4,633 research outputs found
Can parametric statistical methods be trusted for fMRI based group studies?
The most widely used task fMRI analyses use parametric methods that depend on
a variety of assumptions. While individual aspects of these fMRI models have
been evaluated, they have not been evaluated in a comprehensive manner with
empirical data. In this work, a total of 2 million random task fMRI group
analyses have been performed using resting state fMRI data, to compute
empirical familywise error rates for the software packages SPM, FSL and AFNI,
as well as a standard non-parametric permutation method. While there is some
variation, for a nominal familywise error rate of 5% the parametric statistical
methods are shown to be conservative for voxel-wise inference and invalid for
cluster-wise inference; in particular, cluster size inference with a cluster
defining threshold of p = 0.01 generates familywise error rates up to 60%. We
conduct a number of follow up analyses and investigations that suggest the
cause of the invalid cluster inferences is spatial auto correlation functions
that do not follow the assumed Gaussian shape. By comparison, the
non-parametric permutation test, which is based on a small number of
assumptions, is found to produce valid results for voxel as well as cluster
wise inference. Using real task data, we compare the results between one
parametric method and the permutation test, and find stark differences in the
conclusions drawn between the two using cluster inference. These findings speak
to the need of validating the statistical methods being used in the
neuroimaging field
Advancing functional connectivity research from association to causation
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures
Estimating effective connectivity in linear brain network models
Contemporary neuroscience has embraced network science to study the complex
and self-organized structure of the human brain; one of the main outstanding
issues is that of inferring from measure data, chiefly functional Magnetic
Resonance Imaging (fMRI), the so-called effective connectivity in brain
networks, that is the existing interactions among neuronal populations. This
inverse problem is complicated by the fact that the BOLD (Blood Oxygenation
Level Dependent) signal measured by fMRI represent a dynamic and nonlinear
transformation (the hemodynamic response) of neuronal activity. In this paper,
we consider resting state (rs) fMRI data; building upon a linear population
model of the BOLD signal and a stochastic linear DCM model, the model
parameters are estimated through an EM-type iterative procedure, which
alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel
(RTS) smoother, updates the connections among neuronal states and refines the
parameters of the hemodynamic model; sparsity in the interconnection structure
is favoured using an iteratively reweighting scheme. Experimental results using
rs-fMRI data are shown demonstrating the effectiveness of our approach and
comparison with state of the art routines (SPM12 toolbox) is provided
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model
Typical fMRI studies have focused on either the mean trend in the
blood-oxygen-level-dependent (BOLD) time course or functional connectivity
(FC). However, other statistics of the neuroimaging data may contain important
information. Despite studies showing links between the variance in the BOLD
time series (BV) and age and cognitive performance, a formal framework for
testing these effects has not yet been developed. We introduce the Variance
Design General Linear Model (VDGLM), a novel framework that facilitates the
detection of variance effects. We designed the framework for general use in any
fMRI study by modeling both mean and variance in BOLD activation as a function
of experimental design. The flexibility of this approach allows the VDGLM to i)
simultaneously make inferences about a mean or variance effect while
controlling for the other and ii) test for variance effects that could be
associated with multiple conditions and/or noise regressors. We demonstrate the
use of the VDGLM in a working memory application and show that engagement in a
working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure
Serial Correlations in Single-Subject fMRI with Sub-Second TR
When performing statistical analysis of single-subject fMRI data, serial
correlations need to be taken into account to allow for valid inference.
Otherwise, the variability in the parameter estimates might be under-estimated
resulting in increased false-positive rates. Serial correlations in fMRI data
are commonly characterized in terms of a first-order autoregressive (AR)
process and then removed via pre-whitening. The required noise model for the
pre-whitening depends on a number of parameters, particularly the repetition
time (TR). Here we investigate how the sub-second temporal resolution provided
by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
time series. We fit a higher-order AR model and then estimate the optimal AR
model order for a sequence with a TR of less than 600 ms providing whole brain
coverage. We show that physiological noise modelling successfully reduces the
required AR model order, but remaining serial correlations necessitate an
advanced noise model. We conclude that commonly used noise models, such as the
AR(1) model, are inadequate for modelling serial correlations in fMRI using
sub-second TRs. Rather, physiological noise modelling in combination with
advanced pre-whitening schemes enable valid inference in single-subject
analysis using fast fMRI sequences
Stochastic signatures of involuntary head micro-movements can be used to classify females of ABIDE into different subtypes of neurodevelopmental disorders.
© 2017 Torres, Mistry, Caballero and Whyatt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Background: The approximate 5:1 male to female ratio in clinical detection of Autism Spectrum Disorder (ASD) prevents research from characterizing the female phenotype. Current open access repositories [such as those in the Autism Brain Imaging Data Exchange (ABIDE I-II)] contain large numbers of females to help begin providing a new characterization of females on the autistic spectrum. Here we introduce new methods to integrate data in a scale-free manner from continuous biophysical rhythms of the nervous systems and discrete (ordinal) observational scores. Methods: New data-types derived from image-based involuntary head motions and personalized statistical platform were combined with a data-driven approach to unveil sub-groups within the female cohort. Further, to help refine the clinical DSM-based ASD vs. Asperger's Syndrome (AS) criteria, distributional analyses of ordinal score data from Autism Diagnostic Observation Schedule (ADOS)-based criteria were used on both the female and male phenotypes. Results: Separate clusters were automatically uncovered in the female cohort corresponding to differential levels of severity. Specifically, the AS-subgroup emerged as the most severely affected with an excess level of noise and randomness in the involuntary head micro-movements. Extending the methods to characterize males of ABIDE revealed ASD-males to be more affected than AS-males. A thorough study of ADOS-2 and ADOS-G scores provided confounding results regarding the ASD vs. AS male comparison, whereby the ADOS-2 rendered the AS-phenotype worse off than the ASD-phenotype, while ADOS-G flipped the results. Females with AS scored higher on severity than ASD-females in all ADOS test versions and their scores provided evidence for significantly higher severity than males. However, the statistical landscapes underlying female and male scores appeared disparate. As such, further interpretation of the ADOS data seems problematic, rather suggesting the critical need to develop an entirely new metric to measure social behavior in females. Conclusions: According to the outcome of objective, data-driven analyses and subjective clinical observation, these results support the proposition that the female phenotype is different. Consequently the “social behavioral male ruler” will continue to mask the female autistic phenotype. It is our proposition that new observational behavioral tests ought to contain normative scales, be statistically sound and combined with objective data-driven approaches to better characterize the females across the human lifespan.Peer reviewe
Information flow between resting state networks
The resting brain dynamics self-organizes into a finite number of correlated
patterns known as resting state networks (RSNs). It is well known that
techniques like independent component analysis can separate the brain activity
at rest to provide such RSNs, but the specific pattern of interaction between
RSNs is not yet fully understood. To this aim, we propose here a novel method
to compute the information flow (IF) between different RSNs from resting state
magnetic resonance imaging. After haemodynamic response function blind
deconvolution of all voxel signals, and under the hypothesis that RSNs define
regions of interest, our method first uses principal component analysis to
reduce dimensionality in each RSN to next compute IF (estimated here in terms
of Transfer Entropy) between the different RSNs by systematically increasing k
(the number of principal components used in the calculation). When k = 1, this
method is equivalent to computing IF using the average of all voxel activities
in each RSN. For k greater than one our method calculates the k-multivariate IF
between the different RSNs. We find that the average IF among RSNs is
dimension-dependent, increasing from k =1 (i.e., the average voxels activity)
up to a maximum occurring at k =5 to finally decay to zero for k greater than
10. This suggests that a small number of components (close to 5) is sufficient
to describe the IF pattern between RSNs. Our method - addressing differences in
IF between RSNs for any generic data - can be used for group comparison in
health or disease. To illustrate this, we have calculated the interRSNs IF in a
dataset of Alzheimer's Disease (AD) to find that the most significant
differences between AD and controls occurred for k =2, in addition to AD
showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for
publication in Brain Connectivity in its current for
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