationwide from 1995 to 2002 to assess the impact of drug court implementation grants on UCR Part I felony offenses. Consistent with prior findings, drug court implementation grants were associated with net increases in vehicle theft, burglary, larceny, and some violent offenses. Possible explanations for these unintended outcomes are discussed along with recommendations for adjustments to current drug court programs across the nation.

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Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/634165 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 9

Author(s):

Hervé Abdi ◽

Lynne J. Williams ◽

Andrew C. Connolly ◽

M. Ida Gobbini ◽

Joseph P. Dunlop ◽

...

Keyword(s):

Discriminant Analysis ◽

Confidence Intervals ◽

Cross Validation ◽

Regions Of Interest ◽

Fmri Data ◽

Spatial Normalization ◽

Data Table ◽

Statistical Inferences ◽

Random Models ◽

Confusion Matrices

We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap), its performance is evaluated with confusion matrices (for fixed and random models) and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces,) of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject) and with more variables than observations.

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